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Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

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Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy
  • TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
  • CPTAC is NCI proteomic effort
  • Chemical labeling approach now method of choice for quantitative proteomics
  • Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

 

Data access and informatics tools for proteogenomics analysis

Bing Zhang
  • Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
  • Python scripts are available for bioinformatic programming

 

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

·         Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

·         Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

·         Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

·         Validated in METABRIC dataset

·         KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

·         Looked in xenografts and was able to pull out differential kinomes

·         Matched with PDX data so good clinical correlation

·         Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens
  • Deep learning is less likely to saturate as data increases
  • Deep learning attempts to learn multiple layers of information
  • The ultimate goal is prediction but this will be the greatest challenge for ML
  • ML models can integrate data validation and cross database validation
  • What limits the performance of cross validation is the internal noise of data (reproducibility)
  • Learning curves: not the more data but more reproducible data is important
  • Neural networks can outperform classical methods
  • Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter
  • Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
  • Comparative spectral decompositions can be used for global datasets
  • Validation of CNV data using this strategy
  • Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
  • These predictors was significantly better than independent prognostic indicator like age of diagnosis

 

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi
  • Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
  • ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
  • Found a circadian transcription in human lung
  • Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

 

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

 

 

 

 

 

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

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Crystal Resolution in Raman Spetctoscopy for Pharmaceutical Analysis

Curator: Larry H. Bernstein, MD, FCAP

 

Investigating Crystallinity Using Low Frequency Raman Spectroscopy: Applications in Pharmaceutical Analysis

http://images.alfresco.advanstar.com/alfresco_images/pharma/2016/01/21/52099838-6354-4ad4-b6f9-9c7c8061f307/Gordon-figure01_web.jpg

Figure 1: Illustration of an exemplar low-frequency Raman setup with a 785-nm laser.

The second system is based on a pre-built SureBlock XLF-CLM THz-Raman system from Ondax Inc. The laser (830 nm, 200 mW), cleanup filters, and laser line filters are all self-contained inside of the instrument but operate on the same principles as the 785-nm system. The sample is arranged in a 180° backscattering geometry relative to a 10× microscope lens. This system is then coupled via a fiber-optic cable to a Princeton Instruments SP2150i spectrograph and PIXIS 100 CCD camera. The 0.15-m spectrograph is used in conjunction with either a 1200- or 1800-groove/mm blazed diffraction grating to adjust the resolution and spectral range.

Crystalline Versus Amorphous Samples

The Raman spectrum of crystalline and amorphous solids differ greatly in the low-frequency region (see Figure 2) because of the highly ordered and highly disordered molecular environments of the respective solids. However, the mid-frequency region can also be noticeably altered by the changing environment (Figure 3).

 

 

http://images.alfresco.advanstar.com/alfresco_images/pharma/2016/01/21/52099838-6354-4ad4-b6f9-9c7c8061f307/Gordon-figure03_web.jpg

Figure 3: Raman spectra of griseofulvin

Ensuring Accuracy

A potential issue is optical artifacts, and these may be identified by the analysis of both Stokes and anti-Stokes spectra. One advantage of the experimental setups described is that signal from the sample may be measured within minutes and it is nondestructive, thus allowing Raman spectra to be collected from a single sample using both techniques at virtually the same time. This approach permits the examination of low-frequency Raman data with 785-nm and 830-nm excitation and allows comparison with Fourier transform (FT)-Raman spectra, in which it is possible to collect meaningful data down to a Raman shift of 50 cm-1. The benefits are demonstrated in Figure 4. In this data, each technique produces consistent bands with similar Raman shifts and relative intensities. While Raman data were not collected below 50 cm-1 using the 1064-nm system, the bands at 69 and 96 cm-1 are consistent with the 785- and 830-nm data. Furthermore, the latter two methods show consistency with bands appearing around 32 and 46 cm-1 for both techniques.

http://images.alfresco.advanstar.com/alfresco_images/pharma/2016/01/21/52099838-6354-4ad4-b6f9-9c7c8061f307/Gordon-figure04_web.jpg

Figure 4: Comparison of the low-frequency region of three Raman spectroscopic techniques.

Case Studies

So far there have been few studies to utilize low-frequency Raman spectroscopy in the analysis of pharmaceutical crystallinity. Despite this, the literature does contain articles that demonstrate the promising applicability of the technique.

Mah and colleagues (38) studied the level of crystallinity of griseofulvin using low-frequency Raman spectroscopy with PLS analysis. In this study a batch of amorphous griseofulvin (which was checked using X-ray powder diffractometry) was prepared by melting the griseofulvin and rapidly cooling it again using liquid nitrogen. Condensed water was removed by placing the sample over phosphorus pentoxide and the glassy sample was then ground using mortar and pestle. Calibrated samples of 2%, 4%, 6%, 8%, and 10% crystallinity were then created though geometric mixing of the amorphous and crystalline samples; following this mixing, the samples were then pressed into tablets. Many tablets were then stored in differing temperatures (30 °C, 35 °C, and 40 °C) at 0% humidity. Low-frequency 785-nm, mid-frequency 785-nm, and FT-Raman spectroscopies were performed simultaneously on each sample. After PLS analysis, limits of detection (LOD) and limits of quantification (LOQ) were calculated. The results of this research showed that each of these three techniques were capable of quantifying crystallinity. It also showed that FT-Raman and low-frequency Raman techniques were able to both detect and quantify crystallinity earlier than the mid-frequency 785 nm Raman technique. The respective LOD and LOQ values for FT-Raman, low-frequency Raman, and mid-frequency Raman are as follows: LOD values: 0.6%, 1.1%, and 1.5%; LOQ values: 1.8%, 3.4%, and 4.6%. The root mean squared errors of prediction (RMSEP) were also calculated and, like the LOD and LOQ values, indicated that the FT-Raman data had the lowest error, followed by the low-frequency Raman, and mid-frequency Raman had the largest errors of the three techniques. The recrystallization tests that were performed indicated that higher temperatures showed a distinct increase in the rate of recrystallization and that each technique provided similar results (within experimental error). It is also important to note that each technique gave similar spectra (where applicable), which provides supporting evidence that the data is meaningful. Overall, the conclusions of this research were that low-frequency predictions of crystallinity are at least as accurate as the predictions made using mid-frequency Raman techniques. It is arguable that low-frequency Raman is better because of the presence of stronger spectral features and because they are intrinsically linked with crystallinity.

Hédoux and colleagues (36) investigated the crystallinity of indomethacin using low-frequency Raman spectroscopy and compared the results with high frequency data. The ranges of interest were indicated to be 5–250 cm-1and 1500–1750 cm-1 regions. Samples of indomethacin were milled using a cryogenic mill to avoid mechanical heating of the sample, with full amorphous samples being obtained after 25 min of milling. Methods used in this study include Raman spectroscopy, isothermal differential scanning calorimetry (DSC), and X-ray diffractometry as well as the milling technique. The primary objective of this research was to use all of these techniques to monitor the crystallization of amorphous indomethacin to the more stable γ-state while the sample was at room temperature–well below the glass transition temperature,Tg = 43 °C. The results of this research did in fact show that low-frequency Raman spectroscopy is a very sensitive technique for identifying very small amounts of crystallinity within mostly amorphous samples. The data was supported by the well-established methods for monitoring crystallinity: XRD and DSC. This paper particularly noted the benefit of low acquisition times associated with low-frequency Raman spectroscopy compared with the other techniques used.

Low-frequency Raman spectroscopy was also used to monitor two polymorphic forms of caffeine after grinding and pressurization of the samples (39). Pressurization was performed hydrostatically using a gasketed membrane diamond anvil cell (MDAC), while ball milling was used as the method of grinding the sample. Analysis methods used were low-frequency Raman and X-ray diffraction. Low-frequency Raman spectra revealed that, upon slight pressurization, caffeine form I transforms into a metastable state slightly different from that of form II and that a disordered (amorphous) state is achieved in both forms when pressurized above 2 GPa. In contrast, it is concluded that grinding results in the transformation of each form into the other with precise grinding times, thus also generating an intermediate form, which was found to only be observable using low-frequency Raman spectroscopy. The caffeine data, as well as the low-frequency data obtained for indomethacin were further discussed by Hédoux and colleagues (40).

Larkin and colleagues (41) used low-frequency Raman in conjunction with other techniques to characterize several different APIs and their various forms. The other techniques include FT-Raman spectroscopy, X-ray powder diffraction (XRPD), and single-crystal X-ray diffractometry. The APIs studied include carbamazepine, apixaban diacid co-crystals, theophylline, and caffeine and were prepared in various ways that are not detailed here. During this research, low-frequency Raman spectroscopy played an important role in understanding the structures while in their various forms. However, more importantly, low-frequency Raman spectroscopy produced information-rich regions below 200 cm-1 for each of the crystalline samples and noticeably broad features when the APIs were in solution.

Wang and colleagues (42) investigated the applicability of low-frequency Raman spectroscopy in the analysis of respirable dosage forms of various pharmaceuticals. The analyzed pharmaceuticals were involved in the treatment of asthma or chronic obstructive pulmonary disease (COPD) and include salmeterol xinafoate, formoterol fumarate, glycopyrronium bromide, fluticasone propionate, mometasone furoate, and salbutamol sulfate. Various formulations of amino acid excipients were also analyzed in this study. Results indicated that the use of low-frequency Raman analysis was beneficial because of the large features found in the region and allowed for reliable identification of each of the dosage forms. Not only this, it also allowed unambiguous identification of two similar bronchodilators, albuterol (Ventolin) and salbutamol (Airomir).

Heyler and colleagues (43) collected both the low-frequency and fingerprint region of Raman spectra from several polymorphs of carbamazepine, an anticonvulsant and mood stabilizer. This study found that the different polymorphs of this API could be distinguished effectively using these two regions. Similarly, Al-Dulaimi and colleagues (44) demonstrated that polymorphic forms of paracetamol, flufenamic acid, and imipramine hydrochloride could be screened using low-frequency Raman and only milligram quantities of each drug. In this study, paracetamol and flufenamic acid were used as the model compounds for comparison with a previously unstudied system (imipramine hydrochloride). Features within the low-frequency Raman regions of spectra were shown to be significantly different between forms of each drug. Therefore this study also indicated that the polymorphs were highly distinguishable using the technique. Hence, like all other previously mentioned case studies, these investigations further demonstrate the utility of low-frequency Raman spectroscopy as a fast and effective method for screening pharmaceuticals for crystallinity.

Conclusions

Low-frequency Raman spectroscopy is a new technique in the field of pharmaceuticals, as well as in general studies of crystallinity. This is despite indications in previous studies showing an innate ability of the technique for identifying crystalline materials and in some cases, quantifying crystallinity. Arguably one of the most beneficial aspects of this technique is the relatively small amount of time necessary to prepare and analyze samples when compared with XRD or DSC. This should ensure the growing use of low-frequency Raman spectroscopy in, not only pharmaceutical crystallinity studies, but also crystallinity studies of other substances as well.

References

  1. J.R. Ferraro and K. Nakamoto, Introductory Raman Spectroscopy, 1st Edition (Academic Press, San Diego, 1994).
  2. K.C. Gordon and C.M. McGoverin, Int. J. Pharm. 417, 151–162 (2011).
  3. D. Law et al., J. Pharm. Sci. 90, 1015–1025 (2001).
  4. G.H. Ward and R.K. Schultz, Pharm. Res. 12, 773–779 (1995).
  5. M.D. Ticehurst et al., Int. J. Pharm. 193, 247–259 (2000).
  6. M. Rani, R. Govindarajan, R. Surana, and R. Suryanarayanan, Pharm. Res. 23, 2356–2367 (2006).
  7. M.J. Pikal, in Polymorphs of Pharmaceutical Solids, H.G. Brittain, Ed. (Marcel Dekker, New York, 1999), pp. 395–419.
  8. M. Ohta and G. Buckton, Int. J. Pharm. 289, 31–38 (2005).
  9. J. Han and R. Suryanarayanan, Pharm. Dev. Technol. 3, 587–596 (1998).
  10. S. Debnath and R. Suryanarayanan, AAPS PharmSciTech. 5, 1–11 (2004).
  11. C.J. Strachan, T. Rades, D.A. Newnham, K.C. Gordon, M. Pepper, and P.F. Taday, Chem. Phys. Lett. 390, 20–24 (2004).
  12. Y.C. Shen, Int. J. Pharm. 417, 48–60 (2011).
  13. G.W. Chantry, in Submillimeter Spectroscopy: A Guide to the Theoretical and Experimental Physics of the Far Infrared, 1st Edition (Academic Press Inc. Ltd., Waltam, 1971).
  14. D. Tuschel, Spectroscopy 30(9), 18–31 (2015).
  15. P.M.A. Sherwood, Vibrational Spectroscopy of Solids (Cambridge University Press, Cambridge, 1972).
  16. L. Ho et al., J. Control. Release. 119, 253–261 (2007).
  17. V.P. Wallace et al., Faraday Discuss. 126, 255–263 (2004).
  18. F.S. Vieira and C. Pasquini, Anal. Chem. 84, 3780–3786 (2014).
  19. J. Darkwah, G. Smith, I. Ermolina, and M. Mueller-Holtz, Int. J. Pharm.455, 357–364 (2013).
  20. S. Kojima, T. Shibata, H. Igawa, and T. Mori, IOP Conf. Ser. Mater. Sci. Eng. 54, 1–6 (2014).
  21. T. Shibata, T. Mori, and S. Kojima, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 150, 207–211 (2015).
  22. S.P. Delaney, D. Pan, M. Galella, S.X. Yin, and T.M. Korter, Cryst. Growth Des. 12, 5017–5024 (2012).
  23. M.D. King, W.D. Buchanan, and T.M. Korter, Anal. Chem. 83, 3786–3792 (2011).
  24. C.J. Strachan et al., J. Pharm. Sci. 94, 837–846 (2005).
  25. C.M. McGoverin, T. Rades, and K.C. Gordon, J. Pharm. Sci. 97, 4598–4621 (2008).
  26. A. Heinz, C.J. Strachan, K.C. Gordon, and T. Rades, J. Pharm. Pharmacol. 971–988 (2009).,<>
  27. H.G. Brittain, J. Pharm. Sci. 86, 405–412 (1997).
  28. L. Yu, S.M. Reutzel, and G. A. Stephenson, Pharm. Sci. Technol. Today1, 118–127 (1998).
  29. M. Dracínský, E. Procházková, J. Kessler, J. Šebestík, P. Matejka, and P. Bour, J. Phys. Chem. B. 117, 7297–7307 (2013).
  30. P. Sharma et al., J. Raman Spectrosc. DOI 10.1002/jrs.4834, wileyonlinelibrary.com (2015).
  31. A.P. Ayala, Vib. Spectrosc. 45, 112–116 (2007).
  32. J.F. Scott, Spex Speak. 17, 1–12 (1972).
  33. D.P. Strommen and K. Nakamoto, in Laboratory Raman Spectroscopy, 1st Edition (John Wiley & Sons Inc., New York, 1984).
  34. A.L. Glebov, O. Mokhun, A. Rapaport, S. Vergnole, V. Smirnov, and L.B. Glebov, Proc. SPIE. 8428, 84280C1–84280C11 (2012).
  35. E.P.J. Parrott and J.A. Zeitler, Appl. Spectrosc. 69, 1–25 (2015).
  36. A. Hédoux, L. Paccou, Y. Guinet, J.F. Willart, and M. Descamps, Eur. J. Pharm. Sci. 38, 156–164 (2009).
  37. R.L. McCreery, in Raman Spectroscopy for Chemical Analysis, 1st Edition (John Wiley & Sons Inc., New York, 2000).
  38. P.T. Mah, S.J. Fraser, M.E. Reish, T. Rades, K.C. Gordon, and C.J. Strachan, Vib. Spectrosc. 77, 10–16 (2015).
  39. A. Hédoux, A.A. Decroix, Y. Guinet, L. Paccou, P. Derollez, and M. Descamps, J. Phys. Chem. B. 115, 5746–5753 (2011).
  40. A. Hédoux, Y. Guinet, and M. Descamps, Int. J. Pharm. 417, 17–31 (2011).
  41. P.J. Larkin, M. Dabros, B. Sarsfield, E. Chan, J.T. Carriere, and B.C. Smith, Appl. Spectrosc. 68, 758–776 (2014).
  42. H. Wang, M. A. Boraey, L. Williams, D. Lechuga-Ballesteros, and R. Vehring, Int. J. Pharm. 469, 197–205 (2014).
  43. R. Heyler, J. Carriere, and B. Smith, in “Raman Technology for Today’s Spectroscopists,” supplement to Spectroscopy (June), 44–50 (2013).
  44. S. Al-Dulaimi, A. Aina, and J. Burley, CrystEngComm. 12, 1038–1040 (2010).

 

The drawing in Figure 1 is that of a six-membered ring or hexagon. A carbon atom is located at each vertex of the hexagon and a hydrogen atom is attached to each carbon, although it is not written in. The circle inside the ring represents that the electrons are delocalized which is illustrated in Figure 2.

http://images.alfresco.advanstar.com/alfresco_images/pharma/2016/02/12/645ee751-2432-4444-8af1-ded62697ee27/IR-Spectral-figure02_web.jpg

Figure 2: Top: The P orbitals on each of the six carbon atoms in benzene that contribute an electron to the ring. Bottom: the collection of delocalized P orbital electrons forming a cloud of electron density above and below the benzene ring.

Each of the carbon atoms in a benzene ring contains two P orbitals containing a lone electron, and one of these orbitals is perpendicular to the benzene ring as seen in the top of Figure 2. There is enough orbital overlap that these electrons, rather than being confined between two carbon atoms as might be expected, instead delocalize and form clouds of electron density above and below the plane of the ring. This type of bonding is called aromatic bonding(2), and a ring that has aromatic bonding is called an aromatic ring. It is aromatic bonding that gives aromatic rings their unique structures, chemistry, and IR spectra. Benzene is simply a commonly found aromatic ring. Other types of aromatic molecules include polycyclic aromatic hydrocarbons (PAHs), such as naphthalene, that contain two or more benzene rings that are fused (which means adjacent rings share two carbon atoms), and heterocyclic aromatic rings which are aromatic rings that contain a noncarbon atom such as nitrogen. Pyridine is an example of one of these. The interpretation of the IR spectra of these latter aromatic molecules will be discussed in future articles.

The IR Spectrum of Benzene

The IR spectrum of benzene is shown in Figure 3.

http://images.alfresco.advanstar.com/alfresco_images/pharma/2016/02/12/645ee751-2432-4444-8af1-ded62697ee27/IR-Spectral-figure03_web.jpg

 

09:00

Super-Resolution Fluorescence Microscopy: Where To Go Now?
Bernd Rieger, Quantitative Imaging Group Leader, Delft University of Technology

09:30

Keynote Presentation

From Molecules To Whole Organs
Francesco Pavone, Principal Investigator, LENS, University of Florence

Some examples of correlative microscopies, combining linear and non linear techniques will be described. Particular attention will be devoted Alzheimer disease or to neural plasticity after damage as neurobiological application.

10:15

Super-Resolution Imaging by dSTORM
Markus Sauer, Professor, Julius-Maximilians-Universität Würzburg

10:45

Coffee and Networking in Exhibition Hall

11:15

Correlated Fluorescence And X-Ray Tomography: Finding Molecules In Cellular CT Scans
Carolyn Larabell, Professor, University of California San Francisco

11:45

Integrating Advanced Fluorescence Microscopy Techniques Reveals Nanoscale Architecture And Mesoscale Dynamics Of Cytoskeletal Structures Promoting Cell Migration And Invasion
Alessandra Cambi, Assistant Professor, University of Nijmegen

This lecture will describe our efforts to exploit and integrate a variety of advanced microscopy techniques to unravel the nanoscale structural and dynamic complexity of individual podosomes as well as formation, architecture and function of mesoscale podosome clusters.

12:15

Multi-Photon-Like Fluorescence Microscopy Using Two-Step Imaging Probes
George Patterson, Investigator, National Institutes of Health

12:45

Lunch & Networking in Exhibition Hall

14:15

Technology Spotlight

14:30

3D Single Particle Tracking: Following Mitochondria in Zebrafish Embryos
Don Lamb, Professor, Ludwig-Maximilians-University

15:00

Visualizing Mechano-Biology: Quantitative Bioimaging Tools To Study The Impact Of Mechanical Stress On Cell Adhesion And Signalling
Bernhard Wehrle-Haller, Group Leader, University of Geneva

15:30

Superresolution Imaging Of Clathrin-Mediated Endocytosis In Yeast
Jonas Ries, Group Leader, EMBL Heidelberg

We use single-molecule localization microscopy to investigate the dynamic structural organization of the east endocytic machinery. We discovered a striking ring-shaped pre-patterning of the actin nucleation zone, which is key for an efficient force generation and membrane invagination.

16:00

Coffee and Networking in Exhibition Hall

16:30

Optical Imaging of Molecular Mechanisms of Disease
Clemens Kaminski, Professor, University of Cambridge

17:00

3-D Optical Tomography For Ex Vivo And In Vivo Imaging
James McGinty, Professor, Imperial College London

17:30

End Of Day One

Wednesday, 15 June 2016

09:00

Imaging Gene Regulation in Living Cells at the Single Molecule Level
James Zhe Liu, Group Leader, Janelia Research Campus, Howard Hughes Medical Institute

09:30

Keynote Presentation

Super-Resolution Microscopy With DNA Molecules
Ralf Jungmann, Group Leader, Max Planck Institute of Biochemistry

10:15

A Revolutionary Miniaturised Instrument For Single-Molecule Localization Microscopy And FRET
Achillefs Kapanidis, Professor, University of Oxford

10:45

Coffee and Networking in Exhibition Hall

11:15

Democratising Live-Cell High-Speed Super-Resolution Microscopy
Ricardo Henriques, Group Leader, University College London

11:45

Democratising Live-Cell High-Speed Super-Resolution Microscopy

12:15

Information In Localisation Microscopy
Susan Cox, Professor, Kings College London

12:45

Lunch & Networking in Exhibition Hall

14:15

Technology Spotlight

14:30

High-Content Imaging Approaches For Drug Discovery For Neglected Tropical Diseases
Manu De Rycker, Team Leader, University of Dundee

The development of new drugs for intracellular parasitic diseases is hampered by difficulties in developing relevant high-throughput cell-based assays. Here we present how we have used image-based high-content screening approaches to address some of these issues.

15:00

High Resolution In Vivo Histology: Clinical in vivo Subcellular Imaging using Femtoseceond Laser Multiphoton/CARS Tomography
Karsten König, Professor, Saarland University

We report on a certified, medical, transportable multipurpose nonlinear microscopic imagingsystem based on a femtosecond excitation source and a photonic crystal fiber with multiple miniaturized time-correlated single-photon counting detectors.

15:30

Coffee and Networking in Exhibition Hall

16:00

Lateral Organization Of Plasma Membrane Constituents At The Nanoscale
Gerhard Schutz, Professor, Vienna University of Technology

It is of interest how proteins are spatially distributed over the membrane, and whether they conjoin and move as part of multi-molecular complexes. In my lecture, I will discuss methods for approaching the two questions, and provide biological examples.

16:30

Correlative Light And Electron Microscopy In Structural Cell Biology
Wanda Kukulski, Group Leader, University of Cambridge

17:00

Close of Conference

 

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Protein profiling in cancer and metabolic diseases

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Deep Protein Profiling Key

Company has encouraged by two recent reports that emphasise the importance of protein profiling to improve outcomes in cancer treatment.

http://www.technologynetworks.com/Proteomics/news.aspx?ID=190145

Proteome Sciences plc has strongly encouraged by two recent reports that emphasise the importance of protein profiling to improve outcomes in cancer treatment. These highlight the growing need for more detailed, personal assessment of protein profiles to improve the management of cancer treatment.

In the first study two groups from University College London and Cancer Research UK demonstrated that genetic mutations in cancer can lead to changes in the proteins on the cell surface1. These are new sequences which are seen as foreign by the body’s immune system and, with appropriate immunotherapy, the level of response in lung cancer was greatly enhanced.

However many of the patients with these types of mutations unfortunately still did not respond which highlighted the need for deeper analysis of the protein expression in tumours in order to better appreciate the mechanisms that contribute to treatment failure.

The second study, led by Professor Nigel Bundred of Manchester University, reported that use of two drugs that act on the same breast cancer target, an over-expressing protein called Her-2, were able to eradicate detectable tumours in around 10% of those treated in just 11 days, with 87% of those treated having a proteomic change indicating cells had stopped growing and/or cell death had increased2.

Whilst these results appear very promising it is worth noting that the over-expressing Her-2 target is only present in about 20% of breast tumours meaning this combination therapy was successful in clearing tumours in just 2% of the total breast cancer population.

Dr. Ian Pike, Chief Operating Officer of Proteome Sciences commented, “Both these recent studies should rightly be recognised as important steps forward towards better cancer treatment. However, in order to overcome the limitations of current drug therapy programs, a much deeper and more comprehensive analysis of the complex protein networks that regulate tumour growth and survival is required and will be essential to achieve a major advance in the battle to treat cancer.

“Our SysQuant® workflows provide that solution. As an example, in pancreatic cancer3 we have successfully mapped the complex network of regulatory processes and demonstrate the ability to devise personalised treatment combinations on an individual basis for each patient. A retrospective study with SysQuant® to predict response to the targeted drug Sorafenib in liver cancer is in process and we are planning further prospective trials to guide personalised treatment selection in liver cancer.

“We are already delivering systems-wide biology solutions through SysQuant® and TMTcalibrator™ programs to our clients that are generating novel biological data and results using more sensitive profiling that are helping them to better understand their drug development programs and to provide new biomarkers for tracking patient response in clinical trials.

“We are strongly positioned to deliver more comprehensive analysis of proteins and cellular pathways across other areas of disease and in particular to extend the use of SysQuant® with other leading cancer research groups in liver and other cancers.”

Proteome Sciences has also expanded its offering in personalised medicine through the use of its TMTcalibrator™ technology to uniquely identify protein biomarkers that reveal active cancer and other disease processes in body fluid samples. The importance of these ‘mechanistic’ biomarkers is that they are essential to monitor that drugs are being effective and that they can be used as early biomarkers of disease recurrence.

Using SysQuant® and TMTcalibrator™, Proteome Sciences can deliver more comprehensive analysis and provide unparalleled levels of sensitivity and breadth of coverage of the proteome, enabling faster, more efficient drug development and more accurate disease diagnosis.

 

Discovering ‘Outlier’ Enzymes

Researchers at TSRI and Salk Institute have discovered ‘Outlier’ enzymes that could offer new targets to treat type 2 diabetes and inflammatory disorders.

A team led by scientists at The Scripps Research Institute (TSRI) and the Salk Institute for Biological Studies have discovered two enzymes that appear to play a role in metabolism and inflammation—and might someday be targeted with drugs to treat type 2 diabetes and inflammatory disorders. The discovery is unusual because the enzymes do not bear a resemblance—in their structures or amino-acid sequences—to any known class of enzymes.

The team of scientists nevertheless identified them as “outlier” members of the serine/threonine hydrolase class, using newer techniques that detect biochemical activity. “A huge fraction of the human ‘proteome’ remains uncharacterized, and this paper shows how chemical approaches can be used to uncover proteins of a given functionality that have eluded classification based on sequence or predicted structure,” said co-senior author Benjamin F. Cravatt, chair of TSRI’s Department of Chemical Physiology.

“In this study, we found two genes that control levels of lipids with anti-diabetic and anti-inflammatory activity, suggesting exciting targets for diabetes and inflammatory diseases,” said co-senior author Alan Saghatelian, who holds the Dr. Frederik Paulsen Chair at the Salk Institute. The study, which appeared as a Nature Chemical Biology Advance Online Publication on March 28, 2016, began as an effort in the Cravatt laboratory to discover and characterize new serine/threonine hydrolases using fluorophosphonate (FP) probes—molecules that selectively bind and, in effect, label the active sites of these enzymes.

Pulling FP-binding proteins out of the entire proteome of test cells and identifying them using mass spectrometry techniques, the team matched nearly all to known hydrolases. The major outlier was a protein called androgen-induced gene 1 protein (AIG1). The only other one was a distant cousin in terms of sequence, a protein called ADTRP. “Neither of these proteins had been characterized as an enzyme; in fact, there had been little functional characterization of them at all,” said William H. Parsons, a research associate in the Cravatt laboratory who was co-first author of the study.

Experiments on AIG1 and ADTRP revealed that they do their enzymatic work in a unique way. “It looks like they have an active site that is novel—it had never been described in the literature,” said Parsons. Initial tests with panels of different enzyme inhibitors showed that AIG1 and ADTRP are moderately inhibited by inhibitors of lipases—enzymes that break down fats and other lipids. But on what specific lipids do these newly discovered outlier enzymes normally work?

At the Salk Institute, the Saghatelian laboratory was investigating a class of lipids it had discovered in 2014. Known as fatty acid esters of hydroxy fatty acids (FAHFAs), these molecules showed strong therapeutic potential. Saghatelian and his colleagues had found that boosting the levels of one key FAHFA lipid normalizes glucose levels in diabetic mice and also reduces inflammation.

“[Ben Cravatt’s] lab was screening panels of lipids to find the ones that their new enzymes work on,” said Saghatelian, who is a former research associate in the Cravatt laboratory. “We suggested they throw FAHFAs in there—and these turned out to be very good substrates.” The Cravatt laboratory soon developed powerful inhibitors of the newly discovered enzymes, and the two labs began working together, using the inhibitors and genetic techniques to explore the enzymes’ functions in vitro and in cultured cells.

Co-first author Matthew J. Kolar, an MD-PhD student, performed most of the experiments in the Saghatelian lab. The team concluded that AIG1 and ADTRP, at least in the cell types tested, appear to work mainly to break down FAHFAs and not any other major class of lipid. In principle, inhibitors of AIG1 and ADTRP could be developed into FAHFA-boosting therapies.

“Our prediction,” said Saghatelian, “is that if FAHFAs do what we think they’re doing, then using an enzyme inhibitor to block their degradation would make FAHFA levels go up and should thus reduce inflammation as well as improve glucose levels and insulin sensitivity.” The two labs are now collaborating on further studies of the new enzymes—and the potential benefits of inhibiting them—in mouse models of diabetes, inflammation and autoimmune disease.

“One of the neat things this study shows,” said Cravatt, “is that even for enzyme classes as well studied as the hydrolases, there may still be hidden members that, presumably by convergent evolution, arrived at that basic enzyme mechanism despite sharing no sequence or structural homology.”

Other co-authors of the study, “AIG1 and ADTRP are atypical integral membrane hydrolases that degrade bioactive FAHFAs,” were Siddhesh S. Kamat, Armand B. Cognetta III, Jonathan J. Hulce and Enrique Saez, of TSRI; and co-senior author Barbara B. Kahn of Beth Israel Deaconess Medical Center and Harvard Medical School

 

New Weapon Against Breast Cancer

Molecular marker in healthy tissue can predict a woman’s risk of getting the disease, research says.

Harvard Stem Cell Institute (HSCI) researchers at Dana-Farber Cancer Institute (DFCI) and collaborators at Brigham and Women’s Hospital (BWH) have identified a molecular marker in normal breast tissue that can predict a woman’s risk for developing breast cancer, the leading cause of death in women with cancer worldwide.

The work, led by HSCI principal faculty member Kornelia Polyak and Rulla Tamimi of BWH, was published in an early online release and in the April 1 issue of Cancer Research.

The study builds on Polyak’s earlier research finding that women already identified as having a high risk of developing cancer — namely those with a mutation called BRCA1 or BRCA2 — or women who did not give birth before their 30s had a higher number of mammary gland progenitor cells.

In the latest study, Polyak, Tamimi, and their colleagues examined biopsies, some taken as many as four decades ago, from 302 participants in the Nurses’ Health Study and the Nurses’ Health Study II who had been diagnosed with benign breast disease. The researchers compared tissue from the 69 women who later developed cancer to the tissue from the 233 women who did not. They found that women were five times as likely to develop cancer if they had a higher percentage of Ki67, a molecular marker that identifies proliferating cells, in the cells that line the mammary ducts and milk-producing lobules. These cells, called the mammary epithelium, undergo drastic changes throughout a woman’s life, and the majority of breast cancers originate in these tissues.

Doctors already test breast tumors for Ki67 levels, which can inform decisions about treatment, but this is the first time scientists have been able to link Ki67 to precancerous tissue and use it as a predictive tool.

“Instead of only telling women that they don’t have cancer, we could test the biopsies and tell women if they were at high risk or low risk for developing breast cancer in the future,” said Polyak, a breast cancer researcher at Dana-Farber and co-senior author of the paper.

“Currently, we are not able to do a very good job at distinguishing women at high and low risk of breast cancer,” added co-senior author Tamimi, an associate professor at the Harvard T.H. Chan School of Public Health and Harvard Medical School. “By identifying women at high risk of breast cancer, we can better develop individualized screening and also target risk reducing strategies.”

To date, mammograms are the best tool for the early detection, but there are risks associated with screening. False positive and negative results and over-diagnosis could cause psychological distress, delay treatment, or lead to overtreatment, according to the National Cancer Institute (NCI).

Mammography machines also use low doses of radiation. While a single mammogram is unlikely to cause harm, repeated screening can potentially cause cancer, though the NCI writes that the benefits “nearly always outweigh the risks.”

“If we can minimize unnecessary radiation for women at low risk, that would be good,” said Tamimi.

Screening for Ki67 levels would “be easy to apply in the current setting,” said Polyak, though the researchers first want to reproduce the results in an independent cohort of women.

 

AIG1 and ADTRP are atypical integral membrane hydrolases that degrade bioactive FAHFAs

William H ParsonsMatthew J Kolar, …., Barbara B KahnAlan Saghatelian & Benjamin F Cravatt

Nature Chemical Biology 28 March 2016                    http://dx.doi.org:/10.1038/nchembio.2051

Enzyme classes may contain outlier members that share mechanistic, but not sequence or structural, relatedness with more common representatives. The functional annotation of such exceptional proteins can be challenging. Here, we use activity-based profiling to discover that the poorly characterized multipass transmembrane proteins AIG1 and ADTRP are atypical hydrolytic enzymes that depend on conserved threonine and histidine residues for catalysis. Both AIG1 and ADTRP hydrolyze bioactive fatty acid esters of hydroxy fatty acids (FAHFAs) but not other major classes of lipids. We identify multiple cell-active, covalent inhibitors of AIG1 and show that these agents block FAHFA hydrolysis in mammalian cells. These results indicate that AIG1 and ADTRP are founding members of an evolutionarily conserved class of transmembrane threonine hydrolases involved in bioactive lipid metabolism. More generally, our findings demonstrate how chemical proteomics can excavate potential cases of convergent or parallel protein evolution that defy conventional sequence- and structure-based predictions.

Figure 1: Discovery and characterization of AIG1 and ADTRP as FP-reactive proteins in the human proteome.

 

http://www.nature.com/nchembio/journal/vaop/ncurrent/carousel/nchembio.2051-F1.jpg

(a) Competitive ABPP-SILAC analysis to identify FP-alkyne-inhibited proteins, in which protein enrichment and inhibition were measured in proteomic lysates from SKOV3 cells treated with FP-alkyne (20 μM, 1 h) or DMSO using the FP-biotin…

 

  1. Willems, L.I., Overkleeft, H.S. & van Kasteren, S.I. Current developments in activity-based protein profiling. Bioconjug. Chem. 25, 11811191 (2014).
  2. Niphakis, M.J. & Cravatt, B.F. Enzyme inhibitor discovery by activity-based protein profiling.Annu. Rev. Biochem. 83, 341377 (2014).
  3. Berger, A.B., Vitorino, P.M. & Bogyo, M. Activity-based protein profiling: applications to biomarker discovery, in vivo imaging and drug discovery. Am. J. Pharmacogenomics 4,371381 (2004).
  4. Liu, Y., Patricelli, M.P. & Cravatt, B.F. Activity-based protein profiling: the serine hydrolases.Proc. Natl. Acad. Sci. USA 96, 1469414699 (1999).
  5. Simon, G.M. & Cravatt, B.F. Activity-based proteomics of enzyme superfamilies: serine hydrolases as a case study. J. Biol. Chem. 285, 1105111055 (2010).
  6. Bachovchin, D.A. et al. Superfamily-wide portrait of serine hydrolase inhibition achieved by library-versus-library screening. Proc. Natl. Acad. Sci. USA 107, 2094120946 (2010).
  7. Jessani, N. et al. A streamlined platform for high-content functional proteomics of primary human specimens. Nat. Methods 2, 691697 (2005).
  8. Higa, H.H., Diaz, S. & Varki, A. Biochemical and genetic evidence for distinct membrane-bound and cytosolic sialic acid O-acetyl-esterases: serine-active-site enzymes. Biochem. Biophys. Res. Commun. 144, 10991108 (1987).

Academic cross-fertilization by public screening yields a remarkable class of protein phosphatase methylesteras-1 inhibitors

Proc Natl Acad Sci U S A. 2011 Apr 26; 108(17): 6811–6816.    doi:  10.1073/pnas.1015248108
National Institutes of Health (NIH)-sponsored screening centers provide academic researchers with a special opportunity to pursue small-molecule probes for protein targets that are outside the current interest of, or beyond the standard technologies employed by, the pharmaceutical industry. Here, we describe the outcome of an inhibitor screen for one such target, the enzyme protein phosphatase methylesterase-1 (PME-1), which regulates the methylesterification state of protein phosphatase 2A (PP2A) and is implicated in cancer and neurodegeneration. Inhibitors of PME-1 have not yet been described, which we attribute, at least in part, to a dearth of substrate assays compatible with high-throughput screening. We show that PME-1 is assayable by fluorescence polarization-activity-based protein profiling (fluopol-ABPP) and use this platform to screen the 300,000+ member NIH small-molecule library. This screen identified an unusual class of compounds, the aza-β-lactams (ABLs), as potent (IC50 values of approximately 10 nM), covalent PME-1 inhibitors. Interestingly, ABLs did not derive from a commercial vendor but rather an academic contribution to the public library. We show using competitive-ABPP that ABLs are exquisitely selective for PME-1 in living cells and mice, where enzyme inactivation leads to substantial reductions in demethylated PP2A. In summary, we have combined advanced synthetic and chemoproteomic methods to discover a class of ABL inhibitors that can be used to selectively perturb PME-1 activity in diverse biological systems. More generally, these results illustrate how public screening centers can serve as hubs to create spontaneous collaborative opportunities between synthetic chemistry and chemical biology labs interested in creating first-in-class pharmacological probes for challenging protein targets.

Protein phosphorylation is a pervasive and dynamic posttranslational protein modification in eukaryotic cells. In mammals, more than 500 protein kinases catalyze the phosphorylation of serine, threonine, and tyrosine residues on proteins (1). A much more limited number of phosphatases are responsible for reversing these phosphorylation events (2). For instance, protein phosphatase 2A (PP2A) and PP1 are thought to be responsible together for > 90% of the total serine/threonine phosphatase activity in mammalian cells (3). Specificity is imparted on PP2A activity by multiple mechanisms, including dynamic interactions between the catalytic subunit (C) and different protein-binding partners (B subunits), as well as a variety of posttranslational chemical modifications (2, 4). Within the latter category is an unusual methylesterification event found at the C terminus of the catalytic subunit of PP2A that is introduced and removed by a specific methyltransferase (leucine carbxoylmethyltransferase-1 or LCMT1) (5, 6) and methylesterase (protein phosphatase methylesterase-1 or PME-1) (7), respectively (Fig. 1A). PP2A carboxymethylation (hereafter referred to as “methylation”) has been proposed to regulate PP2A activity, at least in part, by modulating the binding interaction of the C subunit with various regulatory B subunits (810). A predicted outcome of these shifts in subunit association is the targeting of PP2A to different protein substrates in cells. PME-1 has also been hypothesized to stabilize inactive forms of nuclear PP2A (11), and recent structural studies have shed light on the physical interactions between PME-1 and the PP2A holoenzyme (12).

There were several keys to the success of our probe development effort. First, screening for inhibitors of PME-1 benefited from the fluopol-ABPP technology, which circumvented the limited throughput of previously described substrate assays for this enzyme. Second, we were fortunate that the NIH compound library contained several members of the ABL class of small molecules. These chiral compounds, which represent an academic contribution to the NIH library, occupy an unusual portion of structural space that is poorly accessed by commercial compound collections. Although at the time of their original synthesis (23) it may not have been possible to predict whether these ABLs would show specific biological activity, their incorporation into the NIH library provided a forum for screening against many proteins and cellular targets, culminating in their identification as PME-1 inhibitors. We then used advanced chemoproteomic assays to confirm the remarkable selectivity displayed by ABLs for PME-1 across (and beyond) the serine hydrolase superfamily. That the mechanism for PME-1 inhibition involves acylation of the enzyme’s conserved serine nucleophile (Fig. 3) suggests that exploration of a more structurally diverse set of ABLs might uncover inhibitors for other serine hydrolases. In this way, the chemical information gained from a single high-throughput screen may be leveraged to initiate probe development programs for additional enzyme targets.

Projecting forward, this research provides an example of how public small-molecule screening centers can serve as a portal for spawning academic collaborations between chemical biology and synthetic chemistry labs. By continuing to develop versatile high-throughput screens and combining them with a small-molecule library of expanding structural diversity conferred by advanced synthetic methodologies, academic biologists and chemists are well-positioned to collaboratively deliver pharmacological probes for a wide range of proteins and pathways in cell biology.

 

New weapon against breast cancer

Molecular marker in healthy tissue can predict a woman’s risk of getting the disease, research says

April 6, 2016 | Popular
BRC_Cancer605

 

New Group of Aging-Related Proteins Discovered

http://www.genengnews.com/gen-news-highlights/new-group-of-aging-related-proteins-discovered/81252599/

Scientists have discovered a group of six proteins that may help to divulge secrets of how we age, potentially unlocking new insights into diabetes, Alzheimer’s, cancer, and other aging-related diseases.

The proteins appear to play several roles in our bodies’ cells, from decreasing the amount of damaging free radicals and controlling the rate at which cells die to boosting metabolism and helping tissues throughout the body respond better to insulin. The naturally occurring amounts of each protein decrease with age, leading investigators to believe that they play an important role in the aging process and the onset of diseases linked to older age.

The research team led by Pinchas Cohen, M.D., dean and professor of the University of Southern California Leonard Davis School of Gerontology, identified the proteins and observed their origin from mitochondria and their game-changing roles in metabolism and cell survival. This latest finding builds upon prior research by Dr. Cohen and his team that uncovered two significant proteins, humanin and MOTS-c, hormones that appear to have significant roles in metabolism and diseases of aging.

Unlike most other proteins, humanin and MOTS-c are encoded in mitochondria. Dr. Cohen’s team used computer analysis to see if the part of the mitochondrial genome that provides the code for humanin was coding for other proteins as well. The analysis uncovered the genes for six new proteins, which were dubbed small humanin-like peptides, or SHLPs, 1 through 6 (pronounced “schlep”).

After identifying the six SHLPs and successfully developing antibodies to test for several of them, the team examined both mouse tissues and human cells to determine their abundance in different organs as well as their functions. The proteins were distributed quite differently among organs, which suggests that the proteins have varying functions based on where they are in the body. Of particular interest is SHLP 2, according to Dr. Cohen.  The protein appears to have insulin-sensitizing, antidiabetic effects as well as neuroprotective activity that may emerge as a strategy to combat Alzheimer’s disease. He added that SHLP 6 is also intriguing, with a unique ability to promote cancer cell death and thus potentially target malignant diseases.

Proteins That May Protect Against Age Related Illnesses Discovered

 

The cell proliferation antigen Ki-67 organises heterochromatin

 Michal Sobecki, 

Antigen Ki-67 is a nuclear protein expressed in proliferating mammalian cells. It is widely used in cancer histopathology but its functions remain unclear. Here, we show that Ki-67 controls heterochromatin organisation. Altering Ki-67 expression levels did not significantly affect cell proliferation in vivo. Ki-67 mutant mice developed normally and cells lacking Ki-67 proliferated efficiently. Conversely, upregulation of Ki-67 expression in differentiated tissues did not prevent cell cycle arrest. Ki-67 interactors included proteins involved in nucleolar processes and chromatin regulators. Ki-67 depletion disrupted nucleologenesis but did not inhibit pre-rRNA processing. In contrast, it altered gene expression. Ki-67 silencing also had wide-ranging effects on chromatin organisation, disrupting heterochromatin compaction and long-range genomic interactions. Trimethylation of histone H3K9 and H4K20 was relocalised within the nucleus. Finally, overexpression of human or Xenopus Ki-67 induced ectopic heterochromatin formation. Altogether, our results suggest that Ki-67 expression in proliferating cells spatially organises heterochromatin, thereby controlling gene expression.

 

A protein called Ki-67 is only produced in actively dividing cells, where it is located in the nucleus – the structure that contains most of the cell’s DNA. Researchers often use Ki-67 as a marker to identify which cells are actively dividing in tissue samples from cancer patients, and previous studies indicated that Ki-67 is needed for cells to divide. However, the exact role of this protein was not clear. Before cells can divide they need to make large amounts of new proteins using molecular machines called ribosomes and it has been suggested that Ki-67 helps to produce ribosomes.

Now, Sobecki et al. used genetic techniques to study the role of Ki-67 in mice. The experiments show that Ki-67 is not required for cells to divide in the laboratory or to make ribosomes. Instead, Ki-67 alters the way that DNA is packaged in the nucleus. Loss of Ki-67 from mice cells resulted in DNA becoming less compact, which in turn altered the activity of genes in those cells.

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Chromatography and Mass Spectroscopy

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Optimization of Chromatography in the Lab

Sanji Bhal & Karim Kassam; ACD/Labs

While analytical laboratories may still rely to some extent on trial-and-error approaches, there is agreement that this is increasingly less effective as systems become more complex. Regulatory bodies are putting increasing pressure on pharmaceutical companies to incorporate Quality by Design (QbD) approaches throughout the drug development process. QbD is defined in the ICH Q8 guideline as “A systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.”

Developing effective and robust separations methods can be a very time-consuming process. A comprehensive approach to method development would be thorough investigation of the design space for any given mixture or sample including buffer, column, solvent, time, temperature, etc. Given the time constraints and limited resources in any R&D laboratory, however, this type of broad scope investigation is unrealistic.

Modeling for the optimization of chromatographic separations of small molecules has been successfully used for approximately 30 years. A large number of articles have been published on this topic by L. Snyder, P. Janderra, P. Schoenmakers et al. Modeling of chromatographic separations continues to be of interest because as the science of chromatographic separations continues to evolve, modeling techniques must evolve with them to support the needs of the community.

New types of chromatographic techniques (UHPLC, HILIC, ion exchange chromatography, etc.) have demanded the need for new modeling tools. This also led to the need for translation of methods from one technique to newer techniques (HPLC to UHPLC, for example). Furthermore, as pharmaceutical R&D has expanded investigation of new drugs from small molecules to proteins and bio-molecules, many of the old rules no longer apply.

The ability to model the behavior of a sample in silico provides chromatographers with a number of advantages:

Greater efficiency in method development—it is difficult to estimate the number of hours required to identify a suitable method for separation of a mixture. An experienced scientist will rely on their knowledge while an inexperienced colleague may struggle with the same separation. As the number of experienced chromatographers decreases across organizations, and the existing scientists are retiring, software to assist those less experienced becomes more attractive.

With such a large number of variables (temperature, gradient, pH, salt concentration, etc.) it is advantageous to use all available knowledge and tools to ‘get ahead’. Increased efficiency can be realized not only in identifying an optimal method faster but also increasing throughput and decreasing scale-up time.

Risk mitigation through robust methods—this is the ideal result of a method development project. By applying QbD principles and understanding the analytical design space of a sample, the chromatographer can understand, reduce, and control sources of variability; and use this information to create a method that is reliable and robust. Simulation of methods provides scientists the luxury of thoroughly investigating method development space with limited consumption of resources and time, for the best result.

Economic considerations—while there is a cost in man hours and time spent on method development, there is also unrecoverable expenditure on consumables (solvents, columns, etc.). In being able to investigate chromatographic space in silico, this time and expenditure can be greatly reduced.

Green chemistry—the ability to model separations not only reduces the volume of waste, it may also help us reduce environmental impact. Consider the case of acetonitrile shortages in recent years. The ability to use alternative methods, i.e., replacing acetonitrile with methanol, not only lead to reduced cost but also has the side effect of more environmentally safe waste.

Software provided with chromatographic instruments delivers many useful capabilities to execute experimental runs and control instruments. Simulation software, however, is typically purchased separately. Several commercial software packages are available, i.e., DryLab, ACD/LC Simulator (from ACD/Labs), ChromSword, and Osiris, each of which provides different advantages and limitations (an exhaustive list is outside the scope of this article).

Commercially available method optimization software is typically built on one of three models—simulations based on molecular structure, retention based modeling, and statistical modeling. Each has its pros and cons with details in their implementation that appeal to different applications.

Data input—flexibility of data import into a system from the instrument is an important consideration when dealing with multiple experiments under varying conditions. Lack of standardization of chromatographic data formats today, however, means that unless data from separations is transferred into Excel or similar software, scientists are left to transcribe information from one system to another. Direct data import from chromatographic runs into third-party modeling software, in the instrument format, is ideal since it avoids transcription errors and saves time in data input. ACD/Labs provides the only software (ACD/LC Simulator and ACD/GC Simulator) with instrument vendor-agnostic support of analytical data at this time.

Data visualization—the ability to review and interrogate data is of utmost importance in method development and optimization, and software vendors implement various tools to meet chromatographers’ requirements. While 3D modeling, offered by DryLab, has enjoyed popularity in the community, the question of applicability still remains. A significant amount of data input (upwards of 45 injections is not unreasonable for simultaneous optimization of 3 factors) is required for effective 3D modeling, which in itself is counter-intuitive if time and resource efficiency is the ultimate goal.

Automation—ACD/AutoChrom (from ACD/Labs) and ChromSword both provide automation through instrument control. AutoChrom provides automation of the most popular Waters Empower and Agilent ChemStation systems and keeps the scientist in control by allowing user input at key stages of the method development process. This software is best suited for challenging separations such as stability indicating methods and forced degradation studies.

Custom Modeling—while third-party modeling software may cover a broad range of structure and method development space, there is nothing better than the ability for scientists to create their own models. ACD/LC Simulator was the only software known to the authors at the time of publication that offers this capability. Work published by world class chromatographers Patrik Pettersson and Mel Eureby demonstrates the use of ACD/LC Simulator in successfully modelling protein and HILIC separations.

Reverse phase HPLC, temperature/gradient optimization as modeled in ACD/LC Simulator. (Credit:  ACD/Labs )

Reverse phase HPLC, temperature/gradient optimization as modeled in ACD/LC Simulator. (Credit: ACD/Labs )

Physicochemical property predictions such as logD and pKa can also help in method development and optimization. In a general sense, being able to predict behavior with respect to pH can offer insights into method development challenges. ChromSword and ACD/Labs software both provide property predictions, and the latter have been leaders in this field for almost two decades with applications across various areas of research and industries.

As the science of separations evolves and the compounds of interest change, the software to support scientific research and development will need to develop alongside. Software vendors need to satisfy the needs of their customer organizations in releasing the time of valuable scientists for innovation thus releasing them from monotonous and tedious tasks. If your organization has yet to invest in software for modeling separations, it will likely come in the future and many of the topics raised here should be kept in mind to ensure you get the best return on investment.

 

Tissue Imaging Mass Spec Detects Early Lipid Changes in Acute Kidney Injury

University of Alabama at Birmingham researchers have made a microscopic snapshot of the early renal lipid changes in acute kidney injury, using a laser-scanning method called MALDI tissue imaging to localize the changes.
These disease-model results, recently published in American Journal of Physiology’s Renal Physiology, show an example of the power of MALDI tissue imaging. MALDI tissue imaging is now available at UAB, and it will be able to aid basic and clinical biomedical research across the campus, said corresponding author Janusz Kabarowski, Ph.D., associate professor of microbiology.
“I think the opportunity to integrate this into existing UAB research centers to facilitate grants is immense,” Kabarowski said. “It can be utilized for any tissue damage. For drugs that can be imaged with MALDI imaging mass spectrometry, you can tell where in a slice of tissue the drugs get to, with obvious implications for testing candidate therapeutic agents in cancer research too. We can capture—at the molecular level—a moment in time.”
The imaging has the power to reveal spatial distribution of complex biochemical processes in an organism, showing where changes in proteins or small molecules take place. Unlike chemical stains, immunohistochemical tags or radioactive labels, it does not require a priori knowledge of the target compounds.
Acute kidney injury is a leading cause of hospital illness or death in critically ill patients. In a mouse model of the injury used by Kabarowski and colleagues, kidneys were made ischemic for 30 minutes. Six hours after reperfusion, and before gross kidney damage was seen, the kidneys were removed and cut in half. The lipids were extracted from one of the halves; the other was flash frozen and cut into thin sections that were mounted on specially coated slides.
Extracted lipids were analyzed using SWATH mass spectrometry, and the UAB researchers found that four were significantly changed at six hours (all were increases). Three of the lipids were ether-linked phospholipids, including a plasmalogen, a type of ether phospholipid thought to have protective anti-oxidant properties. They also found that the levels of these ether-linked phospholipids correlated with levels of plasma creatinine, a marker of acute kidney injury. This suggests a causal or a protective role for them in acute kidney injury, and also suggests they may be an effective early biomarker for injury.
The researchers then used MALDI tissue imaging to find where the most abundant of the ether-linked phospholipids was concentrated. In MALDI, a powerful laser scans the thin tissue section after application of a matrix material by vacuum sublimation, knocking the lipid ions off from the surface of the tissue. The MALDI time-of-flight mass spectrometry and ion fragmentation then allowed identification of the proximal tubules of the kidney as the place where the ether-linked phospholipids were concentrated. The proximal tubules are known to be most prone to developing ischemia-related injury.
Besides Kabarowski, authors of “Early lipid changes in acute kidney injury using SWATH lipidomics coupled with MALDI tissue imaging” are co-first authors Sangeetha Rao, M.D., fellow in the UAB Pediatric Critical Care Medicine, and Kelly B. Walters, UAB departments of Chemistry and Microbiology; Landon Wilson and Stephen Barnes, Ph.D., UAB Department of Pharmacology and Toxicology, Targeted Metabolomics and Proteomics Laboratory; Bo Chen, Ph.D., Subhashini Bolisetty, Ph.D., and Anupam Agarwal, M.D., UAB Division of Nephrology and the Nephrology Research and Training Center; and David Graves, UAB Department of Chemistry.
MALDI imaging mass spectrometry stands for “matrix-assisted laser desorption ionization” imaging mass spectrometry. SWATH mass spectrometry stands for “sequential window acquisition of all theoretical spectra” mass spectrometry.

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microscopy and spatially resolved chemical analysis

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

 

Combining the Power of Mass Spectrometry, Microscopy

Published: Monday, Nov 9, 2015    http://www.technologynetworks.com/news.aspx?ID=184983

A tool that provides world-class microscopy and spatially resolved chemical analysis shows considerable promise for advancing a number of areas of study, including chemical science, pharmaceutical development and disease progression

The hybrid optical microscope/mass spectrometry-based imaging system developed at the Department of Energy’s Oak Ridge National Laboratory operates under ambient conditions and requires no pretreatment of samples to analyze chemical compounds with sub-micron resolution. One micron is equal to about 1/100th the width of a human hair. Results of the work have recently been published by postdoctoral associate Jack Cahill and Gary Van Berkel and Vilmos Kertesz of ORNL’s Chemical Sciences Division.

“Knowing the chemical basis of material interactions that take place at interfaces is vital for designing and advancing new functional materials that are important for DOE missions such as organic photovoltaics for solar energy,” Van Berkel said. “In addition, the new tool can be used to better understand the chemical basis of important biological processes such as drug transport, disease progression and response for treatment.”

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The hybrid instrument transfers tiny amounts of a material such as human tissue or an organic polymer from a sample by a laser ablation process in which material is captured and transported via liquid stream to the ionization source of the mass spectrometer. In just seconds, a computer screen displays the results.

Researchers noted that the resolution of less than one micron is essential to accurately differentiate and distinguish between polymers and sub-components of similar-sized cells.

“Today’s mass spectrometry imaging techniques are not yet up to the task of reliably acquiring molecular information on a wide range of compound types,” Cahill said. “Examples include synthetic polymers used in various functional materials like light harvesting and emitting devices or biopolymers like cellulose in plants or proteins in animal tissue.”

This technology, however, provides the long-sought detailed chemical analysis through a simple interface between a hybrid optical microscope and an electrospray ionization system for mass spectrometry.

 

This technology, however, provides the long-sought detailed chemical analysis through a simple interface between a hybrid optical microscope and an electrospray ionization system for mass spectrometry

 

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Complexity of Protein-Protein Interactions

Curator: Larry H. Bernstein, MD, FCAP

Cracking the Complex

Using mass spec to study protein-protein interactions

By Jeffrey M. Perkel | November 1, 2015

http://www.the-scientist.com//?articles.view/articleNo/44317/title/Cracking-the-Complex/

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Mass spectrometry is a proteomics workhorse. By precisely measuring polypeptide masses, researchers can identify and sequence those molecules, and characterize whether and how they have been chemically modified. To twist a phrase, by their masses you shall know them.

But many proteins do not act in isolation. Critical biological processes such as DNA replication, transcription, translation, cell division, and energy generation rely on the action of massive protein assemblies, many of which comprise dozens of subunits. While these clusters are ripe for study, few traditional mass spectrometric methods can handle them.

Indeed, protein complexes are unwieldy for many types of analysis, says Philip Compton, director of instrumentation at the Proteomics Center of Excellence at Northwestern University in Evanston, Illinois. Most complexes are held together by noncovalent interactions, assemble only transiently, or are located in the cell membrane—all of which complicate sample preparation, he explains. Also, while some complexes are relatively abundant, others are rare, further thwarting detection and analysis.

For mass spectrometry specifically, however, the problem with analyzing protein complexes, which can weigh in at 500 kDa, is size. “In a mass spec, things of that size have traditionally been fairly difficult to handle,” Compton says. Even if you can deliver them into the spectrometer itself, you need a way to figure out which proteins are present, and in what stoichiometry. Plus, normal sample preparation procedures tend to denature proteins, ripping complexes apart.

Still, researchers are increasingly keen to train their mass specs on intact protein assemblies. The Scientistasked four protein-complex experts about the approaches they use in their own labs. This is what they said.

Determining subunit composition 

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GETTING TOGETHER: Lactate dehydrogenase from human skeletal muscle comprises four identical M subunits, shown here in different colors.  FVASCONCELLOS/WIKIMEDIA COMMONS

RESEARCHER: Philip Compton, Director of Instrumentation, Proteomics Center of Excellence, Northwestern University

PROJECT: High-throughput top-down proteomics

SOLUTION: If protein complexes are onions, Compton needs a way to iteratively peel off the layers to see what’s inside. Working with researchers at Thermo Fisher Scientific, Compton is developing an Orbitrap-based mass spectrometer that can do just that, or perform what is called an MS3 study.

Basically, an MS3 experiment involves weighing all the complexes in a sample fraction—there could be as many as 10 or 15 at a time—grabbing one, smashing it into inert-gas molecules to eject a subunit, weighing and sequencing the cast-off piece, and then repeating the process.

That’s the goal, but because that instrument is not yet built, Compton must temporarily content himself with what he calls a “pseudo-MS3” experiment. Basically, instead of one seamless workflow, the instrument shatters the complex, weighs the pieces that come off it, and then repeats the process, only this time capturing and fragmenting those ejected pieces for subsequent analysis (Anal Chem, 85:11163-73, 2013). “We’re kind of splitting it into these two different steps; that accomplishes essentially the same thing,” Compton says.

Compton and his team are still ironing out the kinks, but they have begun applying the approach to protein complexes involved in metabolism. One of these, lactate dehydrogenase (LDH), is a 145-kDa tetramer comprising M (muscle) and H (heart) subunits that can exist in any of five configurations (MMMM, MMMH, MMHH, MHHH, and HHHH). Using the MS3 workflow, Compton says he can differentiate these “multiproteoform assemblies,” as well as any posttranslational modifications those subunits may bear, and determine the abundance of each. Now he hopes to apply the approach to quantify LDH differences between cell and tissue types.

From Protein Complexes to Subunit Backbone Fragments: A Multi-stage Approach to Native Mass Spectrometry

Thermo Fisher Scientific, 28199 Bremen, Germany
Northwestern University, Evanston, Illinois 60208, United States
Anal. Chem., 2013, 85 (23), pp 11163–11173    DOI: http://dx.doi.org:/10.1021/ac4029328
Publication Date (Web): November 15, 2013   Copyright © 2013 American Chemical Society
Abstract Image
Native mass spectrometry (MS) is becoming an important integral part of structural proteomics and system biology research. The approach holds great promise for elucidating higher levels of protein structure: from primary to quaternary. This requires the most efficient use of tandem MS, which is the cornerstone of MS-based approaches. In this work, we advance a two-step fragmentation approach, or (pseudo)-MS3, from native protein complexes to a set of constituent fragment ions. Using an efficient desolvation approach and quadrupole selection in the extended mass-to-charge (m/z) range, we have accomplished sequential dissociation of large protein complexes, such as phosporylase B (194 kDa), pyruvate kinase (232 kDa), and GroEL (801 kDa), to highly charged monomers which were then dissociated to a set of multiply charged fragmentation products. Fragment ion signals were acquired with a high resolution, high mass accuracy Orbitrap instrument that enabled highly confident identifications of the precursor monomer subunits. The developed approach is expected to enable characterization of stoichiometry and composition of endogenous native protein complexes at an unprecedented level of detail.

EXTEND YOUR RANGE: Compton’s team uses a souped-up version of Thermo Fisher’s Orbitrap-based Q Exactive HF mass spectrometer, which among other things features a fourfold wider mass range. Other researchers can perform similar work using Thermo’s Exactive Plus EMR Orbitrap system, an off-the-shelf, “extended mass range” instrument. But, because the EMR lacks the “high-mass isolation capabilities” of Compton’s bespoke hardware, the application range is more limited, he says. “You can still do a similar experiment to us, provided that you have one clean [purified] complex.”

Mapping protein-protein interaction interfaces
RESEARCHER: Igor Kaltashov, Professor of Chemistry, University of Massachusetts Amherst

PROJECT: Probing the interactions of candidate protein therapeutics with their molecular targets

SOLUTION: Most attempts at studying protein complexes deliver them to the mass spec intact. Kaltashov takes a different approach, using a technique called hydrogen-deuterium exchange (HDX).

It works like this: proteins (like other molecules) pass hydrogen atoms back and forth with the solvent that surrounds them. Normally, one hydrogen is simply swapped for another, and nobody is the wiser. But in deuterated (“heavy”) water, as hydrogens are swapped at the protein surface, the protein gets slightly heavier as deuterium molecules replace some of the hydrogens. This allows researchers to probe how accessible different pieces of the protein are to the solvent, based on how much deuterium they pick up from the buffer, and how quickly they do so.

As Kaltashov explains, HDX can be used to study any event that might alter the accessibility of different protein regions to the solvent that surrounds them. Those events include protein folding and aggregation, but also protein-protein interactions. “Once two proteins bind to each other, solvent would be excluded from the interface, and that would be reflected in the hydrogen-deuterium exchange kinetics,” he says. That change is evident when compared to the proteins in isolation.

In a 2009 review, Kaltashov demonstrated the process with transferrin, an iron transport protein, and its receptor. After undergoing the exchange reaction, the proteins were fragmented to peptides and analyzed piecemeal. Some peptides exhibited no hydrogen-deuterium exchange, he says. That suggests they were never exposed to solvent because they were buried inside the protein core. Other peptides exchanged hydrogens with the solvent at the same rate regardless of receptor binding, indicating they are not part of the protein-receptor interface. A third set of peptides, though, exhibited clear differences in the presence and absence of receptor, marking those as elements of the protein-protein interaction domain (Anal Chem, 81:7892-99, 2009).

“You can actually localize these sites and obtain information both on the strength of the binding [interactions] and the structural characteristics of the interface region,” Kaltashov says.

H/D exchange and mass spectrometry in the studies of protein conformation and dynamics: Is there a need for a top-down approach?

Hydrogen/deuterium exchange (HDX) combined with mass spectrometry (MS) detection has matured in recent years to become a powerful tool in structural biology and biophysics. Several limitations of this technique can and will be addressed by tapping into ever expanding arsenal of methods to manipulate ions in the gas phase offered by mass spectrometry.

Keywords: hydrogen/deuterium exchange (HDX), mass spectrometry (MS), protein ion fragmentation, collision-induced dissociation (CAD), electron-capture dissociation (ECD), electron-transfer dissociation (ETD), protein conformation, protein dynamics

Introduction: HDX MS in the context of structural proteomics

The spectacular successes of proteomics and bioinformatics in the past decade have resulted in an explosive growth of information on the composition of complex networks of proteins interacting at the cellular level and beyond. However, a simple inventory of interacting proteins is insufficient for understanding how the components of sophisticated biological machinery work together. Protein interactions with each other, small ligands and other biopolymers are governed by their higher order structure, whose determination on a genome scale is a focus of structural proteomics. Realization that “the structures of individual macromolecules are often uninformative about function if taken out of context”1 is shifting the focus of the inquiry from comprehensive characterization of individual protein structures to structural analysis of protein complexes.

X-ray crystallography remains the mainstay in this field, and high resolution structures of proteins and protein complexes often provide important clues as to how they carry out their diverse functions in vivo. However, individual proteins are not static objects, and their behavior cannot be adequately described based solely on information derived from static snapshots and without taking into consideration their dynamic character.2Conformation and dynamics of small proteins can be probed at high spatial resolution on a variety of time scales using NMR spectroscopy; however, rather unforgiving molecular weight limitations make this technique less suited for the studies of larger proteins and protein complexes.

Mass spectrometry (MS) is playing an increasingly visible role in this field, as it can provide information on protein dynamics on a variety of levels, ranging from interactions with their physiological partners by forming dynamic assemblies3 to large-scale conformational transitions within individual subunits.4 Perhaps one of the most powerful MS-based tools to characterize protein conformation and dynamics is HDX MS, a technique that combined hydrogen/deuterium exchange in solution5 with MS detection of the progress of exchange reactions.6 This technique is certainly not new,7 and in fact already made lasting impact in diverse fields ranging from structural proteomics8 to analysis of biopharmaceutical products.9 Nevertheless, HDX MS methodology is still in a phase where dramatic progress is made, fed by the continued expansion of the experimental armamentarium offered by MS. In particular, better integration of new methods of manipulating ions in the gas phase into HDX MS routine is likely to result in truly transformative changes. This sea change in HDX MS methodology will transform it to a potent tool rivaling NMR in terms of resolution, but without suffering the limitations of this technique.

What information can be deduced from HDX MS measurements? The classic “bottom-up” approach, its challenges and limitations

While the concept of HDX experiment may appear rather transparent (Figure 1), interpretation of the results is usually not. The backbone protection measured in a typical HDX MS experiment is a combination of several factors, as the exchange reaction of each labile hydrogen atom is a convolution of two processes.5The first is a protein motion that makes a particular hydrogen atom exposed to solvent and therefore available for the exchange. This could be a small-scale event, such as relatively frequent local structural fluctuations transiently exposing hydrogen atoms residing close to the protein surface, or a rare global unfolding event exposing atoms sequestered from the solvent in the protein core. The second process is a chemical reaction of exchanging the unprotected labile hydrogen atom with the solvent. The kinetics of this reaction (intrinsic exchange rate) strongly depends on solution temperature and pH (with a minimum at pH 2.5-3 for backbone amides), parameters that obviously have a great influence on the protein dynamics as well.

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Schematic representation of HDX MS experiments: bottom-up (A) and top-down (B) HDX MS.

Since the majority of HDX MS studies target protein dynamics under near-native conditions, the experiments are typically carried out at physiological pH, where the progress of the exchange is followed by monitoring the protein mass change. The direct infusion scheme offers the simplest way to carry out such measurements, either in real time7 or by using on-line rapid mixing.10 However, in many cases these straightforward approaches cannot be used, as they limit the choice of exchange buffer systems to those compatible with electrospray ionization (ESI). To avoid this, HDX can be carried out in any suitable buffer followed by rapid quenching (lowering pH to 2.5-3 and temperature to near 0°C). Dramatic deceleration of the intrinsic exchange rate for backbone amides under these conditions allows the protein solution to be de-salted prior to MS analysis. Additionally, the slow exchange conditions denature most proteins, resulting in facile removal of various binding partners, ranging from small ligands to receptors (their binding to the protein of interest inevitably complicates the HDX MS data interpretation by making accurate mass measurements in the gas phase less straightforward).

An example of such experiments is shown in Figure 2, where HDX is used to probe the higher order structure and conformational dynamics of metal transporter transferrin (Fe2Tf) alone and in the receptor-bound form. Both Tf-metal and Tf-receptor complexes dissociate under the slow exchange conditions prior to MS analysis; therefore, the protein mass evolution in each case reflects solely deuterium uptake in the course of exchange in solution. The extra protection afforded by the receptor binding to Tf persists over an extended period of time, and it may be tempting to assign it to shielding of labile hydrogen atoms at the protein-receptor interface. However, this view is overly simplistic, as the conformational effects of protein binding are frequently felt well beyond the interface region. The difference in the backbone protection levels of receptor-free and receptor-bound forms of Fe2Tf appears to grow during the initial hour of the exchange (Figure 2), reflecting significant stabilization of Fe2Tf higher order structure by the receptor binding. Indeed, while the fast phase of HDX is typically ascribed to frequent local fluctuations (transient perturbations of higher order structure) affecting relatively small protein segments, the slower phases of HDX usually reflect relatively rare, large-scale conformational transitions (transient partial or complete unfolding). This is why global HDX MS measurements similar to those presented in Figure 2 are can be used to obtain quantitative thermodynamic characteristics for protein interaction with a variety of ligands, ranging from metal ions11 and small organic molecules 12 to other proteins13 and oligonucleotides.14

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HDX MS of Fe2Tf in the presence (blue) and the absence (red) of the cognate receptor. The exchange was carried out by diluting the protein stock solution 1:10 in exchange solution (100 mM NH4HCO3 in D2O, pH adjusted to 7.4) and incubating for a certain period of time as indicated on each diagram followed by rapid quenching (lowering pH to 2.5 and temperature to near 0°C). The black trace shows unlabeled protein.

While global HDX MS measurements under near-native conditions provide valuable thermodynamic information on proteins and their interaction with binding partners, structural studies (e.g., localizing the changes in Tf that occur as a result of receptor binding) must rely on the knowledge of exchange kinetics at the local level. This is typically accomplished by carrying out proteolysis under the slow exchange conditions following the quench of HDX.6 Here we will refer to this approach as “bottom-up” HDX MS, by drawing analogy to a bottom-up approach to obtain sequence information.15 An example is shown in Figure 3, where Fe2Tf undergoes exchange in solution in the absence and in the presence of the receptor, followed by rapid quenching of HDX reactions, protein reduction and digestion with pepsin and LC/MS analysis of the deuterium content of individual proteolytic peptides.

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Localizing the influence of the receptor binding on backbone protection of Fe2Tf using bottom-up HDX MS on the physiologically relevant time scale. The panels show isotopic distributions of representative peptic fragments derived from the protein subjected to HDX in the presence (blue) and the absence (red) of the receptor and followed by rapid quenching. Dotted lines indicate deuterium content of unlabeled and fully exchanged peptides. Colored segments within the Fe2Tf/receptor complex show location of the peptic fragments.

Evolution of deuterium content of various peptic fragments in Figure 3 reveals a wide spectrum of protection, which is clearly distributed very unevenly across the protein sequence. While some peptides exhibit nearly complete protection of backbone amides (e.g., segment [396-408] sequestered in the core of the protein C-lobe), exchange in some other segments is fast (e.g., peptide [612-621] in the solvent-exposed loop of the C-lobe). The influence of the receptor binding on the backbone protection is also highly localized. While many segments appear to be unaffected by the receptor binding, there are a few regions where exchange kinetics noticeably decelerates (e.g., segment [71-81] of the N-lobe, which contains several amino acid residues that form Tf/receptor interface according to the available model of the complex based on low-resolution cryo-EM data16).

Although the increased protection of backbone amides proximal to the protein/receptor binding interface is hardly surprising, HDX MS data also reveal a less trivial trend, acceleration of exchange kinetics in some segments of the protein as a result of receptor binding (such behavior is illustrated in Figure 3 with segment [113-134], a part of the N-lobe that is distal to the receptor). Therefore, in addition to mapping binding interface regions, HDX MS also provides a means to localize the protein segments that are affected by the binding indirectly via allosteric mechanisms. However, this example also highlights one of the limitations of HDX MS, namely inadequate spatial resolution. This peptic fragment spans several distinct regions of the protein (an α-helical segment, a β-strand, and two loops). The moderate level of protection observed in this segment in the absence of the receptor binding (fast exchange of three protons followed by slow exchange of the rest) is likely to be a result of averaging out very uneven protection patterns across this peptide. Even smaller peptides may comprise two or more distinct structural elements, such as segment [71-81] spanning three distinct regions of the protein (an α-helical segment, a β-strand, and a loop connecting them).

In some favorable cases spatial resolution in HDX MS of small proteins (<15 kDa) may be enhanced up to a single residue level by analyzing deuterium content of a set of overlapping proteolytic fragments.17However, single-residue resolution has never been demonstrated in HDX MS studies of proteins falling out of the mass range routinely accessible by NMR, although overlapping peptic fragments frequently provide moderate improvement of spatial resolution.

In addition to limited spatial resolution, the “classic” HDX MS scheme frequently suffers from incomplete sequence coverage, especially when applied to larger and extensively glycosylated proteins. Proteins with multiple disulfide bonds constitute another class of targets for which adequate sequence coverage is difficult to achieve, although certain changes in experimental protocol can alleviate this problem, at least for smaller proteins.18 Typically, an 80% level of sequence coverage is considered good, although significantly lower levels may also be adequate, depending on the context of the study.

Protein processing in HDX MS experiments is carried out under the conditions that minimize the exchange rates for backbone amides. Since these slow exchange conditions are highly denaturing for most proteins, both intact protein and its proteolytic fragments lack any protection and inevitably begin to lose their labile isotopic labels, despite low (but finite) intrinsic exchange rates.19 This phenomenon, known as “back-exchange,” may be accelerated during various stages of protein processing, e.g. during the chromatographic step.20 Although back-exchange was frequently evaluated in early HDX MS studies using unstructured model peptides, the utility of this procedure is questionable, since the intrinsic exchange rates are highly sequence-dependent. In many instances, back-exchange may be estimated using algorithms based on context-specific kinetics data (e.g., http://hx2.med.upenn.edu/download.html); it may also be determined experimentally for each proteolytic fragment by processing a fully labeled protein using a series of steps that precisely reproduce those used in HDX MS measurements.9 Typical back-exchange levels reported in recent literature range from 10% to 50%, although significantly higher numbers have also been reported. Even if back-exchange can be accounted for, it nonetheless has very detrimental influence on the quality of HDX MS measurements by reducing the available dynamic range.

Finally, the classic HDX MS scheme is poorly suited for measurements that are carried out under conditions favoring correlated exchange, when HDX kinetics follows the so-called EX1 regime, leading to appearance of bimodal and convoluted multi-modal isotopic distributions of protein ions.21 Carrying out HDX MS measurements under these conditions provides a unique opportunity to visualize and characterize distinct conformational states, which can be populated either transiently10 or at equilibrium.22 The distinction among such states can be made based on the differences in their deuterium contents. However, proteolysis in solution almost always leads to a loss of correlation between the deuterium content of fragment peptides and specific conformers with distinct levels of backbone protection. Therefore, the classic HDX MS scheme does not allow protein higher order structure and dynamics to be characterized in a conformer-specific fashion.

“Top-down” HDX MS: tandem MS allows protein structure to be probed in the conformer-specific fashion but raises the specter of hydrogen scrambling

The problem of characterizing protein conformation and dynamics in a conformer-specific fashion can be addressed using methods of tandem mass spectrometry (the so-called “top-down” HDX MS). Indeed, replacement of proteolysis in solution with protein ion fragmentation in the gas phase following mass selection of precursor ions provides a means to obtain fragment ions originating from a particular conformer with a specific level of deuterium incorporation. Deuterium content of fragment ions would then provide a measure of local protection patterns, assuming there is no internal re-arrangement of labile hydrogen and deuterium atoms during ion activation (vide infra). Although the idea to use polypeptide ion dissociation in the gas phase as an alternative to proteolysis was originally proposed in early 1990s,23 its implementation for proteins only became possible24 following dramatic improvements in FTMS and hybrid TOF analyzers in the late 1990s.

An example of conformer-specific characterization of protein higher order structure using a top-down HDX MS approach is illustrated in Figure 4. The isotopic profile of a fully deuterated 18 kDa protein wt*-CRABPI is recorded following its brief exposure to the 1H-based exchange buffer. The bimodal appearance of the isotopic distribution of the molecular ion (top trace in Figure 4A) clearly indicates the presence of at least two conformers with different levels of backbone protection. Collisional activation of the entire protein ion population generates a set of fragment ions with convoluted isotopic distributions (top trace in Figure 4B). However, mass selection of precursor ions with a specific level of deuterium content allows the top-down HDX MS measurements to be carried out in a conformation-specific fashion, taking full advantage of the HDX MS ability to detect distinct conformers. For example, selective fragmentation of protein ions representing a highly protected conformation is achieved by mass-selecting a narrow population of intact protein ions with high level of retained deuterium (the blue trace in Figure 4A). Mass-selection and subsequent fragmentation of a narrow population of protein ions with significantly lower deuterium content (the red trace in Figure 4A) generates a set of fragment ions whose isotopic distributions provide information on backbone protection within non-native protein states. For example, the data presented in Figure 4 clearly indicate that the C-terminal segment of the protein represented by the y172+ ions retains significant structure even within the partially unfolded conformers: the amount of retained deuterium atoms reduces by only 30% as a result of switching from the precursor ion from highly protected (blue) to less protected (red). At the same time, selection of the precursor ion has a much more dramatic effect on the protection levels exhibited by the N-terminal segment (represented by the b425+ ion), where more than a two-fold decrease in the amount of retained deuterium atoms is observed. Extending this analysis to other protein fragments may allow detailed backbone protection maps to be created for each protein conformer, provided there is no hydrogen scrambling prior to protein ion fragmentation (vide infra).

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Characterization of local dynamics in wt*-CRABP I in a conformer-specific fashion using top-down HDX MS (fully deuterated protein was exposed to 1H2O/CH3CO2N1H4 at pH 3.1 for 10 min; the gray trace at the bottom corresponds to HDX end-point). A: mass selection of precursor ions for subsequent CAD (from top to bottom): broad-band selection of the entire ionic population (not conformer-specific); highly protected conformers; narrow population of less protected conformers; HDX end-point. B: isotopic distributions of two representative fragment ions generated by CAD of precursor ions shown in panel A. Selection of different ion populations as precursor ions for subsequent fragmentation was achieved by varying the width of a mass selection window of a quadrupole filter (Q) in a hybrid quadrupole/time-of-flight mass spectrometer (Qq-TOF MS).

The example shown above illustrates a great promise of top-down HDX MS as a technique uniquely capable of probing structure and dynamics of populations of protein conformers coexisting in solution with high selectivity. Furthermore, this approach often allows one to avoid protein handling under the slow exchange conditions prior to MS analysis, thereby eliminating back-exchange as a factor adversely influencing the quality of measurements. Nonetheless, applications of top-down HDX MS have been limited due to concerns over the possibility of hydrogen scrambling accompanying collision-activated dissociation (CAD) of protein ions. Indeed, several reports pointed out that proton mobility in the gas phase may under certain conditions influence the outcome of top-down HDX MS measurements when CAD is employed to fragment protein ions.25, 26

The occurrence (or the absence) of hydrogen scrambling in the gas phase can be reliably detected by using built-in scrambling indicators. One particularly convenient indicator is a Histag, a 6-30 residues long, histidine-rich segment appended to wild-type sequences to facilitate protein purification on metal affinity columns. Such segments are fully unstructured in solution and, therefore, should lack any backbone protection.27 Alternatively, intrinsic scrambling indicators (e.g., internal flexible loops26), as well as other approaches25 can be used to detect occurrence of scrambling. The available experimental evidence suggests that slow protein ion activation (e.g., SORI CAD) always leads to hydrogen scrambling, while fast activation allows it to be minimized or eliminated in top-down HDX MS experiments.26

Another shortcoming of top-down HDX MS schemes utilizing CAD is the limited extent of protein ion fragmentation, which may lead to sizeable gaps in sequence coverage, particularly for larger proteins,28 and insufficient level of spatial resolution (even for smaller proteins29). Our earlier attempts to solve this problem by employing multi-stage CAD (MSn) were unsuccessful due to massive hydrogen scrambling exhibited by the second generation of fragments.

Electron-induced ion fragmentation in top-down schemes: keeping hydrogen scrambling at bay while enhancing sequence coverage and spatial resolution

Some time ago we suggested that the specter of hydrogen scrambling in top-down HDX MS measurements may be alleviated by using non-ergodic fragmentation processes, where dissociation is induced by ion-electron interaction, rather than collisional activation.30 Indeed, the results of earlier work combining hydrogen exchange of polypeptide ions in the gas phase and electron capture dissociation (ECD) were consistent with the notion of intramolecular rearrangement of hydrogen atoms occurring on a slower time scale compared to ion dissociation.31 A recent study demonstrated that the extent of scrambling was indeed negligible when ECD was used as a means to obtain fragment ions in top-down HDX MS characterization of a small protein ubiquitin.32

Our own recent work suggests that hydrogen scrambling can be avoided when top-down HDX MS employs ECD in characterizing higher order structure of larger proteins (approaching 20 kDa), although experimental conditions must be carefully controlled to minimize proton mobility induced by ion-molecule collisions in the ESI interface. The point in question is illustrated in Figure 5, which shows the results of top-down HDX MS analysis of higher order structure of wt*-CRABP I. The protein retains a significant proportion of labile deuterium label following its complete deuteration and then brief exposure to the 1H-based exchange buffer, as indicated by the isotopic distribution of the surviving molecular ions (red and blue traces in Figure 5A). However, the deuterium content of fragment ions derived from the 21-residue long His-tag region of the protein (e.g., c22 in Figure 5B) is indistinguishable from that of the exchange reaction endpoint, as long as moderate ion desolvation conditions are kept in the ESI interface. This clearly signals that hydrogen scrambling does not affect the outcome of local HDX MS measurements. However, once collision-assisted desolvation of protein ions is attempted in the ESI interface, the appearance of isotopic distributions of larger fragment ions derived from the His-tag region (e.g., c22, red trace in Figure 5B) shifts, indicating apparent deuterium retention and signaling the occurrence of limited hydrogen scrambling. We also demonstrated that deuterium distribution across the protein backbone is preserved when another recently introduced fragmentation technique based on cation-electron interactions, electron transfer dissociation (ETD), is used in top-down HDX MS schemes.33

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Top-down HDX MS of wt*-CRABP I using ECD of the entire protein ion population (fully deuterated protein was exposed to1H2O/CH3CO2N1H4 at pH 3.5 for varying time periods); the black trace at the bottom of corresponds to HDX end-point). A: isotopic distributions of surviving intact protein ions. B: two representative c-ions. Minimal collision-and temperature-induced desolvation was used for acquisition of all mass spectra, except the one top (red trace).

In addition to allowing scrambling to be easily eliminated in top-down HDX MS experiments, both ECD and ETD appear to be superior to CAD in terms of sequence coverage, at least for the proteins in the 20 kDa range. Unlike CAD, protein backbone cleavage in ECD and ETD is less specific,34 leading to a higher number of fragment ions. This translates not only to improved sequence coverage, but also enhanced spatial resolution. Indeed, in some cases it becomes possible to generate patterns of deuterium distribution across the protein backbone down to the single residue level.

One example of such work is shown in Figure 6, where ETD was used as a protein ion fragmentation tool in top-down HDX MS characterization of a 16 kDa variant of CRABP I. The bar graph shows the levels of deuterium retention in a series of c-ions derived from the N-terminal segment of the protein. The bar height at position n in this diagram shows mass difference between two cn-1 fragments, one derived from the fully deuterated protein that was exposed to the protiated exchange buffer at pH 7 for 5 min and then placed under the slow exchange conditions for the duration of the data acquisition cycle, and another one representing the HDX endpoint (raw data for bars at n=14 and 35 are shown in Figure 7). Unchanged height between two adjacent bars at residues n and n+1 indicates no difference in deuterium content of cn-1 and cn fragments, signaling no backbone amide deuterium retention at residue n+1, while bar height increase by one unit indicates complete retention of deuterium at the nth amide.

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Backbone protection pattern of CRABPI mutant (without N-terminal His-tag) obtained from top-down HDX MS measurements using ETD of the entire protein ion population. HDX was initiated by exposing the fully deuterated protein to 1H2O/CH3CO2N1H4 at pH 3.5 for 5 min followed by rapid quenching.

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An example of raw HDX MS data used to generate the protection plot shown in Figure 6. Isotopic distributions of c13 and c34 fragments derived from protein subjected to 5 min HDX exchange in solution (red trace) and protein at the HDX end-point (blue trace) were used to calculate the bar heights at n=12 and 35.

The resulting backbone protection pattern in Figure 6 shows clear correlation with the known higher order structure of the protein (the amino acid sequence and the secondary structure assignment are shown at the top of the graph). Furthermore, the diagram clearly shows uneven distribution of backbone protection even within single structural elements (e.g., lower protection at the fringes vs. the middle of helix α1), as well as unequal protection of similar structural elements participating in the same structural motif (e.g., lower protection of helix α2 vs. helix α1, consistent with the available NMR data). A comparable level of spatial resolution can be achieved with ECD, as shown recently in top-down HDX MS analysis of higher order structure of myoglobin.35

The ability to characterize protein conformation and dynamics at the single residue level is certainly very exciting; however, it comes at a price. Since the protein fragmentation is carried out entirely in the gas phase, no fragment separation can be done prior to mass analysis. A large number of fragment ions with different masses and charges are usually confined to a relatively narrow m/z region, leading to inevitable overlaps of fragment ion isotopic distributions (Figure 7). This places rather stringent requirements on the resolving power of the mass analyzer, effectively narrowing the selection of mass spectrometers suitable for this work to FTMS.

Meeting in the middle: integration of top-down strategies into bottom-up HDX MS schemes

The top-down approach to HDX MS measurements clearly shows a promise to solve many problems that mar the commonly employed bottom-up methodology. The fragmentation efficiency afforded by ECD and ETD provides better spatial resolution, at least for proteins in the 20 kDa range, and this number is likely to grow as there are numerous examples of successful use of these fragmentation techniques to obtain sequence information on significantly larger proteins.36 Unlike the classic bottom-up approach, top-down HDX MS provides an elegant solution to the problem of characterizing higher order structure and dynamics in a conformer-specific fashion (see Figure 4 and discussion in the text). Finally, back-exchange can be eliminated, as outsourcing protein fragmentation to the gas phase often eliminates the need to manipulate the protein in solution under the slow exchange conditions prior to MS analysis.

The top-down/bottom-up dichotomy in HDX MS should not be viewed through the “eitheror” prism. In fact, gas phase fragmentation can enhance the quality of HDX MS data derived from experiments that are built around the bottom-up approach. The suggestion to supplement proteolysis in solution with peptide ion fragmentation in the gas phase to achieve better spatial resolution was made over 10 years ago.37 However, earlier attempts to implement this idea using CAD on a variety of platforms yielded mixed results due to apparent scrambling in some (but not all) fragment ions.37, 38 Later reports showed even more extensive scrambling in small peptide ions subjected to collisional activation,39 an obvious anathema to the proposed marriage of CAD and bottom-up HDX MS. Nonetheless, continued search for a scrambling-free solution to this problem has yielded very encouraging results, with both ECD and ETD showing minimal scrambling when applied to short peptides under carefully controlled conditions40, 41 and feasibility of supplementing proteolytic fragmentation in solution with ETD in the gas phase was recently demonstrated using a small model protein.42 Although these initial steps are relatively modest, they certainly warrant further work in this field.

The two complementary approaches to HDX MS measurements share a set of common challenges that inevitably arise as these techniques gain popularity and the scope of their applications expands. One such challenge is presented by membrane proteins, a notoriously difficult class of biological objects. HDX MS has been shown to have a great potential in this field.43 Interestingly, some initial work in this field was done nearly ten years ago using then-infant top-down HDX MS technique,44 while more recent work in this field utilizes both bottomup18 and top-down45 approaches. Another challenge faced by HDX MS is presented by highly heterogeneous proteins, such as proteins conjugated to other biopolymers and/or synthetic polymers, which constitute a significant fraction of the next generation of biopharmaceuticals. Presently, there are no biophysical techniques capable of characterizing conformation and dynamics of these systems, and there is an urgent need to fill this gap. Finally, nearly all HDX MS work reported to date was carried out in vitro under conditions that some regard as “reductionist.” Although initial HDX work with living objects was carried out over 75 years ago,46 as the years passed only one report on in vivo HDX MS studies was published.47 As mass spectrometry at large is being increasingly used in both in vivo and ex vivo studies, there is a growing pressure on HDX MS to follow the trend, although it remains to be seen how this will be done.

It probably is not an exaggeration to say that we are witnessing a renaissance of HDX MS, with the emergence of the top-down approach not only expanding our experimental arsenal by offering new capabilities, but also serving as a catalyst in enhancing the classic bottom-up methodology. The two techniques are highly complementary, and their synergism will certainly bring about new exciting discoveries and accelerate our progress in solving a variety of problems ranging from very fundamental questions in biophysics to applied problems in drug design.

see more at  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2805115/

WATCH OUT FOR DISULFIDES: If you’re going to try bottom-up HDX experiments, be careful of disulfide bonds, Kaltashov says. Pepsin is one of the very few proteinases that can efficiently digest a protein into its composite peptides under HDX experimental conditions, but it struggles when multiple disulfide bonds are present. In 2014, Kaltashov’s lab published two solutions to that problem. The first employs a fragmentation technique called electron capture dissociation (ECD) to break the disulfide linkage in the mass spec (Anal Chem, 86:5225-31, 2014); the second skips the pepsin digestion altogether—a strategy called top-down analysis (Anal Chem, 86:7293-98, 2014).

Enhancing the Quality of H/D Exchange Measurements with Mass Spectrometry Detection in Disulfide-Rich Proteins Using Electron Capture Dissociation

Anal Chem. 2014 Jun 3; 86(11): 5225–5231.   Published online 2014 May 12. doi:  10.1021/ac500904p
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Hydrogen/deuterium exchange (HDX) mass spectrometry (MS) has become a potent technique to probe higher-order structures, dynamics, and interactions of proteins. While the range of proteins amenable to interrogation by HDX MS continues to expand at an accelerating pace, there are still a few classes of proteins whose analysis with this technique remains challenging. Disulfide-rich proteins constitute one of such groups: since the reduction of thiol–thiol bonds must be carried out under suboptimal conditions (to minimize the back-exchange), it frequently results in incomplete dissociation of disulfide bridges prior to MS analysis, leading to a loss of signal, inadequate sequence coverage, and a dramatic increase in the difficulty of data analysis. In this work, the dissociation of disulfide-linked peptide dimers produced by peptic digestion of the 80 kDa glycoprotein transferrin in the course of HDX MS experiments is carried out using electron capture dissociation (ECD). ECD results in efficient cleavage of the thiol–thiol bonds in the gas phase on the fast LC time scale and allows the deuterium content of the monomeric constituents of the peptide dimers to be measured individually. The measurements appear to be unaffected by hydrogen scrambling, even when high collisional energies are utilized. This technique will benefit HDX MS measurements for any protein that contains one or more disulfides and the potential gain in sequence coverage and spatial resolution would increase with disulfide bond number.
———

Hydrogen/deuterium exchange (HDX) with mass spectrometry (MS) detection has evolved in the past two decades into a powerful tool that is now used to decipher intimate details of processes as diverse as protein folding, recognition and binding, and enzyme catalysis.1,2 While initially being a tool that was used exclusively in fundamental studies, HDX MS is now becoming an indispensable part of the analytical arsenal in the biopharmaceutical sector, where it is utilized increasingly in all stages of protein drug development from discovery to quality control.35 Despite this progress, several areas remain where the application of HDX MS has met with only limited success. Disulfide-rich proteins constitute one such group, where characterization of higher-order structure and dynamics is particularly difficult, because of the suboptimal conditions used for reduction of thiol–thiol bonds following a quench of the exchange reactions. Proteins containing disulfide bonds are encountered very rarely in the protein folding studies where the most popular targets are small proteins lacking cysteine residues (with a notable exception of the oxidative folding studies), as well as in many other fundamental studies focusing on proteins of prokaryotic origin. However, disulfide-rich proteins are encountered very frequently in eukaryotic proteomes6 and constitute a large segment of the biopharmaceutical products,7 where the thiol–thiol bonds are critical elements defining conformation of protein drugs, and also play an important role in stabilizing proteins by endowing them with protease resistance.

While disulfide bond reduction is a relatively trivial task that can be readily accomplished at neutral pH using a variety of reagents, the acidic, low-temperature environment where proteins are placed to quench HDX narrows down the choice to a single reducing agent, TCEP.8 However, the alkaline pH for optimal disulfide reduction by TCEP is substantially higher, compared to the acidic environment of typical “slow exchange conditions” commonly employed to minimize back exchange within proteins and their peptic fragments prior to MS analysis.9 Furthermore, disulfide reduction in HDX MS measurements is usually carried out within a relatively short period of time (a few minutes) and at low temperature (0–4 °C) to limit the extent of the back-exchange, which in many situations does not allow the complete dissociation of thiol–thiol linkages of individual peptic fragments to be achieved in solution prior to LC separation and MS analysis of their deuterium content. Incomplete reduction of disulfide bonds dramatically increases the pool of candidate peptides that should be considered when analyzing proteolytic fragments in HDX MS measurements and frequently reduces sequence coverage and/or spatial resolution. While the former problem can be solved by employing more powerful and robust search engines for peptide identification, the latter one is more difficult to circumvent and can be very detrimental for the quality of HDX MS data and may require significant changes in experimental protocols. Indeed, a complete failure to reduce a certain disulfide bond in a protein will give rise to a thiol–thiol linked peptide dimer, whose constituent monomers do not necessarily represent a contiguous segment of the protein and may have vastly different conformational and dynamic properties. The total deuterium content of the entire dimer (measured by HDX MS) would not provide any meaningful information under these conditions, thereby effectively reducing the sequence coverage in the corresponding segments of the protein.
———-

Disulfide-rich proteins have traditionally been challenging targets for HDX MS studies, because of incomplete reduction of thiol–thiol linkages, which is a consequence of the quench conditions used to minimize amide back-exchange in peptides prior to MS analysis of their deuterium content: limited time, low temperature, and low pH. Traditionally, the principal strategy to address difficult-to-reduce or high-density disulfides in the HDX MS workflow is a brute force approach utilizing high concentrations of reductant and denaturant prior to (or even in combination with) digestion. The effectiveness of this approach is protein-dependent and extended incubation times frequently employed to enhance exposure to reductant invariably result in an undesirable increase in H/D back exchange. More recently, a novel electrochemical approach to reduce disulfides in solution under quench conditions prior to LC-MS has been reported for insulin.32 While electrochemical reduction shows promise, several limitations were identified, an apparent requirement for low-salt conditions, a higher-than-optimal temperature (10 °C), and a current cell pressure limit of 50 bar. In this work, electron capture dissociation (ECD) was used to circumvent the disulfide problem, since it effectively cleaves external disulfide bonds. Dissociation of the disulfide-linked peptide dimers can be accomplished on the fast LC time scale and produces abundant signals for monomeric subunits without interchain hydrogen scrambling, even when collisional activation of ions is applied prior to ion selection and ECD fragmentation. Inclusion of ECD in the HDX MS workflow results in increased sequence coverage and spatial resolution and provides an attractive alternative to extensive chemical reduction of disulfide-rich proteins.

see more at   http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4051250/

Approach to Characterization of the Higher Order Structure of Disulfide-Containing Proteins Using Hydrogen/Deuterium Exchange and Top-Down Mass Spectrometry

Guanbo Wang† and Igor A. Kaltashov*
http://www.chem.umass.edu/people/kaltashovlab/papers/Approach.pdf

Top-down hydrogen/deuterium exchange (HDX) with mass spectrometric (MS) detection has recently matured to become a potent biophysical tool capable of providing valuable information on higher order structure and conformational dynamics of proteins at an unprecedented level of structural detail. However, the scope of the proteins amenable to the analysis by top-down HDX MS still remains limited, with the protein size and the presence of disulfide bonds being the two most important limiting factors. While the limitations imposed by the physical size of the proteins gradually become more relaxed as the sensitivity, resolution and dynamic range of modern MS instrumentation continue to improve at an ever accelerating pace, the presence of the disulfide linkages remains a much less forgiving limitation even for the proteins of relatively modest size. To circumvent this problem, we introduce an online chemical reduction step following completion and quenching of the HDX reactions and prior to the top-down MS measurements of deuterium occupancy of individual backbone amides. Application of the new methodology to the top-down HDX MS characterization of a small (99 residue long) disulfide-containing protein β2- microglobulin allowed the backbone amide protection to be probed with nearly a single-residue resolution across the entire sequence. The high-resolution backbone protection pattern deduced from the top-down HDX MS measurements carried out under native conditions is in excellent agreement with the crystal structure of the protein and high-resolution NMR data, suggesting that introduction of the chemical reduction step to the top-down routine does not trigger hydrogen scrambling either during the electrospray ionization process or in the gas phase prior to the protein ion dissociation.

Since its initial introduction in the late 1990s,1−3 top-down hydrogen/deuterium exchange (HDX) with mass spectrometric (MS) detection evolved to become a potent biophysical tool capable of providing valuable information on higher order structure and conformational dynamics of proteins at an unprecedented level of structural detail. Among the many advantages offered by top-down HDX MS compared to conventional (bottom-up) measurements are significant reduction or indeed complete elimination of the back exchange,4 high spatial resolution,5,6 and the ability to study conformational dynamics in the conformer-specific fashion.7,8 However, despite the spectacular recent advances and the broader acceptance of this technique, the scope of the proteins amenable to the analysis by top-down HDX MS remains limited, with the protein size and the presence of disulfide bonds being the two most important limiting factors. The limitations imposed by the physical size of the proteins gradually become more relaxed as the sensitivity, resolution, and dynamic range of modern MS instrumentation continue to improve at an ever accelerating pace. However, the presence of disulfides remains a much less forgiving limitation even for the proteins of relatively modest size.

In this work we demonstrated feasibility of applying top-down HDX MS measurements to characterize higher order structure and conformational dynamics of disulfide-containing proteins, which have been out of the reach of this technique so far. Use of a moderate amount of a reducing agent TCEP is compatible with the ESI process, while allowing a fraction of the protein molecules to be reduced in solution thereby enabling nearcomplete sequence coverage at high resolution. The agreement between the top-down HDX MS and NMR data sets demonstrate that the new experimental approach is capable of capturing the dynamic picture of protein conformation at high spatial resolution without compromising the quality of the data by triggering hydrogen scrambling in the gas phase. Despite its modest size, β2m is known to be able to populate a non-native state,35 which might be a key player in a variety of processes, including amyloidosis. However, the structure of this non-native state of β2m remains elusive since this conformer exists in dynamic equilibrium with the native state of the protein.36,37 Recently we demonstrated that top-down HDX MS provides an elegant way to selectively probe structure of protein states coexisting in solution at equilibrium;8 however, β2m remained out of reach of this technique until recently due to the presence of a disulfide bond. The ability to expand the scope of top-down HDX MS to disulfide-containing proteins opens up a host of exciting possibilities to explore the structure of β2m, interferon, lysozyme, and a variety of other disulfidecontaining proteins in a conformer-specific fashion, where physiologically important non-native states may play important roles in processes as diverse as folding, recognition, signaling, and amyloidosis. ■ ASSOCIATED CONTENT *S Supporting Information Representative examples of isotopic distributions of fragment ions that have (Supplementary Figure 1) and have not (Supplementary Figure 2) been used to calculate the deuterium occupancy at individual backbone amides of β2m in top-down HDX MS measurements. This material is available free of charge via the Internet at http://pubs.acs.org.

Determining surface topology of protein complexes

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SUSSING OUT THE SURFACE: Protein topology can be probed by firing low-energy electrons (white circles) at intact protein complexes within a high-resolution mass spectrometer. That reaction, called electron capture dissociation, causes the protein complex to fracture on its surface, revealing the exposed amino acid residues.     COURTESY OF PIRIYA WONGKONGKATHEP AND HUILIN LI, UCLA

RESEARCHER: Joseph Loo, Professor of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles)

PROJECT: Studying protein-ligand and protein-protein interactions

SOLUTION: Loo is less interested in complex identification than in how the protein subunits assemble. Specifically, he wants to know which amino acid residues lie on the complex’s surface and which are buried inside or interacting with ligands.

It’s a question of structural biology, he explains: “How is this thing folded in a way that these residues are on the outside?”

To work that out, Loo combines high-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR) with electron-capture dissociation (ECD), a mass spec fragmentation method in which an ion in the mass spectrometer interacts with free electrons, causing the protein to fracture along its peptide backbone. By measuring the mass of those fragments with high precision, researchers can determine the protein’s amino acid sequence.

In Loo’s case, though, that fragmentation is not uniform along the length of the protein. Proteins usually are denatured for mass spectrometry analysis, but the protein complexes in his studies are intact—a process called native mass spectrometry. Fragmentation thus occurs preferentially on the surface of the complex, like the cracks in the shell of a hard-boiled egg. “You get limited sequence information, but that sequence information comes from regions that are specific to its 3-D structure,” he says (Anal Chem, 86:317-20, 2014).

Native Top-Down ESI-MS of 158 kDa Protein Complex by High Resolution Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) delivers high resolving power, mass measurement accuracy, and the capabilities for unambiguously sequencing by a top-down MS approach. Here, we report isotopic resolution of a 158 kDa protein complex – tetrameric aldolase with an average absolute deviation of 0.36 ppm and an average resolving power of ~520,000 at m/z 6033 for the 26+ charge state in magnitude mode. Phase correction further improves the resolving power and average absolute deviation by 1.3 fold. Furthermore, native top-down electron capture dissociation (ECD) enables the sequencing of 149 C-terminal amino acid (AA) residues out of 463 total AAs. Combining the data from top-down MS of native and denatured aldolase complexes, a total of 58% of the backbone cleavages efficiency is achieved. The observation of complementary product ion pairs confirms the correctness of the sequence and also the accuracy of the mass fitting of the isotopic distribution of the aldolase tetramer. Top-down MS of the native protein provides complementary sequence information to top-down ECD and CAD MS of the denatured protein. Moreover, native top-down ECD of aldolase tetramer reveals that ECD fragmentation is not limited only to the flexible regions of protein complexes and that regions located on the surface topology are prone to ECD cleavage.

“Native” mass spectrometry (MS) is an emerging technique that has been successfully used to characterize intact, noncovalently-bound protein complexes, providing stoichiometry and structural information that is complementary to data supplied by conventional structural biology techniques.13 To confidently characterize protein complexes, electrospray ionization (ESI)-MS measurements acquired with isotopic resolving power (RP) and high mass accuracy and capabilities for deriving primary structure, i.e., sequence, information would be ideal. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) is prominent for its superior resolving power and mass accuracy and its utility for tandem MS (MS/MS) with a variety of fragmentation techniques; FT-ICR MS is noted for characterizating posttranslational modifications (PTMs) and protein-ligand and protein-protein interactions.49 However, it remains challenging to isotopically resolving large biomolecules over 100 kDa due to sample heterogeneity, cation/solvent/buffer addition, space charge effects, and electric and magnetic field inhomogeneity (for FT-ICR).1013 Unit mass resolution has been achieved for a few denatured proteins, including a 112 kDa protein with 3 Da mass error using a 9.4 T FT-ICR MS,14 a 115 kDa protein by a 7 T instrument with a mass error of 5 ppm,4 and a 148 kDa protein with a mass error of 1 Da by a 9.4 T FTMS.10

Compared to denatured proteins, it is more difficult to achieve isotopic resolution for inherently lower charged (and thus, higher m/z) native protein complexes because (1) the peak height is proportional to its charge state, (2) the resolving power is inversely proportional to mass-to-charge ratio for FT-ICR MS, and (3) the broader isotope distribution of large biomolecules reduces overall signal-to-noise ratio.15 However, the introduction of a new FT-ICR analyzer cell – the ParaCell, by Nikolaev and coworkers has significantly increased the resolving power of FT-ICR MS.16, 17 By dynamically harmonizing the electric field potential at any radius of cyclotron motion in the entire cell volume, a resolving power of 39 M has been achieved for the alkaloid, resperine (m/z 609), using a 7 T system.18 In addition, a few native protein complexes, including enolase dimer (93 kDa, RP ~ 800,000 at m/z 4250), alcohol dehydrogenase tetramer (147 kDa, RP ~ 500,000 at m/z 5465), and enolase tetramer (186 kDa), have been isotopically resolved with a 12 T FT-ICR system with the new ICR cell.18 Although Mitchell and Smith reported that cyclotron phase locking due to Coulombic interactions limits the highest mass that unit mass resolution can be achieved by FT-ICR MS (Mmax ≈ 1×104B, where B is magnetic field strength),19 the ParaCell has made it significantly easier and promising to measure high resolution mass spectra for large native protein complexes.

……

Native top-down CAD and ISD were performed for the aldolase tetramer; dissociation of the tetramer to yield monomer was observed in both approaches and no sequence information was obtained. The cleavage sites from ECD (colored in red) and CAD (colored in green) of the denatured aldolase monomer (26+) are overlaid with the native ECD results for aldolase tetramer (Figure 2B). As shown in Figure 2B, in contrast to the limited number of c-ion fragments observed in the ECD of aldolase tetramer, ECD of denatured aldolase monomer induces extensive c-ion fragments in the N-terminal region and enables the assignment of first 156 N-terminal AA residues. Surprisingly, the number of z-ions observed from ECD of the denatured aldolase monomer is much less compared to the ECD of the native aldolase tetramer. Although it may be possible that the z-ions may undergo secondary fragmentation due to excess available energy, electrons, or long ion-electron reaction times during the ECD experiment, ECD experiments with reduced reaction time and bias voltages were performed and the results argue against this assumption. Overall, 58% of the total number of backbone bonds are cleaved from combining top-down MS of native aldolase complex and denatured aldolase monomer (20% for native ECD of aldolase tetramer, 37% for ECD of denatured aldolase, and 5% for CAD of denatured aldolase).

The three dimensional structure of the aldolase tetramer is shown in Figure 3. To compare the flexibility of the structure to the data from ECD of the aldolase tetramer, one of the subunits (B-chain) is presented as B-factor putty and the D-chain is shown with its native ECD backbone cleavage regions colored in red. The remaining A- and C-chains are shown in grey. Although the C-terminal region (AA 340–363) of each subunit is highly flexible based on the crystallography B-factor (see B-chain in Figure 3A), only 4 out of 75 backbone cleavage sites are from the AA 340–363 region. Instead, the native ECD fragments largely originate from surface regions of the protein structure (see D-chain in Figure 3A). The N-terminal regions are not directly involved in the interfaces between subunits, but they are located in regions that are partially buried, which is consistent with the limited c-ions observed. To better show the native ECD backbone cleavage regions, the D-chain is rotated 90 degrees clockwise (Figure 3B). It is clear that, although protein structure flexibility might play a role in the native top-down ECD fragmentation pattern, for aldolase the ECD cleavage sites are not limited to the flexible region. In addition, backbone cleavage regions from CAD (yellow) and ECD (cyan) of denatured aldolase are complementary with the native ECD results.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3908771/bin/nihms548404f3.jpg

A) Structure of tetrameric aldolase (1ZAH)29. A- and C-chains are shown as grey ribbons, the B-chain is shown in B-factor putty, and the D-chain is in cartoon with native ECD cleavage sites colored in red, CAD cleavage sites of denatured aldolase in yellow, and ECD cleavage sites of the N-terminal region from ECD of denatured aldolase in cyan. B) The D-chain is rotated 90 degrees clockwise to show the outer surface region of the subunit structure.

Also evident in such data sets are protein–small molecule interactions. As the proteins break apart, Loo explains, ligands often remain attached to the polypeptide shards that are produced. In one recent publication, for instance, his team mapped zinc binding sites in eukaryotic alcohol dehydrogenase, a 147-kDa tetrameric complex (J Am Soc Mass Spectrom, 25:2060-8, 2014).

Revealing Ligand Binding Sites and Quantifying Subunit Variants of Non-Covalent Protein Complexes in a Single Native Top-Down FTICR MS Experiment

“Native” mass spectrometry (MS) has been proven increasingly useful for structural biology studies of macromolecular assemblies. Using horse liver alcohol dehydrogenase (hADH) and yeast alcohol dehydrogenase (yADH) as examples, we demonstrate that rich information can be obtained in a single native top-down MS experiment using Fourier transform ion cyclotron mass spectrometry (FTICR MS). Beyond measuring the molecular weights of the protein complexes, isotopic mass resolution was achieved for yeast ADH tetramer (147 kDa) with an average resolving power of 412,700 at m/z 5466 in absorption mode and the mass reflects that each subunit binds to two zinc atoms. The N-terminal 89 amino acid residues were sequenced in a top-down electron capture dissociation (ECD) experiment, along with the identifications of the zinc binding site at Cys46 and a point mutation (V58T). With the combination of various activation/dissociation techniques, including ECD, in-source dissociation (ISD), collisionally activated dissociation (CAD), and infrared multiphoton dissociation (IRMPD), 40% of the yADH sequence was derived directly from the native tetramer complex. For hADH, native top-down ECD-MS shows that both E and S subunits are present in the hADH sample, with a relative ratio of 4:1. Native top-down ISD MS hADH dimer shows that each subunit (E and S chain) binds not only to two zinc atoms, but also the NAD+/NADH ligand, with a higher NAD+/NADH binding preference for the S chain relative to the E chain. In total, 32% sequence coverage was achieved for both E and S chains.

Studying how proteins interact with one another and assemble on a structural basis is key to understanding biological processes and their function. As a complementary technique to conventional technologies used in structural biology, such as nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography, and electron microscopy, “native” mass spectrometry (MS) has established its crucial role in the characterization of intact noncovalently-bound protein complexes, revealing the composition, stoichiometry, dynamics, stability, and also spatial information of subunit arrangements in protein assemblies [111]. To date, most native MS studies of protein complexes have been performed using quadrupole time-of-flight (Q-TOF) MS instruments with electrospray ionization (ESI). Because of the efficient transmission of high mass and highm/z ions using TOF analyzers, large proteins with molecular weights up to 18 MDa have been studied [12,13]. The coupling of ion mobility spectrometry (IMS) with mass spectrometry provides a new dimension to the analysis of biomolecules [14]. With IMS, ions are separated based on size and shape, and the IMS-derived collision cross-section information can be used to understand the topological properties of gas phase protein complexes. Surface induced dissociation (SID) has been recently added for the purposes of disassembling protein complexes into sub-complexes that appear to better reflect the structure of the solution phase complexes [1517]. The capability of Orbitrap MS has been extended significantly for the analysis of macromolecules, with greatly improved mass (and m/z) range and resolving power to measure the binding of ADP and ATP to the 800 kDa GroEL complex [18].

Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) is known for its superior resolving power and mass accuracy and its capabilities for tandem MS (MS/MS) with a variety of fragmentation techniques. Particularly, after the introduction of electron capture dissociation (ECD) [19], FTICR MS quickly established its utility for protein top-down protein sequencing, post-translational modification characterization, and protein gas phase studies [2034]. Polypeptide backbone bonds are cleaved by ECD, but non-covalent interactions are preserved, which therefore makes the native top-down MS study of the non-covalent interaction sites of protein-ligands complexes more feasible. Our group and others have successfully applied top-down ECD-MS to pinpoint the interaction sites of several protein-ligand system [3538], and this can be enhanced by “supercharging” [35]. An early attempt of applying ECD-MS to the study of large protein complexes was made by Heeren and Heck, but little topology and sequence information was derived [39]. However, the Gross group starting in 2010 made the first breakthrough for the study of large protein complexes using native top-down ECD with FTICR MS. Besides obtaining molecular weight, sequence, and metal-binding site information in a single MS experiment, they correlated the origins of ECD product ions to the flexible regions of proteins as determined by the “B-factor” from the X-ray crystal structures of protein complexes [40, 41]. Therefore, native top-down ECD has been proposed as a tool to probe the flexible regions of protein complexes. Our group recently also demonstrated the capability of obtaining sequence information and isotopic mass resolution of a noncovalently-bound protein complex of 158 kDa using native top-down FTICR MS, and most importantly, we found that the origin of ECD fragments is not limited only to the flexible region of the protein complex (e.g., tetrameric aldolase), but also largely from the surface of the complex [42].

The application of FTICR MS for native top-down interrogation of large non-covalent bound protein complexes is still in its infancy. Here, for the purpose of further exploring the capability of FTICR MS in the analysis of large protein complexes, various fragmentation techniques including in-source dissociation (ISD), collisionally activated dissociation (CAD), ECD, and infrared multiphoton dissociation (IRMPD) were applied in the native top-down MS studies of a 80 kDa dimeric protein complex and a 147 kDa tetrameric protein complex. The results demonstrate that with the superior resolving power, mass accuracy, and versatile fragmentation techniques of FTICR MS, rich information, including isotopic mass resolution, amino acid sequence, point mutations, metal/ligand binding sites, and identification and quantification of subunit variants can be accomplished in a single native top-down FTICR MS experiment.

see more at   http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4444062/

Still, Loo admits, the technique “is not really ready for prime time.” His team is collecting ECD data on a bank of proteins of known structure to ensure the data they collect really do reflect protein topology. In the meantime, they are working to extend the size of the complexes they can analyze. The technique’s current limit is 800 kDa.

GO NATIONAL: FTICR mass spectrometers offer top-of-the-line accuracy and resolution, with price tags to match. Few researchers have direct access to them, Loo says, but they can always try the national laboratories. Both the National High Magnetic Field Laboratory at Florida State University and the Environmental Molecular Sciences Laboratory at the Pacific Northwest National Laboratory have user facilities open to worthy projects.

Determining the architecture of protein complexes

RESEARCHER: Vicki Wysocki, Ohio Eminent Scholar and Professor of Chemistry and Biochemistry, Ohio State University

PROJECT: Instrumentation development for whole-complex analysis

SOLUTION: An analytical chemist by training, Wysocki focuses on instrumentation development for protein-complex analysis. Among the discoveries in her lab is a method called surface-induced dissociation (SID).

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HIT THE WALL, JACK: When it comes to molecular collision in a mass spectrometer, size matters. Collide a complex with small gas molecules, and proteins in the complex will simply unravel (top). By smacking them into a “wall”—a process called surface-induced dissociation—the complex dissociates to reveal its underlying architecture.  COURTESY OF VICKI WYSOCKI

Like many other fragmentation approaches, SID works by forcing an ion in the mass spectrometer to collide with another object. Usually that object is a small gas molecule, with the energy of collision sufficient to crack the peptide backbone. But for large protein complexes, bigger is better, and the collision partner in SID is as big as it can get: the method slams protein ions of interest into a nonreactive surface inside the instrument—essentially, a wall—causing complexes to fracture into subcomplexes that reveal the assembly’s inner architecture.

Wysocki combined this approach with ion-mobility separation—a kind of gas-phase electrophoresis that resolves molecules by their size and shape—to dissect an enzyme involved in antibiotic production. The enzyme, they found, has two copies each of three subunits, alpha, beta, and gamma, arranged as a pair of triads sitting on top of one another, with the alpha and beta subunits of one triad linked more tightly to each other than either is to gamma (Anal Chem, 83:2862-65, 2011).

Such information can be valuable to protein engineers, Wysocki says, especially as this particular complex otherwise falls into a structural biology knowledge gap: “It doesn’t crystallize, and it’s too small for the cryoEM and a little bit large for NMR,” she says. “And so, mass spec turned out to be a great tool.”

Revealing the Quaternary Structure of a Heterogeneous Noncovalent Protein Complex through Surface-Induced Dissociation

Anne E. Blackwell, Eric D. Dodds,† Vahe Bandarian, and Vicki H. Wysocki*
https://research.cbc.osu.edu/wysocki.11/wp-content/uploads/2012/09/Blackwell-2011-Revealing-the-Quater.pdf

As scientists begin to appreciate the extent to which quaternary structure facilitates protein function, determination of the subunit arrangement within noncovalent protein complexes is increasingly important. While native mass spectrometry shows promise for the study of noncovalent complexes, few developments have been made toward the determination of subunit architecture, and no mass spectrometry activation method yields complete topology information. Here, we illustrate the surface-induced dissociation of a heterohexamer, toyocamycin nitrile hydratase, directly into its constituent trimers. We propose that the single-step nature of this activation in combination with high energy deposition allows for dissociation prior to significant unfolding or other large-scale rearrangement. This method can potentially allow for dissociation of a protein complex into subcomplexes, facilitating the mapping of subunit contacts and thus determination of quaternary structure of protein complexes.

normal.img-000.jpg

http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/ancham/2011/ancham.2011.83.issue-8/ac200452b/production/pdfimages_v02/normal.img-000.jpg

The majority of proteins exist and perform their functions as multimers of varing stoichiometries and architecture.1 However, very few methods are available that can provide insights into subunit interactions. Native mass spectrometry (MS) is increasingly being used to study noncovalent protein complexes, as many structural features found in solution may be maintained in the gas phase.2,3 While subunit stoichiometries are readily obtainable by mass measurement alone, the determination of subunit arrangement within protein complexes remains a significant challenge. This is particularly true for heterogeneous complexes with multiple types of subunits. Considerable progress has been made using solution-phase disruption to divide the original protein complex into smaller subcomplexes, which may be readily measured by MS.4,5 The composition of the stable subcomplexes provides insight on the topology of the protein complex. However, MS activation methods used to date have fallen short of providing subunit topology. Here, we present the first evidence for subunit arrangement obtained directly from gas-phase experiments on a heterogeneous complex via surfaceinduced dissociation (SID). We have demonstrated previously the ability of SID to yield unique dissociation pathways for protein complexes, resulting in complementary information to collision-induced dissociation (CID).68 While the SID process is not yet well understood for macromolecules, there is a large body of work concerning SID of small molecules; influential factors such as collision energy, surface composition, and translational-to-vibrational energy conversion have been well-studied.911 The higher effective mass of a surface relative to that of neutral gas atoms used in CID (typically argon) results in significantly higher energy deposited through a single surface collision.9 As SID is a single-collision activation process, rather than activation via thousands of less energetic collisions as in CID, dissociation pathways other than those of the lowest energies become accessible

……

This is the only study to date demonstrating an ion activation method capable of yielding extensive dissociation, as well as the release of intact subcomplexes, thus providing relevant substructure information on a noncovalent, hetero-oligomeric protein complex. The capacity to produce intact, charge-symmetric subcomplexes suggests that dissociation occurs faster than subunit unfolding and that a significant degree of secondary and tertiary structure is maintained up to the point of dissociation and for some period of time afterward. Identification of trimeric substructure in TNH provides insight into a protein with little previous structural characterization and indicates a promising advancement of MS as a tool for structural biology.

Such information can be valuable to protein engineers, Wysocki says, especially as this particular complex otherwise falls into a structural biology knowledge gap: “It doesn’t crystallize, and it’s too small for the cryoEM and a little bit large for NMR,” she says. “And so, mass spec turned out to be a great tool.”

CHOOSE MASS: Mass spec may not be the only method for quickly working out protein structure, but it surely is the fastest, Wysocki says. She recalls one instance when a colleague sent over a complex that his group couldn’t crack. “In one afternoon, my student gave them a prediction of the structure: this one’s a heptamer, with a large subunit sitting atop a hexameric ring.” Even if the experiment doesn’t work, she adds, that fast turnaround time can be a boon, as collaborators can get rapid feedback for tweaking their experimental conditions. “Mass is a great thing.”

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Insights in Biological and Synthetic Medicinal Chemistry

Larry H. Bernstein, M.D., FCAP, Curator

Leaders in Pharmaceutical Intelligence

Series E. 2;  10

Selected Articles Linking the Biological and Synthetic Worlds

The worlds of biological and synthetic chemistry both offer incredible diversity. Biology provides complex architectures including proteins, nucleic acids, and polysaccharides. Synthetic chemistry, on the other hand, provides a tool for atom-by-atom control over molecular structure that can be used to obtain molecules and materials inaccessible through biology.

In this ACS Select Virtual Issue, we highlight some of the recent advances in bioconjugation chemistry. These publications describe new strategies for functionalization of biomacromolecules, as well as the use of synthetic molecules as building blocks for assembly using biological machinery. The resultant conjugate systems have new and exciting properties, as demonstrated in new therapeutic and imaging applications.

– Vincent Rotello, Editor-in-Chief, Bioconjugate Chemistry
– C. Dale Poulter, Editor-in-Chief, The Journal of Organic Chemistry
– Amos Smith, III, Editor-in-Chief, Organic Letters

10.1  Bioconjugate Chemistry

10.1.1 Production of Site-Specific Antibody-Drug Conjugates Using Optimized Non-Natural Amino Acids in a Cell-Free Expression System
Zimmerman, E. S.; Heibeck, T. H.; Gill, A.; Li, X. F.; Murray, C. J.; Madlansacay, M. R.; Tran, C.; Uter, N. T.; Yin, G.; Rivers, P. J.; Yam, A. Y.; Wang, W. D.; Steiner, A. R.; Bajad, S. U.; Penta, K.; Yang, W. J.; Hallam, T. J.; Thanos, C. D.; Sato, A. K.
Bioconjugate Chem.201425 (2), pp 351-361
DOI: 10.1021/bc400490z

10.1.2 General Chemoselective and Redox-Responsive Ligation and Release Strategy
Park, S.; Westcott, N. P.; Luo, W.; Dutta, D.; Yousaf, M. N.
Bioconjugate Chem.201425 (3), pp 543-551
DOI: 10.1021/bc400565y

10.1.3 Chemoenzymatic Fc Glycosylation via Engineered Aldehyde Tags
Smith, E. L.; Giddens, J. P.; Iavarone, A. T.; Godula, K.; Wang, L. X.; Bertozzi, C. R.
Bioconjugate Chem.201425 (4), pp 788-795
DOI: 10.1021/bc500061s

10.1.4 Triazine-Based Tool Box for Developing Peptidic PET Imaging Probes: Syntheses, Microfluidic Radio labeling, and Structure-Activity Evaluation
Li, H. R.; Zhou, H. Y.; Krieger, S.; Parry, J. J.; Whittenberg, J. J.; Desai, A. V.; Rogers, B. E.; Kenis, P. J. A.; Reichert, D. E.
Bioconjugate Chem.201425 (4), pp 761-772
DOI: 10.1021/bc500034n

10.1.5 Developments in the Field of Bioorthogonal Bond Forming Reactions-Past and Present Trends
King, M.; Wagner, A.
Bioconjugate Chem.201425 (5), pp 825-839
DOI: 10.1021/bc500028d

10.1.6 Diels-Alder Cycloadditions on Synthetic RNA in Mammalian Cells
Pyka, A. M.; Domnick, C.; Braun, F.; Kath-Schorr, S.
Bioconjugate Chem.201425 (8), pp 1438-1443
DOI: 10.1021/bc500302y

10.1.7 High-Density Functionalization and Cross-Linking of DNA: “Click” and “Bis-Click” Cycloadditions Performed on Alkynylated Oligonucleotides with Fluorogenic Anthracene Azides
Pujari, S. S.; Ingale, S. A.; Seela, F.
Bioconjugate Chem.201425 (10), pp 1855-1870
DOI: 10.1021/bc5003532

10.1.8 Surface Functionalization of Exosomes Using Click Chemistry
Smyth, T.; Petrova, K.; Payton, N. M.; Persaud, I.; Redzic, J. S.; Gruner, M. W.; Smith-Jones, P.; Anchordoquy, T. J.
Bioconjugate Chem.201425 (10), pp 1777-1784
DOI: 10.1021/bc500291r

10.1.9 Site-Specific Antibody-Drug Conjugates: The Nexus of Biciorthogonal Chemistry, Protein Engineering, and Drug Development.
Agarwal, P.; Bertozzi, C. R.
Bioconjugate Chem.201526 (2), pp 176-192
DOI: 10.1021/bc5004982

10.1.10 Strain-Promoted Oxidation-Controlled Cyclooctyne-1,2-Quinone Cycloaddition (SPOCQ) for Fast and Activatable Protein Conjugation
Borrmann, A.; Fatunsin, O.; Dommerholt, J.; Jonker, A. M.; Lowik, D.; van Hest, J. C. M.; van Delft, F. L.
Bioconjugate Chem.201526 (2), pp 257-261
DOI: 10.1021/bc500534d

10.2 The Journal of Organic Chemistry

10.2.1 Sequential “Click” – “Photo-Click” Cross-Linker for Catalyst-Free Ligation of Azide-Tagged Substrates
Arumugam, S.; Popik, V. V.
J. Org. Chem.201479 (6), pp 2702-2708
DOI: 10.1021/jo500143v

10.2.3 Diazirine-Containing RNA Photo-Cross-Linking Probes for Capturing microRNA Targets
Nakamoto, K.; Ueno, Y.
J. Org. Chem.201479 (6), pp 2463-2472
DOI: 10.1021/jo402738t

10.2.4 Interstrand Cross-Link and Bioconjugate Formation in RNA from a Modified Nucleotide
Sloane, J. L.; Greenberg, M. M.
J. Org. Chem.201479 (20), pp 9792-9798
DOI: 10.1021/jo501982r

10.2.5 Synthesis of Base-Modified 2 ‘-Deoxyribonucleoside Triphosphates and Their Use in Enzymatic Synthesis of Modified DNA for Applications in Bioanalysis and Chemical Biology
Hocek, M.
J. Org. Chem.201479 (21), pp 9914-9921
DOI: 10.1021/jo5020799

10.2.6 Site-specific PEGylation of Proteins: Recent Developments
Nischan, N.; Hackenberger, C. P. R.
J. Org. Chem.201479 (22), pp 10727-10733
DOI: 10.1021/jo502136n

10.3 Organic Letters

10.3.1 One-Pot Peptide Ligation-Desulfurization at Glutamate
Cergol, K. M.; Thompson, R. E.; Malins, L. R.; Turner, P.; Payne, R. J.
Org. Lett.201416 (1), pp 290-293
DOI: 10.1021/ol403288n

10.3.2 Semisynthesis of Peptoid-Protein Hybrids by Chemical Ligation at Serine
Levine, P. M.; Craven, T. W.; Bonneau, R.; Kirshenbaum, K
Org. Lett.201416 (2), pp 512-515
DOI: 10.1021/ol4033978

10.3.3 A Photoinduced, Benzyne Click Reaction
Gann, A. W.; Amoroso, J. W.; Einck, V. J.; Rice, W. P.; Chambers, J. J.; Schnarr, N. A.
Org. Lett.201416 (7), pp 2003-2005
DOI: 10.1021/ol500389t

10.3.4 Amine-Selective Bioconjugation Using Arene Diazonium Salts
Diethelm, S.; Schafroth, M. A.; Carreira, E. M.
Org. Lett.201416 (15), pp 3908-3911
DOI: 10.1021/ol5016509

10.3 5 Efficient and Facile Synthesis of Acrylamide Libraries for Protein-Guided Tethering
Allen, C. E.; Curran, P. R.; Brearley, A. S.; Boissel, V.; Sviridenko, L.; Press, N. J.; Stonehouse, J. P.; Armstrong, A.
Org. Lett.201517 (3), pp 458-460
DOI: 10.1021/ol503486t

10.4 Synthesis, Design and Molecular Function

This Special Issue on “Synthesis, Design and Molecular Function”, guest-edited by Paul Wender, is intended to explore the many exciting new advances and challenges associated with designing and making molecules in the 21st century. It features contributions from thought leaders in the field directed at new reactions, reagents and catalysts, process technologies and screening strategies.

See guest editorial by Paul Wender

10.4.1 Art, Architecture, and the Molecular Frontier
Paul A. Wender (Guest Editor)
DOI10.1021/acs.accounts.5b00332

10.4.2 From Synthesis to Function via Iterative Assembly of N-Methyliminodiacetic Acid Boronate Building Blocks
Junqi Li, Anthony S. Grillo, and Martin D. Burke *
DOI10.1021/acs.accounts.5b00128

10.4.3 Trimethylenemethane Diyl Mediated Tandem Cycloaddition Reactions: Mechanism Based Design of Synthetic Strategies
Hee-Yoon Lee *
DOI10.1021/acs.accounts.5b00178

10.4.4 Intermolecular Reaction Screening as a Tool for Reaction Evaluation
Karl D. Collins* and Frank Glorius*
DOI10.1021/ar500434f

10.4.5 Development of Globo-H Cancer Vaccine
Samuel J. Danishefsky*, Youe-Kong Shue, Michael N. Chang, and Chi-Huey Wong*
DOI10.1021/ar5004187

10.4.6 Total Synthesis of Vinblastine, Related Natural Products, and Key Analogues and Development of Inspired Methodology Suitable for the Systematic Study of Their Structure–Function Properties
Justin E. Sears and Dale L. Boger*
DOI10.1021/ar500400w

10.4.7 Reaction Design, Discovery, and Development as a Foundation to Function-Oriented Synthesis
Glenn C. Micalizio* and Sarah B. Hale
DOI10.1021/ar500408e

10.4.8 Copy, Edit, and Paste: Natural Product Approaches to Biomaterials and Neuroengineering
Karl Gademann*
DOI10.1021/ar500435b

10.4.9 Catalytic Enantioselective Construction of Quaternary Stereocenters: Assembly of Key Building Blocks for the Synthesis of Biologically Active Molecules
Yiyang Liu, Seo-Jung Han, Wen-Bo Liu, and Brian M. Stoltz*
DOI10.1021/ar5004658

10.4.10 Focused Library with a Core Structure Extracted from Natural Products and Modified: Application to Phosphatase Inhibitors and Several Biochemical Findings
Go Hirai* and Mikiko Sodeoka*
DOI10.1021/acs.accounts.5b00048

10.5 Ionization Methods in Mass Spectrometry

Mass spectrometry has undoubtedly boomed over the last two decades and has become a major analytical tool in many disciplines. The technique relies on the separation of ions of different m/z, and its success hinges on efficient ionization methods that furthermore should be tailored to the task at hand. Depending on the application, ionization should be soft, hard, selective, as efficient as possible, etc. This virtual issue pulls together publications from Analytical Chemistry that showcase the exemplary developments in ionization techniques.

10.5.1 From the editorial by Renato Zenobi
DOI 10.1021/acs.analchem.5b01062

10.5.2 Nanophotonic Ionization for Ultratrace and Single-Cell Analysis by Mass Spectrometry
Bennett N. Walker, Jessica A. Stolee, and Akos Vertes
DOI: 10.1021/ac301238k

10.5.3 Unraveling the Mechanism of Electrospray Ionization
Lars Konermann, Elias Ahadi, Antony D. Rodriguez, and Siavash Vahidi
DOI: 10.1021/ac302789c

10.5.4 Ambient Surface Mass Spectrometry Using Plasma-Assisted Desorption Ionization: Effects and Optimization of Analytical Parameters for Signal Intensities of Molecules and Polymers
T. L. Salter, I. S. Gilmore, A. Bowfield, O. T. Olabanji, and J. W. Bradley
DOI: 10.1021/ac302677m

10.5.5 Fast Surface Acoustic Wave-Matrix-Assisted Laser Desorption Ionization Mass Spectrometry of Cell Response from Islets of Langerhans
Loreta Bllaci, Sven Kjellström, Lena Eliasson, James R. Friend, Leslie Y. Yeo, and Staffan Nilsson
DOI: 10.1021/ac3019125

10.5.6 Electrospun Nanofibers as Substrates for Surface-Assisted Laser Desorption/Ionization and Matrix-Enhanced Surface-Assisted Laser Desorption/Ionization Mass Spectrometry
Tian Lu and Susan V. Olesik
DOI: 10.1021/ac303292e

10.5.7 Capillary Photoionization: A High Sensitivity Ionization Method for Mass Spectrometry
Markus Haapala, Tina Suominen, and Risto Kostiainen
DOI: 10.1021/ac4002673

10.5.8 High-Speed Tandem Mass Spectrometric in Situ Imaging by Nanospray Desorption Electrospray Ionization Mass Spectrometry
Ingela Lanekoff, Kristin Burnum-Johnson, Mathew Thomas, Joshua Short, James P. Carson, Jeeyeon Cha, Sudhansu K. Dey, Pengxiang Yang, Maria C. Prieto Conaway, and Julia Laskin
DOI: 10.1021/ac401760s

10.5.9 Atomic Force Microscope Controlled Topographical Imaging and Proximal Probe Thermal Desorption/Ionization Mass Spectrometry Imaging
Olga S. Ovchinnikova, Kevin Kjoller, Gregory B. Hurst, Dale A. Pelletier, and Gary J. Van Berkel
DOI: 10.1021/ac4026576

10.5.10 Droplet Electrospray Ionization Mass Spectrometry for High Throughput Screening for Enzyme Inhibitors
Shuwen Sun and Robert T. Kennedy
DOI: 10.1021/ac502542z

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Pathway Specific Targeting in Anticancer Therapies

Writer and Curator: Larry H. Bernstein, MD, FCAP 

 

7.7 Pathway specific targeting in anticancer therapies

7.7.1 Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism

7.7.2 Sonic hedgehog (Shh) signaling promotes tumorigenicity and stemness via activation of epithelial-to-mesenchymal transition (EMT) in bladder cancer.

7.7.3 Differential activation of NF-κB signaling is associated with platinum and taxane resistance in MyD88 deficient epithelial ovarian cancer cells

7.7.4 Activation of apoptosis by caspase-3-dependent specific RelB cleavage in anticancer agent-treated cancer cells

7.7.5 Identification of Liver Cancer Progenitors Whose Malignant Progression Depends on Autocrine IL-6 Signaling

7.7.6 Acetylation Stabilizes ATP-Citrate Lyase to Promote Lipid Biosynthesis and Tumor Growth

7.7.7 Monoacylglycerol Lipase Regulates a Fatty Acid Network that Promotes Cancer Pathogenesis

7.7.8 Pirin regulates epithelial to mesenchymal transition and down-regulates EAF/U19 signaling in prostate cancer cells

7.7.9 O-GlcNAcylation at promoters, nutrient sensors, and transcriptional regulation

 

7.7.1 Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism

Thangavelua, CQ Pana, …, BC Lowa, and J. Sivaramana
Proc Nat Acad Sci 2012; 109(20):7705–7710
http://dx.doi.org:/10.1073/pnas.1116573109

Besides thriving on altered glucose metabolism, cancer cells undergo glutaminolysis to meet their energy demands. As the first enzyme in catalyzing glutaminolysis, human kidney-type glutaminase isoform (KGA) is becoming an attractive target for small molecules such as BPTES [bis-2-(5 phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide], although the regulatory mechanism of KGA remains unknown. On the basis of crystal structures, we reveal that BPTES binds to an allosteric pocket at the dimer interface of KGA, triggering a dramatic conformational change of the key loop (Glu312-Pro329) near the catalytic site and rendering it inactive. The binding mode of BPTES on the hydrophobic pocket explains its specificity to KGA. Interestingly, KGA activity in cells is stimulated by EGF, and KGA associates with all three kinase components of the Raf-1/Mek2/Erk signaling module. However, the enhanced activity is abrogated by kinase-dead, dominant negative mutants of Raf-1 (Raf-1-K375M) and Mek2 (Mek2-K101A), protein phosphatase PP2A, and Mek-inhibitor U0126, indicative of phosphorylation-dependent regulation. Furthermore, treating cells that coexpressed Mek2-K101A and KGA with suboptimal level of BPTES leads to synergistic inhibition on cell proliferation. Consequently, mutating the crucial hydrophobic residues at this key loop abrogates KGA activity and cell proliferation, despite the binding of constitutive active Mek2-S222/226D. These studies therefore offer insights into (i) allosteric inhibition of KGA by BPTES, revealing the dynamic nature of KGA’s active and inhibitory sites, and (ii) cross-talk and regulation of KGA activities by EGF-mediated Raf-Mek-Erk signaling. These findings will help in the design of better inhibitors and strategies for the treatment of cancers addicted with glutamine metabolism.

The Warburg effect in cancer biology describes the tendency of cancer cells to take up more glucose than most normal cells, despite the availability of oxygen (12). In addition to altered glucose metabolism, glutaminolysis (catabolism of glutamine to ATP and lactate) is another hallmark of cancer cells (23). In glutaminolysis, mitochondrial glutaminase catalyzes the conversion of glutamine to glutamate (4), which is further catabolized in the Krebs cycle for the production of ATP, nucleotides, certain amino acids, lipids, and glutathione (25).

Humans express two glutaminase isoforms: KGA (kidney-type) and LGA (liver-type) from two closely related genes (6). Although KGA is important for promoting growth, nothing is known about the precise mechanism of its activation or inhibition and how its functions are regulated under physiological or pathophysiological conditions. Inhibition of rat KGA activity by antisense mRNA results in decreased growth and tumorigenicity of Ehrlich ascites tumor cells (7), reduced level of glutathione, and induced apoptosis (8), whereas Myc, an oncogenic transcription factor, stimulates KGA expression and glutamine metabolism (5). Interestingly, direct suppression of miR23a and miR23b (9) or activation of TGF-β (10) enhances KGA expression. Similarly, Rho GTPase that controls cytoskeleton and cell division also up-regulates KGA expression in an NF-κB–dependent manner (11). In addition, KGA is a substrate for the ubiquitin ligase anaphase-promoting complex/cyclosome (APC/C)-Cdh1, linking glutaminolysis to cell cycle progression (12). In comparison, function and regulation of LGA is not well studied, although it was recently shown to be linked to p53 pathway (1314). Although intense efforts are being made to develop a specific KGA inhibitor such as BPTES [bis-2-(5-phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide] (15), its mechanism of inhibition and selectivity is not yet understood. Equally important is to understand how KGA function is regulated in normal and cancer cells so that a better treatment strategy can be considered.

The previous crystal structures of microbial (Mglu) and Escherichia coli glutaminases show a conserved catalytic domain of KGA (1617). However, detailed structural information and regulation are not available for human glutaminases especially the KGA, and this has hindered our strategies to develop inhibitors. Here we report the crystal structure of the catalytic domain of human apo KGA and its complexes with substrate (L-glutamine), product (L-glutamate), BPTES, and its derived inhibitors. Further, Raf-Mek-Erk module is identified as the regulator of KGA activity. Although BPTES is not recognized in the active site, its binding confers a drastic conformational change of a key loop (Glu312-Pro329), which is essential in stabilizing the catalytic pocket. Significantly, EGF activates KGA activity, which can be abolished by the kinase-dead, dominant negative mutants of Mek2 (Mek2-K101A) or its upstream activator Raf-1 (Raf-1-K375M), which are the kinase components of the growth-promoting Raf-Mek2-Erk signaling node. Furthermore, coexpression of phosphatase PP2A and treatment with Mek-specific inhibitor or alkaline phosphatase all abolished enhanced KGA activity inside the cells and in vitro, indicating that stimulation of KGA is phosphorylation dependent. Our results therefore provide mechanistic insights into KGA inhibition by BPTES and its regulation by EGF-mediated Raf-Mek-Erk module in cell growth and possibly cancer manifestation.

Structures of cKGA and Its Complexes with L-Glutamine and L-Glutamate.
The human KGA consists of 669 amino acids. We refer to Ile221-Leu533 as the catalytic domain of KGA (cKGA) (Fig. 1A). The crystal structures of the apo cKGA and in complex with L-glutamine or L-glutamate were determined (Table S1). The structure of cKGA has two domains with the active site located at the interface. Domain I comprises (Ile221-Pro281 and Cys424 -Leu533) of a five-stranded anti-parallel β-sheet (β2↓β1↑β5↓β4↑β3↓) surrounded by six α-helices and several loops. The domain II (Phe282-Thr423) mainly consists of seven α-helices. L-Glutamine/L-glutamate is bound in the active site cleft (Fig. 1B and Fig. S1B). Overall the active site is highly basic, and the bound ligand makes several hydrogen-bonding contacts to Gln285, Ser286, Asn335, Glu381, Asn388, Tyr414, Tyr466, and Val484 (Fig. 1C and Fig. S1C), and these residues are highly conserved among KGA homologs (Fig. S1D). Notably, the putative serine-lysine catalytic dyad (286-SCVK-289), corresponding to the SXXK motif of class D β-lactamase (17), is located in close proximity to the bound ligand. In the apo structure, two water molecules were located in the active site, one of them being displaced by glutamine in the substrate complex. The substrate side chain is within hydrogen-bonding distance (2.9 Å) to the active site Ser286. Other key residues involved in catalysis, such as Lys289, Tyr414, and Tyr466, are in the vicinity of the active site. Lys289 is within hydrogen-bonding distance to Ser286 (3.1 Å) and acts as a general base for the nucleophilic attack by accepting the proton from Ser286. Tyr466, which is close to Ser286 and in hydrogen-bonding contact (3.2 Å) with glutamine, is involved in proton transfer during catalysis. Moreover, the carbonyl oxygen of the glutamine is hydrogen-bonded with the main chain amino groups of Ser286 and Val484, forming the oxyanion hole. Thus, we propose that in addition to the putative catalytic dyad (Ser286 XX Lys289), Tyr466 could play an important role in the catalysis (Fig. 1Cand Fig. S2).

structure of the cKGA-L-glutamine complex

structure of the cKGA-L-glutamine complex

http://www.pnas.org/content/109/20/7705/F1.medium.gif

Fig. 1.  Schematic view and structure of the cKGA-L-glutamine complex. (A) Human KGA domains and signature motifs (refer to Fig. S1A for details). (B) Structure of the of cKGA and bound substrate (L-glutamine) is shown as a cyan stick. (C) Fourier 2Fo-Fc electron density map (contoured at 1 σ) for L-glutamine, that makes hydrogen bonds with active site residues are shown.

Allosteric Binding Pocket for BPTES. The chemical structure of BPTES has an internal symmetry, with two exactly equivalent parts including a thiadiazole, amide, and a phenyl group (Fig. S3A), and it equally interacts with each monomer. The thiadiazole group and the aliphatic linker are well buried in a hydrophobic cluster that consists of Leu321, Phe322, Leu323, and Tyr394 from both monomers, which forms the allosteric pocket (Fig. 2 B–E). The side chain of Phe322 is found at the bottom of the allosteric pocket. The phenyl-acetamido moiety of BPTES is partially exposed on the loop (Asn324-Glu325), where it interacts with Phe318, Asn324, and the aliphatic part of the Glu325 side chain. On the basis of our observations we synthesized a series of BPTES-derived inhibitors (compounds2–5) (Fig. S3 AF and SI Results) and solved their cocrystal structure of compounds 2–4. Similar to BPTES, compounds 24 all resides within the hydrophobic cluster of the allosteric pocket (Fig. S3 CF).

Fig. 2. Structure of cKGA: BPTES complex and the allosteric binding mode of BPTES.

Allosteric Binding of BPTES Triggers Major Conformational Change in the Key Loop Near the Active Site.  The overall structure of these inhibitor complexes superimposes well with apo cKGA. However, a major conformational change at the Glu312 to Pro329 loop was observed in the BPTES complex (Fig. 2F). The most conformational changes of the backbone atoms that moved away from the active site region are found at the center of the loop (Leu316-Lys320). The backbone of the residues Phe318 and Asn319 is moved ≈9 Å and ≈7 Å, respectively, compared with the apo structure, whereas the side chain of these residues moved ≈14 Å and ≈12 Å, respectively. This loop rearrangement in turn brings Phe318 closer to the phenyl group of the inhibitor and forms the inhibitor binding pocket, whereas in the apo structure the same loop region (Leu316-Lys320) was found to be adjacent to the active site and forms a closed conformation of the active site.

Binding of BPTES Stabilizes the Inactive Tetramers of cKGA.  To understand the role of oligomerization in KGA function, dimers and tetramers of cKGA were generated using the symmetry-related monomers (Fig. 2 A–E and Fig. S4 D and E). The dimer interface in the cKGA: BPTES complex is formed by residues from the helix Asp386-Lys398 of both monomers and involves hydrogen bonding, salt bridges, and hydrophobic interactions (Phe389, Ala390, Tyr393, and Tyr394), besides two sulfate ions located in the interface (Fig. 2E). The dimers are further stabilized by binding of BPTES, where it binds to loop residues (Glu312-Pro329) and Tyr394 from both monomers (Fig. 2 D and E). Similarly, residues from Lys311-Asn319 loop and Arg454, His461, Gln471, and Asn529-Leu533 are involved in the interface with neighboring monomers to form the tetramer in the BPTES complex.

BPTES Induces Allosteric Conformational Changes That Destabilize Catalytic Function of KGA

Fig. 3A shows that 293T cells overexpressing KGA produced higher level of glutamate compared with the vector control cells. Most significantly, all of these mutants, except Phe322Ala, greatly diminished the KGA activity.

Fig. 3. Mutations at allosteric loop and BPTES binding pocket abrogate KGA activity and BPTES sensitivity.

Raf-Mek-Erk Signaling Module Regulates KGA Activity. Because KGA supports cell growth and proliferation, we first validated that treatment of cells with BPTES indeed inhibits KGA activity and cell proliferation (Fig. S5 A–D and SI Results). Next, as cells respond to various physiological stimuli to regulate their metabolism, with many of the metabolic enzymes being the primary targets of modulation (18), we examined whether KGA activity can be regulated by physiological stimuli, in particular EGF, which is important for cell growth and proliferation. Cells overexpressing KGA were made quiescent and then stimulated with EGF for various time points. Fig. 4A shows that the basal KGA activity remained unchanged 30 min after EGF stimulation, but the activity was substantially enhanced after 1 h and then gradually returned to the basal level after 4 h. Because EGF activates the Raf-Mek-Erk signaling module (19), treatment of cells with Mek-specific inhibitor U0126 could block the enhanced KGA activity with parallel inhibition of Erk phosphorylation (Fig. 4A). Interestingly, such Mek-induced KGA activity is specific to EGF and lysophosphatidic acid (LPA) but not with other growth factors, such as PDGF, TGF-β, and basic FGF (bFGF), despite activation of Mek-Erk by bFGF (Fig. S6A).

The results show that KGA could interact equally well with the wild-type or mutant forms of Raf-1 and Mek2 (Fig. 4C). Importantly, endogenous Raf-1 or Erk1/2, including the phosphorylated Erk1/2 (Fig. 4 C and D), could be detected in the KGA complex. Taken together, these results indicate that the activity of KGA is directly regulated by Raf-Mek-Erk downstream of EGF receptor. To further show that Mek2-enhanced KGA activity requires both the kinase activity of Mek2 and the core residues for KGA catalysis, wild-type or triple mutant (Leu321Ala/Phe322Ala/Leu323Ala) of KGA was coexpressed with dominant negative Mek2-KA or the constitutive active Mek2-SD and their KGA activities measured. The result shows that the presence of Mek2-KA blocks KGA activity, whereas the triple mutant still remains inert even in the presence of the constitutively active Mek2 (Fig. 4E), and despite Mek2 binding to the KGA triple mutant (Fig. S7B). Consequently, expressing triple mutant did not support cell proliferation as well as the wild-type control (Fig. S7C).

Fig. 4. EGFR-Raf-Mek-Erk signaling stimulates KGA activity.

When cells expressing both KGA and Mek2-K101A were treated with subthreshold levels of BPTES, there was a synergistic reduction in cell proliferation (Fig. S6C and SI Results). Lastly, to determine whether regulation of KGA by Raf-Mek-Erk depends on its phosphorylation status, cells were transfected with KGA with or without the protein phosphatase PP2A and assayed for the KGA activity. PP2A is a ubiquitous and conserved serine/threonine phosphatase with broad substrate specificity. The results indicate that KGA activity was reduced down to the basal level in the presence of PP2A (Fig. 5A). Coimmunoprecipitation study also revealed that KGA interacts with PP2A (Fig. 5B), suggesting a negative feedback regulation by this protein phosphatase. Furthermore, treatment of immunoprecipitated and purified KGA with calf-intestine alkaline phosphatase (CIAP) almost completely abolished the KGA activity in vitro (Fig. S6D). Taken together, these results indicate that KGA activity is regulated by Raf-Mek2, and KGA activation by EGF could be part of the EGF-stimulated Raf-Mek-Erk signaling program in controlling cell growth and proliferation (Fig. 5C).

KGA activity is regulated by phosphorylation

KGA activity is regulated by phosphorylation

http://www.pnas.org/content/109/20/7705/F5.medium.gif

Fig. 5. KGA activity is regulated by phosphorylation. (C) Schematic model depicting the synergistic cross-talk between KGA-mediated glutaminolysis and EGF-activated Raf-Mek-Erk signaling. Exogenous glutamine can be transported across the membrane and converted to glutamate by glutaminase (KGA), thus feeding the metabolite to the ATP-producing tricarboxylic acid (TCA) cycle. This process can be stimulated by EGF receptor-mediated Raf-Mek-Erk signaling via their phosphorylation-dependent pathway, as evidenced by the inhibition of KGA activity by the kinase-dead and dominant negative mutants of Raf-1 (Raf-1-K375M) and Mek2 (Mek2-K101A), protein phosphatase PP2A, and Mek-specific inhibitor U0126. Consequently, inhibiting KGA with BPTES and blocking Raf-Mek pathway with Mek2-K101A provide a synergistic inhibition on cell proliferation.

Small-molecule inhibitors that target glutaminase activity in cancer cells are under development. Earlier efforts targeting glutaminase using glutamine analogs have been unsuccessful owing to their toxicities (2). BPTES has attracted much attention as a selective, nontoxic inhibitor of KGA (15), and preclinical testing of BPTES toward human cancers has just begun (20). BPTES selectively suppresses the growth of glioma cells (21) and inhibits the growth of lymphoma tumor growth in animal model studies (22). Wang et al. (11) reported a small molecule that targets glutaminase activity and oncogenic transformation. Despite extensive studies, nothing is known about the structural and molecular basis for KGA inhibitory mechanisms and how their function is regulated during normal and cancer cell metabolism. Such limited information impedes our effort in producing better generations of inhibitors for better treatment regimens.

Comparison of the complex structures with apo cKGA structure, which has well-defined electron density for the key loop, we provide the atomic view of an allosteric binding pocket for BPTES and elucidate the inhibitory mechanism of KGA by BPTES. The key residues of the loop (Glu312-Pro329) undergo major conformational changes upon binding of BPTES. In addition, structure-based mutagenesis studies suggest that this loop is essential for stabilizing the active site. Therefore, by binding in an allosteric pocket, BPTES inhibits the enzymatic activity of KGA through (i) triggering a major conformational change on the key residues that would normally be involved in stabilizing the active sites and regulating its enzymatic activity; and (ii) forming a stable inactive tetrameric KGA form. Our findings are further supported by two very recent reports on KGA isoform (GAC) (2324), although these studies lack full details owing to limitation of their electron density maps. BPTES is specific to KGA but not to LGA (15). Sequence comparison of KGA with LGA (Fig. S8A) reveals two unique residues on KGA, Phe318 and Phe322, which upon mutation to LGA counterparts, become resistant to BPTES. Thus, our study provides the molecular basis of BPTES specificity.

7.7.2 Sonic hedgehog (Shh) signaling promotes tumorigenicity and stemness via activation of epithelial-to-mesenchymal transition (EMT) in bladder cancer.

Islam SS, Mokhtari RB, Noman AS, …, van der Kwast T, Yeger H, Farhat WA.
Molec Carcinogenesis mar 2015; 54(5). http://dx.doi.org:/10.1002/mc.22300

shh sonic hedgehog signaling pathway nri2151-f1

shh sonic hedgehog signaling pathway nri2151-f1

Activation of the sonic hedgehog (Shh) signaling pathway controls tumorigenesis in a variety of cancers. Here, we show a role for Shh signaling in the promotion of epithelial-to-mesenchymal transition (EMT), tumorigenicity, and stemness in the bladder cancer. EMT induction was assessed by the decreased expression of E-cadherin and ZO-1 and increased expression of N-cadherin. The induced EMT was associated with increased cell motility, invasiveness, and clonogenicity. These progression relevant behaviors were attenuated by treatment with Hh inhibitors cyclopamine and GDC-0449, and after knockdown by Shh-siRNA, and led to reversal of the EMT phenotype. The results with HTB-9 were confirmed using a second bladder cancer cell line, BFTC905 (DM). In a xenograft mouse model TGF-β1 treated HTB-9 cells exhibited enhanced tumor growth. Although normal bladder epithelial cells could also undergo EMT and upregulate Shh with TGF-β1 they did not exhibit tumorigenicity. The TGF-β1 treated HTB-9 xenografts showed strong evidence for a switch to a more stem cell like phenotype, with functional activation of CD133, Sox2, Nanog, and Oct4. The bladder cancer specific stem cell markers CK5 and CK14 were upregulated in the TGF-β1 treated xenograft tumor samples, while CD44 remained unchanged in both treated and untreated tumors. Immunohistochemical analysis of 22 primary human bladder tumors indicated that Shh expression was positively correlated with tumor grade and stage. Elevated expression of Ki-67, Shh, Gli2, and N-cadherin were observed in the high grade and stage human bladder tumor samples, and conversely, the downregulation of these genes were observed in the low grade and stage tumor samples. Collectively, this study indicates that TGF-β1-induced Shh may regulate EMT and tumorigenicity in bladder cancer. Our studies reveal that the TGF-β1 induction of EMT and Shh is cell type context dependent. Thus, targeting the Shh pathway could be clinically beneficial in the ability to reverse the EMT phenotype of tumor cells and potentially inhibit bladder cancer progression and metastasis

Sonic_hedgehog_pathway

Sonic_hedgehog_pathway

7.7.3 Differential activation of NF-κB signaling is associated with platinum and taxane resistance in MyD88 deficient epithelial ovarian cancer cells

Gaikwad SM, Thakur B, Sakpal A, Singh RK, Ray P.
Int J Biochem Cell Biol. 2015 Apr; 61:90-102
http://dx.doi.org:/10.1016/j.biocel.2015.02.001

Development of chemoresistance is a major impediment to successful treatment of patients suffering from epithelial ovarian carcinoma (EOC). Among various molecular factors, presence of MyD88, a component of TLR-4/MyD88 mediated NF-κB signaling in EOC tumors is reported to cause intrinsic paclitaxel resistance and poor survival. However, 50-60% of EOC patients do not express MyD88 and one-third of these patients finally relapses and dies due to disease burden. The status and role of NF-κB signaling in this chemoresistant MyD88(negative) population has not been investigated so far. Using isogenic cellular matrices of cisplatin, paclitaxel and platinum-taxol resistant MyD88(negative) A2780 ovarian cancer cells expressing a NF-κB reporter sensor, we showed that enhanced NF-κB activity was required for cisplatin but not for paclitaxel resistance. Immunofluorescence and gel mobility shift assay demonstrated enhanced nuclear localization of NF-κB and subsequent binding to NF-κB response element in cisplatin resistant cells. The enhanced NF-κB activity was measurable from in vivo tumor xenografts by dual bioluminescence imaging. In contrast, paclitaxel and the platinum-taxol resistant cells showed down regulation in NF-κB activity. Intriguingly, silencing of MyD88 in cisplatin resistant and MyD88(positive) TOV21G and SKOV3 cells showed enhanced NF-κB activity after cisplatin but not after paclitaxel or platinum-taxol treatments. Our data thus suggest that NF-κB signaling is important for maintenance of cisplatin resistance but not for taxol or platinum-taxol resistance in absence of an active TLR-4/MyD88 receptor mediated cell survival pathway in epithelial ovarian carcinoma.

7.7.4 Activation of apoptosis by caspase-3-dependent specific RelB cleavage in anticancer agent-treated cancer cells

Kuboki MIto ASimizu SUmezawa K.
Biochem Biophys Res Commun. 2015 Jan 16; 456(3):810-4
http://dx.doi.org:/10.1016/j.bbrc.2014.12.024

Activation of caspase 3 and caspase-dependent apoptosis  nrmicro2071-f1

Activation of caspase 3 and caspase-dependent apoptosis nrmicro2071-f1

Highlights

  • We have prepared RelB mutants that are resistant to caspase 3-induced scission.
  • Vinblastine induced caspase 3-dependent site-specific RelB cleavage in cancer cells.
  • Cancer cells expressing cleavage-resistant RelB showed less sensitivity to vinblastine.
  • Caspase 3-induced RelB cleavage may provide positive feedback mechanism in apoptosis.

DTCM-glutarimide (DTCM-G) is a newly found anti-inflammatory agent. In the course of experiments with lymphoma cells, we found that DTCM-G induced specific RelB cleavage. Anticancer agent vinblastine also induced the specific RelB cleavage in human fibrosarcoma HT1080 cells. The site-directed mutagenesis analysis revealed that the Asp205 site in RelB was specifically cleaved possibly by caspase-3 in vinblastine-treated HT1080 cells. Moreover, the cells stably overexpressing RelB Asp205Ala were resistant to vinblastine-induced apoptosis. Thus, the specific Asp205 cleavage of RelB by caspase-3 would be involved in the apoptosis induction by anticancer agents, which would provide the positive feedback mechanism.

apoptotic-caspases-control-microglia-activation-cdd2011107f3

apoptotic-caspases-control-microglia-activation-cdd2011107f3

 

 

7.7.5 Identification of Liver Cancer Progenitors Whose Malignant Progression Depends on Autocrine IL-6 Signaling

He GDhar DNakagawa HFont-Burgada JOgata HJiang Y, et al.
Cell. 2013 Oct 10; 155(2):384-96
http://dx.doi.org/10.1016%2Fj.cell.2013.09.031

Il-6 signaling in cancer cells

Il-6 signaling in cancer cells

Hepatocellular carcinoma (HCC) is a slowly developing malignancy postulated to evolve from pre-malignant lesions in chronically damaged livers. However, it was never established that premalignant lesions actually contain tumor progenitors that give rise to cancer. Here, we describe isolation and characterization of HCC progenitor cells (HcPCs) from different mouse HCC models. Unlike fully malignant HCC, HcPCs give rise to cancer only when introduced into a liver undergoing chronic damage and compensatory proliferation. Although HcPCs exhibit a similar transcriptomic profile to bipotential hepatobiliary progenitors, the latter do not give rise to tumors. Cells resembling HcPCs reside within dysplastic lesions that appear several months before HCC nodules. Unlike early hepatocarcinogenesis, which depends on paracrine IL-6 production by inflammatory cells, due to upregulation of LIN28 expression, HcPCs had acquired autocrine IL-6 signaling that stimulates their in vivo growth and malignant progression. This may be a general mechanism that drives other IL-6-producing malignancies.

Clonal evolution and selective pressure may cause some descendants of the initial progenitor to cross the bridge of no return and form a premalignant lesion. Cancer genome sequencing indicates that most cancers require at least five genetic changes to evolve (Wood et al., 2007). It has been difficult to isolate and propagate cancer progenitors prior to detection of tumor masses. Further, it is not clear whether cancer progenitors are the precursors for the  cancer stem cells (CSCs)isolated from cancers. An answer to these critical questions depends on identification and isolation of cancer progenitors, which may also enable definition of molecular markers and signaling pathways suitable for early detection and treatment.

Hepatocellular carcinoma (HCC), the end product of chronic liver diseases, requires several decades to evolve (El-Serag, 2011). It is the third most deadly and fifth most common cancer worldwide, and in the United States its incidence has doubled in the past two decades. Furthermore, 8% of the world’s population are chronically infected with hepatitis B or C viruses (HBV and HCV) and are at a high risk of new HCC development (El-Serag, 2011). Up to 5% of HCV patients will develop HCC in their lifetime, and the yearly HCC incidence in patients with cirrhosis is 3%–5%. These tumors may arise from premalignant lesions, ranging from dysplastic foci to dysplastic hepatocyte nodules that are often seen in damaged and cirrhotic livers and are more proliferative than the surrounding parenchyma (Hytiroglou et al., 2007). There is no effective treatment for HCC and, upon diagnosis, most patients with advanced disease have a remaining lifespan of 4–6 months. Premalignant lesions, called foci of altered hepatocytes (FAH), were described in chemically induced HCC models (Pitot, 1990), but it was questioned whether these lesions harbor tumor progenitors or result from compensatory proliferation (Sell and Leffert, 2008). The aim of this study was to determine whether HCC progenitor cells (HcPCs) exist and if so, to isolate these cells and identify some of the signaling networks that are involved in their maintenance and progression.

We now describe HcPC isolation from mice treated with the procarcinogen diethyl nitrosamine (DEN), which induces poorly differentiated HCC nodules within 8 to 9 months (Verna et al., 1996). The use of a chemical carcinogen is justified because the finding of up to 121 mutations per HCC genome suggests that carcinogens may be responsible for human HCC induction (Guichard et al., 2012). Furthermore, 20%–30% of HCC, especially in HBV-infected individuals, evolve in noncirrhotic livers (El-Serag, 2011). Nonetheless, we also isolated HcPCs fromTak1Δhep mice, which develop spontaneous HCC as a result of progressive liver damage, inflammation, and fibrosis caused by ablation of TAK1 (Inokuchi et al., 2010). Although the etiology of each model is distinct, both contain HcPCs that express marker genes and signaling pathways previously identified in human HCC stem cells (Marquardt and Thorgeirsson, 2010) long before visible tumors are detected. Furthermore, DEN-induced premalignant lesions and HcPCs exhibit autocrine IL-6 production that is critical for tumorigenic progression. Circulating IL-6 is a risk indicator in several human pathologies and is strongly correlated with adverse prognosis in HCC and cholangiocarcinoma (Porta et al., 2008Soresi et al., 2006). IL-6 produced by in-vitro-induced CSCs was suggested to be important for their maintenance (Iliopoulos et al., 2009). Little is known about the source of IL-6 in HCC.

DEN-Induced Collagenase-Resistant Aggregates of HCC Progenitors

A single intraperitoneal (i.p.) injection of DEN into 15-day-old BL/6 mice induces HCC nodules first detected 8 to 9 months later. However, hepatocytes prepared from macroscopically normal livers 3 months after DEN administration already contain cells that progress to HCC when transplanted into the permissive liver environment of MUP-uPA mice (He et al., 2010), which express urokinase plasminogen activator (uPA) from a mouse liver-specific major urinary protein (MUP) promoter and undergo chronic liver damage and compensatory proliferation (Rhim et al., 1994). HCC markers such as α fetoprotein (AFP), glypican 3 (Gpc3), and Ly6D, whose expression in mouse liver cancer was reported (Meyer et al., 2003), were upregulated in aggregates from DEN-treated livers, but not in nonaggregated hepatocytes or aggregates from control livers (Figure S1A). Using 70 μm and 40 μm sieves, we separated aggregated from nonaggregated hepatocytes (Figure 1A) and tested their tumorigenic potential by transplantation into MUP-uPA mice (Figure 1B). To facilitate transplantation, the aggregates were mechanically dispersed and suspended in Dulbecco’s modified Eagle’s medium (DMEM). Five months after intrasplenic (i.s.) injection of 104 viable cells, mice receiving cells from aggregates developed about 18 liver tumors per mouse, whereas mice receiving nonaggregated hepatocytes developed less than 1 tumor each (Figure 1B). The tumors exhibited typical trabecular HCC morphology and contained cells that abundantly express AFP (Figure S1B).

Only liver tumors were formed by the transplanted cells. Other organs, including the spleen into which the cells were injected, remained tumor free (Figure 1B), suggesting that HcPCs progress to cancer only in the proper microenvironment. Indeed, no tumors appeared after HcPC transplantation into normal BL/6 mice. But, if BL/6 mice were first treated with retrorsine (a chemical that permanently inhibits hepatocyte proliferation [Laconi et al., 1998]), intrasplenically transplanted with HcPC-containing aggregates, and challenged with CCl4 to induce liver injury and compensatory proliferation (Guo et al., 2002), HCCs readily appeared (Figure 1C). CCl4 omission prevented tumor development. Notably, MUP-uPA or CCl4-treated livers are fragile, rendering direct intrahepatic transplantation difficult. CCl4-induced liver damage, especially within a male liver, generates a microenvironment that drives HcPC proliferation and malignant progression. To examine this point, we transplanted GFP-labeled HcPC-containing aggregates into retrorsine-treated BL/6 mice and examined their ability to proliferate with or without subsequent CCl4 treatment. Indeed, the GFP+ cells formed clusters that grew in size only in CCl4-treated host livers (Figure S1E). Omission of CC14 prevented their expansion.

Because CD44 is expressed by HCC stem cells (Yang et al., 2008Zhu et al., 2010), we dispersed the aggregates and separated CD44+ from CD44 cells and transplanted both into MUP-uPA mice. Whereas as few as 103 CD44+ cells gave rise to HCCs in 100% of recipients, no tumors were detected after transplantation of CD44 cells (Figure 1E). Remarkably, 50% of recipients developed at least one HCC after receiving as few as 102 CD44+ cells.

HcPC-Containing Aggregates in Tak1Δhep Mice

We applied the same HcPC isolation protocol to Tak1Δhep mice, which develop HCC of different etiology from DEN-induced HCC. Importantly, Tak1Δhep mice develop HCC as a consequence of chronic liver injury and fibrosis without carcinogen or toxicant exposure (Inokuchi et al., 2010). Indeed, whole-tumor exome sequencing revealed that DEN-induced HCC contained about 24 mutations per 106 bases (Mb) sequenced, with B-RafV637E being the most recurrent, whereas 1.4 mutations per Mb were detected inTak1Δhep HCC’s exome (Table S1). By contrast, Tak1Δhep HCC exhibited gene copy number changes. HCC developed in 75% of MUP-uPA mice that received dispersed Tak1Δhep aggregates, but no tumors appeared in mice receiving nonaggregated Tak1Δhep or totalTak1f/f hepatocytes (Figure 2B). bile duct ligation (BDL) or feeding with 3,5-dicarbethoxy-1,4-dihydrocollidine (DDC), treatments that cause cholestatic liver injuries and oval cell expansion (Dorrell et al., 2011), did increase the number of small hepatocytic cell aggregates (Figure S2A). Nonetheless, no tumors were observed 5 months after injection of such aggregates into MUP-uPA mice (Figure S2B). Thus, not all hepatocytic aggregates contain HcPCs, and HcPCs only appear under tumorigenic conditions.

The HcPC Transcriptome Is Similar to that of HCC and Oval Cells

To determine the relationship between DEN-induced HcPCs, normal hepatocytes, and fully transformed HCC cells, we analyzed the transcriptomes of aggregated and nonaggregated hepatocytes from male littermates 5 months after DEN administration, HCC epithelial cells from DEN-induced tumors, and normal hepatocytes from age- and gender-matched littermate controls. Clustering analysis distinguished the HCC samples from other samples and revealed that the aggregated hepatocyte samples did not cluster with each other but rather with nonaggregated hepatocytes derived from the same mouse (Figure S3A). 57% (583/1,020) of genes differentially expressed in aggregated relative to nonaggregated hepatocytes are also differentially expressed in HCC relative to normal hepatocytes (Figure 3B, top), a value that is highly significant (p < 7.13 × 10−243). More specifically, 85% (494/583) of these genes are overexpressed in both HCC and HcPC-containing aggregates (Figure 3B, bottom table). Thus, hepatocyte aggregates isolated 5 months after DEN injection contain cells that are related in their gene expression profile to HCC cells isolated from fully developed tumor nodules.

Figure 3 Aggregated Hepatocytes Exhibit an Altered Transcriptome Similar to that of HCC Cells

We examined which biological processes or cellular compartments were significantly overrepresented in the induced or repressed genes in both pairwise comparisons (Gene Ontology Analysis). As expected, processes and compartments that were enriched in aggregated hepatocytes relative to nonaggregated hepatocytes were almost identical to those that were enriched in HCC relative to normal hepatocytes (Figure 3C). Several human HCC markers, including AFP, Gpc3 and H19, were upregulated in aggregated hepatocytes (Figures 3D and 3E). Aggregated hepatocytes also expressed more Tetraspanin 8 (Tspan8), a cell-surface glycoprotein that complexes with integrins and is overexpressed in human carcinomas (Zöller, 2009). Another cell-surface molecule highly expressed in aggregated cells is Ly6D (Figures 3D and 3E). Immunofluorescence (IF) analysis revealed that Ly6D was undetectable in normal liver but was elevated in FAH and ubiquitously expressed in most HCC cells (Figure S3C). A fluorescent-labeled Ly6D antibody injected into HCC-bearing mice specifically stained tumor nodules (Figure S3D). Other cell-surface molecules that were upregulated in aggregated cells included syndecan 3 (Sdc3), integrin α 9 (Itga9), claudin 5 (Cldn5), and cadherin 5 (Cdh5) (Figure 3D). Aggregated hepatocytes also exhibited elevated expression of extracellular matrix proteins (TIF3 and Reln1) and a serine protease inhibitor (Spink3). Elevated expression of such proteins may explain aggregate formation. Aggregated hepatocytes also expressed progenitor cell markers, including the epithelial cell adhesion molecule (EpCAM) (Figure 3E) and Dlk1 (Figure 3D). We compared the HcPC and HCC (Figure 3A) to the transcriptome of DDC-induced oval cells (Shin et al., 2011). This analysis revealed a striking similarity between the HCC, HcPC, and the oval cell transcriptomes (Figure S3B). Despite these similarities, some genes that were upregulated in HcPC-containing aggregates and HCC were not upregulated in oval cells. Such genes may account for the tumorigenic properties of HcPC and HCC.

Figure 4  DEN-Induced HcPC Aggregates Express Pathways and Markers Characteristic of HCC and Hepatobiliary Stem Cells

We examined the aggregates for signaling pathways and transcription factors involved in hepatocarcinogenesis. Many aggregated cells were positive for phosphorylated c-Jun and STAT3 (Figure 4A), transcription factors involved in DEN-induced hepatocarcinogenesis (Eferl et al., 2003He et al., 2010). Sox9, a transcription factor that marks hepatobiliary progenitors (Dorrell et al., 2011), was also expressed by many of the aggregated cells, which were also positive for phosphorylated c-Met (Figure 4A), a receptor tyrosine kinase that is activated by hepatocyte growth factor (HGF) and is essential for liver development (Bladt et al., 1995) and hepatocarcinogenesis (Wang et al., 2001). Few of the nonaggregated hepatocytes exhibited activation of these signaling pathways. Despite different etiology, HcPC-containing aggregates from Tak1Δhep mice exhibit upregulation of many of the same markers and pathways that are upregulated in DEN-induced HcPC-containing aggregates. Flow cytometry confirmed enrichment of CD44+ cells as well as CD44+/CD90+ and CD44+/EpCAM+ double-positive cells in the HcPC-containing aggregates from either DEN-treated or Tak1Δhep livers (Figure S4B).

HcPC-Containing Aggregates Originate from Premalignant Dysplastic Lesions

FAH are dysplastic lesions occurring in rodent livers exposed to hepatic carcinogens (Su et al., 1990). Similar lesions are present in premalignant human livers (Su et al., 1997). Yet, it is still debated whether FAH correspond to premalignant lesions or are a reaction to liver injury that does not lead to cancer (Sell and Leffert, 2008). In DEN-treated males, FAH were detected as early as 3 months after DEN administration (Figure 5A), concomitant with the time at which HcPC-containing aggregates were detected. In females, FAH development was delayed. FAH contained cells positive for the same progenitor cell markers and activated signaling pathways present in HcPC-containing aggregates, including AFP, CD44, and EpCAM (Figure 5C). FAH also contained cells positive for activated STAT3, c-Jun, and PCNA (Figure 5C).

HcPCs Exhibit Autocrine IL-6 Expression Necessary for HCC Progression

In situ hybridization (ISH) and immunohistochemistry (IHC) revealed that DEN-induced FAH contained IL-6-expressing cells (Figures 6A, 6B, and S5), and freshly isolated DEN-induced aggregates contained more IL-6 messenger RNA (mRNA) than nonaggregated hepatocytes (Figure 6C). We examined several factors that control IL-6 expression and found that LIN28A and B were significantly upregulated in HcPCs and HCC (Figures 6D and 6E). LIN28-expressing cells were also detected within FAH (Figure 6F). As reported (Iliopoulos et al., 2009), knockdown of LIN28B in cultured HcPC or HCC cell lines decreased IL-6 expression (Figure 6G). LIN28 exerts its effects through downregulation of the microRNA (miRNA) Let-7 (Iliopoulos et al., 2009).

Figure 6  Liver Premalignant Lesions and HcPCs Exhibit Elevated IL-6 and LIN28 Expression

Figure 7  HCC Growth Depends on Autocrine IL-6 Production

The isolation and characterization of cells that can give rise to HCC only after transplantation into an appropriate host liver undergoing chronic injury demonstrates that cancer arises from progenitor cells that are yet to become fully malignant. Importantly, unlike fully malignant HCC cells, the HcPCs we isolated cannot form s.c. tumors or even liver tumors when introduced into a nondamaged liver. Liver damage induced by uPA expression or CCl4 treatment provides HcPCs with the proper cytokine and growth factor milieu needed for their proliferation. Although HcPCs produce IL-6, they may also depend on other cytokines such as TNF, which is produced by macrophages that are recruited to the damaged liver. In addition, uPA expression and CCl4 treatment may enhance HcPC growth and progression through their fibrogenic effect on hepatic stellate cells. Although HCC and other cancers have been suspected to arise from premalignant/dysplastic lesions (Hruban et al., 2007Hytiroglou et al., 2007), a direct demonstration that such lesions progress into malignant tumors has been lacking. Based on expression of common markers—EpCAM, CD44, AFP, activated STAT3, and IL-6—that are not expressed in normal hepatocytes, we postulate that HcPCs originate from FAH or dysplastic foci, which are first observed in male mice within 3 months of DEN exposure.

7.7.6 Acetylation Stabilizes ATP-Citrate Lyase to Promote Lipid Biosynthesis and Tumor Growth

Lin R1Tao RGao XLi TZhou XGuan KLXiong YLei QY.
Mol Cell. 2013 Aug 22; 51(4):506-18
http://dx.doi.org:/10.1016/j.molcel.2013.07.002

Increased fatty acid synthesis is required to meet the demand for membrane expansion of rapidly growing cells. ATP-citrate lyase (ACLY) is upregulated or activated in several types of cancer, and inhibition of ACLY arrests proliferation of cancer cells. Here we show that ACLY is acetylated at lysine residues 540, 546, and 554 (3K). Acetylation at these three lysine residues is stimulated by P300/calcium-binding protein (CBP)-associated factor (PCAF) acetyltransferase under high glucose and increases ACLY stability by blocking its ubiquitylation and degradation. Conversely, the protein deacetylase sirtuin 2 (SIRT2) deacetylates and destabilizes ACLY. Substitution of 3K abolishes ACLY ubiquitylation and promotes de novo lipid synthesis, cell proliferation, and tumor growth. Importantly, 3K acetylation of ACLY is increased in human lung cancers. Our study reveals a crosstalk between acetylation and ubiquitylation by competing for the same lysine residues in the regulation of fatty acid synthesis and cell growth in response to glucose.

Fatty acid synthesis occurs at low rates in most nondividing cells of normal tissues that primarily uptake lipids from circulation. In contrast, increased lipogenesis, especially de novo lipid synthesis, is a key characteristic of cancer cells. Many studies have demonstrated that in cancer cells, fatty acids are preferred to be derived from de novo synthesis instead of extracellular lipid supply (Medes et al., 1953Menendez and Lupu, 2007;Ookhtens et al., 1984Sabine et al., 1967). Fatty acids are key building blocks for membrane biogenesis, and glucose serves as a major carbon source for de novo fatty acid synthesis (Kuhajda, 2000McAndrew, 1986;Swinnen et al., 2006). In rapidly proliferating cells, citrate generated by the tricarboxylic acid (TCA) cycle, either from glucose by glycolysis or glutamine by anaplerosis, is preferentially exported from mitochondria to cytosol and then cleaved by ATP citrate lyase (ACLY) (Icard et al., 2012) to produce cytosolic acetyl coenzyme A (acetyl-CoA), which is the building block for de novo lipid synthesis. As such, ACLY couples energy metabolism with fatty acids synthesis and plays a critical role in supporting cell growth. The function of ACLY in cell growth is supported by the observation that inhibition of ACLY by chemical inhibitors or RNAi dramatically suppresses tumor cell proliferation and induces differentiation in vitro and in vivo (Bauer et al., 2005Hatzivassiliou et al., 2005). In addition, ACLY activity may link metabolic status to histone acetylation by providing acetyl-CoA and, therefore, gene expression (Wellen et al., 2009).

While ACLY is transcriptionally regulated by sterol regulatory element-binding protein 1 (SREBP-1) (Kim et al., 2010), ACLY activity is regulated by the phosphatidylinositol 3-kinase (PI3K)/Akt pathway (Berwick et al., 2002Migita et al., 2008Pierce et al., 1982). Akt can directly phosphorylate and activate ACLY (Bauer et al., 2005Berwick et al., 2002Migita et al., 2008Potapova et al., 2000). Covalent lysine acetylation has recently been found to play a broad and critical role in the regulation of multiple metabolic enzymes (Choudhary et al., 2009Zhao et al., 2010). In this study, we demonstrate that ACLY protein is acetylated on multiple lysine residues in response to high glucose. Acetylation of ACLY blocks its ubiquitinylation and degradation, thus leading to ACLY accumulation and increased fatty acid synthesis. Our observations reveal a crosstalk between protein acetylation and ubiquitylation in the regulation of fatty acid synthesis and cell growth.

Acetylation of ACLY at Lysines 540, 546, and 554

Recent mass spectrometry-based proteomic analyses have potentially identified a large number of acetylated proteins, including ACLY (Figure S1A available online; Choudhary et al., 2009Zhao et al., 2010). We detected the acetylation level of ectopically expressed ACLY followed by western blot using pan-specific anti-acetylated lysine antibody. ACLY was indeed acetylated, and its acetylation was increased by nearly 3-fold after treatment with nicotinamide (NAM), an inhibitor of the SIRT family deacetylases, and trichostatin A (TSA), an inhibitor of histone deacetylase (HDAC) class I and class II (Figure 1A). Experiments with endogenous ACLY also showed that TSA and NAM treatment enhanced ACLY acetylation (Figure 1B).

Figure 1  ACLY Is Acetylated at Lysines 540, 546, and 554

Ten putative acetylation sites were identified by mass spectrometry analyses (Table S1). We singly mutated each lysine to either a glutamine (Q) or an arginine (R) and found that no single mutation resulted in a significant reduction of ACLY acetylation (data not shown), indicating that ACLY may be acetylated at multiple lysine residues. Three lysine residues, K540, K546, and K554, received high scores in the acetylation proteomic screen and are evolutionarily conserved from C. elegans to mammals (Figure S1A). We generated triple Q and R mutants of K540, K546, and K554 (3KQ and 3KR) and found that both 3KQ and 3KR mutations resulted in a significant (~60%) decrease in ACLY acetylation (Figure 1C), indicating that 3K are the major acetylation sites of ACLY.  Further, we found that the acetylation of endogenous ACLY is clearly increased after treatment of cells with NAM and TSA (Figure 1D). These results demonstrate that ACLY is acetylated at K540, K546, and K554.

Glucose Promotes ACLY Acetylation to Stabilize ACLY

In mammalian cells, glucose is the main carbon source for de novo lipid synthesis. We found that ACLY levels increased with increasing glucose concentration, which also correlated with increased ACLY 3K acetylation (Figure 1E). Furthermore, to confirm whether the glucose level affects ACLY protein stability in vivo, we intraperitoneally injected glucose in BALB/c mice and found that high glucose resulted in a significant increase of ACLY protein levels (Figure 1F).

To determine whether ACLY acetylation affects its protein levels, we treated HeLa and Chang liver cells with NAM and TSA and found an increase in ACLY protein levels (Figure S1G, upper panel). ACLY mRNA levels were not significantly changed by the treatment of NAM and TSA (Figure S1G, lower panel), indicating that this upregulation of ACLY is mostly achieved at the posttranscriptional level. Indeed, ACLY protein was also accumulated in cells treated with the proteasome inhibitor MG132, indicating that ACLY stability could be regulated by the ubiquitin-proteasome pathway (Figure 1G). Blocking deacetylase activity stabilized ACLY (Figure S1H). The stabilization of ACLY induced by high glucose was associated with an increase of ACLY acetylation at K540, K546, and K554. Together, these data support a notion that high glucose induces both ACLY acetylation and protein stabilization and prompted us to ask whether acetylation directly regulates ACLY stability. We then generated ACLYWT, ACLY3KQ, and ACLY3KRstable cells after knocking down the endogenous ACLY. We found that the ACLY3KR or ACLY3KQmutant was more stable than the ACLYWT (Figures 1I and S1I). Collectively, our results suggest that glucose induces acetylation at K540, 546, and 554 to stabilize ACLY.

Acetylation Stabilizes ACLY by Inhibiting Ubiquitylation

To determine the mechanism underlying the acetylation and ACLY protein stability, we first examined ACLY ubiquitylation and found that it was actively ubiquitylated (Figure 2A). Previous proteomic analyses have identified K546 in ACLY as a ubiquitylation site (Wagner et al., 2011). In order to identify the ubiquitylation sites, we tested the ubiquitylation levels of double mutants 540R–546R and 546–554R (Figure S2A). We found that the ubiquitylation of the 540R-546R and 546R-554R mutants is partially decreased, while mutation of K540, K546, and K554 (3KR), which changes all three putative acetylation lysine residues of ACLY to arginine residues, dramatically reduced the ACLY ubiquitylation level (Figures 2B and S2A), indicating that 3K lysines might also be the ubiquitylation target residues. Moreover, inhibition of deacetylases by NAM and TSA decreased ubiquitylation of WT but not 3KQ or 3KR mutant ACLY (Figure 2C). These results implicate an antagonizing role of the acetylation towards the ubiquitylation of ACLY at these three lysine residues.

Figure 2  Acetylation Protects ACLY from Proteasome Degradation by Inhibiting Ubiquitylation

We found that ACLY acetylation was only detected in the nonubiquitylated, but not the ubiquitylated (high-molecular-weight), ACLY species. This result indicates that ACLY acetylation and ubiquitylation are mutually exclusive and is consistent with the model that K540, K546, and K554 are the sites of both ubiquitylation and acetylation. Therefore, acetylation of these lysines would block ubiquitylation.

We also found that glucose upregulates ACLY acetylation at 3K and decreases its ubiquitylation (Figure S2B). High glucose (25 mM) effectively decreased ACLY ubiquitylation, while inhibition of deacetylases clearly diminished its ubiquitylation (Figure 2E). We conclude that acetylation and ubiquitylation occur mutually exclusively at K540, K546, and K554 and that high-glucose-induced acetylation at these three sites blocks ACLY ubiquitylation and degradation.

UBR4 Targets ACLY for Degradation

UBR4 was identified as a putative ACLY-interacting protein by affinity purification coupled with mass spectrometry analysis (data not shown). To address if UBR4 is a potential ACLY E3 ligase, we determined the interaction between ACLY and UBR4 and found that ACLY interacted with the E3 ligase domain of UBR4; this interaction was enhanced by MG132 treatment (Figure 3A). UBR4 knockdown in A549 cells resulted in an increase of endogenous ACLY protein level (Figure 3C). Moreover, UBR4 knockdown significantly stabilized ACLY (Figure 3D) and decreased ACLY ubiquitylation (Figure 3E). Taken together, these results indicate that UBR4 is an ACLY E3 ligase that responds to glucose regulation.

Figure 3  UBR4 Is the E3 Ligase of ACLY

PCAF Acetylates ACLY

PCAF knockdown significantly reduced acetylation of 3K, indicating that PCAF is a potential 3K acetyltransferase in vivo (Figure 4C, upper panel). Furthermore, PCAF knockdown decreased the steady-state level of endogenous ACLY, but not ACLY mRNA (Figure 4C, middle and lower panels). Moreover, we found that PCAF knockdown destabilized ACLY (Figure 4D). In addition, overexpression of PCAF decreases ACLY ubiquitylation (Figure 4E), while PCAF inhibition increases the interaction between UBR4 E3 ligase domain and wild-type ACLY, but not 3KR (Figure 4F). Together, our results indicate that PCAF increases ACLY protein level, possibly via acetylating ACLY at 3K.

Figure 4  PCAF Is the Acetylase of ACLY

SIRT2 Deacetylates ACLY

Figure 5  SIRT2 Decreases ACLY Acetylation and Increases Its Protein Levels In Vivo

Acetylation of ACLY Promotes Cell Proliferation and De Novo Lipid Synthesis

The protein levels of ACLY 3KQ and 3KR were accumulated to a level higher than the wild-type cells upon extended culture in low-glucose medium (Figure S6A, right panel), indicating a growth advantage conferred by ACLY stabilization resulting from the disruption of both acetylation and ubiquitylation at K540, K546, and K554. Cellular acetyl-CoA assay showed that cells expressing 3KQ or 3KR mutant ACLY produce more acetyl-CoA than cells expressing the wild-type ACLY under low glucose (Figures 6B and S6B), further supporting the conclusion that 3KQ or 3KR mutation stabilizes ACLY.

Figure 6  Acetylation of ACLY at 3K Promotes Lipogenesis and Tumor Cell Proliferation

ACLY is a key enzyme in de novo lipid synthesis. Silencing ACLY inhibited the proliferation of multiple cancer cell lines, and this inhibition can be partially rescued by adding extra fatty acids or cholesterol into the culture media (Zaidi et al., 2012). This prompted us to measure extracellular lipid incorporation in A549 cells after knockdown and ectopic expression of ACLY. We found that when cultured in low glucose (2.5 mM), cells expressing wild-type ACLY uptake significantly more phospholipids compared to cells expressing 3KQ or 3KR mutant ACLY (Figures 6C, 6D, and S6D). When cultured in the presence of high glucose (25 mM), however, cells expressing either the wild-type, 3KQ, or 3KR mutant ACLY all have reduced, but similar, uptake of extracellular phospholipids (Figures 6C, 6D, and S6D). The above results are consistent with a model that acetylation of ACLY induced by high glucose increases its stability and stimulates de novo lipid synthesis.

3K Acetylation of ACLY Is Increased in Lung Cancer

ACLY is reported to be upregulated in human lung cancer (Migita et al., 2008). Many small chemicals targeting ACLY have been designed for cancer treatment (Zu et al., 2012). The finding that 3KQ or 3KR mutant increased the ability of ACLY to support A549 lung cancer cell proliferation prompted us to examine 3K acetylation in human lung cancers. We collected a total of 54 pairs of primary human lung cancer samples with adjacent normal lung tissues and performed immunoblotting for ACLY protein levels. This analysis revealed that, when compared to the matched normal lung tissues, 29 pairs showed a significant increase of total ACLY protein using b-actin as a loading control (Figures 7A and S7A). The tumor sample analyses demonstrate that ACLY protein levels are elevated in lung cancers, and 3K acetylation positively correlates with the elevated ACLY protein. These data also indicate that ACLY with 3K acetylation may be potential biomarker for lung cancer diagnosis.

Figure 7
  Acetylation of ACLY at 3K Is Upregulated in Human Lung Carcinoma

Dysregulation of cellular metabolism is a hallmark of cancer (Hanahan and Weinberg, 2011Vander Heiden et al., 2009). Besides elevated glycolysis, increased lipogenesis, especially de novo lipid synthesis, also plays an important role in tumor growth. Because most carbon sources for fatty acid synthesis are from glucose in mammalian cells (Wellen et al., 2009), the channeling of carbon into de novo lipid synthesis as building blocks for tumor cell growth is primarily linked to acetyl-CoA production by ACLY. Moreover, the ACLY-catalyzed reaction consumes ATP. Therefore, as the key cellular energy and carbon source, one may expect a role for glucose in ACLY regulation. In the present study, we have uncovered a mechanism of ACLY regulation by glucose that increases ACLY protein level to meet the enhanced demand of lipogenesis in growing cells, such as tumor cells (Figure 7C). Glucose increases ACLY protein levels by stimulating its acetylation.

Upregulation of ACLY is common in many cancers (Kuhajda, 2000Milgraum et al., 1997Swinnen et al., 2004Yahagi et al., 2005). This is in part due to the transcriptional activation by SREBP-1 resulting from the activation of the PI3K/AKT pathway in cancers (Kim et al., 2010Nadler et al., 2001Wang and Dey, 2006). In this study, we report a mechanism of ACLY regulation at the posttranscriptional level. We propose that acetylation modulated by glucose status plays a crucial role in coordinating the intracellular level of ACLY, hence fatty acid synthesis, and glucose availability. When glucose is sufficient, lipogenesis is enhanced. This can be achieved, at least in part, by the glucose-induced stabilization of ACLY. High glucose increases ACLY acetylation, which inhibits its ubiquitylation and degradation, leading to the accumulation of ACLY and enhanced lipogenesis. In contrast, when glucose is limited, ACLY is not acetylated and thus can be ubiquitylated, leading to ACLY degradation and reduced lipogenesis. Moreover, our data indicate that acetylation and ubiquitylation in ACLY may compete with each other by targeting the same lysine residues at K540, K546, and K554. Consistently, previous proteomic analyses have identified K546 in ACLY as a ubiquitylation site (Wagner et al., 2011). Similar models of different modifications on the same lysine residues have been reported in the regulation of other proteins (Grönroos et al., 2002Li et al., 20022012). We propose that acetylation and ubiquitylation have opposing effects in the regulation of ACLY by competitively modifying the same lysine residues. The acetylation-mimetic 3KQ and the acetylation-deficient 3KR mutants behaved indistinguishably in most biochemical and functional assays, mainly due to the fact that these mutations disrupt lysine ubiquitylation that primarily occurs on these three residues.

ACLY is increased in lung cancer tissues compared to adjacent tissues. Consistently, ACLY acetylation at 3K is also significantly increased in lung cancer tissues. These observations not only confirm ACLY acetylation in vivo, but also suggest that ACLY 3K acetylation may play a role in lung cancer development. Our study reveals a mechanism of ACLY regulation in response to glucose signals.

 

7.7.7 Monoacylglycerol Lipase Regulates a Fatty Acid Network that Promotes Cancer Pathogenesis

Nomura DK1Long JZNiessen SHoover HSNg SWCravatt BF.
Cell. 2010 Jan 8; 140(1):49-61
http://dx.doi.org/10.1016.2Fj.cell.2009.11.027

Highlights

  • Monoacylglycerol lipase (MAGL) is elevated in aggressive human cancer cells
  • Loss of MAGL lowers fatty acid levels in cancer cells and impairs pathogenicity
  • MAGL controls a signaling network enriched in protumorigenic lipids
  • A high-fat diet can restore the growth of tumors lacking MAGL in vivo
monoacylglycerol-lipase-magl-is-highly-expressed-in-aggressive-human-cancer-cells-and-primary-tumors

monoacylglycerol-lipase-magl-is-highly-expressed-in-aggressive-human-cancer-cells-and-primary-tumors

http://www.cell.com/cms/attachment/1082768/7977146/fx1.jpg

Tumor cells display progressive changes in metabolism that correlate with malignancy, including development of a lipogenic phenotype. How stored fats are liberated and remodeled to support cancer pathogenesis, however, remains unknown. Here, we show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in nonaggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity—phenotypes that are reversed by an MAGL inhibitor. Impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of protumorigenic signals.

We show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in non-aggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity — phenotypes that are reversed by an MAGL inhibitor. Interestingly, impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of pro-tumorigenic signals.

The conversion of cells from a normal to cancerous state is accompanied by reprogramming of metabolic pathways (Deberardinis et al., 2008Jones and Thompson, 2009Kroemer and Pouyssegur, 2008), including those that regulate glycolysis (Christofk et al., 2008Gatenby and Gillies, 2004), glutamine-dependent anaplerosis (DeBerardinis et al., 2008DeBerardinis et al., 2007Wise et al., 2008), and the production of lipids (DeBerardinis et al., 2008Menendez and Lupu, 2007). Despite a growing appreciation that dysregulated metabolism is a defining feature of cancer, it remains unclear, in many instances, how such biochemical changes occur and whether they play crucial roles in disease progression and malignancy.

Among dysregulated metabolic pathways, heightened de novo lipid biosynthesis, or the development a “lipogenic” phenotype (Menendez and Lupu, 2007), has been posited to play a major role in cancer. For instance, elevated levels of fatty acid synthase (FAS), the enzyme responsible for fatty acid biosynthesis from acetate and malonyl CoA, are correlated with poor prognosis in breast cancer patients, and inhibition of FAS results in decreased cell proliferation, loss of cell viability, and decreased tumor growth in vivo (Kuhajda et al., 2000Menendez and Lupu, 2007Zhou et al., 2007). FAS may support cancer growth, at least in part, by providing metabolic substrates for energy production (via fatty acid oxidation) (Buzzai et al., 2005Buzzai et al., 2007Liu, 2006). Many other features of lipid biochemistry, however, are also critical for supporting the malignancy of cancer cells, including:

Prominent examples of lipid messengers that contribute to cancer include:

Here, we use functional proteomic methods to discover a lipolytic enzyme, monoacylglycerol lipase (MAGL), that is highly elevated in aggressive cancer cells from multiple tissues of origin. We show that MAGL, through hydrolysis of monoacylglycerols (MAGs), controls free fatty acid (FFA) levels in cancer cells. The resulting MAGL-FFA pathway feeds into a diverse lipid network enriched in pro-tumorigenic signaling molecules and promotes migration, survival, and in vivo tumor growth. Aggressive cancer cells thus pair lipogenesis with high lipolytic activity to generate an array of pro-tumorigenic signals that support their malignant behavior.

Activity-Based Proteomic Analysis of Hydrolytic Enzymes in Human Cancer Cells

To identify enzyme activities that contribute to cancer pathogenesis, we conducted a functional proteomic analysis of a panel of aggressive and non-aggressive human cancer cell lines from multiple tumors of origin, including melanoma [aggressive (C8161, MUM2B), non-aggressive (MUM2C)], ovarian [aggressive (SKOV3), non-aggressive (OVCAR3)], and breast [aggressive (231MFP), non-aggressive (MCF7)] cancer. Aggressive cancer lines were confirmed to display much greater in vitro migration and in vivo tumor-growth rates compared to their non-aggressive counterparts (Figure S1), as previously shown (Jessani et al., 2004;Jessani et al., 2002Seftor et al., 2002Welch et al., 1991). Proteomes from these cancer lines were screened by activity-based protein profiling (ABPP) using serine hydrolase-directed fluorophosphonate (FP) activity-based probes (Jessani et al., 2002Patricelli et al., 2001). Serine hydrolases are one of the largest and most diverse enzyme classes in the human proteome (representing ~ 1–1.5% of all human proteins) and play important roles in many biochemical processes of potential relevance to cancer, such as proteolysis (McMahon and Kwaan, 2008Puustinen et al., 2009), signal transduction (Puustinen et al., 2009), and lipid metabolism (Menendez and Lupu, 2007Zechner et al., 2005). The goal of this study was to identify hydrolytic enzyme activities that were consistently altered in aggressive versus non-aggressive cancer lines, working under the hypothesis that these conserved enzymatic changes would have a high probability of contributing to the pathogenic state of cancer cells.

Among the more than 50 serine hydrolases detected in this analysis (Tables S13), two enzymes, KIAA1363 and MAGL, were found to be consistently elevated in aggressive cancer cells relative to their non-aggressive counterparts, as judged by spectral counting (Jessani et al., 2005Liu et al., 2004). We confirmed elevations in KIAA1363 and MAGL in aggressive cancer cells by gel-based ABPP, where proteomes are treated with a rhodamine-tagged FP probe and resolved by 1D-SDS-PAGE and in-gel fluorescence scanning (Figure 1A). In both cases, two forms of each enzyme were detected (Figure 1A), due to differential glycoslyation for KIAA1363 (Jessani et al., 2002), and possibly alternative splicing for MAGL (Karlsson et al., 2001). We have previously shown that KIAA1363 plays a role in regulating ether lipid signaling pathways in aggressive cancer cells (Chiang et al., 2006). On the other hand, very little was known about the function of MAGL in cancer.

Figure 1  MAGL is elevated in aggressive cancer cells, where the enzyme regulates monoacylgycerol (MAG) and free fatty acid (FFA) levels

The heightened activity of MAGL in aggressive cancer cells was confirmed using the substrate C20:4 MAG (Figure 1B). Since several enzymes have been shown to display MAG hydrolytic activity (Blankman et al., 2007), we confirmed the contribution that MAGL makes to this process in cancer cells using the potent and selective MAGL inhibitor JZL184 (Long et al., 2009a).

MAGL Regulates Free Fatty Acid Levels in Aggressive Cancer Cells

MAGL is perhaps best recognized for its role in degrading the endogenous cannabinoid 2-arachidonoylglycerol (2-AG, C20:4 MAG), as well as other MAGs, in brain and peripheral tissues (Dinh et al., 2002Long et al., 2009aLong et al., 2009bNomura et al., 2008). Consistent with this established function, blockade of MAGL by JZL184 (1 μM, 4 hr) produced significant elevations in the levels of several MAGs, including 2-AG, in each of the aggressive cancer cell lines (Figure 1C and Figure S2). Interestingly, however, MAGL inhibition also caused significant reductions in the levels of FFAs in aggressive cancer cells (Figure 1D and Figure S2). This surprising finding contrasts with the function of MAGL in normal tissues, where the enzyme does not, in general, control the levels of FFAs (Long et al., 2009aLong et al., 2009b;Nomura et al., 2008).

Metabolic labeling studies using the non-natural C17:0-MAG confirmed that MAGs are converted to LPC and LPE by aggressive cancer cells, and that this metabolic transformation is significantly enhanced by treatment with JZL184 (Figure S1). Finally, JZL184 treatment did not affect the levels of MAGs and FFAs in non-aggressive cancer lines (Figure 1C, D), consistent with the negligible expression of MAGL in these cells (Figure 1A, B).

We next stably knocked down MAGL expression by RNA interference technology using two independent shRNA probes (shMAGL1, shMAGL2), both of which reduced MAGL activity by 70–80% in aggressive cancer lines (Figure 2A, D and Figure S2). Other serine hydrolase activities were unaffected by shMAGL probes (Figure 2A, D and Figures S2), confirming the specificity of these reagents. Both shMAGL probes caused significant elevations in MAGs and corresponding reductions in FFAs in aggressive melanoma (Figure 2B, C), ovarian (Figure 2E, F), and breast cancer cells (Figure S2).

Figure 2  Stable shRNA-mediated knockdown of MAGL lowers FFA levels in aggressive cancer cells.

Together, these data demonstrate that both acute (pharmacological) and stable (shRNA) blockade of MAGL cause elevations in MAGs and reductions in FFAs in aggressive cancer cells. These intriguing findings indicate that MAGL is the principal regulator of FFA levels in aggressive cancer cells. Finally, we confirmed that MAGL activity (Figure 3A, B) and FFA levels (Figure 3C) are also elevated in high-grade primary human ovarian tumors compared to benign or low-grade tumors. Thus, heightened expression of the MAGL-FFA pathway is a prominent feature of both aggressive human cancer cell lines and primary tumors.

Figure 3  High-grade primary human ovarian tumors possess elevated MAGL activity and FFAs compared to benign tumors.

Disruption of MAGL Expression and Activity Impairs Cancer Pathogenicity

shMAGL cancer lines were next examined for alterations in pathogenicity using a set of in vitro and in vivo assays. shMAGL-melanoma (C8161), ovarian (SKOV3), and breast (231MFP) cancer cells exhibited significantly reduced in vitro migration (Figure 4A, F and Figure S2), invasion (Figure 4B, G and Figure S2), and cell survival under serum-starvation conditions (Figure 4C, H and Figure S2). Acute pharmacological blockade of MAGL by JZL184 also decreased cancer cell migration (Figure S2), but not survival, possibly indicating that maximal impairments in cancer aggressiveness require sustained inhibition of MAGL.

Figure 4  shRNA-mediated knockdown and pharmacological inhibition of MAGL impair cancer aggressiveness.

MAGL Overexpression Increases FFAs and the Aggressiveness of Cancer Cells

Stable MAGL-overexpressing (MAGL-OE) and control [expressing an empty vector or a catalytically inactive version of MAGL, where the serine nucleophile was mutated to alanine (S122A)] variants of MUM2C and OVCAR3 cells were generated by retroviral infection and evaluated for their respective MAGL activities by ABPP and C20:4 MAG substrate assays. Both assays confirmed that MAGL-OE cells possess greater than 10-fold elevations in MAGL activity compared to control cells (Figure 5A and Figure S4). MAGL-OE cells also showed significant reductions in MAGs (Figure 5B andFigure S4) and elevated FFAs (Figure 5C and Figure S4). This altered metabolic profile was accompanied by increased migration (Figure 5D and Figure S4), invasion (Figure 5E and Figure S4), and survival (Figure S4) in MAGL-OE cells. None of these effects were observed in cancer cells expressing the S122A MAGL mutant, indicating that they require MAGL activity.  MAGL-OE MUM2C cells also showed enhanced tumor growth in vivo compared to control cells (Figure 5F). Notably, the increased tumor growth rate of MAGL-OE MUM2C cells nearly matched that of aggressive C8161 cells (Figure S4). These data indicate that the ectopic expression of MAGL in non-aggressive cancer cells is sufficient to elevate their FFA levels and promote pathogenicity both in vitro and in vivo.

Figure 5 Ectopic expression of MAGL elevates FFA levels and enhances the in vitro and in vivo pathogenicity of MUM2C melanoma cells.

Metabolic Rescue of Impaired Pathogenicity in MAGL-Disrupted Cancer Cells

MAGL could support the aggressiveness of cancer cells by either reducing the levels of its MAG substrates, elevating the levels of its FFA products, or both. Among MAGs, the principal signaling molecule is the endocannabinoid 2-AG, which activates the CB1 and CB2 receptors (Ahn et al., 2008Mackie and Stella, 2006). The endocannabinoid system has been implicated previously in cancer progression and, depending on the specific study, shown to promote (Sarnataro et al., 2006Zhao et al., 2005) or suppress (Endsley et al., 2007Wang et al., 2008) cancer pathogenesis. Neither a CB1 or CB2 antagonist rescued the migratory defects of shMAGL cancer cells (Figure S5). CB1 and CB2 antagonists also did not affect the levels of MAGs or FFAs in cancer cells (Figure S5).

We then determined whether increased FFA delivery could rectify the tumor growth defect observed for shMAGL cells in vivo. Immune-deficient mice were fed either a normal chow or high-fat diet throughout the duration of a xenograft tumor growth experiment. Notably, the impaired tumor growth rate of shMAGL-C8161 cells was completely rescued in mice fed a high-fat diet. In contrast, shControl-C8161 cells showed equivalent tumor growth rates on a normal versus high-fat diet. The recovery in tumor growth for shMAGL-C8161 cells in the high-fat diet group correlated with significantly increases levels of FFAs in excised tumors (Figure 6D). Collectively, these results indicate that MAGL supports the pathogenic properties of cancer cells by maintaining tonically elevated levels of FFAs.

Figure 6  Recovery of the pathogenic properties of shMAGL cancer cells by treatment with exogenous fatty acids.

MAGL Regulates a Fatty Acid Network Enriched in Pro-Tumorigenic Signals

Studies revealed that neither

  • the MAGL-FFA pathway might serve as a means to regenerate NAD+ (via continual fatty acyl glyceride/FFA recycling) to fuel glycolysis, or
  • increased lipolysis could be to generate FFA substrates for β-oxidation, which may serve as an important energy source for cancer cells (Buzzai et al., 2005), or
  • CPT1 blockade (reduced expression of CPT1 in aggressive cancer cells (data not shown) has been reported previously (Deberardinis et al., 2006))

providing evidence against a role for β-oxidation as a downstream mediator of the pathogenic effects of the MAGL-fatty acid pathway.

Considering that FFAs are fundamental building blocks for the production and remodeling of membrane structures and signaling molecules, perturbations in MAGL might be expected to affect several lipid-dependent biochemical networks important for malignancy. To test this hypothesis, we performed lipidomic analyses of cancer cell models with altered MAGL activity, including comparisons of:

  1. MAGL-OE versus control cancer cells (OVCAR3, MUM2C), and
  2. shMAGL versus shControl cancer cells (SKOV3, C8161).

Complementing these global profiles, we also conducted targeted measurements of specific bioactive lipids (e.g., prostaglandins) that are too low in abundance for detection by standard lipidomic methods. The resulting data sets were then mined to identify a common signature of lipid metabolites regulated by MAGL, which we defined as metabolites that were significantly increased or reduced in MAGL–OE cells and showed the opposite change in shMAGL cells relative to their respective control groups (Figure 7A, B and Table S4).

Figure 7  MAGL regulates a lipid network enriched in pro-tumorigenic signaling molecules.

Most of the lipids in the MAGL-fatty acid network, including several lysophospholipids (LPC, LPA, LPE), ether lipids (MAGE, alkyl LPE), phosphatidic acid (PA), and prostaglandin E2 (PGE2), displayed similar profiles to FFAs, being consistently elevated and reduced in MAGL-OE and shMAGL cells, respectively. Only MAGs were found to show the opposite profile (elevated and reduced in shMAGL and MAGL-OE cells, respectively). Interestingly, virtually this entire lipidomic signature was also observed in aggressive cancer cells when compared to their non-aggressive counterparts (e.g., C8161 versus MUM2C and SKOV3 versus OVCAR3, respectively; Table S4). These findings demonstrate that MAGL regulates a lipid network in aggressive cancer cells that consists of not only FFAs and MAGs, but also a host of secondary lipid metabolites. Increases (rather than decreases) in LPCs and LPEs were observed in JZL184-treated cells (Figure S1 and Table S4). These data indicate that acute and chronic blockade of MAGL generate distinct metabolomic effects in cancer cells, likely reflecting the differential outcomes of short- versus long-term depletion of FFAs.

Within the MAGL-fatty acid network are several pro-tumorigenic lipid messengers, including LPA and PGE2, that have been reported to promote the aggressiveness of cancer cells (Gupta et al., 2007Mills and Moolenaar, 2003). Metabolic labeling studies confirmed that aggressive cancer cells can convert both MAGs and FFAs (Figure S1) to LPA and PGE2 and, for MAGs, this conversion was blocked by JZL184 (Figure S1). Interestingly, treatment with either LPA or PGE2 (100 nM, 4 hr) rescued the impaired migration of shMAGL cancer cells at concentrations that did not affect the migration of shControl cells (Figure 7E).

Heightened lipogenesis is an established early hallmark of dysregulated metabolism and pathogenicity in cancer (Menendez and Lupu, 2007). Cancer lipogenesis appears to be driven principally by FAS, which is elevated in most transformed cells and important for survival and proliferation (De Schrijver et al., 2003;Kuhajda et al., 2000Vazquez-Martin et al., 2008). It is not yet clear how FAS supports cancer growth, but most of the proposed mechanisms invoke pro-tumorigenic functions for the enzyme s fatty acid products and their lipid derivatives (Menendez and Lupu, 2007). This creates a conundrum, since the fatty acid molecules produced by FAS are thought to be rapidly incorporated into neutral- and phospho-lipids, pointing to the need for complementary lipolytic pathways in cancer cells to release stored fatty acids for metabolic and signaling purposes (Prentki and Madiraju, 2008Przybytkowski et al., 2007). Consistent with this hypothesis, we found that acute treatment with the FAS inhibitor C75 (40 μM, 4 h) did not reduce FFA levels in cancer cells (data not shown). Furthermore, aggressive and non-aggressive cancer cells exhibited similar levels of FAS (data not shown), indicating that lipogenesis in the absence of paired lipolysis may be insufficient to confer high levels of malignancy.

Here we show that aggressive cancer cells do indeed acquire the ability to liberate FFAs from neutral lipid stores as a consequence of heightened expression of MAGL. MAGL and its FFA products were found to be elevated in aggressive human cancer cell lines from multiple tissues of origin, as well as in high-grade primary human ovarian tumors. These data suggest that the MAGL-FFA pathway may be a conserved feature of advanced forms of many types of cancer. Further evidence in support of this premise originates from gene expression profiling studies, which have identified increased levels of MAGL in primary human ductal breast tumors compared to less malignant medullary breast tumors (Gjerstorff et al., 2006). The key role that MAGL plays in regulating FFA levels in aggressive cancer cells contrasts with the function of this enzyme in normal tissues, where it mainly controls the levels of MAGs, but not FFAs (Long et al., 2009b). These data thus provide a striking example of the co-opting of an enzyme by cancer cells to serve a distinct metabolic purpose that supports their pathogenic behavior.

Taken together, our results indicate that MAGL serves as key metabolic hub in aggressive cancer cells, where the enzyme regulates a fatty acid network that feeds into a number of pro-tumorigenic signaling pathways.

 

7.7.8 Pirin regulates epithelial to mesenchymal transition and down-regulates EAF/U19 signaling in prostate cancer cells

7.7.8.1  Pirin regulates epithelial to mesenchymal transition independently of Bcl3-Slug signaling

Komai K1Niwa Y1Sasazawa Y1Simizu S2.
FEBS Lett. 2015 Mar 12; 589(6):738-43
http://dx.doi.org:/10.1016/j.febslet.2015.01.040

Highlights

  • Pirin decreases E-cadherin expression and induces EMT.
  • The induction of EMT by Pirin is achieved through a Bcl3 independent pathway.
  • Pirin may be a novel target for cancer therapy.

Epithelial to mesenchymal transition (EMT) is an important mechanism for the initial step of metastasis. Proteomic analysis indicates that Pirin is involved in metastasis. However, there are no reports demonstrating its direct contribution. Here we investigated the involvement of Pirin in EMT. In HeLa cells, Pirin suppressed E-cadherin expression and regulated the expression of other EMT markers. Furthermore, cells expressing Pirin exhibited a spindle-like morphology, which is reminiscent of EMT. A Pirin mutant defective for Bcl3 binding decreased E-cadherin expression similar to wild-type, suggesting that Pirin regulates E-cadherin independently of Bcl3-Slug signaling. These data provide direct evidence that Pirin contributes to cancer metastasis.

Pirin regulates the expression of E-cadherin and EMT markers

In melanoma, Pirin enhances NF-jB activity and increases Slug expression by binding Bcl3 [31], and it may also be involved in adenoid cystic tumor metastasis [23]. Since Slug suppresses E-cadherin transcription and is recognized as a major EMT inducer, we hypothesized that Pirin may regulate EMT through inducing Slug expression. To investigate whether Pirin regulates EMT, we measured E-cadherin expression following Pirin knockdown. As shown in Fig. 1A and B, E-cadherin expression was significantly increased following Pirin knockdown indicating that it may promote EMT. To confirm this, we established Pirin-expressing HeLa cells (Fig. 1C), which inhibited the expression of E-cadherin (Fig. 1D). Additionally, the expression of Occludin, an epithelial marker, was decreased, and several mesenchymal markers, including Fibronectin, N-cadherin, and Vimentin, were increased by Pirin expression (Fig. 1D). These data suggest that Pirin promotes EMT.

Pirin induces EMT-associated cell morphological changes

As mentioned above, cells undergo morphological changes during EMT. Therefore, we next analyzed whether Pirin expression affects cell morphology. Quantitative analysis of morphological changes was based on cell circularity, {4p(area)/(perimeter)2}100, which decreases during EMT-associated morphological changes [34–36]. Indeed, TGF-b or TNF-a exposure induced EMTassociated cell morphological changes in HeLa cells (data not shown). Employing this parameter of circularity, we compared the morphology of our established HeLa/Pirin-GFP cells with control HeLa/GFP cells. Although the control HeLa/GFP cells displayed a cobblestone-like morphology, HeLa/Pirin-GFP cells were elongated in shape (Fig. 2A). Indeed, compared with control cells, the circularity of HeLa/Pirin-GFP cells was significantly decreased (Fig. 2B). To confirm that these observations were dependent on Pirin expression, HeLa/Pirin-GFP cells were treated with an siRNA targeting Pirin. HeLa/Pirin-GFP cells recovered a cobblestone-like morphology (Fig. 2C) and circularity (Fig. 2D) when treated with Pirin siRNA indicating that Pirin expression induces EMT.

Pirin induces cell migration

During EMT cells acquire migratory capabilities. Therefore, we analyzed whether Pirin affects cell migration. HeLa cells were treated with an siRNA targeting Pirin and migration was assessed using a wound healing assay. Although Pirin knockdown had no effect on cell proliferation (data not shown), wound repair was inhibited in Pirin-depleted HeLa cells (Fig. 3A and B) suggesting that Pirin promoted cell migration. Furthermore, camptothecin treatment of HeLa/GFP cells caused decreased cell viability in a dose-dependent manner, whereas HeLa/Pirin-GFP cells were more resistantto drugtreatment (datanot shown).These results suggest that Pirin induces EMT-like phenotypes, such as cell migration and anticancer drug resistance.
Pirin regulates EMT independently of Bcl3-Slug signaling

To investigate whether Pirin controls E-cadherin expression at the transcriptional level, we measured E-cadherin promoter activity with a reporter assay. Indeed, the luciferase reporter analysis indicated that Pirin inhibited E-cadherin promoter activity (Fig. 4A and B). To determine if Bcl3 is involved in Pirin-induced EMT, we tested whether a Pirin mutant defective in Bcl3 binding could inhibit E-cadherin expression. We generated a mutation in the metal-binding cavity of Pirin(E103A) and confirmed that it disrupted Bcl3 binding. In vitro GST pull-down analysis using recombinant Pirin and Bcl3/ARD demonstrated that the Pirin mutant was defective for Bcl3 binding compared to wild-type (Fig. 5A). Interestingly, expression of both wild-type Pirin and the mutant defective in Bcl3 binding reduced E-cadherin gene and protein expression (Fig. 5B and C). Taken together these results indicate that Pirin decreases E-cadherin expression without binding Bcl3, and suggest that Pirin regulates EMT independently of Bcl3-Slug signaling.

Discussion

A characteristic feature of EMT is the disruption of epithelial cell–cell contact, which is achieved by reduced E-cadherin expression. Therefore, revealing the regulatory pathways controlling E-cadherin expression may elucidate the mechanisms of EMT. Several transcription factors regulate E-cadherin transcription. For instance,Snail,Slug,Twist,and Zebact as mastertranscriptional regulators that bind the consensus E-box sequence in the E-cadherin gene promoter and decrease the transcriptional activity [38]. Since Pirin regulates the transcription of Slug [31], we hypothesized that Pirin may also regulate EMT. In this study we demonstrated that Pirin decreases E-cadherin expression, and induces EMT and cancer malignant phenotypes. Since EMT is an initial step of metastasis, Pirin may contribute to cancer progression. We next examined whether the regulation of EMT by Pirin is attributed to Bcl3 binding and the induction of Slug. To this end, we generated a Pirin mutant (E103A) defective for Bcl3 binding (Fig. 5A). Single Fe2+ ion chelating is coordinated by His56, His58, His101, and Glu103 of Pirin, and the N-terminal domain containing these residues is highly conserved between mammals, plants, fungi, and prokaryotic organisms [15,27]. Therefore, it has been predicted that this N-terminal domain containing the metal-binding cavity is important for Pirin function [20,26,31]. Indeed, TPh A inserts into the metal-binding cavity and inhibits binding to Bcl3 suggesting that the interaction occurs with the metal-binding cavity of Pirin [31]. In contrast, Hai Pang suggests that a Pirin–Bcl3– (p50)2 complex forms between acidic regions of the N-terminal Pirin domain at residues 77–82, 97–103 and 124–128 with a basic patch of Bcl3 [27]. In this study, we mutated Glutamic acid 103, a residue common between Hai Pang’s model and Pirin’s metalbinding cavity. Pull-down analysis indicated that an E103A mutant is defectiveinfor Bcl3binding(Fig.5A). Thisis the firstexperimental demonstration showing that Glu103 of Pirin is important Bcl3 binding. However, expression of the E103A mutant suppressed Ecadherin gene expression similarly to wild-type Pirin (Fig. 5B and C). Although the Bcl3–(p50)2 complex participates in oncogene addiction in cervical cells [39,40], expression of Pirin in HeLa cells did not increase Slug expression (data not shown). Therefore, we concludethatPirindecreasesE-cadherinexpressionindependently of Bcl3-Slug signaling. To understand how Pirin suppresses E-cadherin gene expression, we analyzed E-cadherin promoter activity (Fig. 4). Since Pirin decreased the activity of the E-cadherin promoter (995+1), we constructed a series of promoter deletion mutants (795+1, 565+1, 365+1, 175+1) to identify a region important for Pirin-mediated regulation. Expression of Pirin decreased the transcriptional activity of all constructs (Supplementary Fig. S1A), suggesting that Pirin may suppress E-cadherin expression through element(s) in region 175+1. Yan-Nan Liu and colleagues proposed that this region contains four Sp1-binding sites and two E-boxes that regulate E-cadherin expression.

Fig. 1. Pirin regulates E-cadherin gene expression. (A, B) HeLa cells were transfected with siRNA targeting Pirin (siPirin#1 or #2) or control siRNA (siCTRL). Forty-eight hours after transfection, cDNA was used for PCR using primer sets specific against Pirin, E-cadherin and GAPDH (A). Forty-eight hours after transfection, HeLa cells were lysed and the lysates were analyzed by Western blot with the indicated antibodies (B). (C) Lysates from HeLa/Pirin-GFP and HeLa/GFP cells were analyzed by Western blot with the indicated antibodies. (D) cDNA from HeLa/GFP or HeLa/Pirin-GFP cells was used for PCR to determine the effect of Pirin on the expression of EMT marker genes.

Fig. 2. Pirin induces cell morphological changes associated with EMT. (A) Phase contrast and fluorescence microscopic images were taken of HeLa/GFP and HeLa/Pirin-GFP cells. (B) Cell circularity was defined as form factor, {4p(area)/(perimeter)2}100 [%], and calculated using Image J software. A random selection of 100 cells from each condition was measured. (C, D) Phase contrast and fluorescence microscopic images were taken of siRNA-treated HeLa/GFP and HeLa/Pirin-GFP cells. Each cell line was transfected with siPirin#2 or siCTRL. Cells were observed by microscopy 48 h after transfection (C) and circularity was measured (D). Data shown are means ± s.d. ⁄P <0.05, bars 100lm.

Fig. 3. Pirin knockdown suppresses cell migration. (A, B) HeLa cells were transfected with siPirin#2 or siCTRL. An artificial wound was created with a tip 24h after transfection and cells were cultured for an additional 12 h. For quantification, the cells were photographed after 12h of incubation (A) and the area covered by cells was measured using Image J and normalized to control cells (B).

Fig. 4. Pirin regulates E-cadherin promoter activity.(A). HeLacells were transfected with siPirin#2 or siGFP (control) and cultured for 24 h. The E-cadherin promoter construct (995+1) and phRL-TK vectorwere transfected and cellswere cultured for an additional 24 h. Luciferase activities were measured and normalized to Renilla luciferase activity. (B) HeLa cells were transfected with the promoter construct (995+1), phRL-TK vector, and a Pirin expression vector. After 24 h, luciferase activities were measured and normalized to Renilla luciferase activity. Data are the mean ± s.d. ⁄P < 0.05.

Fig. 5. Pirin decreases E-cadherin expression in a Bcl3-independent manner. (A) Purified His6-Pirin and His6-Pirin(E103A) were incubated with Glutathione-Sepharose beads conjugated to GST or GST-Bcl3/ARD. The samples were analyzed by Western blot. (B, C) HeLa cells were transfected with vectors encoding GFP, Pirin-GFP, or Pirin(E103A)GFP. Cells were lysed 48 h after transfection and lysates were analyzed by Western blot (B). RNA collected at 48h was used for RT-PCR with the specified primer sets for each gene (C).

7.7.8.2 1324 PIRIN DOWN-REGULATES THE EAF2/U19 SIGNALING AND RETARDS THE GROWTH INHIBITION INDUCED BY EAF2/U19 IN PROSTATE CANCER CELLS

Zhongjie Qiao, Dan Wang, Zhou Wang
The Journal of Urology Apr 2013; 189(4), Supplement: e541
http://dx.doi.org/10.1016/j.juro.2013.02.2678
EAF2/U19, as the tumor suppressor, has been reported to induce apoptosis of LNCaP cells and suppress AT6.1 xenograft prostate tumor growth in vivo, and its expression level is down-regulated in advanced human prostate cancer. EAF2/U19 is also a putative transcription factor with a transactivation domain and capability of sequence-specific DNA binding. Identification and characterization of the binding partners and regulators of EAF2/U19 is essential to understand its function in regulating apoptosis/survival of prostate cancer cells.

7.7.8.3 Pirin Inhibits Cellular Senescence in Melanocytic Cells

Cellular senescence has been widely recognized as a tumor suppressing mechanism that acts as a barrier to cancer development after oncogenic stimuli. A prominent in vivo model of the senescence barrier is represented by nevi, which are composed of melanocytes that, after an initial phase of proliferation induced by activated oncogenes (most commonly BRAF), are blocked in a state of cellular senescence. Transformation to melanoma occurs when genes involved in controlling senescence are mutated or silenced and cells reacquire the capacity to proliferate. Pirin (PIR) is a highly conserved nuclear protein that likely functions as a transcriptional regulator whose expression levels are altered in different types of tumors. We analyzed the expression pattern of PIR in adult human tissues and found that it is expressed in melanocytes and has a complex pattern of regulation in nevi and melanoma: it is rarely detected in mature nevi, but is expressed at high levels in a subset of melanomas. Loss of function and overexpression experiments in normal and transformed melanocytic cells revealed that PIR is involved in the negative control of cellular senescence and that its expression is necessary to overcome the senescence barrier. Our results suggest that PIR may have a relevant role in melanoma progression

Cellular senescence is a physiological process through which normal somatic cells lose their ability to divide and enter an irreversible state of cell cycle arrest, although they remain viable and metabolically active.1,2The specific molecular circuitry underlying the onset of cellular senescence is dependent on the type of stimulus and on the cellular context. A central role is held by the activation of the tumor suppressor proteins p53 and retinoblastoma susceptibility protein (pRB),3–5 which act by interfering with the transcriptional program of the cell and ultimately arresting cell cycle progression.

In the last decade, senescence has been recognized as a major barrier against the development of tumors in mammals.6–8 One of the most prominent in vivo examples is represented by nevi, in which cells proliferate after oncogene activation and then become senescent. Melanoma is a highly aggressive form of neoplasm often observed to derive from nevi, and the transition implies suppression of the mechanisms that sustain the onset and maintenance of senescence.9 In fact, many of the melanoma-associated tumor suppressor genes identified to date are themselves involved in control of senescence, including BRAF (encoding serine/threonine-protein kinase B-raf), CKD4 (cyclin-dependent kinase 4), and CDKN2A (encoding cyclin-dependent kinase inhibitor 2A isoforms p16INK4a and p19ARF).3,10

Nevi frequently harbor oncogenic mutations of the tyrosine kinase BRAF gene, particularly V600E,11 andBRAFV600E is also found in approximately 70% of cutaneous melanomas.12 Expression of BRAFV600E in human melanocytes leads to oncogene-induced senescence,8 which can be considered as a mechanism that protects from malignant progression. In time, some cells may eventually escape senescence, probably through the acquisition of additional genetic abnormalities, thus favoring transformation to melanoma.13

Pirin (PIR) is a highly conserved nuclear protein belonging to the Cupin superfamily14 whose function is, to date, poorly characterized. It has been described as a putative transcriptional regulator on the basis of its physical association with the nuclear I/CCAAT box transcription factor NFI/CTF115 and with the B-cell lymphoma protein, BCL-3, a regulator of NF-κB/Rel activity. A recent report shows that PIR controls melanoma cell migration through the transcriptional regulation of snail homolog 2, SNAI2 (previously SLUG).16 Other reports described quercetinase enzymatic activity,17 and regulation of apoptosis18,19 and stress response, unveiling a high degree of cell-type and species specificity in PIR function.

There is evidence of variations in PIR expression levels in different types of malignancies, but a systematic analysis of PIR expression in human tumors has been lacking. We analyzed PIR expression pattern in a collection of normal and neoplastic human tissues and found that it is expressed in scattered melanocytes, virtually absent in more mature regions of nevi, and present at high levels in a subset of melanomas. Functional studies performed in normal and transformed melanocytic cells revealed that PIR ablation results in cellular senescence, and that PIR levels decrease in response to senescence stimuli. Our results suggest that PIR may be a relevant player in the negative control of cellular senescence in PIR-expressing melanomas.

PIR overexpression in melanoma

Figure 3  PIR overexpression in PIR melanoma cells has no effect on proliferation.
PIR Expression Is Down-Regulated by BRAF Activation and Camptothecin Treatment

BRAF mutations are frequent in nevi, and are directly linked to the induction of oncogene-induced senescence. Variations in PIR expression levels were therefore investigated in an experimental model of senescence induced by oncogenic BRAF. Human diploid fibroblasts (TIG3–hTERT) expressing a conditional form of constitutively activated BRAF fused to the ligand-binding domain of the estrogen receptor (ER) rapidly undergo oncogene-induced senescence on treatment with 4-hydroxytamoxifen (OHT).28,29 PIR protein and mRNA levels were measured in TIG3-BRAF-ER cells at different time points of treatment with 800 nmol/L OHT. PIR expression was significantly repressed both at the mRNA and at the protein level after BRAF activation (Figure 6A), and remained at low levels after 120 hours, suggesting that a significant reduction of PIR expression is associated with the establishment of oncogene-induced senescence in different cell types.

7.7.9 O-GlcNAcylation at promoters, nutrient sensors, and transcriptional regulation

Brian A. Lewis
Biochim et Biophys Acta (BBA) – Gene Regulatory Mechanisms Nov 2013; 1829(11): 1202–1206
http://dx.doi.org/10.1016/j.bbagrm.2013.09.003

Highlights

  • This review article discusses recent advances in the links between O-GlcNAc and transcriptional regulation.
  • Discusses several systems to illustrate O-GlcNAc dynamics: Tet proteins, MLL complexes, circadian clock proteins and RNA pol II.
  • Suggests that promoters are nutrient sensors.

Post-translational modifications play important roles in transcriptional regulation. Among the less understood PTMs is O-GlcNAcylation. Nevertheless, O-GlcNAcylation in the nucleus is found on hundreds of transcription factors and coactivators and is often found in a mutually exclusive ying–yang relationship with phosphorylation. O-GlcNAcylation also links cellular metabolism directly to the proteome, serving as a conduit of metabolic information to the nucleus. This review serves as a brief introduction to O-GlcNAcylation, emphasizing its important thematic roles in transcriptional regulation, and highlights several recent and important additions to the literature that illustrate the connections between O-GlcNAc and transcription.

links between O-GlcNAc and transcriptional regulation.

links between O-GlcNAc and transcriptional regulation.

http://ars.els-cdn.com/content/image/1-s2.0-S1874939913001351-gr1.sml
links between O-GlcNAc and transcriptional regulation.

systems to illustrate O-GlcNAc dynamics

systems to illustrate O-GlcNAc dynamics

http://ars.els-cdn.com/content/image/1-s2.0-S1874939913001351-gr2.sml
systems to illustrate O-GlcNAc dynamics

7.7.10 O-GlcNAcylation in cellular functions and human diseases

Yang YR1Suh PG2.
Adv Biol Regul. 2014 Jan; 54:68-73
http://dx.doi.org:/10.1016/j.jbior.2013.09.007

O-GlcNAcylation is dynamic and a ubiquitous post-translational modification. O-GlcNAcylated proteins influence fundamental functions of proteins such as protein-protein interactions, altering protein stability, and changing protein activity. Thus, aberrant regulation of O-GlcNAcylation contributes to the etiology of chronic diseases of aging, including cancer, cardiovascular disease, metabolic disorders, and Alzheimer’s disease. Diverse cellular signaling systems are involved in pathogenesis of these diseases. O-GlcNAcylated proteins occur in many different tissues and cellular compartments and affect specific cell signaling. This review focuses on the O-GlcNAcylation in basic cellular functions and human diseases.

O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

http://ars.els-cdn.com/content/image/1-s2.0-S2212492613000717-gr2.sml
O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

aberrant regulation of O-GlcNAcylation in disease

aberrant regulation of O-GlcNAcylation in disease

http://ars.els-cdn.com/content/image/1-s2.0-S2212492613000717-gr3.sml
aberrant regulation of O-GlcNAcylation in disease

 Comment:

Body of review in energetic metabolic pathways in malignant T cells

Antigen stimulation of T cell receptor (TCR) signaling to nuclear factor (NF)-B is required for T cell proliferation and differentiation of effector cells.
The TCR-to-NF-B pathway is generally viewed as a linear sequence of events in which TCR engagement triggers a cytoplasmic cascade of protein-protein interactions and post-translational modifications, ultimately culminating in the nuclear translocation of NF-B.
Activation of effect or T cells leads to increased glucose uptake, glycolysis, and lipid synthesis to support growth and proliferation.
Activated T cells were identified with CD7, CD5, CD3, CD2, CD4, CD8 and CD45RO. Simultaneously, the expression of CD95 and its ligand causes apoptotic cells death by paracrine or autocrine mechanism, and during inflammation, IL1-β and interferon-1α. The receptor glucose, Glut 1, is expressed at a low level in naive T cells, and rapidly induced by Myc following T cell receptor (TCR) activation. Glut1 trafficking is also highly regulated, with Glut1 protein remaining in intracellular vesicles until T cell activation.

Dr. Aurel,
Targu Jiu

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Manipulate Signaling Pathways

Writer and Curator: Larry H Bernstein, MD, FCAP 

 

7.6  Manipulate Signaling Pathways

7.6.1 The Dynamics of Signaling as a Pharmacological Target

7.6.2 A Protein-Tagging System for Signal Amplification in Gene Expression and Fluorescence Imaging

7.6.3 IQGAPs choreograph cellular signaling from the membrane to the nucleus

7.6.4 Signaling cell death from the endoplasmic reticulum stress response

7.6.5 An Enzyme that Regulates Ether Lipid Signaling Pathways in Cancer Annotated by Multidimensional Profiling

7.6.6 Peroxisomes – A Nexus for Lipid Metabolism and Cellular Signaling

7.6.7 A nexus for cellular homeostasis- the interplay between metabolic and signal transduction pathways

7.6.8 Mechanisms-of-intercellular-signaling

7.6.9 Cathepsin B promotes colorectal tumorigenesis, cell invasion, and metastasis

 

 

7.6.1 The Dynamics of Signaling as a Pharmacological Target

Marcelo Behar, Derren Barken, Shannon L. Werner, Alexander Hoffmann
Cell  10 Oct 2013; 155(2):448–461
http://dx.doi.org/10.1016/j.cell.2013.09.018

Highlights

  • Drugs targeting signaling hubs may block specific dynamic features of the signal
  • Specific inhibition of dynamic features may introduce pathway selectivity
  • Phase space analysis reveals principles for drug targeting signaling dynamics
  • Based on these principles, NFκB dynamics can be manipulated with specificity

Summary

Highly networked signaling hubs are often associated with disease, but targeting them pharmacologically has largely been unsuccessful in the clinic because of their functional pleiotropy. Motivated by the hypothesis that a dynamic signaling code confers functional specificity, we investigated whether dynamic features may be targeted pharmacologically to achieve therapeutic specificity. With a virtual screen, we identified combinations of signaling hub topologies and dynamic signal profiles that are amenable to selective inhibition. Mathematical analysis revealed principles that may guide stimulus-specific inhibition of signaling hubs, even in the absence of detailed mathematical models. Using the NFκB signaling module as a test bed, we identified perturbations that selectively affect the response to cytokines or pathogen components. Together, our results demonstrate that the dynamics of signaling may serve as a pharmacological target, and we reveal principles that delineate the opportunities and constraints of developing stimulus-specific therapeutic agents aimed at pleiotropic signaling hubs.

http://www.cell.com/cms/attachment/2021777732/2041663648/fx1.jpg

Intracellular signals link the cell’s genome to the environment. Misregulation of such signals often cause or exacerbate disease (Lin and Karin, 2007 and Weinberg, 2007) (so-called “signaling diseases”), and their rectification has been a major focus of biomedical and pharmaceutical research (Cohen, 2002Frelin et al., 2005 and Ghoreschi et al., 2009). For the identification of therapeutic targets, the concept of discrete signaling pathways that transmit intracellular signals to connect cellular sensor/receptors with cellular core machineries has been influential. In this framework, molecular specificity of therapeutic agents correlates well with their functional or phenotypic specificity. However, in practice, clinical outcomes for many drugs with high molecular specificity has been disappointing (e.g., inhibitors of IKK, MAPK, and JNK; Berger and Iyengar, 2011DiDonato et al., 2012Röring and Brummer, 2012 and Seki et al., 2012).

Many prominent signaling mediators are functionally pleiotropic, playing roles in multiple physiological functions (Chavali et al., 2010 and Gandhi et al., 2006). Indeed, signals triggered by different stimuli often travel through shared network segments that operate as hubs before reaching the effectors of the cellular response (Bitterman and Polunovsky, 2012 and Gao and Chen, 2010). Hubs’ inherent pleiotropy means that their inhibition may have broad and likely undesired effects (Karin, 2008Berger and Iyengar, 2011,Force et al., 2007Oda and Kitano, 2006 and Zhang et al., 2008); this is a major obstacle for the efficacy of drugs targeting prominent signaling hubs such as p53, MAPK, or IKK.

Recent studies have begun to address how signaling networks generate stimulus-specific responses (Bardwell, 2006Haney et al., 2010Hao et al., 2008 and Zalatan et al., 2012). For example, the activity of some pleiotropic kinases may be steered to particular targets by scaffold proteins (Park et al., 2003,Schröfelbauer et al., 2012 and Zalatan et al., 2012). Alternatively, or in addition, some signaling hubs may rely on stimulus-specific signal dynamics to activate selective downstream branches in a stimulus-specific manner in a process known as temporal or dynamic coding or multiplexing (Behar and Hoffmann, 2010,Chalmers et al., 2007Hoffmann et al., 2002Kubota et al., 2012Marshall, 1995 and Purvis et al., 2012;Purvis and Lahav, 2013Schneider et al., 2012 and Werner et al., 2005).

Although the importance of signaling scaffolds and their pharmacological promise is widely appreciated (Klussmann et al., 2008 and Zalatan et al., 2012) and isolated studies have altered the stimulus-responsive signal dynamics (Purvis et al., 2012Park et al., 2003Sung et al., 2008 and Sung and Simon, 2004), the capacity for modulating signal dynamics for pharmacological gain has not been addressed in a systematic manner. In this work, we demonstrate by theoretical means that, when signal dynamics are targeted, pharmacological perturbations can produce stimulus-selective results. Specifically, we identify combinations of signaling hub topology and input-signal dynamics that allow for pharmacological perturbations with dynamic feature-specific or input-specific effects. Then, we investigate stimulus-specific drug targeting in the IKK-NFκB signaling hub both in silico and in vivo. Together, our work begins to define the opportunities for pharmacological targeting of signaling dynamics to achieve therapeutic specificity.

Dynamic Signaling Hubs May Be Manipulated to Mute Specific Signals

Previous work has shown how stimulus-specific signal dynamics may allow a signaling hub to selectively route effector functions to different downstream branches (Behar et al., 2007). Here, we investigated the capacity of simple perturbations to kinetic parameters (caused for example by drug treatments) to produce stimulus-specific effects. For this, we examined a simple model of an idealized signaling hub (Figure 1A), reminiscent of the NFκB p53 or of MAPK signaling modules. The hub X reacts with strong but transient activity to stimulus S1 and sustained, slowly rising activity to stimulus S2. These stimulus-specific signaling dynamics are decoded by two effector modules, regulating transcription factors TF1 and TF2. TF1, regulated by a strongly adaptive negative feedback, is sensitive only to fast-changing signals, whereas TF2, regulated by a slowly activating two-state switch, requires sustained signals for activation (Figure 1B). We found it useful to characterize the X, TF1, and TF2 responses in terms of two dynamic features, namely the maximum early amplitude (“E,” time < 15′) and the average late amplitude (“L,” 15′ < t < 6 hr). These features, calculated using a mathematical model of the network (see Experimental Procedures) show good fidelity and specificity (Komarova et al., 2005) (Figure 1C), as S1 causes strong activation of TF1 with minimal crosstalk to TF2, and vice versa for S2.

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Figure 1. Pharmacologic Perturbations with Stimulus-Specific Effects

(A) A negative-feedback module transduces input signals S1 and S2, producing outputs that are decoded by downstream effectors circuits that may distinguish between different dynamics.

(B) Unperturbed dynamics of X, TF1, and TF2 in response to S1 (red) and S2 (blue). Definition of early (E) and late (L) parts of the signal is indicated.

(C) Specificity and fidelity of E and L for TF1 and TF2, as defined in Komarova et al., 2005).

(D) Partial inhibition of X activation (A) abolishes the response to S1, but not S2, whereas a perturbation targeting the feedback regulator (FBR) suppresses the response to S2, but not S1.

(E) Perturbation phenotypes defined as difference between unperturbed and perturbed values of the indicated quantities (arbitrary scales for X, TF1, and TF2). Perturbation A inhibits E and TF1, but not TF2; perturbation FBR inhibits L and TF2, but not TF1.

(F) Virtual screening pipeline showing the experimental design and the two analysis branches for characterizing feature- and input-specific effects.

See also in Experimental Procedures and Table S1.

Seeking simple (affecting a single reaction) perturbations that selectively inhibit signaling by S1 or S2, we found that perturbation A, partially inhibiting the activation of X, was capable of suppressing hub activity in response to a range of S1 amplitudes while still allowing for activity in response to S2 (Figure 1D). Consequently, this perturbation significantly reduced TF1 activity in response to S1 but had little effect on TF2 activity elicited by S2. We also found that the most effective way to inhibit S2 signaling was by targeting the deactivation of negative feedback regulator Y (FBR). This perturbation caused almost complete abrogation of late X activity yet allows for significant levels of early activity. As a result, TF2 was nearly completely abrogated in response to S2, but stimulus S1 still produced a solid TF1 response. The early (E) and late (L) amplitudes could be used to quantify the input-signal-specific effects of these perturbations (Figure 1E).

This numerical experiment showed that it is possible to selectively suppress transient or sustained dynamic signals transduced through a common negative-feedback-containing signaling hub. Moreover, the dynamic features E and L could be independently inhibited. To study how prevalent such opportunities for selective inhibition are, we established a computational pipeline for screening reaction perturbations within multiple network topologies and in response to multiple dynamic input signals; the simulation results were analyzed to identify cases of either “input-signal-specific” inhibition or “dynamic feature-specific” inhibition (Figure 1F).

A Computational Screen to Identify Opportunities for Input-Signal-Specific Inhibition

The computational screen involved small libraries of one- and two-component regulatory modules and temporal profiles of input signals (Figure 2A), both commonly found in intracellular signaling networks. All modules (M1–M7, column on left) contained a species X that, upon stimulation by an input signal, is converted into an active form X (the output) that propagates the signal to downstream effectors. One-component modules included a reversible two-state switch (M1) and a three-state cycle with a refractory state (M2). Two-component modules contained a species Y that, upon activation via a feedback (M3 and M5) or feedforward (M4 and M6) loop, either deactivates X (M3 and M4) or inhibits (M5 and M6) its activation. We also included the afore-described topology that mimics the IκB-NFκB or the Mdm2-p53 modules (M7). Mathematical descriptions may be found in the Experimental Procedures. Although many biological signaling networks may conform to one of these simple topologies, others may be abstracted to one that recapitulates the physiologically relevant emergent properties

Figure 2. A Virtual Screen for Stimulus Specificity in Pharmacologic Perturbations

(A) Signaling modules (left) and input library (top) used in the screen. Dotted lines indicate enzymatic reactions (perturbation names indicated in letter code). Time courses of hub activity for each module/input combination for the unperturbed (black) and perturbed cases (blue indicates a decrease, red an increase in parameter value).

(B) Relative sensitivity of the stimulus response to the indicated perturbation (defined as the perturbation’s effect on the area under the curve), normalized per row.

See also Experimental ProceduresFigure S1, and Tables S2 and S3.

The library of stimuli (S1–S10; Figure 2A, top row) comprises ten input functions with different combinations of “fast” and “slow” initiation and decay phases (see Experimental Procedures). The virtual screen was performed by varying the kinetic parameter for each reaction over a range of values, thereby modeling simple perturbations of different strengths and recording the temporal profile of X abundance. To quantify stimulus-specific inhibition, we measured the area under the normalized dose-response curves (time average of X versus perturbation dose) for each module-input combination (Experimental ProceduresFigure 2B, and Figure S1 available online).

Phase Space Analysis Reveals Underlying Regulatory Principles

To understand the origin of dynamic feature-specific inhibition, we investigated the perturbation effects analytically on each module’s phase space, i.e., the space defined by X∗ and Y∗ quasi-equilibrium surfaces (Figures 4 and S4). These surfaces (“q.e. surfaces”) represent the dose response of X∗ as a function of Y∗ and a stationary input signal S (“X surface”) and the dose response of Y∗ as a function of X∗ and S (“Y surface”) (Figure 4A). The points at which the surfaces intersect correspond to the concentrations of X∗ and Y∗ in equilibrium for a given value of S. In the basal state, when S is low, the system is resting at an equilibrium point close to the origin of coordinates. When S increases, the concentrations of X∗ and Y∗ adjust until the signal settles at some stationary value (Figure 4A). Gradually, changing input signals cause the concentrations to follow trajectories close to the q.e. surfaces (quasi-equilibrium dynamics), following the line defined by the intersection of the surfaces (“q.e. line”) in the extreme of infinitely slow inputs. Fast-changing stimuli drive the system out of equilibrium, causing the trajectories to deviate markedly from the q.e. surfaces.

Two main principles emerged: (1) perturbations that primarily affect the shape of a q.e. surface tend to affect steady-state levels or responses that evolve close to quasi-equilibrium, and (2) perturbations that primarily affect the balance of timescales (X, Y activation, and S) tend to affect transient out-of-equilibrium parts of the response. These principles reflect the fact that out-of-equilibrium parts of a signal are largely insensitive to the precise shape of the underlying dose-response surfaces (they may still be bounded by them) but depend on the balance between the timescales of the biochemical processes involved. Perturbation of these balances affects how a system approaches steady state (thus affecting out-of-equilibrium and quasi-equilibrium dynamics), but not steady-state levels. To illustrate these principles, we present selected results for modules M3 and M4 and discuss additional cases in the supplement (Figure S3).

Detailed Analysis of Modules M3 and M4, Related to Figure 4

Time courses and projections of the phase space for modules M3 and M4. Color coding similar to Figure 4.

In the feedback-based modules (M3 and M5), the early peak of activity in response to rapidly changing signals is an out-of-equilibrium feature that occurs when the timescale of Y activation is significantly slower than that of X. Under these conditions, the concentration of X increases rapidly (out of equilibrium) before decaying along the X surface (in quasi-equilibrium) as more Y gets activated (Figure 4A, parameters modified to better illustrate the effects being discussed; see Table S2). For input signals that settle at some stationary level of S, Y activation eventually catches up and the concentration of X settles at the equilibrium point where the X and Y curves intersect. Gradually changing signals allow X and Yactivation to continuously adapt, and the system evolves closer to the q.e. line.

In such modules, perturbation A (X activation) changes both the shape of the q.e. surface for X and the kinetics of activation. When in the unperturbed system Y saturates, perturbation A primarily reduces Xsteady-state level (Figures 4B and 4C, left and center). When Y does not saturate in the unperturbed system, the primary effect is the reduced activation kinetics. Thus the perturbation affects the out-of-equilibrium peak (Figures 4B and 4C, center and right), with only minor reduction of steady-state levels (especially when Y’s dose response respect to X is steep). The transition from saturated to not-saturated feedback (as well as the perturbation strength) underlies the dose-dependent switch from L to E observed in the screen. In both saturated and unsaturated regimes, the shift in the shape of the surfaces does change the q.e. line and thus affects responses occurring in quasi-equilibrium. In contrast, perturbation of the feedback recovery (FBR) shifts the Y surface vertically (Figure 4D), specifically affecting the steady-state levels and late signaling; the effect on Y kinetics is limited because the reaction is relatively slow. Perturbation FBA also shifts the Y surface, but the net effect is less specific because the associated increase in the rate of Y activation tends to equalize X and Y kinetics affecting also the out-of-equilibrium peak.

In resting cells, NFκB is held inactive through its association with inhibitors IκBα, β, and ε. Upon stimulation, these proteins are phosphorylated by the kinase IKK triggering their degradation. Free nuclear NFκB activates the expression of target genes, including IκB-encoding genes, which thereby provide negative feedback (Figure 5A). The IκB-NFκB-signaling module is a complex dynamic system; however, by abstracting the control mechanism to its essentials, we show below that the above-described principles can be applied profitably.

IκB-NFκB signaling module

IκB-NFκB signaling module

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Figure 5. Modulating NFκB Signaling Dynamics

(A) The IκB-NFκB signaling module.

(B) Equilibrium dose-response relationship for NFκB versus IKK.

(C) Three IKK curves representative of three stimulation regimes; TNFc (red), TNFp (green), and LPS (blue) function as inputs into the model, which computes the corresponding NFκB activity dynamics (bottom). The quasi-equilibrium line (black) was obtained by transforming the IKK temporal profiles by the dose response in (B). Deviation from the quasi-equilibrium line for the TNF response indicates out-of-equilibrium dynamics.

(D) Coarse-grained model of the IκB-NFκB module and predicted effects of perturbations.

(E) Selected perturbations with specific effects on out-of-equilibrium (top three) or steady state (bottom two). (Left to right) Feature maps in the E-L space (E: t < 60 ′, L: 120′ < t < 300′), tangent angle at the unperturbed point (θ > 0 indicates L is more suppressed than E and vice versa), and time courses (green, TNF chronic; red, TNF pulse; blue, LPS). Only inhibitory perturbations are shown. Additional perturbations are shown in Figure S4.

See also Experimental Procedures and Table S7.

Here, we delineate the potential of achieving stimulus-specific inhibition when targeting molecular reactions within pleiotropic signaling hubs. We found that it is theoretically possible to design perturbations that (1) selectively attenuate signaling in response to one stimulus but not another, (2) selectively attenuate undesirable features of dynamic signals or enhance desirable ones, or (3) remodulate output signals to fit a dynamic profile normally associated with a different stimulus.

These opportunities—not all of them possible for every signaling module topology or biological scenario—are governed by two general principles based on timescale and dose-response relationships between upstream signal dynamics and intramodule reaction kinetics (Figure 4 and Table S4). In short, a steady-state or quasi-equilibrium part of a response may be selectively affected by perturbations that introduce changes in the relevant dose-response surfaces. Out-of-equilibrium responses that are not sensitive to the precise shape of a dose-response curve may be selectively attenuated by perturbations that modify the relative timescales. Dose responses and timescales cannot, in general, be modified independently by simple perturbations (combination treatments are required), but as we show, in some cases, one effect dominates resulting in feature or stimulus specificity.

The degree to which specific dynamic features of a signaling profile or the dynamic responses to specific stimuli can be selectively inhibited depends on how distinctly they rely on quasi-equilibrium and out-of-equilibrium control. Signals that contain both features may be partially inhibited by both types of perturbation, limiting the specific inhibition achievable by simple perturbations. In practice, this limited the degree to which NFκB signaling could be inhibited in a stimulus-specific manner (Figure 5) and the associated therapeutic dose window (Figure 6). The most selective stimulus-specific effects can be introduced when a signal is heavily dependent on a particular dynamic feature; for example, suppression of out-of-equilibrium transients will abrogate the response to stimuli that produce such transients. For a selected group of target genes, this specificity at the signal level translated directly to expression patterns (Figure 6B, middle). More generally, selective inhibition of early or late phases of a signal may allow for specific control of early and late response genes (Figure 6C), a concept that remains to be studied at genomic scales. Though the principles are general, how they apply to specific signaling pathways depends not only on the regulatory topology, but also on the dynamic regime determined by the parameters. As demonstrated with the IκB-NFκB module, analysis of a coarse-grained topology in terms of the principles may allow the prediction of perturbations with a desired specificity.

 

7.6.2 A Protein-Tagging System for Signal Amplification in Gene Expression and Fluorescence Imaging

Marvin E. Tanenbaum, Luke A. Gilbert, Lei S. Qi, Jonathan S. Weissman, Ronald D. Vale
Cell 23 Oct 2014; 159(3): 635–646
http://dx.doi.org/10.1016/j.cell.2014.09.039

Highlights

  • SunTag allows controlled protein multimerization on a protein scaffold
  • SunTag enables long-term single-molecule imaging in living cells
  • SunTag greatly improves CRISPR-based activation of gene expression

Summary

Signals in many biological processes can be amplified by recruiting multiple copies of regulatory proteins to a site of action. Harnessing this principle, we have developed a protein scaffold, a repeating peptide array termed SunTag, which can recruit multiple copies of an antibody-fusion protein. We show that the SunTag can recruit up to 24 copies of GFP, thereby enabling long-term imaging of single protein molecules in living cells. We also use the SunTag to create a potent synthetic transcription factor by recruiting multiple copies of a transcriptional activation domain to a nuclease-deficient CRISPR/Cas9 protein and demonstrate strong activation of endogenous gene expression and re-engineered cell behavior with this system. Thus, the SunTag provides a versatile platform for multimerizing proteins on a target protein scaffold and is likely to have many applications in imaging and controlling biological outputs.

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SunTag, which can recruit multiple copies of an antibody-fusion protein
Development of the SunTag, a System for Recruiting Multiple Protein Copies to a Polypeptide Scaffold Protein multimerization on a single RNA or DNA template is made possible by identifying protein domains that bind with high affinity to a relatively short nucleic acid motif. We therefore sought a protein-based system with similar properties, specifically a protein that can bind tightly to a short peptide sequence (Figures 1A and1B).Antibodies arecapable ofbindingto short,unstructured peptide sequences with high affinity and specificity, and, importantly, peptide epitopes can be designed that differ from naturally occurring sequences in the genome. Furthermore, whereas antibodies generally do not fold properly in the cytoplasm, single-chain variable fragment (scFv) antibodies, in which the epitope-binding regions of the light and heavy chains of the antibody are fused to forma single polypeptide, have been successfully expressed in soluble form in cells (Colby et al., 2004; Lecerf et al., 2001; Wo ¨rn et al., 2000).
We expressed three previously developed single-chain antibodies (Colby et al., 2004; Lecerf et al., 2001; Wo ¨rn et al., 2000) fused to EGFP in U2OS cells and coexpressed their cognate peptides (multimerized in four tandem copies) fused to the cytoplasmic side of the mitochondrial protein mitoNEET (Colca et al., 2004) (referred to here as Mito, Figure S1A). We then assayed whether the antibody-GFP fusion proteins would be recruited to the mitochondria by fluorescence microscopy, which would indicate binding between antibody and peptide (Figure 1B). Of the three antibody-peptide pairs tested, only the GCN4 antibody-peptide pair showed robust and specific binding while not disrupting normal mitochondrial morphology (Figures 1C and S1B). Thus, we focused our further efforts on the GCN4 antibody-peptide pair. The GCN4 antibody was optimized to allow intracellular expression in yeast (Wo ¨rn et al., 2000). In human cells, however, we still observed some protein aggregates of scFv-GCN4-GFP at high expression levels (Figure S2A). To improve scFv-GCN4 stability, we added a variety of N- and C-terminal fusion proteins known to enhance protein solubility and found that fusion of superfolder-GFP (sfGFP) alone
(Pe’delacq et al., 2006) or along with the small solubility tag GB1 (Gronenborn et al., 1991) to the C terminus of the GCN4 antibody almost completely eliminated protein aggregation, even at high expression levels (Figure S2A). Thus, we performed all further experiments with scFv-GCN4-sfGFP-GB1 (hereafter referred to as scFvGCN4-GFP). Very tight binding of the antibody-peptide pair in vivo is critical fortheformation ofmultimersonaproteinscaffoldbackbone.To determine the dissociation rate of the GCN4 antibody-peptide interaction, we performed fluorescence recovery after photobleaching (FRAP) experiments on scFv-GCN4-GFP bound to the mitochondrial-localized mito-mCherry-4xGCN4pep. After photobleaching, very slow GFP recovery was observed (halflife of 5–10 min [Figures 2A and 2B]), indicating that the antibody bound very tightly to the peptide. It is also important to optimize the spacing of the scFv-GCN4 binding sites within the protein scaffold so that they could be saturated by scFvGCN4 because steric hindrance of neighboring peptide binding sites is a concern. We varied the spacing between neighboring GCN4 peptides and quantified the antibody occupancy on the peptide array.

Figure 1. Identification of an Antibody-Peptide Pair that Binds Tightly In Vivo (A) Schematic of the antibody-peptide labeling strategy. (B) Schematic of the experiment described in (C) in which the mitochondrial targeting domain of mitoNEET (yellow box, mito) fused to mCherry and four tandem copies of a peptide recruits a GFP-tagged intracellular antibody to mitochondria. (C) ScFv-GCN4-GFP was coexpressed with either mito-mCherry-4xGCN4peptide (bottom) or mito-mCherry-FKBP as a control (top) in U2OS cells, and cells were imaged using spinning-disk confocal microscopy. Scale bars, 10 mm. See also Figure S1.

Figure 2. Characterization of the Off Rate and Stoichiometry of the Binding Interaction between the scFv-GCN4 Antibody and the GCN4 Peptide Array In Vivo (A) Mito-mCherry-24xGCN4pep was cotransfected with scFv-GCN4-GFP in HEK293 cells, and their colocalization on mitochondria in a single cell is shown (10 s). At 0 s, the mitochondria-localized GFP signal was photobleached in a single z plane using a 472 nm laser, and fluorescence recovery was followed by time-lapse microscopy. Scale bar, 5 mm. (B) The FRAP was quantified for 20 cells. (C–E) Indicated constructs were transfected in HEK293 cells, and images were acquired 24 hr after transfection with identical image acquisition settings. Representative images are shown in (C). Note that the GFP signal intensity in the mito-mCherry-24xGCN4pep + scFv-GCN4-GFP is highly saturated when the same scaling is used as in the other panels. Bottom row shows a zoom of a region of interest: dynamic scaling was different for the GFP and mCherry signals, so that both could be observed. Scale bars, 10 mm. (D and E) Quantifications of the GFP:mCherry fluorescence intensity ratio on mitochondria after normalization. Eachdot represents a single cell, and dashed lines indicates the average value. See also Figure S2.

Figure 3. The SunTag Allows Long-Term Single-Molecule Fluorescence Imaging in the Cytoplasm (A–H) U2OS cells were transfected with indicated SunTag24x constructs together with the scFv-GCN4-GFP-NLS and were imaged by spinning-disk confocal microscopy 24 hr after transfection. (A) A representative image of SunTag24x-CAAX-GFP is shown (left), as well as the fluorescence intensities quantification of the foci (right, blue bars). As a control, U2OS were transfected with sfGFP-CAAX and fluorescence intensities of single sfGFP-CAAX molecules were also quantified (red bars). The average fluorescence intensity of the single sfGFP-CAAX was set to 1. Dotted line marks the outline of the cell (left). Scale bar, 10 mm. (B) Cells expressing K560-SunTag24x-GFP were imaged by spinning disk confocal microscopy (image acquisition every 200 ms). Movement is revealed by a maximum intensity projection of 50 time points (left) and a kymograph (right). Scale bar, 10 mm. (C and D) Cells expressing both EB3-tdTomato and K560-SunTag24x-GFP were imaged, and moving particles were tracked manually. Red and blue tracks (bottom) indicate movement toward the cell interior and periphery, respectively (C). The duration of the movie was 20 s. Scale bar, 5 mm. Dots in (D) represent individual cells with between 5 and 20 moving particles scored per cell. The mean and SD are indicated. (E and F) Cells expressing Kif18b-SunTag24x-GFP were imaged with a 250 ms time interval. Images in (E) show a maximum intensity projection (50 time- points, left) and a kymograph (right). Speeds of moving molecules were quantified from ten different cells (F). Scale bar, 10 mm. (G and H) Cells expressing both mCherry-a-tubulin and K560rig-SunTag24x-GFP were imaged with a 600 ms time interval.The entire cell is shown in (G), whereas H shows zoomed-instills of atime series from the same cell. Open circlestrack two foci on the same microtubule,which is indicated bythe dashed line. Asterisks indicate stationary foci. Scale bars, 10 and 2 mm (G and H), respectively. See also Figure S3 and Movies S1, S2, S3, S4, S5, and S6.
The GCN4 peptide contains many hydrophobic residues (Figure 4B) and is largely unstructured in solution (Berger et al., 1999); thus, the poor expression of the peptide array could be due to its unstructured and hydrophobic nature. To test this idea, we designed several modified peptide sequence that were predicted to increase a-helical propensity and reduce hydrophobicity. One of these optimized peptides (v4, Figure 4B) was expressed moderately well as a 243 peptide array, and even higher expression was achieved with a 103 peptide array (Figure 4C). Importantly, fluorescence imaging revealed that thescFv-GCN4antibody robustlyboundto theGCN4v4peptide array in vivo and FRAP analysis suggests that the scFv-GCN4 antibody dissociates with a similar slow off rate from the GCN4
v4 peptide array as the original peptide (Figures 4D and 4E). Furthermore, K560 motility could be observed when it was tagged with the optimized v4 243 peptide array, indicating that the optimized v4 peptide array did not interfere with protein function (Movie S7). Together, these results identify a second version of the peptide array that can be used for applications requiring higher expression.
Activation of Gene Transcription Using Cas9-SunTag Because the SunTag system could be used for amplification of a fluorescence signal, we tested whether it also could be used to amplify regulatory signals involved in gene expression. Transcription of a gene is strongly enhanced by recruiting multiple copies of transcriptional activators to endogenous or artificial gene promoters (Anderson and Freytag, 1991; Chen et al., 1992; Pettersson and Schaffner, 1990). Thus, we thought that robust, artificial activation of gene transcription might also be achieved by recruiting multiple copies of a synthetic transcriptional activator to a gene using the SunTag.

Figure 4. An Optimized Peptide Array for High Expression (A) Indicated constructs were transfected in HEK293 cells and imaged 24 hr after transfection using wide-field microscopy. All images were acquired using identical acquisition parameters. Representative images are shown (left), and fluorescence intensities were quantified (n = 3) (right). (B) Sequence of the first and second generation GCN4 peptide (modified or added residues are colored blue, hydrophobic residues are red, and linker residues are yellow). (C–E) Indicated constructs were transfected in HEK293 cells and imaged 24 hr after transfection using wide-field (C) or spinning-disk confocal (D and E) microscopy. (C) Representative images are shown (left), and fluorescence intensities were quantified (n = 3) (right). (D and E) GFP signal on mitochondria was photobleached, and fluorescence recovery was determined over time. The graph (E) represents an average of six cells per condition. (E) shows an image of a representative cell before photobleaching. Scale bars in (A) and (C), 50 mm; scale bars in (D) and (E), 10 mm. Error bars in (A) and (C) represent SDs. See also Movie S7.

Figure 5. dCas9-SunTag Allows Genetic Rewiring of Cells through Activation of Endogenous Genes (A) Schematic of gene activation by dCas9-VP64 and dCas9-SunTag-VP64. dCas9 binds to a gene promoter through its sequence-specific sgRNA (red line). Direct fusion of VP64 to dCas9 (top) results in a single VP64 domain at the promoter, which poorly activates transcription of the downstream gene. In contrast, recruitment of many VP64 domains using the SunTag potently activates transcription of the gene (bottom). (B–D) K562 cells stably expressing dCas9-VP64 or dCas9-SunTag-VP64 were infected with lentiviral particles encoding indicated sgRNAs, as well as BFP and a puromycin resistance gene and selected with 0.7 mg/ml puromycin for 3 days to kill uninfected cells. (B and C) Cells were stained for CXCR4 using adirectlylabeleda-CXCR4 antibody, and fluorescence was analyzed by FACS. (D) Trans-well migration assays (see Experimental Procedures) were performed with indicated sgRNAs. Results are displayed as the fold change in directional migrating cells over control cell migration. (E) dCas9-VP64 or dCas9-SunTag-VP64 induced transcription of CDKN1B with several sgRNAs. mRNA levels were quantified by qPCR. (F) Doubling timeofcontrolcells orcells expressing indicated sgRNAs was determined (see Experimental Procedures section). Graphs in (C), (D), and (F) are averages of three independent experiments. Graph in (E) is average of two biological replicates, each with two or three technical replicates. All error bars indicate SEM. See also Figure S4

 

7.6.3 IQGAPs choreograph cellular signaling from the membrane to the nucleus

Jessica M. Smith, Andrew C. Hedman, David B. Sacks
Trends Cell Biol Mar 2015; 25(3): 171–184
http://dx.doi.org/10.1016/j.tcb.2014.12.005

Highlights

  • IQGAP proteins scaffold diverse signaling molecules.
  • IQGAPs mediate crosstalk between signaling pathways.
  • IQGAP1 regulates nuclear processes, including transcription.

Since its discovery in 1994, recognized cellular functions for the scaffold protein IQGAP1 have expanded immensely. Over 100 unique IQGAP1-interacting proteins have been identified, implicating IQGAP1 as a critical integrator of cellular signaling pathways. Initial research established functions for IQGAP1 in cell–cell adhesion, cell migration, and cell signaling. Recent studies have revealed additional IQGAP1 binding partners, expanding the biological roles of IQGAP1. These include crosstalk between signaling cascades, regulation of nuclear function, and Wnt pathway potentiation. Investigation of the IQGAP2 and IQGAP3 homologs demonstrates unique functions, some of which differ from those of IQGAP1. Summarized here are recent observations that enhance our understanding of IQGAP proteins in the integration of diverse signaling pathways.

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7.6.4 Signaling cell death from the endoplasmic reticulum stress response

Shore GC1, Papa FR, Oakes SA
Curr Opin Cell Biol. 2011 Apr; 23(2):143-9
http://dx.doi.org/10.1016%2Fj.ceb.2010.11.003

Inability to meet protein folding demands within the endoplasmic reticulum (ER) activates the unfolded protein response (UPR), a signaling pathway with both adaptive and apoptotic outputs. While some secretory cell types have a remarkable ability to increase protein folding capacity, their upper limits can be reached when pathological conditions overwhelm the fidelity and/or output of the secretory pathway.

The lumen of the ER is a unique cellular environment optimized to carry out the three primary tasks of this organelle:

  1. calcium storage and release,
  2. protein folding and secretion, and
  3. lipid biogenesis [1].

A range of cellular disturbances lead to accumulation of misfolded proteins in the ER, including

  • point mutations in secreted proteins that disrupt their proper folding,
  • sustained secretory demands on endocrine cells,
  • viral infection with ER overload of virus-encoding protein, and
  • loss of calcium homeostasis with detrimental effects on ER-resident calcium-dependent chaperones [24].

 

The tripartite UPR consists of three ER transmembrane proteins (IRE1α, PERK, ATF6) that

  • alert the cell to the presence of misfolded proteins in the ER and
  • attempt to restore homeostasis in this organelle through increasing ER biogenesis,
  1. decreasing the influx of new proteins into the ER,
  2. promoting the transport of damaged proteins from the ER to the cytosol for degradation, and
  3. upregulating protein folding chaperones [5].

The adaptive responses of the UPR can markedly expand the protein folding capacity of the cell and restore ER homeostasis [6]. However, if these adaptive outputs fail to compensate because ER stress is excessive or prolonged, the UPR induces cell death.

The cell death pathways collectively triggered by the UPR include both caspase-dependent apoptosis and caspase-independent necrosis. While many details remain unknown, we are beginning to understand how cells determine when ER stress is beyond repair and communicate this information to the cell death machinery. For the purposes of this review, we focus on the apoptotic outputs triggered by the UPR under irremediable ER stress.

Connections from the UPR to the Mitochondrial Apoptotic Pathway

Connections from the UPR to the Mitochondrial Apoptotic Pathway

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078187/bin/nihms256554f1.jpg

Figure 1 Connections from the UPR to the Mitochondrial Apoptotic Pathway

Under excessive ER stress, the ER transmembrane sensors IRE1α and PERK send signals through the BCL-2 family of proteins to activate the mitochondrial apoptotic pathway. In response to unfolded proteins, IRE1α oligomerizes and induces endonucleolytic decay of hundreds of ER-localized mRNAs, depleting ER protein folding components and leading to worsening ER stress. Phosphorylated IRE1α also recruits TNF receptor-associated factor 2 (TRAF2) and activates apoptosis signaling kinase 1 (ASK1) and its downstream target c-Jun NH2-terminal kinase (JNK). JNK then activates pro-apoptotic BIM and inhibits anti-apoptotic BCL-2. These conditions result in dimerization of PERK and activation of its kinase domain to phosphorylate eukaryotic translation initiation factor 2α (eIF2α), which causes selective translation of activating transcription factor-4 (ATF4). ATF4 upregulates expression of the CHOP/GADD153 transcription factor, which inhibits the gene encoding anti-apoptotic BCL-2 while inducing expression of pro-apoptotic BIM. ER stress also promotes p53-dependent transcriptional upregulation of Noxa and Puma, two additional pro-apoptotic BH3-only proteins. Furthermore, high levels of UPR signaling induce initiator caspase-2 to proteolytically cleave and activate pro-apoptotic BID upstream of the mitochondrion. In addition to antagonizing pro-survival BCL-2 members, cleaved BID, BIM and PUMA activate Bax and/or Bak. Hence, in response to excessive UPR signaling, the balance of BCL-2 family proteins shifts in the direction of apoptosis and leads to the oligomerization of BAX and BAK, two multi-domain pro-apoptotic BCL-2 family proteins that then drive the permeabilization of the outer mitochondrial membrane, apoptosome formation and activation of executioner caspases such as Caspase-3. Figure adapted with permission from the Journal of Cell Science [58].

The proximal unfolded protein response sensors

UPR signaling is initiated by three ER transmembrane proteins:

  1. IRE1α,
  2. PERK, and

The most ancient ER stress sensor, IRE1α, contains

  1. an ER lumenal domain,
  2. a cytosolic kinase domain and
  3. a cytosolic RNase domain [9,10].

In the presence of unfolded proteins, IRE1α’s ER lumenal domains homo-oligomerize, leading

  • first to kinase trans-autophosphorylation and
  • subsequent RNase activation.

Dissociation of the ER chaperone BiP from IRE1α’s lumenal domain in order to engage unfolded proteins may facilitate IRE1α oligomerization [11]; alternatively, the lumenal domain may bind unfolded proteins directly [12]. PERK’s ER lumenal domain is thought to be activated similarly [13,14]. The ATF6 activation mechanism is less clear. Under ER stress, ATF6 translocates to the Golgi and is cleaved by Site-1 and Site-2 proteases to generate the ATF6(N) transcription factor [15].

All three UPR sensors have outputs that attempt to tilt protein folding demand and capacity back into homeostasis. PERK contains a cytosolic kinase that phosphorylates eukaryotic translation initiation factor 2α (eIF2α), which impedes translation initiation to reduce the protein load on the ER [16]. IRE1α splices XBP1mRNA, to produce the homeostatic transcription factor XBP1s [17,18]. Together with ATF6(N), XBP1s increases transcription of genes that augment ER size and function[19]. When eIF2α is phosphorylated, the translation of the activating transcription factor-4 (ATF4) is actively promoted and leads to the transcription of many pro-survival genes [20]. Together, these transcriptional events act as homeostatic feedback loops to reduce ER stress. If successful in reducing the amount of unfolded proteins, the UPR attenuates.

However, when these adaptive responses prove insufficient, the UPR switches into an alternate mode that promotes apoptosis. Under irremediable ER stress, PERK signaling can induce ATF-4-dependent upregulation of the CHOP/GADD153 transcription factor, which inhibits expression of the gene encoding anti-apoptotic BCL-2 while upregulating the expression of oxidase ERO1α to induce damaging ER oxidation [21,22]. Sustained IRE1α oligomerization leads to activation of apoptosis signal-regulating kinase 1 (ASK1) and its downstream target c-Jun NH2-terminal kinase (JNK) [23,24]. Phosphorylation by JNK has been reported to both activate pro-apoptotic BIM and inhibit anti-apoptotic BCL-2 (see below). Small molecule modulators of ASK1 have been shown to protect cultured cells against ER stress-induced apoptosis, emphasizing the importance of the IRE1α-ASK1-JNK output as a death signal in this pathway [25]. In response to sustained oligomerization, the IRE1α RNase also causes endonucleolytic decay of hundreds of ER-localized mRNAs [26]. By depleting ER cargo and protein folding components, IRE1α-mediated mRNA decay may worsen ER stress, and could be a key aspect of IRE1α’s pro-apoptotic program [27]. Recently, inhibitors of IRE1α’s kinase pocket have been shown to conformationally activate its adjacent RNase domain in a manner that enforces homeostatic XBP1s without causing destructive mRNA decay [27], a potentially exciting strategy for preventing ER stress-induced cell loss.

The BCL-2 family and the Mitochondrial Apoptotic Pathway

A wealth of genetic and biochemical data argues that the intrinsic (mitochondrial) apoptotic pathway is the major cell death pathway induced by the UPR, at least in most cell types. This apoptotic pathway is set in motion when several toxic proteins (e.g., cytochrome c, Smac/Diablo) are released from mitochondria into the cytosol where they lead to activation of downstream effector caspases (e.g., Caspase-3) [30]. The BCL-2 family, a large class of both pro- and anti- survival proteins, tightly regulates the intrinsic apoptotic pathway by controlling the integrity of the outer mitochondrial membrane [31]. This pathway is set in motion when cell injury leads to the transcriptional and/or post-translational activation of one or more BH3-only proteins that share sequence similarity in a short alpha helix (~9–12 a.a.) known as the Bcl-2 homology 3 (BH3) domain [32]. Once activated, BH3-only proteins lead to loss of mitochondrial integrity by disabling mitochondrial protecting proteins that drive the permeabilization of the outer mitochondrial membrane.

ER stress has been reported to activate at least four distinct BH3-only proteins (BID, BIM, NOXA, PUMA) that then signal the mitochondrial apoptotic machinery (i.e., BAX/BAK) [3335]. Each of these BH3-only proteins is activated by ER stress in a unique way. Cells individually deficient in any of these BH3-only proteins are modestly protected against ER stress-inducing agents, but not nearly as resistant as cells null for their common downstream targets BAX and BAK [36]—the essential gatekeepers to the mitochondrial apoptotic pathway. Moreover, cells genetically deficient in both Bim andPuma are more protected against ER stress-induced apoptosis than Bim or Puma single knockout cells [37].

The ER stress sensor that signals these BH3-only proteins is known in a few cases (i.e., BIM is downstream of PERK); however, we do not yet understand how the UPR communicates with most of the BH3-only proteins. Moreover, it is not known if all of the above BH3-only proteins are simultaneously set in motion by all forms of ER stress or if a subset is activated under specific pathological stimuli that injure this organelle. Understanding the molecular details of how ER damage is communicated to the mitochondrial apoptotic machinery is critical if we want to target disease specific apoptotic signals sent from the ER.

Initiator and Executor Caspases

Caspases, or cysteine-dependent aspartate-directed proteases, play essential roles in both initiating apoptotic signaling (initiator caspases- 2, 4, 8, 12) and executing the final stages of cell demise (executioner caspases- 3, 7, 9) [38]. It is not surprising that the executioner caspases (casp-3,7,9) are critical for cell death resulting from damage to this organelle. Caspase 12 was the first caspase reported to localize to the ER downstream of BAX/BAK-dependent mitochondrial permeabilization becomes activated by UPR signaling in murine cells [39],but humans fail to express a functional Caspase 12 [41. Genetic knockdown or pharmacological inhibition of caspase-2 confers resistance to ER stress-induced apoptosis [42]. How the UPR activates caspase-2 and whether other initiator caspasesare also involved remains to be determined.

Calcium and Cell Death

Although an extreme depletion of ER luminal Ca2+ concentrations is a well-documented initiator of the UPR and ER stress-induced apoptosis or necrosis, it represents a relatively non-physiological stimulus. Ca2+ signaling from the ER is likely coupled to most pathways leading to apoptosis. UPR-induced activation of ERO1-α via CHOP in macrophages results in stimulation of inositol 1,4,5-triphosphate receptor (IP3R) [43]. All three sub-groups of the Bcl-2 family at the ER regulate IP3R activity. A significant fraction of IP3R is a constituent of highly specialized tethers that physically attach ER cisternae to mitochondria (mitochondrial-associated membrane) and regulate local Ca2+ dynamics at the ER-mitochondrion interface [4546]. This results in propagation of privileged IP3R-mediated Ca2+ oscillations into mitochondria. In an extreme scenario, massive transmission of Ca2+ into mitochondria results in Ca2+ overload and cell death by caspase-dependent and –independent means [46,47]. More refined transmission regulated by the Bcl-2 axis at the ER can influence cristae junctions and the availability of cytochrome c for its release across the outer mitochondrial membrane [48]. Finally, such regulated Ca2+transmission to mitochondria is a key determinant of mitochondrial bioenergetics [49].

ER Stress-Induced Cell Loss and Disease

Mounting evidence suggests that ER stress-induced apoptosis contributes to a range of human diseases of cell loss, including diabetes, neurodegeneration, stroke, and heart disease, to name a few (reviewed in REF [50]). The cause of ER stress in these distinct diseases varies depending on the cell type affected and the intracellular and/or extracellular conditions that disrupt proteostasis. Both mutant SOD1 and mutant huntingtin proteins aggregate, exhaust proteasome activity, and result in secondary accumulations of misfolded proteins in the ER [5152].

In the case of IRE1α, it may be possible to use kinase inhibitors to activate its cytoprotective signaling and shut down its apoptotic outputs [27]. Whether similar strategies will work for PERK and/or ATF6 remains to be seen. Alternatively, blocking the specific apoptotic signals that emerge from the UPR is perhaps a more straightforward strategy to prevent ER stress-induced cell loss. To this end, small molecular inhibitors of ASK and JNK are currently being tested in a variety preclinical models of ER stress [5253,5657]. This is just the beginning, and much work needs to be done to validate the best drugs targets in the ER stress pathway.

Conclusions

The UPR is a highly complex signaling pathway activated by ER stress that sends out both adaptive and apoptotic signals. All three transmembrane ER stress sensors (IRE1α, PERK, AFT6) have outputs that initially decrease the load and increase capacity of the ER secretory pathway in an effort to restore ER homeostasis. However, under extreme ER stress, continuous engagement of IRE1α and PERK results in events that simultaneously exacerbate protein misfolding and signal death, the latter involving caspase-dependent apoptosis and caspase-independent necrosis. Advances in our molecular understanding of how these stress sensors switch from life to death signaling will hopefully lead to new strategies to prevent diseases caused by ER stress-induced cell loss.

7.6.5 An Enzyme that Regulates Ether Lipid Signaling Pathways in Cancer Annotated by Multidimensional Profiling

Chiang KP, Niessen S, Saghatelian A, Cravatt BF.
Chem Biol. 2006 Oct; 13(10):1041-50.
http://dx.doi.org/10.1016/j.chembiol.2006.08.008

Hundreds, if not thousands, of uncharacterized enzymes currently populate the human proteome. Assembly of these proteins into the metabolic and signaling pathways that govern cell physiology and pathology constitutes a grand experimental challenge. Here, we address this problem by using a multidimensional profiling strategy that combines activity-based proteomics and metabolomics. This approach determined that KIAA1363, an uncharacterized enzyme highly elevated in aggressive cancer cells, serves as a central node in an ether lipid signaling network that bridges platelet-activating factor and lysophosphatidic acid. Biochemical studies confirmed that KIAA1363 regulates this pathway by hydrolyzing the metabolic intermediate 2-acetyl monoalkylglycerol. Inactivation of KIAA1363 disrupted ether lipid metabolism in cancer cells and impaired cell migration and tumor growth in vivo. The integrated molecular profiling method described herein should facilitate the functional annotation of metabolic enzymes in any living system.

Elucidation of the metabolic and signaling networks that regulate health and disease stands as a principal goal of postgenomic research. The remarkable complexity of these molecular pathways has inspired the advancement of “systems biology” methods for their characterization [1]. Toward this end, global profiling technologies, such as DNA microarrays 2 and 3 and mass spectrometry (MS)-based proteomics 4 and 5, have succeeded in generating gene and protein signatures that depict key features of many human diseases. However, extricating from these associative relationships the roles that specific biomolecules play in cell physiology and pathology remains problematic, especially for proteins of unknown biochemical or cellular function.

The functions of certain proteins, such as adaptor or scaffolding proteins, can be gleaned from large-scale protein-interaction maps generated by technologies like yeast two-hybrid 6 and 7, protein microarrays [8], and MS analysis of immunoprecipitated protein complexes 9 and 10. In contrast, enzymes contribute to biological processes principally through catalysis. Thus, elucidation of the activities of the many thousands of enzymes encoded by eukaryotic and prokaryotic genomes requires knowledge of their endogenous substrates and products. The functional annotation of enzymes in prokaryotic systems has been facilitated by the clever analysis of gene clusters or operons 11 and 12, which correspond to sets of genes adjacently located in the genome that encode for enzymes participating in the same metabolic cascade. The assembly of eukaryotic enzymes into metabolic pathways is more problematic, however, as their corresponding genes are not, in general, physically organized into operons, but rather are scattered randomly throughout the genome.

We hypothesized that the determination of endogenous catalytic activities for uncharacterized enzymes could be accomplished directly in living systems by the integrated application of global profiling technologies that survey both the enzymatic proteome and its primary biochemical output (i.e., the metabolome). Here, we have tested this premise by utilizing multidimensional profiling to characterize an integral membrane enzyme of unknown function that is highly elevated in human cancer.

Development of a Selective Inhibitor for the Uncharacterized Enzyme KIAA1363

Previous studies using the chemical proteomic technology activity-based protein profiling (ABPP) 15, 16 and 17 have identified enzyme activity signatures that distinguish human cancer cells based on their biological properties, including tumor of origin and state of invasiveness [18]. A primary component of these signatures was the protein KIAA1363, an uncharacterized integral membrane hydrolase found to be upregulated in aggressive cancer cells from multiple tissues of origin. To investigate the role that KIAA1363 plays in cancer cell metabolism and signaling, a selective inhibitor of this enzyme was generated by competitive ABPP 20 and 21.

Previous competitive ABPP screens that target the serine hydrolase superfamily identified a set of trifluoromethyl ketone (TFMK) inhibitors that showed activity in mouse brain extracts [20]. These TFMK inhibitors showed only limited activity in living human cells (data not shown). We postulated that the activity of KIAA1363 inhibitors could be enhanced by replacing the TFMK group with a carbamate, which inactivates serine hydrolases via a covalent mechanism (Figure S1; see the Supplemental Data available with this article online). Carbamate AS115 (Figure 1A) was synthesized and tested for its effects on the invasive ovarian cancer cell line SKOV-3 by competitive ABPP (Figure 1B). AS115 was found to potently and selectively inactivate KIAA1363, displaying an IC50 value of 150 nM, while other serine hydrolase activities were not affected by this agent (IC50 values > 10 μM) (Figures 1B and 1C). AS115 also selectively inhibited KIAA1363 in other aggressive cancer cell lines that possess high levels of this enzyme, including the melanoma lines C8161 and MUM-2B (Figure S2B).

Figure 1. Characterization of AS115, a Selective Inhibitor of the Cancer-Related Enzyme KIAA1363

Profiling the Metabolic Effects of KIAA1363 Inactivation in Cancer Cells

We next compared the global metabolite profiles of SKOV-3 cells treated with AS115 to identify endogenous small molecules regulated by KIAA1363, using a recently described, untargeted liquid chromatography-mass spectrometry (LC-MS) platform for comparative metabolomics [22]. AS115 (10 μM, 4 hr) was found to cause a dramatic reduction in the levels of a specific set of lipophilic metabolites (m/z 317, 343, and 345) in SKOV-3 cells ( Figure 2A). These metabolites did not correspond to any of the typical lipid species found in cells, none of which were significantly altered by AS115 treatment ( Table S1). High-resolution MS of the m/z 317 metabolite provided a molecular formula of C19H40O3 ( Figure 2B), which suggests that this compound might represent a monoalkylglycerol ether bearing a C16:0 alkyl chain (C16:0 MAGE).  This structure assignment was corroborated by tandem MS and LC analysis, in which the endogenous m/z 317 product and synthetic C16:0 MAGE displayed equivalent fragmentation and migration patterns, respectively ( Figure S3). By extension, the m/z 343 and 345 metabolites were interpreted to represent the C18:1 and C18:0 MAGEs, respectively. A control carbamate inhibitor, URB597, which targets other hydrolytic enzymes [23], but not KIAA1363, did not affect MAGE levels in cancer cells ( Figure S4).

Pharmacological Inhibition of KIAA1363 Reduces Monoalkylglycerol Ether, MAGE, Levels in Human Cancer Cells

Pharmacological Inhibition of KIAA1363 Reduces Monoalkylglycerol Ether, MAGE, Levels in Human Cancer Cells

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Figure 2. Pharmacological Inhibition of KIAA1363 Reduces Monoalkylglycerol Ether, MAGE, Levels in Human Cancer Cells

(A) Global metabolite profiling of AS115-treated SKOV-3 cells (10 μM AS115, 4 hr) with untargeted LC-MS methods [22]revealed a specific reduction in a set of structurally related metabolites with m/z values of 317, 343, and 345 (p < 0.001 for AS115- versus DMSO-treated SKOV-3 cells). Results represent the average fold change for three independent experiments. See Table S1for a more complete list of metabolite levels.

(B) High-resolution MS analysis of the sodium adduct of the purified m/z 317 metabolite provided a molecular formula of C19H40O3, which, in combination with tandem MS and LC analysis ( Figure S3), led to the determination of the structure of this small molecule as C16:0 monoalkylglycerol ether (C16:0 MAGE).

Biochemical Characterization of KIAA1363 as a 2-Acetyl MAGE Hydrolase

The correlation between KIAA1363 inactivation and reduced MAGE levels suggests that these lipids are products of a KIAA1363-catalyzed reaction. A primary route for the biosynthesis of MAGEs has been proposed to occur via the enzymatic hydrolysis of their 2-acetyl precursors 24 and 25. This 2-acetyl MAGE hydrolysis activity was first detected in cancer cell extracts over a decade ago [25], but, to date, it has eluded molecular characterization. To test whether KIAA1363 functions as a 2-acetyl MAGE hydrolase, this enzyme was transiently transfected into COS7 cells. KIAA1363-transfected cells possessed significantly higher 2-acetyl MAGE hydrolase activity compared to mock-transfected cells, and this elevated activity was blocked by treatment with AS115 (Figure 3A). In contrast, KIAA1363- and mock-transfected cells showed no differences in their respective hydrolytic activity for 2-oleoyl MAGE, monoacylglycerols, or phospholipids (e.g., platelet-activating factor [PAF], phosphatidylcholine) (Figure S5A). These data indicate that KIAA1363 selectively catalyzes the hydrolysis of 2-acetyl MAGEs to MAGEs.

KIAA1363 Regulates an Ether Lipid Signaling Network that Bridges Platelet-Activating Factor and the Lysophospholipids

Examination of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [26] suggests that the KIAA1363-MAGE pathway might serve as a unique metabolic node linking the PAF [27] and lysophospholipid [28] signaling systems in cancer cells (Figure 4A). Consistent with a direct pathway leading from MAGEs to these lysophospholipids, addition of 13C-MAGE to SKOV-3 cells resulted in the formation of 13C-labeled alkyl-LPC and alkyl-LPA (Figure 4C).
Conversely, the levels of 2-acetyl MAGE in SKOV-3 cells, as judged by metabolic labeling experiments, were significantly stabilized by treatment with AS115, which, in turn, led to an accumulation of PAF (Figure 4D).  A comparison of the metabolite profiles of SKOV-3 and OVCAR-3 cells revealed significantly higher levels of MAGE, alkyl-LPC, and alkyl-LPA in the former line (Figure 4E). These data indicate that the lysophospholipid branch of the MAGE network is elevated in aggressive cancer cells, and that this metabolic shift is regulated by KIAA1363.

Figure 4. KIAA1363 Serves as a Key Enzymatic Node in a Metabolic Network that Connects the PAF and Lysophospholipid Families of Signaling Lipids

Stable Knockdown of KIAA1363 Impairs Tumor Growth In Vivo

Figure 6. KIAA1363 Contributes to Ovarian Tumor Growth and Cancer Cell Migration

The decrease in tumorigenic potential of shKIAA1363 cells was not associated with a change in proliferation potential in vitro (Figure S8). shKIAA1363 cells were, however, impaired in their in vitro migration capacity compared to control cells (Figure 6B). Neither MAGE nor alkyl-LPC impacted cancer cell migration at concentrations up to 1 μM (Figure 6B). In contrast, alkyl-LPA (10 nM) completely rescued the reduced migratory activity of shKIAA1363 cells. Collectively, these results indicate that KIAA1363 contributes to the pathogenic properties of cancer cells in vitro and in vivo, possibly through regulating the levels of the bioactive lipid LPA.

We have determined by integrated enzyme and small-molecule profiling that KIAA1363, a protein of previously unknown function, is a 2-acetyl MAGE hydrolase that serves as a key regulator of a lipid signaling network that contributes to cancer pathogenesis. Although we cannot yet conclude which of the specific metabolites regulated by KIAA1363 supports tumor growth in vivo, the rescue of the reduced migratory phenotype of shKIAA1363 cancer cells by LPA is consistent with previous reports showing that this lipid signals through a family of G protein-coupled receptors to promote cancer cell migration and invasion 2829 and 30. LPA is also an established biomarker in ovarian cancer, and the levels of this metabolite are elevated nearly 10-fold in ascites fluid and plasma of patients with ovarian cancer [31]. Our results suggest that additional components in the KIAA1363-ether lipid network, including MAGE, alkyl LPC, and KIAA1363 itself, might also merit consideration as potential diagnostic markers for ovarian cancer. Consistent with this premise, our preliminary analyses have revealed highly elevated levels of KIAA1363 in primary human ovarian tumors compared to normal ovarian tissues (data not shown). The heightened expression of KIAA1363 in several other cancers, including breast 18 and 32, melanoma [18], and pancreatic cancer [33], indicates that alterations in the KIAA1363-ether lipid network may be a conserved feature of tumorigenesis. Considering further that reductions in KIAA1363 activity were found to impair tumor growth of both ovarian and breast cancer cells, it is possible that inhibitors of this enzyme may prove to be of value for the treatment of multiple types of cancer.

 

7.6.6 Peroxisomes – A Nexus for Lipid Metabolism and Cellular Signaling

Lodhi IJ, Semenkovich CF
Cell Metab. 2014 Mar 4; 19(3):380-92
http://dx.doi.org/10.1016%2Fj.cmet.2014.01.002

Peroxisomes are often dismissed as the cellular hoi polloi, relegated to cleaning up reactive oxygen chemical debris discarded by other organelles. However, their functions extend far beyond hydrogen peroxide metabolism. Peroxisomes are intimately associated with lipid droplets and mitochondria, and their ability to carry out fatty acid oxidation and lipid synthesis, especially the production of ether lipids, may be critical for generating cellular signals required for normal physiology. Here we review the biology of peroxisomes and their potential relevance to human disorders including cancer, obesity-related diabetes, and degenerative neurologic disease.

Peroxisomes are multifunctional organelles present in virtually all eukaryotic cells. In addition to being ubiquitous, they are also highly plastic, responding rapidly to cellular or environmental cues by modifying their size, number, morphology, and function (Schrader et al., 2013). Early ultrastructural studies of kidney and liver cells revealed cytoplasmic particles enclosed by a single membrane containing granular matrix and a crystalline core (Rhodin, 1958). These particles were linked with the term “peroxisome” by Christian de Duve, who first identified the organelle in mammalian cells when enzymes such as oxidases and catalases involved in hydrogen peroxide metabolism co-sedimented in equilibrium density gradients (De Duve and Baudhuin, 1966). Based on these studies, it was originally thought that the primary function of these organelles was the metabolism of hydrogen peroxide. Novikoff and colleagues observed a large number of peroxisomes in tissues active in lipid metabolism such as liver, brain, intestinal mucosa, and adipose tissue (Novikoff and Novikoff, 1982;Novikoff et al., 1980). Peroxisomes in different tissues vary greatly in shape and size, ranging from 0.1-0.5 μM in diameter. In adipocytes, peroxisomes tend to be small in size and localized in the vicinity of lipid droplets. Notably, a striking increase in the number of peroxisomes was observed during differentiation of adipogenic cells in culture (Novikoff and Novikoff, 1982). These findings suggest that peroxisomes may be involved in lipid metabolism.

Lazarow and de Duve hypothesized that peroxisomes in animal cells were capable of carrying out fatty acid oxidation. This was confirmed when they showed that purified rat liver peroxisomes contained fatty acid oxidation activity that was robustly increased by treatment of animals with clofibrate (Lazarow and De Duve, 1976). In a series of experiments, Hajra and colleagues discovered that peroxisomes were also capable of lipid synthesis (Hajra and Das, 1996). Over the past three decades, multiple lines of evidence have solidified the concept that peroxisomes play fundamentally important roles in lipid metabolism. In addition to removal of reactive oxygen species, metabolic functions of peroxisomes in mammalian cells include β-oxidation of very long chain fatty acids, α-oxidation of branched chain fatty acids, and synthesis of ether-linked phospholipids as well as bile acids (Figure 1). β-oxidation also occurs in mitochondria, but peroxisomal β-oxidation involves distinctive substrates and complements mitochondrial function; the processes of α-oxidation and ether lipid synthesis are unique to peroxisomes and important for metabolic homeostasis.

Structure and functions of peroxisomes

Structure and functions of peroxisomes

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951609/bin/nihms-555068-f0001.jpg

Figure 1 Structure and functions of peroxisomes

The peroxisome is a single membrane-enclosed organelle that plays an important role in metabolism. The main metabolic functions of peroxisomes in mammalian cells include β-oxidation of very long chain fatty acids, α-oxidation of branched chain fatty acids, synthesis of bile acids and ether-linked phospholipids and removal of reactive oxygen species. Peroxisomes in many, but not all, cell types contain a dense crystalline core of oxidative enzymes.

Here we highlight the established role of peroxisomes in lipid metabolism and their emerging role in cellular signaling relevant to metabolism. We describe the origin of peroxisomes and factors involved in their assembly, division, and function. We address the interaction of peroxisomes with lipid droplets and implications of this interaction for lipid metabolism. We consider fatty acid oxidation and lipid synthesis in peroxisomes and their importance in brown and white adipose tissue (sites relevant to lipid oxidation and synthesis) and disease pathogenesis.

peroxisomal biogenesis and protein import

peroxisomal biogenesis and protein import

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951609/bin/nihms-555068-f0002.jpg

Potential pathways to peroxisomal biogenesis. Peroxisomes are generated autonomously through division of pre-existing organelles (top) or through a de novo process involving budding from the ER followed by import of matrix proteins (bottom). B. Peroxisomal membrane protein import. Peroxisomal membrane proteins (PMPs) are imported post-translationally to the peroxisomal membrane. Pex19 is a soluble chaperone that binds to PMPs and transports them to the peroxisomal membrane, where it docks with a complex containing Pex16 and Pex3. Following insertion of the PMP, Pex19 is recycled back to the cytosol.

Regardless of their origin, peroxisomes require a group of proteins called peroxins for their assembly, division, and inheritance. Over 30 peroxins, encoded by Pex genes, have been identified in yeast (Dimitrov et al., 2013). At least a dozen of these proteins are conserved in mammals, where they regulate various aspects of peroxisomal biogenesis, including factors that control assembly of the peroxisomal membrane, factors that interact with peroxisomal targeting sequences allowing proteins to be shuttled to peroxisomes, and factors that act as docking receptors for peroxisomal proteins.

At least three peroxins (Pex3, Pex16 and Pex19) appear to be critical for assembly of the peroxisomal membrane and import of peroxisomal membrane proteins (PMPs) (Figure 2B). Pex19 is a soluble chaperone and import receptor for newly synthesized PMPs (Jones et al., 2004). Pex3 buds from the ER in a pre-peroxisomal vesicle and functions as a docking receptor for Pex19 (Fang et al., 2004). Pex16 acts as a docking site on the peroxisomal membrane for recruitment of Pex3 (Matsuzaki and Fujiki, 2008). Peroxisomal matrix proteins are translated on free ribosomes in the cytoplasm prior to their import. These proteins have specific peroxisomal targeting sequences (PTS) located either at the carboxyl (PTS1) or amino (PTS2) terminus (Gould et al., 1987Swinkels et al., 1991).

 

7.6.7 A nexus for cellular homeostasis- the interplay between metabolic and signal transduction pathways

Ana P Gomes, John Blenis
Current Opinion in Biotechnology Aug 2015; 34:110–117
http://dx.doi.org/10.1016/j.copbio.2014.12.007

Highlights

  • Signaling networks sense intracellular and extracellular cues to maintain homeostasis.
  • PI3K/AKT and Ras/ERK signaling induces anabolic reprogramming.
  • mTORC1 is a master node of signaling integration that promotes anabolism.
  • AMPK and SIRT1 fine tune signaling networks in response to energetic status.

In multicellular organisms, individual cells have evolved to sense external and internal cues in order to maintain cellular homeostasis and survive under different environmental conditions. Cells efficiently adjust their metabolism to reflect the abundance of nutrients, energy and growth factors. The ability to rewire cellular metabolism between anabolic and catabolic processes is crucial for cells to thrive. Thus, cells have developed, through evolution, metabolic networks that are highly plastic and tightly regulated to meet the requirements necessary to maintain cellular homeostasis. The plasticity of these cellular systems is tightly regulated by complex signaling networks that integrate the intracellular and extracellular information. The coordination of signal transduction and metabolic pathways is essential in maintaining a healthy and rapidly responsive cellular state.

AMPK and SIRT1 fine tune signaling networks in response to energetic status

AMPK and SIRT1 fine tune signaling networks in response to energetic status

 

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AMPK and SIRT1 fine tune signaling networks in response to energetic status

 

http://ars.els-cdn.com/content/image/1-s2.0-S0958166914002225-gr1.sml

mTORC1 is a master node of signaling integration that promotes anabolism.

 

http://ars.els-cdn.com/content/image/1-s2.0-S0958166914002225-gr2.sml

Fine-tuning signaling networks

 PI3K/Akt signaling-induced anabolic reprogramming

Growth factors and other ligands activate PI3K signaling upon binding and consequent activation of their cell surface receptors, such as receptor tyrosine kinases (RTKs) and G protein-coupled
receptors (GPCRs). This leads to the phosphorylation of membrane phosphatidylinositiol lipids and the recruitment and activation of several protein kinases, which perpetuate the extracellular
signals to modulate intracellular processes [3,4]. One of the most crucial signal propagators regulated by PI3K signaling is protein kinase B/Akt [3,4]. Indeed, Akt rewires metabolism in response
to environmental cues by three distinct means;
(i) by the direct phosphorylation and regulation of metabolic enzymes,
(ii) by activating/inactivating metabolism altering transcriptional factors, and
(iii) by modulating other kinases that themselves regulate metabolism [5].
Akt regulates glucose metabolism, inducing both glucose uptake and glycolytic flux by increasing the expression of the glucose transporter genes and regulating the activity of glycolytic enzymes,
respectively [6–8]. Moreover, the ability of Akt to induce glycolysis is also mediated by the regulation of Hexokinase (HK). HK performs the first step of glycolysis.

Figure 1 Anabolic rewiring induced by PI3K/Akt, Ras/ERK and mTORC1 signaling.
Extracellular signals activate two major signaling cascades controlled by the activation of PI3K and Ras. PI3K and Ras regulate Akt and ERK, which in turn induce changes in intermediate metabolism
to promote anabolic processes. In addition, they also induce the activation of  mTORC1, thus further supporting the rewiring of cellular metabolism towards anabolic processes. Through various mechanisms
Akt, ERK and mTORC1 stimulate mRNA translation, aerobic glycolysis, glutamine anaplerosis, lipid synthesis, the pentose phosphate and pyrimidine synthesis, thus producing the major components
necessary for cell growth and proliferation.

Figure 2. Regulation of intermediate metabolism by nutrient and energy sensors.
Nutrient and energy-responsive pathways fine-tune the output of signaling cascades, allowing for the correct balance between the availability of nutrients and the cellular capacity to use them effectively.
AMPK and SIRT1 respond to the energy status of the cells through sensing of AMP and NAD+ levels respectively. When energy is scarce, these sensors are activated inducing a rewiring of intermediate
metabolism to catabolic processes in order to produce energy and restore homeostasis. When nutrients (such as glucose and amino acids) and energy are available, AMPK, SIRT1, SIRT3 and SIRT6 are
repressed and mTORC1 is active, thus promoting a shift towards anabolic processes and energy production. These networks of signaling cascades, their interconnection and regulation allow the cells
to maintain energetic balance and allow for the physiological adaptation to the ever-changing environment.

 

7.6.8 Mechanisms-of-intercellular-signaling

7.6.8.1 Activation and signaling of the p38 MAP kinase pathway

Tyler Zarubin1 and Jiahuai Han
Cell Research (2005) 15, 11–18
http://dx.doi.org:/10.1038/sj.cr.7290257

The family members of the mitogen-activated protein (MAP) kinases mediate a wide variety of cellular behaviors in response to extracellular stimuli. One of the four main sub-groups, the p38 group of MAP kinases, serve as a nexus for signal transduction and play a vital role in numerous biological processes. In this review, we highlight the known characteristics and components of the p38 pathway along with the mechanism and consequences of p38 activation. We focus on the role of p38 as a signal transduction mediator and examine the evidence linking p38 to inflammation, cell cycle, cell death, development, cell differentiation, senescence and tumorigenesis in specific cell types. Upstream and downstream components of p38 are described and questions remaining to be answered are posed. Finally, we propose several directions for future research on p38.

Cellular behavior in response to extracellular stimuli is mediated through intracellular signaling pathways such as the mitogen-activated protein (MAP) kinase pathways 1. MAP kinases are members of discrete signaling cascades and serve as focal points in response to a variety of extracellular stimuli. Four distinct subgroups within the MAP kinase family have been described:

  • extracellular signal-regulated kinases (ERKs),
  • c-jun N-terminal or stress-activated protein kinases (JNK/SAPK),
  • ERK/big MAP kinase 1 (BMK1), and
  • the p38 group of protein kinases.

The focus of this review will be to highlight the characteristics of

  • the p38 kinases,
  • components of this kinase cascade,
  • activation of this pathway, and
  • the biological consequences of its activation.

p38 (p38) was first isolated as a 38-kDa protein rapidly tyrosine phosphorylated in response to LPS stimulation 23. p38 cDNA was also cloned as a molecule that binds puridinyl imidazole derivatives which are known to inhibit biosynthesis of inflammatory cytokines such as interleukin-1 (IL-1) and tumor-necrosis factor (TNF) in LPS stimulated monocytes 4. To date, four splice variants of the p38 family have been identified: p38, p38 5, p38 (ERK6, SAPK3) 67, and p38(SAPK4) 89. Of these, p38 and p38 are ubiquitously expressed while p38 and p38 are differentially expressed depending on tissue type. All p38 kinases can be categorized by a Thr-Gly-Tyr (TGY) dual phosphorylation motif 10. Sequence comparisons have revealed that each p38 isoform shares 60% identity within the p38 group but only 40–45% to the other three MAP kinase family members.

Mammalian p38s activation has been shown to occur in response to extracellular stimuli such as UV light, heat, osmotic shock, inflammatory cytokines (TNF- & IL-1), and growth factors (CSF-1) 13151617,18192021. This plethora of activators conveys the complexity of the p38 pathway and this matter is further complicated by the observation that activation of p38 is not only dependent on stimulus, but on cell type as well. For example, insulin can stimulate p38 in 3T3-L1 adipocytes 22, but downregulates p38 activity in chick forebrain neuron cells 23. The activation of p38 isoforms can be specifically controlled through different regulators and coactivated by various combinations of upstream regulators 2426.

Like all MAP kinases, p38 kinases are activated by dual kinases termed the MAP kinase kinases (MKKs). However, despite conserved dual phosphorylation sites among p38 isoforms, selective activation by distinct MKKs has been observed. There are two main MAPKKs that are known to activate p38, MKK3 and MKK6. It is proposed that upstream kinases can differentially regulate p38 isoforms as evidenced by the inability of MKK3 to effectively activate p38 while MKK6 is a potent activator despite 80% homology between these two MKKs 27. Also, it has been shown that MKK4, an upstream kinase of JNK, can aid in the activation of p38 and p38 in specific cell types 8. This data suggests then, that activation of p38 isoforms can be specifically controlled through different regulators and coactivated by various combinations of upstream regulators. Furthermore, substrate selectivity may be a reason why each MKK has a distinct function. In addition to the activation by upstream kinases, there is a MAPKK-independent mechanism of p38 MAPK activation involving TAB1 (transforming growth factor–activated protein kinase 1 (TAK1)-binding protein) 28. The activation of p38 in this pathway is achieved by the autophosphorylation of p38 after interaction with TAB1.

The activation of p38 in response to the wide range of extracellular stimuli can be seen in part by the diverse range of MKK kinases (MAP3K) that participate in p38 activation. These include TAK1 33, ASK1/MAPKKK5 34, DLK/MUK/ZPK 3536, and MEKK4 353738. Overexpression of these MAP3Ks leads to activation of both p38 and JNK pathways which is possibly one reason why these two pathways are often co-activated. Also contributing to p38 activation upstream of MAPK kinases are low molecular weight GTP-binding proteins in the Rho family such as Rac1 and Cdc42 4041. Rac1 can bind to MEKK1 or MLK1 while Cdc42 can only bind to MLK1 and both result in activation of p38 via MAP3Ks 3542.

Dephosphorylation, would seem to play a major role in the downregulation of MAP kinase activity. Many dual-specificity phosphatases have been identified that act upon various members of the MAP kinase pathway and are grouped as the MAP kinase phosphatase (MKP) family 45. Several members can efficiently dephosphorylate p38 and p38 4647; however, p38 and p38 are resistant to all known MKP family members.

The first p38 substrate identified was the MAP kinase-activated protein kinase 2 (MAPKAPK2 or MK2) 11552. This substrate, along with its closely related family member MK3 (3pk), were both shown to activate various substrates including small heat shock protein 27 (HSP27) 53, lymphocyte-specific protein 1 (LSP1) 54, cAMP response element-binding protein (CREB) 55, transcription factor ATF1 55, SRF 56, and tyrosine hydroxylase 57. p38 regulated/activated kinase (PRAK) is a p38 and/or p38activated kinase that shares 20-30% sequence identity to MK2 and is thought to regulate heat shock protein 27 (HSP27) 61. Mitogen- and stress-activated protein kinase-1 (MSK1) can be directly activated by p38 and ERK, and may mediate activation of CREB 626364.

Another group of substrates that are activated by p38 comprise transcription factors. Many transcription factors encompassing a broad range of action have been shown to be phosphorylated and subsequently activated by p38. Examples include activating transcription factor 1, 2 & 6 (ATF-1/2/6), SRF accessory protein (Sap1), CHOP (growth arrest and DNA damage inducible gene 153, or GADD153), p53, C/EBP, myocyte enhance factor 2C (MEF2C), MEF2A, MITF1, DDIT3, ELK1, NFAT, and high mobility group-box protein 1 (HBP1) 175566676869707172,73747576. An important cis-element, AP-1 appears to be influenced by p38 through several different mechanisms.  Taken together, all the data suggest that the p38 pathway has a wide variety of functions.

Abundant evidence for p38 involvement in apoptosis exists to date and is based on concomitant activation of p38 and apoptosis induced by a variety of agents such as NGF withdrawal and Fas ligation 959697. Cysteine proteases (caspases) are central to the apoptotic pathway and are expressed as inactive zymogens 98,99. Caspase inhibitors then can block p38 activation through Fas cross-linking, suggesting p38 functions downstream of caspase activation 97100. However, overexpression of dominant active MKK6b can also induce caspase activity and cell death thus implying that p38 may function both upstream and downstream of caspases in apoptosis 101102. It must be mentioned that the role of p38 in apoptosis is cell type and stimulus dependent. While p38 signaling has been shown to promote cell death in some cell lines, in different cell lines p38 has been shown to enhance survival, cell growth, and differentiation.

p38 now seems to have a role in tumorigenesis and sensescence. There have been reports that activation of MKK6 and MKK3 led to a senescent phenotype dependent upon p38 MAPK activity. Also, p38 MAPK activity was shown responsible for senescence in response to telomere shortening, H2O2 exposure, and chronic RAS oncogene signaling 117118119. A common feature of tumor cells is a loss of senescence and p38 may be linked to tumorigenesis in certain cells. It has been reported that p38 activation may be reduced in tumors and that loss of components of the p38 pathway such as MKK3 and MKK6 resulted in increased proliferation and likelihood of tumorigenic conversion regardless of the cell line or the tumor induction agent used in these studies 29.

Although all research done on the p38 pathway cannot be reviewed here, certain conclusions can still be made regarding the operation of p38 as a signal transduction mediator. The p38 family (,,,) is activated by both stress and mitogenic stimuli in a cell dependent manner and certain isoforms can either directly or indirectly target proteins to control pre/post transcription. p38 MAPKs also have the ability to activate other kinases and consequently regulate numerous cellular responses. Because p38 signaling has been implicated in cellular responses including inflammation, cell cycle, cell death, development, cell differentiation, senescence, and tumorigenesis, emphasis must be placed on p38 function with respect to specific cell types.

Regulation of the p38 pathway is not an isolated cascade and many different upstream signals can lead to p38 activation. These signals may be p38 specific (MKK3/6), general MAPKKs (MKK4), or MAPKK independent signals (TAB1). Downstream signaling pathways of p38 are quite divergent and each component may interact with other cellular components, both upstream and downstream, to coordinate cellular processes such as feedback mechanisms. Furthermore, in vivo p38 is not an isolated event and exists in the presence of other MAP kinases and a plethora of other signaling pathways. The subcellular location of p38 activation may also play a critical role determining the resulting effect and may add yet another order of complexity to the investigation of p38 function.

 

7.6.8.2 Mitogen-Activated Protein Kinase Pathways Mediated by ERK, JNK, and p38 Protein Kinases

Gary L. Johnson and Razvan Lapadat
Science 6 Dec 2002; 298: 1911-1912.

Multicellular organisms have three well-characterized subfamilies of mitogen activated protein kinases (MAPKs) that control a vast array of physiological processes. These enzymes are regulated by a characteristic phosphorelay system in which a series of three protein kinases phosphorylate and activate one another. The extracellular signal–regulated kinases (ERKs) function in the control of cell division, and inhibitors of these enzymes are being explored as anticancer agents. The c-Jun amino-terminal kinases ( JNKs) are critical regulators of transcription, and JNK inhibitors may be effective in control of rheumatoid arthritis. The p38 MAPKs are activated by inflammatory cytokines and environmental stresses.

Protein kinases are enzymes that covalently attach phosphate to the side chain of either serine, threonine, or tyrosine of specific proteins inside cells. Such phosphorylation of proteins can control their enzymatic activity, their interaction with other proteins and molecules, their location in the cell, and their propensity for degradation by proteases. Mitogen-activated protein kinases (MAPKs) compose a family of protein kinases whose function and regulation have been conserved during evolution from unicellular organisms such as brewers’ yeast to complex organisms including humans (1). MAPKs phosphorylate specific serines and threonines of target protein substrates and regulate cellular activities ranging from gene expression, mitosis, movement, metabolism, and programmed death. Because of the many important cellular functions controlled by MAPKs, they have been studied extensively to define their roles in physiology and human disease. MAPK-catalyzed phosphorylation of substrate proteins functions as a switch to turn on or off the activity of the substrate protein.

MAPKs are part of a phosphorelay system composed of three sequentially activated kinases, and, like their substrates, MAPKs are regulated by phosphorylation (Fig. 1) (2). MKK-catalyzed phosphorylation activates the MAPK and increases its activity in catalyzing the phosphorylation of its own substrates. MAPK phosphatases reverse the phosphorylation and return the MAPK to an inactive state. MKKs are highly selective in phosphorylating specific MAPKs. MAPK kinase kinases (MKKKs) are the third component of the phosphorelay system. MKKKs phosphorylate and activate specific MKKs. MKKKs have distinct motifs in their sequences that selectively confer their activation in response to different stimuli.

Fig. 1. MAPK phosphorelay systems.

The modules shown are representative of pathway connections for the respective MAPK phosphorelay systems.There are multiple component MKKKs, MKKs, and MAPKs for each system.For example, there are three Raf proteins (c-Raf1, B-Raf, A-Raf), two MKKs (MKK1 and MKK2), and two ERKs (ERK1 and ERK2) that can compose MAPK phosphorelay systems responsive to growth factors.The ERK, JNK, and p39 pathways in the STKE Connections Map demonstrate the potential complexity of these systems.

ERKs 1 and 2 are both components of a three-kinase phosphorelay module that includes the MKKK c-Raf1, B-Raf, or A-Raf, which can be activated by the proto-oncogene Ras. Mutations that convert Ras to an activated oncogene are common oncogenic mutations in many human tumors. Oncogenic Ras persistently activates the ERK1 and ERK2 pathways, which contributes to the increased proliferative rate of tumor cells. For this reason, inhibitors of the ERK pathways are entering clinical trials as potential anticancer agents.

Regulation of the JNK pathway is extremely complex and is influenced by many MKKKs. As depicted in the STKE JNK Pathway Connections Map, there are 13 MKKKs that regulate the JNKs. This diversity of MKKKs allows a wide range of stimuli to activate this MAPK pathway. JNKs are important in controlling programmed cell death or apoptosis (9). The inhibition of JNKs enhances chemotherapy-induced inhibition of tumor cell growth, suggesting that JNKs may provide a molecular target for the treatment of cancer. The pharmaceutical industry is bringing JNK inhibitors into clinical trials.

Recently, a major paradigm shift for MAPK regulation was developed for p38. The p38 enzyme is activated by the protein TAB1 (12), but TAB1 is not a MKK. Rather, TAB1 appears to be an adaptor or scaffolding protein and has no known catalytic activity. This is the first demonstration that another mechanism exists for the regulation of MAPKs in addition to the MKKK-MKKMAPK regulatory module.

The importance of MAPKs in controlling cellular responses to the environment and in regulating gene expression, cell growth, and apoptosis has made them a priority for research related to many human diseases. The ERK, JNK, and p38 pathways are all molecular targets for drug development, and inhibitors of MAPKs will undoubtedly be one of the next group of drugs developed for the treatment of human disease (13).

7.6.9 Cathepsin B promotes colorectal tumorigenesis, cell invasion, and metastasis

B Bian, S Mongrain, S Cagnol, Marie-Josée Langlois, J Boulanger, et al.
Molec Carcinogen 25 Mar 2015; 54(5). http://dx.doi.org:/10.1002/mc.22312

Cathepsin B is a cysteine proteinase that primarily functions as an endopeptidase within endolysosomal compartments in normal cells. However, during tumoral expansion, the regulation of cathepsin B can be altered at multiple levels, thereby resulting in its overexpression and export outside of the cell. This may suggest a possible role of cathepsin B in alterations leading to cancer progression. The aim of this study was to determine the contribution of intracellular and extracellular cathepsin B in growth, tumorigenesis, and invasion of colorectal cancer (CRC) cells. Results show that mRNA and activated levels of cathepsin B were both increased in human adenomas and in CRCs of all stages. Treatment of CRC cells with the highly selective and non-permeant cathepsin B inhibitor Ca074 revealed that extracellular cathepsin B actively contributed to the invasiveness of human CRC cells while not essential for their growth in soft agar. Cathepsin B silencing by RNAi in human CRC cells inhibited their growth in soft agar, as well as their invasion capacity, tumoral expansion, and metastatic spread in immunodeficient mice. Higher levels of the cell cycle inhibitor p27Kip1 were observed in cathepsin B-deficient tumors as well as an increase in cyclin B1. Finally, cathepsin B colocalized with p27Kip1 within the lysosomes and efficiently degraded the inhibitor. In conclusion, the present data demonstrate that cathepsin B is a significant factor in colorectal tumor development, invasion, and metastatic spreading and may, therefore, represent a potential pharmacological target for colorectal tumor therapy

Colorectal cancer (CRC),a major malignancy worldwide and the second leading cause of cancer death in North America, develops through multiple steps. The ability of cancers to invade and metastasize depends on the action of proteases actively taking center stage in extracellular proteolysis [2]. Of all the proteases, the cysteine protease cathepsin B is of significant importance [3]. Cathepsin B primarily functions as an endopeptidase within endolysosomal compartments in normal cells. However, during malignant transformation cathepsin B can be upregulated [3, 4]. Cathepsin B in tumors can either be secreted, bound to the cell membrane or released by shedding vesicles [4]. Expression and redistribution of active cathepsin B to the basal plasma membrane occurs in late colon adenomas [5, 6] coincident with the activation of KRAS [1]. In line with these results, Cavallo-Medved et al. [7] have demonstrated that trafficking of cathepsin B to caveolae and its secretion are regulated by active KRAS in CRC cells in culture. Accordingly, secretion of cathepsin B, increased in the extracellular environment of CRC [8, 9], is suspected to play an essential role in disrupting extracellular matrix barriers, facilitating invasion and metastasis [10-12]. These data are consistent with the link between cathepsin B protein expression in colorectal carcinomas and shortened patient survival [6].

In a recent prospective cohort study of 558 men and women with colonic tumors [13] 82% of patients had tumors that expressed cathepsin B, irrespective of stage, while the remaining 18% had tumors that did not express cathepsin B. Other studies have suggested that cathepsin B expression or activity may actually peak during early stage cancer and subsequently decline with advanced disease [14, 15]. This points to a possible role of cathepsin B in both early and late alterations leading to colonic cancer.

This study used two strategies to specifically counteract the action of cathepsin B. The first involved the use of RNA interference (RNAi) to inhibit the expression of cathepsin B protein into CRC cells while the second approach employed the highly selective cathepsin inhibitor Ca074 to block extracellular cathepsin B activity. Results suggest that extracellular cathepsin B is involved in cell invasion whereas intracellular cathepsin B controls malignant properties of CRC cells. Further, biochemical analysis suggests that intracellular cathepsin B regulates tumorigenesis by degrading the p27Kip1 cell cycle inhibitor.

mRNA and Activated Levels of Cathepsin B Are Increased in Adenomas and in Colorectal Tumors of All Stages

Cathepsin B expression was analyzed at both the mRNA and protein levels in a series of human paired specimens at various tumor stages. As shown in Figure 1A, increased transcript levels of cathepsin B were observed in colorectal tumors, regardless of tumor stage, including in adenomas. Of note, increased cathepsin B expression was more prominent in tumors exhibiting APC mutations. By contrast, there did not appear to be a significant difference relative to KRAS mutations (Figure 1B). To establish whether these increased mRNA levels could be correlated with increased cathepsin B protein levels and more importantly with increased activity, expression of the active processed forms of the protease (25 and 30 kDa) was analyzed by Western blot. Both pro-cathepsin B and active cathepsin B were also increased in colorectal tumors compared to normal tissues (Figure 1C and D). These data hence suggest that increased transcription contributes to a greater expression of active cathepsin B in CRC.

Extracellular Cathepsin B Contributes to Invasiveness of Human CRC Cells but is Dispensable for Their Growth in Soft Agar

Cathepsin B protein levels were next examined in lysates obtained from various human CRC cell lines. As shown in Figure 2A, the proactive and catalytically active processed forms of cathepsin B were detected at various levels in CRC cell lines. Selected cathepsin B presence was also confirmed in conditioned culture medium of CRC cells, again at various levels (Figure 2A, lower panel). However, while the pro-form of cathepsin B was readily observed in conditioned culture medium of all CRC cells, the catalytically-active processed forms of cathepsin B were not detected in Western blot analyses. Additionally, using a fluorescence-based enzymatic assay, no cathepsin B enzyme activity was detected in conditioned medium. Since the pro-protease form might be activated under acidic pH conditions (peri- or extracellular) and by extracellular components of the extracellular matrix, the impact of extracellular inhibition of cathepsin B activation on CRC cell invasion was verified using Biocoat Matrigel chambers. HT-29, DLD1, and SW480 CRC cell lines secreting different levels of pro-cathepsin B (Figure 2A) were tested. Experiments were performed using the highly selective and non-permeant inhibitor Ca074 to reduce extracellular cathepsin B activity. At 10 μM, Ca074 produced a >99% inhibition of recombinant cathepsin B levels while barely reducing intracellular cathepsin B, that is, 5–8%, even upon 12 h exposure to the inhibitor (data not shown). Of note, treatment with 10 μM Ca074 significantly inhibited Matrigel invasion by approximately 45–60% in HT29, DLD1, and SW480 CRC cell lines (Figure 2B). By contrast, treatment with Ca074 had no significant effect on their capacity to form colonies in soft agarose (Figure 2C).

Cathepsin B Silencing in Human CRC Cells Inhibits Tumorigenicity and Metastasis in Immunodeficient Mice

Suppression of cathepsin B expression was found to significantly attenuate the metastatic potential of CRC cells in vivo in experimental metastasis assays. Indeed, immunodeficient mice injected with control CRC cells into the tail vein showed extensive lung metastasis within 28 d, whereas cells expressing shRNA against cathepsin B exhibited reduced lung colonization (Figure 4A). Cathepsin B silencing also altered the capacity of CRC cells to form tumors in mice as assessed by subcutaneous xenograft assays. HT29 cells induced palpable tumors with a short latency period of 9 d after their injection while downregulation of cathepsin B expression in these cells severely impaired their capacity to grow as tumors (Figure 4B).

Cathepsin B Silencing in Human CRC Cells Inhibits Growth in Soft Agar and Invasion Capacity

Recombinant lentiviruses encoding anti-cathepsin B short hairpin RNA (shRNA) were developed in order to stably suppress cathepsin B expression in CRC cells. As shown in Figure 3A, intracellular cathepsin B mRNA and protein levels were decreased in HT29 and DLD1 cells in comparison to a control shRNA which had no effect. Reduction of cathepsin B expression modestly slowed the proliferation rate of HT29 and DLD1 populations in 2D cell culture (Figure 3B). Conversely, cathepsin B silencing significantly reduced the ability of HT29 and DLD1 cells to form colonies in soft agarose (Figure 3C). This indicates that intracellular cathepsin B controls anchorage-independent growth of CRC cells given the absence of Ca074 effect (Figure 2C). Moreover, cathepsin B silencing also reduced the number of invading HT29 and DLD1 cells to a similar extent as Ca074 treatment (Figure 3D vs. Figure 2B).

Cathepsin B Silencing in Human CRC Cells Inhibits Tumorigenicity and Metastasis in Immunodeficient Mice

Suppression of cathepsin B expression was found to significantly attenuate the metastatic potential of CRC cells in vivo in experimental metastasis assays. Indeed, immunodeficient mice injected with control CRC cells into the tail vein showed extensive lung metastasis within 28 d, whereas cells expressing shRNA against cathepsin B exhibited reduced lung colonization (Figure 4A). Cathepsin B silencing also altered the capacity of CRC cells to form tumors in mice as assessed by subcutaneous xenograft assays. HT29 cells induced palpable tumors with a short latency period of 9 d after their injection while downregulation of cathepsin B expression in these cells severely impaired their capacity to grow as tumors (Figure 4B).

Cathepsin B Cleaves the Cell Cycle Inhibitor p27Kip1

In order to verify whether p27Kip1 is in fact a substrate for cathepsin B, both proteins were first overexpressed in 293 T cells and cells subsequently lysed 2 d later for Western blot analysis of their respective expression. As shown in Figure 5A, forced expression of cathepsin B in 293 T cells dose-dependently reduced p27Kip1 protein levels. Next, to determine whether p27Kip1 could be degraded by cathepsin B in vitro, lysates from 293 T cells overexpressing HA-tagged p27Kip1 were incubated with purified cathepsin B and analyzed by Western blot. Figure 5B and C shows that cathepsin B degraded p27Kip1 in a time-dependent manner as visualized by the accumulation of three lower molecular mass species (26, 20, and 12 kDa) in addition to the full-length p27Kip1 protein (see arrows versus arrowhead).

Cathepsin B is capable of endopeptidase, peptidyl-dipeptidase, and carboxydipeptidase activities [18-20]. Cathepsin B also possesses a basic amino acid in the catalytic subsite in position S2 enabling the protease to preferentially split its substrates after Arg–Arg or Lys–Arg or Arg–Lys sequences. At least five of these sequences can be found within the human p27Kip1 sequence (Figure 5D). Therefore, the first amino acid of these doublets was mutated into alanine to test whether it would affect the degradation by cathepsin B. Mutation of arginine 58 (Figure 5E) and lysine 189 (Figure 5F) did not alter the cleavage profile of p27Kip1 by cathepsin B. Mutation of lysine 165 and arginine 194 also had no altering effect (not shown). On the other hand, mutation of arginine 152 into alanine markedly reduced the detection of the 20-kDa fragment (Figure 5E).

The protein stability of wild-type p27Kip1 was then compared to that of the p27Kip1 R152A/Δ189–198 mutant, which is more resistant to cathepsin B cleavage. 293T cells were transiently transfected with either wild-type p27Kip1 or p27Kip1 mutant and subsequently treated with cycloheximide to inhibit protein neosynthesis. Thereafter, cells were lysed at different time intervals in order to analyze protein expression levels of p27Kip1 forms. As shown in Figure 6A, following cycloheximide treatment, protein levels of the p27Kip1 mutant decreased much more slowly than that of wild-type protein. Specifically, 10 h after cycloheximide addition, expression of p27Kip1 protein was clearly decreased while expression of the p27Kip1 mutant remained at control (time 0) levels. Of note, forced expression of cathepsin B in 293 T cells dose-dependently reduced the wild-type form of p27Kip1 protein levels while expression of p27Kip1 R152A/Δ189–198 mutant was only very slightly affected (Figure 6B).

Colocalization of Endogenous p27Kip1 With Cathepsin B Into Lysosomes

As shown in Figure 7A, the anti-cathepsin B antibody confirmed the colocalization of cathepsin B (in green) with the lysosomal acidotropic probe LysoTracker (in red). As expected, most of p27Kip1 staining (in green) was observed in the cell nucleus (Figure 7B). However, certain areas of colocalization were observed between endogenous p27Kip1 (in green) and cathepsin B (in red) (Figure 7B, asterisks). Moreover, Western blot analyses revealed the presence of p27Kip1 protein in lysosome-enriched fractions obtained from differential centrifugation of Caco-2/15 and SW480 cell lysates (Figure 7C and D). These lysosomal fractions were enriched in lysosome-associated membrane protein 1 (LAMP1) and exhibited very low or undetectable levels of the nuclear lamin B protein.

The most extensive literature to date regarding cathepsin B highlights a key role of this protease in the invasiveness and metastasis of various carcinoma cells [3, 8, 10-12]. The present findings demonstrate that cathepsin B has not only a role in facilitating CRC invasion and metastasis, but also in mediating early premalignant processes. Results herein show that cathepsin B promotes anchorage-independent CRC cell growth, which translates in vivo to enhanced tumor growth. In addition, cathepsin B was identified as a new protease capable of proteolytic cleavage of the cell cycle inhibitor p27Kip1. This is especially relevant since the loss of p27Kip1 expression has been strongly associated with aggressive tumor behavior and poor clinical outcome in CRC [22, 23].

These data are reminiscent of the immunohistochemistry data reported by Chan et al. [13] showing that cathepsin B protein was expressed in the vast majority of colon cancers analyzed (558 tumors), which was also independent of tumor stage. The present data also revealed that increased transcription of cathepsin B was associated with the presence of mutations in APC but not in KRAS, thus emphasizing the fact that cathepsin B gene expression is already deregulated in early stages of colorectal carcinoma. Indeed, most CRCs acquire loss-of-function mutations in both copies of the APC gene, resulting in inefficient breakdown of intracellular β-catenin and enhanced nuclear signaling [27]. Given the importance of the Wnt/APC/β-catenin pathway in human tumorigenesis initiation, the present data showing an association between cathepsin B expression and APC mutations are particularly noteworthy.

 

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The Union of Biomarkers and Drug Development


The Union of Biomarkers and Drug Development

Author and Curator: Larry H. Bernstein, MD, FCAP

There has been consolidation going on for over a decade in both thr pharmaceutical and in the diagnostics industry, and at the same time the page is being rewritten for health care delivery.  I shall try to work through a clear picture of these not coincidental events.

Key notables:

  1. A growing segment of the US population is reaching Medicare age
  2. There is also a large underserved population in both metropolitan and nonurban areas and a fragmentation of the middle class after a growth slowdown in the economy since the 2008 deep recession.
  3. The deep recession affecting worldwide economies was only buffered by availability of oil or natural gas.
  4. In addition, there was a self-destructive strategy to cut spending on national scales that withdrew the support that would bolster support for infrastrucrue renewl.
  5. There has been a dramatic success in the clinical diagnostics industry, with a long history of being viewed as a loss leader, and this has been recently followed by the pharmaceutical industry faced with inability to introduce new products, leading to more competition in off-patent medications.
  6. The introduction of the Accountable Care Act has opened the opportunities for improved care, despite political opposition, and has probably sustained opportunity in the healthcare market.

Let’s take a look at this three headed serpent. – Pharma, Diagnostics, New Entity
?  The patient  ?
?  Insurance    ?
?  Physician    ?

Part I.   The Concept

When Illumina Buys Roche: The Dawning Of The Era Of Diagnostics Dominance

Robert J. Easton, Alain J. Gilbert, Olivier Lesueur, Rachel Laing, and Mark Ratner
http://PharmaMedtechBI.com    | IN VIVO: The Business & Medicine Report Jul/Aug 2014; 32(7).

  • With current technology and resources, a well-funded IVD company can create and pursue a strategy of information gathering and informatics application to create medical knowledge, enabling it to assume the risk and manage certain segments of patients
  • We see the first step in the process as the emergence of new specialty therapy companies coming from an IVD legacy, most likely focused in cancer, infection, or critical care

When Illumina Inc. acquired the regulatory consulting firm Myraqa, a specialist in in vitro diagnostics (IVD), in July, the press release announcement characterized the deal as one that would bolster illumina’s in-house capabilities for clinical readiness and help prepare for its next growth phase in regulated markets. That’s not surprising given the US Food and Drug Administration’s (FDA) approval a year and a half ago of its MiSeq next-generation sequencer for clinical use. But the deal could also suggest illumina is beginning to move along the path toward taking on clinical risk – that is, eventually

  • advising physicians and patients, which would mean facing regulators directly

Such a move – by illumina, another life sciences tools firm, or an information specialist from the high-tech universe – is inevitable given

  • the emerging power of diagnostics and traditional health care players’ reluctance to themselves take on such risk.

Alternatively, we believe that a well-funded diagnostics company could establish this position. either way, such a champion would establish dominion over and earn higher valuation than less-aggressive players who

  • only supply compartmentalized drug and device solutions.

Diagnostics companies have long been dogged by a fundamental issue:

  1. they are viewed and valued more along the lines of a commodity business than as firms that deliver a unique product or service
  2. diagnostics companies are in position to do just that today because they are now advantaged by having access to more data points.
  3. if they were to cobble together the right capabilities, diagnostics companies would have the ability to turn information into true medical knowledge

Example: PathGEN PathChip

nucleic-acid-based platform detects 296 viruses, bacteria, fungi & parasites

http://ow.ly/d/2GvQhttp://ow.ly/DSORV

This puts the diagnostics player in an unfamiliar realm where it can ask the question of what value they offer compared with a therapeutic. The key is that diagnostics can now offer unique information and potentially unique tools to capture that information. In order to do so, it has to create information from the data it generates, and then to supply that knowledge to users who will value and act on that knowledge. Complex genomic tests, as much as physical examination, may be the first meaningful touch point for physicians’ classification of disease.

Even if lab tests are more expensive, it is a cheaper means for deciding what to do first for a patient than the trial and error of prescribing medication without adequate information. Information is gaining in value as the amount of treatment data available on genomically characterizable subpopulations increases. In such a circumstance
it is the ability to perform that advisory function that will add tremendous value above what any test provides, the leverage of being able to apply a proprietary diagnostics platform – and importantly, the data it generates. It is the ability to perform that advisory function that will add tremendous value above what any test provides.

Integrated Diagnostics Inc. and Biodesix Inc. with mass spectrometry has the tools for unraveling disease processes, and numerous players are quite visibly in or are getting into the business of providing medical knowledge and clinical decision support in pursuit of a huge payout for those who actually solve important disease mysteries. Of course one has to ask whether MS/MS is sufficient for the assigned task, and also whether the technology is ready for the kind of workload experienced in a clinical service compared to a research vehicle.  My impression (as a reviewer) is that it is not now the time to take this seriously.

Roche has not realized its intent with Ventana: failing to deliver on the promise of boosting Roche’s pipeline, which was a significant factor in the high price Roche paid. The combined company was to be “uniquely positioned to further expand Ventana’s business globally and together develop more cost-efficient, differentiated, and targeted medicines.  On the other hand,  Biodesix decided to use Veristrat to look back and analyze important trial data to try to ascertain which patients would benefit from ficlatuzumab (subset). The predictive effect for the otherwise unimpressive trial results was observed in both progression-free survival and overall survival endpoints, and encouraged the companies to conduct a proof-of-concept study of ficlatuzumab in combination with Tarceva in advanced Non Small Cell Lung Cancer Patients (NSCLC) selected using the Veristrat test.

A second phase of IVD evolution will be far more challenging to pharma, when the most accomplished companies begin to assemble and integrate much broader data
sets, thereby gaining knowledge sufficient to actually manage patients and dictate therapy, including drug selection. No individual physician has or will have access to all of this information on thousands of patients, combined with the informatics to tease out from trillions of data points the optimal personalized medical approach. When the IVD-origin knowledge integrator amasses enough data and understanding to guide therapy decisions in large categories, particularly drug choices, it will become more valuable than any of the drug suppliers.

This is an apparent reversal of fortune. The pharmaceutical industry has been considered the valued provider, while the IVD manufacturer has been the low valued cousin. Now, it is by an ability to make kore accurate the drug administration that the IVD company can control the drug bill, to the detriment of drug developers, by finding algorithms that generate equal-to-innovative-drug outcomes using generics for most of the patients, thereby limiting the margins of drug suppliers and the upsides for new drug discovery/development.

It is here that there appears to be a misunderstanding of the whole picture of the development of the healthcare industry.  The pharmaceutical industry had a high value added only insofar it could replace market leaders for treatment before or at the time of patent expiration, which largely depended either introducing a new class of drug, or by relieving the current drug in its class of undesired toxicities or “side effects”.  Otherwise, the drug armamentarium was time limited to the expiration date. In other words, the value was dependent on a window of no competition.  In addition, as the regulation of healthcare costs were tightening under managed care, the introduction of new products that were deemed to be only marginally better, could be substitued by “off-patent” drug products.

The other misunderstanding is related to the IVD sector.  Laboratory tests in the 1950’s were manual, and they could be done by “technicians” who might not have completed a specialized training in clinical laboratory sciences.  The first sign of progress was the introduction of continuous flow chemistry, with a sampling probe, tubing to bring the reacting reagents into a photocell, and the timing of the reaction controlled by a coiled glass tubing before introducing the colored product into a uv-visible photometer.  In perhaps a decade, the Technicon SMA 12 and 6 instruments were introduced that could do up to 18 tests from a single sample.

Part 2. Emergence of an IVD Clinical Automated Diagnostics Industry

Why tests are ordered

  1. Screening
  2. Diagnosis
  3. Monitoring

Historical Perspective

Case in Point 1:  Outstanding Contributions in Clinical Chemistry. 1991. Arthur Karmen.

Dr. Karmen was born in New York City in 1930. He graduated from the Bronx High School of Science in 1946 and earned an A.B. and M.D. in 1950 and 1954, respectively, from New York University. In 1952, while a medical student working on a summer project at Memorial-Sloan Kettering, he used paper chromatography of amino acids to demonstrate the presence of glutamic-oxaloacetic and glutaniic-pyruvic ransaminases (aspartate and alanine aminotransferases) in serum and blood. In 1954, he devised the spectrophotometric method for measuring aspartate aminotransferase in serum, which, with minor modifications, is still used for diagnostic testing today. When developing this assay, he studied the reaction of NADH with serum and demonstrated the presence of lactate and malate dehydrogenases, both of which were also later used in diagnosis. Using the spectrophotometric method, he found that aspartate aminotransferase increased in the period immediately after an acute myocardial infarction and did the pilot studies that showed its diagnostic utility in heart and liver diseases.  This became as important as the EKG. It was replaced in cardiology usage by the MB isoenzyme of creatine kinase, which was driven by Burton Sobel’s work on infarct size, and later by the troponins.

Case in point 2: Arterial Blood Gases.  Van Slyke. National Academy of Sciences.

The test is used to determine the pH of the blood, the partial pressure of carbon dioxide and oxygen, and the bicarbonate level. Many blood gas analyzers will also report concentrations of lactate, hemoglobin, several electrolytes, oxyhemoglobin, carboxyhemoglobin and methemoglobin. ABG testing is mainly used in pulmonology and critical care medicine to determine gas exchange which reflect gas exchange across the alveolar-capillary membrane.

DONALD DEXTER VAN SLYKE died on May 4, 1971, after a long and productive career that spanned three generations of biochemists and physicians. He left behind not only a bibliography of 317 journal publications and 5 books, but also more than 100 persons who had worked with him and distinguished themselves in biochemistry and academic medicine. His doctoral thesis, with Gomberg at University of Michigan was published in the Journal of the American Chemical Society in 1907.  Van Slyke received an invitation from Dr. Simon Flexner, Director of the Rockefeller Institute, to come to New York for an interview. In 1911 he spent a year in Berlin with Emil Fischer, who was then the leading chemist of the scientific world. He was particularly impressed by Fischer’s performing all laboratory operations quantitatively —a procedure Van followed throughout his life. Prior to going to Berlin, he published the  classic nitrous acid method for the quantitative determination of primary aliphatic amino groups,  the first of the many gasometric procedures devised by Van Slyke, and made possible the determination of amino acids. It was the primary method used to study amino acid

composition of proteins for years before chromatography. Thus, his first seven postdoctoral years were centered around the development of better methodology for protein composition and amino acid metabolism.

With his colleague G. M. Meyer, he first demonstrated that amino acids, liberated during digestion in the intestine, are absorbed into the bloodstream, that they are removed by the tissues, and that the liver alone possesses the ability to convert the amino acid nitrogen into urea.  From the study of the kinetics of urease action, Van Slyke and Cullen developed equations that depended upon two reactions: (1) the combination of enzyme and substrate in stoichiometric proportions and (2) the reaction of the combination into the end products. Published in 1914, this formulation, involving two velocity constants, was similar to that arrived at contemporaneously by Michaelis and Menten in Germany in 1913.

He transferred to the Rockefeller Institute’s Hospital in 2013, under Dr. Rufus Cole, where “Men who were studying disease clinically had the right to go as deeply into its fundamental nature as their training allowed, and in the Rockefeller Institute’s Hospital every man who was caring for patients should also be engaged in more fundamental study”.  The study of diabetes was already under way by Dr. F. M. Allen, but patients inevitably died of acidosis.  Van Slyke reasoned that if incomplete oxidation of fatty acids in the body led to the accumulation of acetoacetic and beta-hydroxybutyric acids in the blood, then a reaction would result between these acids and the bicarbonate ions that would lead to a lower than-normal bicarbonate concentration in blood plasma. The problem thus became one of devising an analytical method that would permit the quantitative determination of bicarbonate concentration in small amounts of blood plasma.  He ingeniously devised a volumetric glass apparatus that was easy to use and required less than ten minutes for the determination of the total carbon dioxide in one cubic centimeter of plasma.  It also was soon found to be an excellent apparatus by which to determine blood oxygen concentrations, thus leading to measurements of the percentage saturation of blood hemoglobin with oxygen. This found extensive application in the study of respiratory diseases, such as pneumonia and tuberculosis. It also led to the quantitative study of cyanosis and a monograph on the subject by C. Lundsgaard and Van Slyke.

In all, Van Slyke and his colleagues published twenty-one papers under the general title “Studies of Acidosis,” beginning in 1917 and ending in 1934. They included not only chemical manifestations of acidosis, but Van Slyke, in No. 17 of the series (1921), elaborated and expanded the subject to describe in chemical terms the normal and abnormal variations in the acid-base balance of the blood. This was a landmark in understanding acid-base balance pathology.  Within seven years after Van moved to the Hospital, he had published a total of fifty-three papers, thirty-three of them coauthored with clinical colleagues.

In 1920, Van Slyke and his colleagues undertook a comprehensive investigation of gas and electrolyte equilibria in blood. McLean and Henderson at Harvard had made preliminary studies of blood as a physico-chemical system, but realized that Van Slyke and his colleagues at the Rockefeller Hospital had superior techniques and the facilities necessary for such an undertaking. A collaboration thereupon began between the two laboratories, which resulted in rapid progress toward an exact physico-chemical description of the role of hemoglobin in the transport of oxygen and carbon dioxide, of the distribution of diffusible ions and water between erythrocytes and plasma,
and of factors such as degree of oxygenation of hemoglobin and hydrogen ion concentration that modified these distributions. In this Van Slyke revised his volumetric gas analysis apparatus into a manometric method.  The manometric apparatus proved to give results that were from five to ten times more accurate.

A series of papers on the CO2 titration curves of oxy- and deoxyhemoglobin, of oxygenated and reduced whole blood, and of blood subjected to different degrees of oxygenation and on the distribution of diffusible ions in blood resulted.  These developed equations that predicted the change in distribution of water and diffusible ions between blood plasma and blood cells when there was a change in pH of the oxygenated blood. A significant contribution of Van Slyke and his colleagues was the application of the Gibbs-Donnan Law to the blood—regarded as a two-phase system, in which one phase (the erythrocytes) contained a high concentration of nondiffusible negative ions, i.e., those associated with hemoglobin, and cations, which were not freely exchaThe importance of Vanngeable between cells and plasma. By changing the pH through varying the CO2 tension, the concentration of negative hemoglobin charges changed in a predictable amount. This, in turn, changed the distribution of diffusible anions such as Cl” and HCO3″ in order to restore the Gibbs-Donnan equilibrium. Redistribution of water occurred to restore osmotic equilibrium. The experimental results confirmed the predictions of the equations.

As a spin-off from the physico-chemical study of the blood, Van undertook, in 1922, to put the concept of buffer value of weak electrolytes on a mathematically exact basis.
This proved to be useful in determining buffer values of mixed, polyvalent, and amphoteric electrolytes, and put the understanding of buffering on a quantitative basis. A
monograph in Medicine entitled “Observation on the Courses of Different Types of Bright’s Disease, and on the Resultant Changes in Renal Anatomy,” was a landmark that
related the changes occurring at different stages of renal deterioration to the quantitative changes taking place in kidney function. During this period, Van Slyke and R. M. Archibald identified glutamine as the source of urinary ammonia. During World War II, Van and his colleagues documented the effect of shock on renal function and, with R. A. Phillips, developed a simple method, based on specific gravity, suitable for use in the field.

Over 100 of Van’s 300 publications were devoted to methodology. The importance of Van Slyke’s contribution to clinical chemical methodology cannot be overestimated.
These included the blood organic constituents (carbohydrates, fats, proteins, amino acids, urea, nonprotein nitrogen, and phospholipids) and the inorganic constituents (total cations, calcium, chlorides, phosphate, and the gases carbon dioxide, carbon monoxide, and nitrogen). It was said that a Van Slyke manometric apparatus was almost all the special equipment needed to perform most of the clinical chemical analyses customarily performed prior to the introduction of photocolorimeters and spectrophotometers for such determinations.

The progress made in the medical sciences in genetics, immunology, endocrinology, and antibiotics during the second half of the twentieth century obscures at times the progress that was made in basic and necessary biochemical knowledge during the first half. Methods capable of giving accurate quantitative chemical information on biological material had to be painstakingly devised; basic questions on chemical behavior and metabolism had to be answered; and, finally, those factors that adversely modified the normal chemical reactions in the body so that abnormal conditions arise that we characterize as disease states had to be identified.

Viewed in retrospect, he combined in one scientific lifetime (1) basic contributions to the chemistry of body constituents and their chemical behavior in the body, (2) a chemical understanding of physiological functions of certain organ systems (notably the respiratory and renal), and (3) how such information could be exploited in the
understanding and treatment of disease. That outstanding additions to knowledge in all three categories were possible was in large measure due to his sound and broadly based chemical preparation, his ingenuity in devising means of accurate measurements of chemical constituents, and the opportunity given him at the Hospital of the Rockefeller Institute to study disease in company with physicians.

In addition, he found time to work collaboratively with Dr. John P. Peters of Yale on the classic, two-volume Quantitative Clinical Chemistry. In 1922, John P. Peters, who had just gone to Yale from Van Slyke’s laboratory as an Associate Professor of Medicine, was asked by a publisher to write a modest handbook for clinicians describing useful chemical methods and discussing their application to clinical problems. It was originally to be called “Quantitative Chemistry in Clinical Medicine.” He soon found that it was going to be a bigger job than he could handle alone and asked Van Slyke to join him in writing it. Van agreed, and the two men proceeded to draw up an outline and divide up the writing of the first drafts of the chapters between them. They also agreed to exchange each chapter until it met the satisfaction of both.At the time it was published in 1931, it contained practically all that could be stated with confidence about those aspects of disease that could be and had been studied by chemical means. It was widely accepted throughout the medical world as the “Bible” of quantitative clinical chemistry, and to this day some of the chapters have not become outdated.

History of Laboratory Medicine at Yale University.

The roots of the Department of Laboratory Medicine at Yale can be traced back to John Peters, the head of what he called the “Chemical Division” of the Department of Internal Medicine, subsequently known as the Section of Metabolism, who co-authored with Donald Van Slyke the landmark 1931 textbook Quantitative Clinical Chemistry (2.3); and to Pauline Hald, research collaborator of Dr. Peters who subsequently served as Director of Clinical Chemistry at Yale-New Haven Hospital for many years. In 1947, Miss Hald reported the very first flame photometric measurements of sodium and potassium in serum (4). This study helped to lay the foundation for modern studies of metabolism and their application to clinical care.

The Laboratory Medicine program at Yale had its inception in 1958 as a section of Internal Medicine under the leadership of David Seligson. In 1965, Laboratory Medicine achieved autonomous section status and in 1971, became a full-fledged academic department. Dr. Seligson, who served as the first Chair, pioneered modern automation and computerized data processing in the clinical laboratory. In particular, he demonstrated the feasibility of discrete sample handling for automation that is now the basis of virtually all automated chemistry analyzers. In addition, Seligson and Zetner demonstrated the first clinical use of atomic absorption spectrophotometry. He was one of the founding members of the major Laboratory Medicine academic society, the Academy of Clinical Laboratory Physicians and Scientists.

Davenport fig 10.jpg

Case in Point 3.  Nathan Gochman.  Developer of Automated Chemistries.

Nathan Gochman, PhD, has over 40 years of experience in the clinical diagnostics industry. This includes academic teaching and research, and 30 years in the pharmaceutical and in vitro diagnostics industry. He has managed R & D, technical marketing and technical support departments. As a leader in the industry he was President of the American Association for Clinical Chemistry (AACC) and the National Committee for Clinical Laboratory Standards (NCCLS, now CLSI). He is currently a Consultant to investment firms and IVD companies.

Nathan Gochman

Nathan Gochman

The clinical laboratory has become so productive, particularly in chemistry and immunology, and the labor, instrument and reagent costs are well determined, that today a physician’s medical decisions are 80% determined by the clinical laboratory.  Medical information systems have lagged far behind.  Why is that?  Because the decision for a MIS has historical been based on billing capture.  Moreover, the historical use of chemical profiles were quite good at validating healthy dtatus in an outpatient population, but the profiles became restricted under Diagnostic Related Groups.    Thus, it came to be that the diagnostics was considered a “commodity”.  In order to be competitive, a laboratory had to provide “high complexity” tests that were drawn in by a large volume of “moderate complexity”tests.

Part 3. Biomarkers in Medical Practice

Case in Point 1.

A Solid Prognostic Biomarker

HDL-C: Target of Therapy or Fuggedaboutit?

Steven E. Nissen, MD, MACC, Peter Libby, MD

DisclosuresNovember 06, 2014

Steven E. Nissen, MD, MACC: I am Steve Nissen, chairman of the Department of Cardiovascular Medicine at the Cleveland Clinic. I am here with Dr Peter Libby, chief of cardiology at the Brigham and Women’s Hospital and professor of medicine at Harvard Medical School. We are going to discuss high-density lipoprotein cholesterol (HDL-C), a topic that has been very controversial recently. Peter, HDL-C has been a pretty good biomarker. The question is whether it is a good target.

Peter Libby, MD: Since the early days in Berkley, when they were doing ultracentrifugation, and when it was reinforced and put on the map by the Framingham Study,[1] we have known that HDL-C is an extremely good biomarker of prospective cardiovascular risk with an inverse relationship with all kinds of cardiovascular events. That is as solid a finding as you can get in observational epidemiology. It is a very reliable prospective marker. It’s natural that the pharmaceutical industry and those of us who are interested in risk reduction would focus on HDL-C as a target. That is where the controversies come in.

Dr Nissen: It has been difficult. My view is that the trials that have attempted to modulate HDL-C or the drugs they used have been flawed. Although the results have not been promising, the jury is yet out. Torcetrapib, the cholesteryl ester transfer protein (CETP) inhibitor developed by Pfizer, had anoff-target toxicity.[2] Niacin is not very effective, and there are a lot of downsides to the drug. That has been an issue, but people are still working on this. We have done some studies. We did our ApoA-1 Milano infusion study[3]about a decade ago, which showed very promising results with respect to shrinking plaques in coronary arteries. I remain open to the possibility that the right drug in the right trial will work.

Dr Libby: What do you do with the genetic data that have come out in the past couple of years? Sekar Kathiresan masterminded and organized an enormous collaboration[4] in which they looked, with contemporary genetics, at whether HDL had the genetic markers of being a causal risk factor. They came up empty-handed.

Dr Nissen: I am cautious about interpreting those data, like I am cautious about interpreting animal studies of atherosclerosis. We have both lived through this problem in which something works extremely well in animals but doesn’t work in humans, or it doesn’t work in animals but it works in humans. The genetic studies don’t seal the fate of HDL. I have an open mind about this. Drugs are complex. They work by complex mechanisms. It is my belief that what we have to do is test these hypotheses in well-designed clinical trials, which are rigorously performed with drugs that are clean—unlike torcetrapib—and don’t have off-target toxicities.

An Unmet Need: High Lp(a) Levels

Dr Nissen: I’m going to push back on that and make a couple of points. The HPS2-THRIVE study was flawed. They studied the wrong people. It was not a good study, and AIM-HIGH[8] was underpowered. I am not putting people on niacin. What do you do with a patient whose Lp(a) is 200 mg/dL?

Dr Libby: I’m waiting for the results of the PCSK9 and anacetrapib studies. You can tell me about evacetrapib.[9]Reducing Lp(a) is an unmet medical need. We both care for kindreds with high Lp(a) levels and premature coronary artery disease. We have no idea what to do with them other than to treat them with statins and lower their LDL-C levels.

Dr Nissen: I have taken a more cautious approach with respect to taking people off of niacin. If I have patients who are doing well and tolerating it (depending on why it was started), I am discontinuing niacin in some people. I am starting very few people on the drug, but I worry about the quality of the trial.

Dr Libby: So you are of the “don’t start don’t stop” school?

Dr Nissen: Yes. It’s difficult when the trial is fatally flawed. There were 11,000 patients from China in this study. I have known for years that if you give niacin to people of Asiatic ethnic descent, they have terrible flushing and they won’t continue the drug. One question is, what was the adherence? The adverse events would have been tolerable had there been efficacy. The concern here is that this study was destined to fail because they studied a low LDL/high HDL population, a group of people for whom niacin just isn’t used.

Triglycerides and HDL: Do We Have It Backwards?

Dr Libby: What about the recent genetic[10] and epidemiologic data that support triglycerides, and apolipoprotein C3 in particular as a causal risk factor? Have we been misled through all of the generations in whom we have been adjusting triglycerides for HDL-C and saying that triglycerides are not a causal risk factor because once we adjust for HDL, the risk goes away? Do you think we got it backwards?

Dr Nissen: The tricky factor here is that because of this intimate inverse relationship between triglycerides and HDL, we may be talking about the same phenomenon. That is one of the reasons that I am not certain we are not going to be able to find a therapy. What if you had a therapy that lowered triglycerides and raised HDL-C? Could that work? Could that combination be favorable? I want answers from rigorous, well-designed clinical trials that ask the right questions in the right populations. I am disappointed, just as I have been disappointed by the fibrate trials.[11,12] There is a class of drugs that raises HDL-C a little and lowers triglycerides a lot.

Dr Nissen: But the gemfibrozil studies (VA-HIT[13] and Helsinki Heart[14]) showed benefit.

The Dyslipidemia Bar Has Been Raised

Dr Libby: Those studies were from the pre-statin era. We both were involved in trials in which patients were on high-dose statins at baseline. Do you think that this is too high a bar?

Dr Nissen: The bar has been raised, and for the pharmaceutical industry, the studies that we need to find out whether lowering triglycerides or raising HDL is beneficial are going to be large. We are doing a study with evacetrapib. It has 12,000 patients. It’s fully enrolled. Evacetrapib is a very clean-looking drug. It doesn’t have such a long biological half-life as anacetrapib, so I am very encouraged that it won’t have that baggage of being around for 2-4 years. We’ve got a couple of shots on goal here. Don’t forget that we have multiple ongoing studies of HDL-C infusion therapies that are still under development. Those have some promise too. The jury is still out.

Dr Libby: We agree on the need to do rigorous, large-scale endpoint trials. Do the biomarker studies, but don’t wait to start the endpoint trial because that’s the proof in the pudding.

Dr Nissen: Exactly. We have had a little controversy about HDL-C. We often agree, but not always, and we may have a different perspective. Thanks for joining me in this interesting discussion of what will continue to be a controversial topic for the next several years until we get the results of the current ongoing trials.

Case in Point 2.

NSTEMI? Honesty in Coding and Communication?

Melissa Walton-Shirley

November 07, 2014

The complaint at ER triage: Weakness, fatigue, near syncope of several days’ duration, vomiting, and decreased sensorium.

The findings: O2sat: 88% on room air. BP: 88 systolic. Telemetry: Sinus tachycardia 120 bpm. Blood sugar: 500 mg/dL. Chest X ray: atelectasis. Urinalysis: pyuria. ECG: T-wave-inversion anterior leads. Echocardiography: normal left ventricular ejection fraction (LVEF) and wall motion. Troponin I: 0.3 ng/mL. CT angiography: negative for pulmonary embolism (PE). White blood cell count: 20K with left shift. Blood cultures: positive for Gram-negative rods.

The treatment: Intravenous fluids and IV levofloxacin—changed to ciprofloxacin.

The communication at discharge: “You had a severe urinary-tract infection and grew bacteria in your bloodstream. Also, you’ve had a slight heart attack. See your cardiologist immediately upon discharge-no more than 5 days from now.”

The diagnoses coded at discharge: Urosepsis and non-ST segment elevation MI (NSTEMI) 410.1.

One year earlier: This moderately obese patient was referred to our practice for a preoperative risk assessment. The surgery planned was a technically simple procedure, but due to the need for precise instrumentation, general endotracheal anesthesia (GETA) was being considered. The patient was diabetic, overweight, and short of air. A stress exam was equivocal for CAD due to poor exercise tolerance and suboptimal imaging. Upon further discussion, symptoms were progressive; therefore, cardiac cath was recommended, revealing angiographically normal coronaries and a predictably elevated left ventricular end diastolic pressure (LVEDP) in the mid-20s range. The patient was given a diagnosis of diastolic dysfunction, a prescription for better hypertension control, and in-depth discussion on exercise and the Mediterranean and DASH diets for weight loss. Symptoms improved with a low dose of diuretic. The surgery was completed without difficulty. Upon follow-up visit, the patient felt well, had lost a few pounds, and blood pressure was well controlled.

Five days after ER workup: While out of town, the patient developed profound weakness and went to the ER as described above. Fast forward to our office visit in the designated time frame of “no longer than 5 days’ postdischarge,” where the patient and family asked me about the “slight heart attack” that literally came on the heels of a normal coronary angiogram.

But the patient really didn’t have a “heart attack,” did they? The cardiologist aptly stated that it was likely nonspecific troponin I leak in his progress notes. Yet the hospitalist framed the diagnosis of NSTEMI as item number 2 in the final diagnoses.

The motivations on behalf of personnel who code charts are largely innocent and likely a direct result of the lack of understanding of the coding system on behalf of us as healthcare providers. I have a feeling, though, that hospitals aren’t anxious to correct this misperception, due to an opportunity for increased reimbursement. I contacted a director of a coding department for a large hospital who prefers to remain anonymous. She explained that NSTEMI ICD9 code 410.1 falls in DRG 282 with a weight of .7562. The diagnosis of “demand ischemia,” code 411.89, a slightly less inappropriate code for a nonspecific troponin I leak, falls in DRG 311 with a weight of .5662. To determine reimbursement, one must multiply the weight by the average hospital Medicare base rate of $5370. Keep in mind that each hospital’s base rate and corresponding payment will vary. The difference in reimbursement for a large hospital bill between these two choices for coding is substantial, at over $1000 difference ($4060 vs $3040).

Although hospitals that are already reeling from shrinking revenues will make more money on the front end by coding the troponin leak incorrectly as an NSTEMI, when multiple unnecessary tests are generated to follow up on a nondiagnostic troponin leak, the amount of available Centers for Medicare & Medicaid Services (CMS) reimbursement pie shrinks in the long run. Furthermore, this inappropriate categorization generates extreme concern on behalf of patients and family members that is often never laid to rest. The emotional toll of a “heart-attack” diagnosis has an impact on work fitness, quality of life, cost of medication, and the cost of future testing. If the patient lived for another 100 years, they will likely still list a “heart attack” in their medical history.

As a cardiologist, I resent the loose utilization of one of “my” heart-attack codes when it wasn’t that at all. At discharge, we need to develop a better way of communicating what exactly did happen. Equally important, we need to communicate what exactly didn’t happen as well.

Case in Point 3.

Blood Markers Predict CKD Heart Failure 

Published: Oct 3, 2014 | Updated: Oct 3, 2014

Elevated levels of high-sensitivity troponin T (hsTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) strongly predicted heart failure in patients with chronic kidney disease followed for a median of close to 6 years, researchers reported.

Compared with patients with the lowest blood levels of hsTnT, those with the highest had a nearly five-fold higher risk for developing heart failure and the risk was 10-fold higher in patients with the highest NT-proBNP levels compared with those with the lowest levels of the protein, researcher Nisha Bansal, MD, of the University of Washington in Seattle, and colleagues wrote online in the Journal of the American Society of Nephrology.

A separate study, published online in theJournal of the American Medical Association earlier in the week, also examined the comorbid conditions of heart and kidney disease, finding no benefit to the practice of treating cardiac surgery patients who developed acute kidney injury with infusions of the antihypertensive drug fenoldopam.

The study, reported by researcher Giovanni Landoni, MD, of the IRCCS San Raffaele Scientific Institute, Milan, Italy, and colleagues, was stopped early “for futility,” according to the authors, and the incidence of hypotension during drug infusion was significantly higher in patients infused with fenoldopam than placebo (26% vs. 15%; P=0.001).

Blood Markers Predict CKD Heart Failure

The study in patients with mild to moderate chronic kidney disease (CKD) was conducted to determine if blood markers could help identify patients at high risk for developing heart failure.

Heart failure is the most common cardiovascular complication among people with renal disease, occurring in about a quarter of CKD patients.

The two markers, hsTnT and NT-proBNP, are associated with overworked cardiac myocytes and have been shown to predict heart failure in the general population.

However, Bansal and colleagues noted, the markers have not been widely used in diagnosing heart failure among patients with CKD due to concerns that reduced renal excretion may raise levels of these markers, and therefore do not reflect an actual increase in heart muscle strain.

To better understand the importance of elevated concentrations of hsTnT and NT-proBNP in CKD patients, the researchers examined their association with incident heart failure events in 3,483 participants in the ongoing observational Chronic Renal Insufficiency Cohort (CRIC) study.

All participants were recruited from June 2003 to August 2008, and all were free of heart failure at baseline. The researchers used Cox regression to examine the association of baseline levels of hsTnT and NT-proBNP with incident heart failure after adjustment for demographic influences, traditional cardiovascular risk factors, makers of kidney disease, pertinent medication use, and mineral metabolism markers.

At baseline, hsTnT levels ranged from ≤5.0 to 378.7 pg/mL and NT-proBNP levels ranged from ≤5 to 35,000 pg/mL. Compared with patients who had undetectable hsTnT, those in the highest quartile (>26.5 ng/mL) had a significantly higher rate of heart failure (hazard ratio 4.77; 95% CI 2.49-9.14).

Compared with those in the lowest NT-proBNP quintile (<47.6 ng/mL), patients in the highest quintile (>433.0 ng/mL) experienced an almost 10-fold increase in heart failure risk (HR 9.57; 95% CI 4.40-20.83).

The researchers noted that these associations remained robust after adjustment for potential confounders and for the other biomarker, suggesting that while hsTnT and NT-proBNP are complementary, they may be indicative of distinct biological pathways for heart failure.

Even Modest Increases in NP-proBNP Linked to Heart Failure

The findings are consistent with an earlier analysis that included 8,000 patients with albuminuria in the Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study, which showed that hsTnT was associated with incident cardiovascular events, even after adjustment for eGFR and severity of albuminuria.

“Among participants in the CRIC study, those with the highest quartile of detectable hsTnT had a twofold higher odds of left ventricular hypertrophy compared with those in the lowest quartile,” Bansal and colleagues wrote, adding that the findings were similar after excluding participants with any cardiovascular disease at baseline.

Even modest elevations in NT-proBNP were associated with significantly increased rates of heart failure, including in subgroups stratified by eGFR, proteinuria, and diabetic status.

“NT-proBNP regulates blood pressure and body fluid volume by its natriuretic and diuretic actions, arterial dilation, and inhibition of the renin-aldosterone-angiotensin system and increased levels of this marker likely reflect myocardial stress induced by subclinical changes in volume or pressure, even in persons without clinical disease,” the researchers wrote.

The researchers concluded that further studies are needed to develop and validate risk prediction tools for clinical heart failure in patients with CKD, and to determine the potential role of these two biomarkers in a heart failure risk prediction and prevention strategy.

Fenoldopam ‘Widely Promoted’ in AKI Cardiac Surgery Setting

The JAMA study examined whether the selective dopamine receptor D agonist fenoldopam mesylate can reduce the need for dialysis in cardiac surgery patients who develop acute kidney injury (AKI).

Fenoldopam induces vasodilation of the renal, mesenteric, peripheral, and coronary arteries, and, unlike dopamine, it has no significant affinity for D2 receptors, meaning that it theoretically induces greater vasodilation in the renal medulla than in the cortex, the researchers wrote.

“Because of these hemodynamic effects, fenoldopam has been widely promoted for the prevention and therapy of AKI in the United States and many other countries with apparent favorable results in cardiac surgery and other settings,” Landoni and colleagues wrote.

The drug was approved in 1997 by the FDA for the indication of in-hospital, short-term management of severe hypertension. It has not been approved for renal indications, but is commonly used off-label in cardiac surgery patients who develop AKI.

Although a meta analysis of randomized trials, conducted by the researchers, indicated a reduction in the incidence and progression of AKI associated with the treatment, Landoni and colleagues wrote that the absence of a definitive trial “leaves clinicians uncertain as to whether fenoldopam should be prescribed after cardiac surgery to prevent deterioration in renal function.”

To address this uncertainty, the researchers conducted a prospective, randomized, parallel-group trial in 667 patients treated at 19 hospitals in Italy from March 2008 to April 2013.

All patients had been admitted to ICUs after cardiac surgery with early acute kidney injury (≥50% increase of serum creatinine level from baseline or low output of urine for ≥6 hours). A total of 338 received fenoldopam by continuous intravenous infusion for a total of 96 hours or until ICU discharge, while 329 patients received saline infusions.

The primary end point was the rate of renal replacement therapy, and secondary end points included mortality (intensive care unit and 30-day mortality) and the rate of hypotension during study drug infusion.

Study Showed No Benefit, Was Stopped Early

Yale Lampoon – AA Liebow.   1954

Not As a Doctor
[Fourth Year]

These lyrics, sung by John Cole, Jack Gariepy and Ed Ransenhofer to music borrowed from Gilbert and Sullivan’s The Mikado, lampooned Averill Liebow, M.D., a pathologist noted for his demands on students. (CPC stands for clinical pathology conference.)

If you want to know what this is,
it’s a medical CPC
Where we give the house staff
the biz, for there’s no one so
wise as we!
We pathologists show them how,
Although it is too late now.
Our art is a sacred cow!

American physician, born 1911, Stryj in Galicia, Austria (now in Ukraine); died 1978.

Averill Abraham Liebow, born in Austria, was the “founding father” of pulmonary pathology in the United States. He started his career as a pathologist at Yale, where he remained for many years. In 1968 he moved to the University of California School of Medicine, San Diego, where he taught for 7 years as Professor and Chairman, Department of Pathology.

His studies include many classic studies of lung diseases. Best known of these is his famous classification of interstitial lung disease. He also published papers on sclerosing pneumocytoma, pulmonary alveolar proteinosis, meningothelial-like nodules, pulmonary hypertension, pulmonary veno-occlusive disease, lymphomatoid granulomatosis, pulmonary Langerhans cell histiocytosis, pulmonary epithelioid hemangioendothelioma and pulmonary hyalinizing granuloma .

As a Lieutenant Colonel in the US Army Medical Corps, He was a member of the Atomic Bomb Casualty Commission who studied the effects of the atomic bomb in Hiroshima and Nagasaki.

We thank Sanjay Mukhopadhyay, M.D., for information submitted.

As a resident at UCSD, Dr. Liebow held “Organ Recitals” every morning, including Mother’s day.  The organs had to be presented in specified order… heart, lung, and so forth.  On one occasion, we needed a heart for purification of human lactate dehydrogenase for a medical student project, so I presented the lung out of order.  Dr. Liebow asked where the heart was, and I told the group it was noprmal and I froze it for enzyme purification (smiles).  In the future show it to me first. He was generous to those who showed interest.  As I was also doing research in Nathan Kaplan’s laboratory, he made special arrangements for me to mentor Deborah Peters, the daughter of a pulmonary physician, and granddaughter of the Peters who collaborated with Van Slyke.  I mentored many students with great reward since then.  He could look at a slide and tell you what the x-ray looked like.  I didn’t encounter that again until he sent me to the Armed Forces Institute of Pathology, Washington, DC during the Vietnam War and Watergate, and I worked in Orthopedic Pathology with Lent C. Johnson.  He would not review a case without the x-ray, and he taught the radiologists.

Part 3

My Cancer Genome from Vanderbilt University: Matching Tumor Mutations to Therapies & Clinical Trials

Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2014/11/05/my-cancer-genome-from-vanderbilt-university-matching-tumor-mutations-to-therapies-clinical-trials/

GenomOncology and Vanderbilt-Ingram Cancer Center (VICC) today announced a partnership for the exclusive commercial development of a decision support tool based on My Cancer Genome™, an online precision cancer medicine knowledge resource for physicians, patients, caregivers and researchers.

Through this collaboration, GenomOncology and VICC will enhance My Cancer Genome through the development of a new genomics content management tool. The MyCancerGenome.org website will remain free and open to the public. In addition, GenomOncology will develop a decision support tool based on My Cancer Genome™ data that will enable automated interpretation of mutations in the genome of a patient’s tumor, providing actionable results in hours versus days.

Vanderbilt-Ingram Cancer Center (VICC) launched My Cancer Genome™ in January 2011 as an integral part of their Personalized Cancer Medicine Initiative that helps physicians and researchers track the latest developments in precision cancer medicine and connect with clinical research trials. This web-based information tool is designed to quickly educate clinicians on the rapidly expanding list of genetic mutations that impact cancers and enable the research of treatment options based on specific mutations. For more information on My Cancer Genome™visit www.mycancergenome.org/about/what-is-my-cancer-genome.

Therapies based on the specific genetic alterations that underlie a patient’s cancer not only result in better outcomes but often have less adverse reactions

Up front fee

Nominal fee covers installation support, configuring the Workbench to your specification, designing and developing custom report(s) and training your team.

Per sample fee

GenomOncology is paid on signed-out clinical reports. This philosophy aligns GenomOncology with your Laboratory as we are incentivized to offer world-class support and solutions to differentiate your clinical NGS program. There is no annual license fee.

Part 4

Clinical Trial Services: Foundation Medicine & EmergingMed to Partner

Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2014/11/03/clinical-trial-services-foundation-medicine-emergingmed-to-partner/

Foundation Medicine and EmergingMed said today that they will partner to offer clinical trial navigation services for health care providers and their patients who have received one of Foundation Medicine’s tumor genomic profiling tests.

The firms will provide concierge services to help physicians

  • identify appropriate clinical trials for patients
  • based on the results of FoundationOne or FoundationOne Heme.

“By providing clinical trial navigation services, we aim to facilitate

  • timely and accurate clinical trial information and enrollment support services for physicians and patients,
  • enabling greater access to treatment options based on the unique genomic profile of a patient’s cancer

Currently, there are over 800 candidate therapies that target genomic alterations in clinical trials,

  • but “patients and physicians must identify and act on relevant options
  • when the patient’s clinical profile is aligned with the often short enrollment window for each trial.

These investigational therapies are an opportunity to engage patients with cancer whose cancer has progressed or returned following standard treatment in a most favorable second option after relapse.  The new service is unique in notifying when new clinical trials emerge that match a patient’s genomic and clinical profile.

Google signs on to Foundation Medicine cancer Dx by offering tests to employees

By Emily Wasserman

Diagnostics luminary Foundation Medicine ($FMI) is generating some upward momentum, fueled by growing revenues and the success of its clinical tests. Tech giant Google ($GOOG) has taken note and is signing onto the company’s cancer diagnostics by offering them to employees.

Foundation Medicine CEO Michael Pellini said during the company’s Q3 earnings call that Google will start covering its DNA tests for employees and their family members suffering from cancer as part of its health benefits portfolio, Reuters reports.

Both sides stand to benefit from the deal, as Google looks to keep a leg up on Silicon Valley competitors and Foundation Medicine expands its cancer diagnostics platform. Last month, Apple ($AAPL) and Facebook ($FB) announced that they would begin covering the cost of egg freezing for female employees. A diagnostics partnership and attractive health benefits could work wonders for Google’s employee retention rates and bottom line.

In the meantime, Cambridge, MA-based Foundation Medicine is charging full speed ahead with its cancer diagnostics platform after filing for an IPO in September 2013. The company chalked up 6,428 clinical tests during Q3 2014, an eye-popping 149% increase year over year, and brought in total revenue for the quarter of $16.4 million–a 100% leap from last year. Foundation Medicine credits the promising numbers in part to new diagnostic partnerships and extended coverage for its tests.

In January, the company teamed up with Novartis ($NVS) to help the drugmaker evaluate potential candidates for its cancer therapies. In April, Foundation Medicine announced that it would develop a companion diagnostic test for a Clovis Oncology ($CLVS) drug under development to treat patients with ovarian cancer, building on an ongoing collaboration between the two companies.

Foundation Medicine also has its sights set on China’s growing diagnostics market, inking a deal in October with WuXi PharmaTech ($WX) that allows the company to perform lab testing for its FoundationOne assay at WuXi’s Shanghai-based Genome Center.

a nod to the deal with Google during a corporate earnings call on Wednesday, according to a person who listened in. Pellini said Google employees were made aware of this new benefit last week.

Foundation Medicine teams with MD Anderson for new trial of cancer Dx

Second study to see if targeted therapy can change patient outcomes

August 15, 2014 | By   FierceDiagnostics

Foundation Medicine ($FMI) is teaming up with the MD Anderson Cancer Center in Texas for a new trial of the the Cambridge, MA-based company’s molecular diagnostic cancer test that targets therapies matched to individual patients.

The study is called IMPACT2 (Initiative for Molecular Profiling and Advanced Cancer Therapy) and is designed to build on results from the the first IMPACT study that found

  • 40% of the 1,144 patients enrolled had an identifiable genomic alteration.

The company said that

  • by matching specific gene alterations to therapies,
  • 27% of patients in the first study responded versus
  • 5% with an unmatched treatment, and
  • “progression-free survival” was longer in the matched group.

The FoundationOne molecular diagnostic test

  • combines genetic sequencing and data gathering
  • to help oncologists choose the best treatment for individual patients.

Costing $5,800 per test, FoundationOne’s technology can uncover a large number of genetic alterations for 200 cancer-related genes,

  • blending genomic sequencing, information and clinical practice.

“Based on the IMPACT1 data, a validated, comprehensive profiling approach has already been adopted by many academic and community-based oncology practices,” Vincent Miller, chief medical officer of Foundation Medicine, said in a release. “This study has the potential to yield sufficient evidence necessary to support broader adoption across most newly diagnosed metastatic tumors.”

The company got a boost last month when the New York State Department of Health approved Foundation Medicine’s two initial cancer tests: the FoundationOne test and FoundationOne Heme, which creates a genetic profile for blood cancers. Typically,

  • diagnostics companies struggle to win insurance approval for their tests
  • even after they gain a regulatory approval, leaving revenue growth relatively flat.

However, Foundation Medicine reported earlier this week its Q2 revenue reached $14.5 million compared to $5.9 million for the same period a year ago. Still,

  1. net losses continue to soar as the company ramps up
  2. its commercial and business development operation,
  • hitting $13.7 million versus a $10.1 million deficit in the second quarter of 2013.

Oncology

There has been a remarkable transformation in our understanding of

  • the molecular genetic basis of cancer and its treatment during the past decade or so.

In depth genetic and genomic analysis of cancers has revealed that

  • each cancer type can be sub-classified into many groups based on the genetic profiles and
  • this information can be used to develop new targeted therapies and treatment options for cancer patients.

This panel will explore the technologies that are facilitating our understanding of cancer, and

  • how this information is being used in novel approaches for clinical development and treatment.
Oncology _ Reprted by Dr. Aviva Lev-Ari, Founder, Leaders in Pharmaceutical Intelligence

Opening Speaker & Moderator:

Lynda Chin, M.D.
Department Chair, Department of Genomic Medicine
MD Anderson Cancer Center

  • Who pays for PM?
  • potential of Big data, analytics, Expert systems, so not each MD needs to see all cases, Profile disease to get same treatment
  • business model: IP, Discovery, sharing, ownership — yet accelerate therapy
  • security of healthcare data
  • segmentation of patient population
  • management of data and tracking innovations
  • platforms to be shared for innovations
  • study to be longitudinal,
  • How do we reconcile course of disease with PM
  • phinotyping the disease vs a Patient in wait for cure/treatment

Panelists:

Roy Herbst, M.D., Ph.D.
Ensign Professor of Medicine and Professor of Pharmacology;
Chief of Medical Oncology, Yale Cancer Center and Smilow Cancer Hospital

Development new drugs to match patient, disease and drug – finding the right patient for the right Clinical Trial

  • match patient to drugs
  • partnerships: out of 100 screened patients, 10 had the gene, 5 were able to attend the trial — without the biomarker — all 100 patients would participate for the WRONG drug for them (except the 5)
  • patients wants to participate in trials next to home NOT to have to travel — now it is in the protocol
  • Annotated Databases – clinical Trial informed consent – adaptive design of Clinical Trial vs protocol
  • even Academic MD can’t read the reports on Genomics
  • patients are treated in the community — more training to MDs
  • Five companies collaborating – comparison og 6 drugs in the same class
  • if drug exist and you have the patient — you must apply PM

Summary and Perspective:

The current changes in Biotechnology have been reviewed with an open question about the relationship of In Vitro Diagnostics to Biopharmaceuticals switching, with the potential, particularly in cancer and infectious diseases, to added value in targeted therapy by matching patients to the best potential treatment for a favorable outcome.

This reviewer does not see the movement of the major diagnostics leaders entering into the domain of direct patient care, even though there are signals in that direction.  The Roche example is perhaps the most interesting because Roche already became the elephant in the room after the introduction of Valium,  subsequently bought out Boehringer Mannheim Diagnostics to gain entry into the IVD market, and established a huge presence in Molecular Diagnostics early.  If it did anything to gain a foothold in the treatment realm, it would more likely forge a relationship with Foundation Medicine.  Abbott Laboratories more than a decade ago was overextended, and it had become the leader in IVD as a result of the specialty tests, but it fell into difficulties with quality control of its products in the high volume testing market, and acceeded to Olympus, Roche, and in the mid volume market to Beckman and Siemens.  Of course, Dupont and Kodak, pioneering companies in IVD, both left the market.

The biggest challenge in the long run is identified by the ability to eliminate many treatments that would be failures for a large number of patients. That has already met the proof of concept.  However, when you look at the size of the subgroups, we are not anywhere near a large scale endeavor.  In addition, there is a lot that has to be worked out that is not related to genomic expression by the “classic” model, but has to take into account the emrging knowledge and greater understanding of regulation of cell metabolism, not only in cancer, but also in chronic inflammatory diseases.

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