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Summary of Proteomics

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

 

We have completed a series of discussions on proteomics, a scientific endeavor that is essentially 15 years old.   It is quite remarkable what has been accomplished in that time.  The interest is abetted by the understanding of the limitations of the genomic venture that has preceded it.  The thorough, yet incomplete knowledge of the genome, has led to the clarification of its limits.  It is the coding for all that lives, but all that lives has evolved to meet a demanding and changing environment with respect to

1. availability of nutrients
2. salinity
3. temperature
4. radiation exposure
5. toxicities in the air, water, and food
6. stresses – both internal and external

We have seen how both transcription and translation of the code results in a protein, lipoprotein, or other complex than the initial transcript that was modeled from tRNA. What you see in the DNA is not what you get in the functioning cell, organ, or organism.  There are comparabilities as well as significant differences between plants, prokaryotes, and eukaryotes.  There is extensive variation.  The variation goes beyond genomic expression, and includes the functioning cell, organ type, and species.

Here, I return to the introductory discussion.  Proteomics is a goal directed, sophisticated science that uses a combination of methods to find the answers to biological questions. Graves PR and Haystead TAJ.  Molecular Biologist’s Guide to Proteomics.
Microbiol Mol Biol Rev. Mar 2002; 66(1): 39–63.  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC120780/

Peptide mass tag searching. Shown is a schematic of how information from an unknown peptide (top) is matched to a peptide sequence in a database (bottom) for protein identification. The partial amino acid sequence or “tag” obtained by MS/MS is combined with the peptide mass (parent mass), the mass of the peptide at the start of the sequence (mass tag 1), and the mass of the peptide at the end of the sequence (mass tag 2). The specificity of the protease used (trypsin is shown) can also be included in the search.

The ICAT method for measuring differential protein expression. (A) Structure of the ICAT reagent. ICAT consists of a biotin affinity group, a linker region that can incorporate heavy (deuterium) or light (hydrogen) atoms, and a thiol-reactive end group for linkage to cysteines. (B) ICAT strategy. Proteins are harvested from two different cell states and labeled on cysteine residues with either the light or heavy form of the ICAT reagent. Following labeling, the two protein samples are mixed and digested with a protease such as trypsin. Peptides labeled with the ICAT reagent can be purified by virtue of the biotin tag by using avidin chromatography. Following purification, ICAT-labeled peptides can be analyzed by MS to quantitate the peak ratios and proteins can be identified by sequencing the peptides with MS/MS.

Strategies for determination of phosphorylation sites in proteins. Proteins phosphorylated in vitro or in vivo can be isolated by protein electrophoresis and analyzed by MS. (A) Identification of phosphopeptides by peptide mass fingerprinting. In this method, phosphopeptides are identified by comparing the mass spectrum of an untreated sample to that of a sample treated with phosphatase. In the phosphatase-treated sample, potential phosphopeptides are identified by a decrease in mass due to loss of a phosphate group (80 Da). (B) Phosphorylation sites can be identified by peptide sequencing using MS/MS. (C) Edman degradation can be used to monitor the release of inorganic ³²P to provide information about phosphorylation sites in peptides.

Proteome-mining strategy. Proteins are isolated on affinity column arrays from a cell line, organ, or animal source and purified to remove nonspecific adherents. Then, compound libraries are passed over the array and the proteins eluted are analyzed by protein electrophoresis. Protein information obtained by MS or Edman degradation is then used to search DNA and protein databases. If a relevant target is identified, a sublibrary of compounds can be evaluated to refine the lead. From this method a protein target and a drug lead can be simultaneously identified.

Although the technology for the analysis of proteins is rapidly progressing, it is still not feasible to study proteins on a scale equivalent to that of the nucleic acids. Most of proteomics relies on methods, such as protein purification or PAGE, that are not high-throughput methods. Even performing MS can require considerable time in either data acquisition or analysis. Although hundreds of proteins can be analyzed quickly and in an automated fashion by a MALDI-TOF mass spectrometer, the quality of data is sacrificed and many proteins cannot be identified. Much higher quality data can be obtained for protein identification by MS/MS, but this method requires considerable time in data interpretation. In our opinion, new computer algorithms are needed to allow more accurate interpretation of mass spectra without operator intervention. In addition, to access unannotated DNA databases across species, these algorithms should be error tolerant to allow for sequencing errors, polymorphisms, and conservative substitutions. New technologies will have to emerge before protein analysis on a large-scale (such as mapping the human proteome) becomes a reality.

Another major challenge for proteomics is the study of low-abundance proteins. In some eukaryotic cells, the amounts of the most abundant proteins can be 10⁶-fold greater than those of the low-abundance proteins. Many important classes of proteins (that may be important drug targets) such as transcription factors, protein kinases, and regulatory proteins are low-copy proteins. These low-copy proteins will not be observed in the analysis of crude cell lysates without some purification. Therefore, new methods must be devised for subproteome isolation.

Tissue Proteomics for the Next Decade?  Towards a Molecular Dimension in Histology

R Longuespe´e, M Fle´ron, C Pottier, F Quesada-Calvo, Marie-Alice Meuwis, et al.
OMICS A Journal of Integrative Biology 2014; 18: 9.    http://dx.doi.org:/10.1089/omi.2014.0033

The concept of tissues appeared more than 200 years ago, since textures and attendant differences were described within the whole organism components. Instrumental developments in optics and biochemistry subsequently paved the way to transition from classical to molecular histology in order to decipher the molecular contexts associated with physiological or pathological development or function of a tissue. In 1941, Coons and colleagues performed the first systematic integrated examination of classical histology and biochemistry when his team localized pneumonia antigens in infected tissue sections. Most recently, in the early 21st century, mass spectrometry (MS) has progressively become one of the most valuable tools to analyze biomolecular compounds. Currently, sampling methods, biochemical procedures, and MS instrumentations
allow scientists to perform ‘‘in depth’’ analysis of the protein content of any type of tissue of interest. This article reviews the salient issues in proteomics analysis of tissues. We first outline technical and analytical considerations for sampling and biochemical processing of tissues and subsequently the instrumental possibilities for proteomics analysis such as shotgun proteomics in an anatomical context. Specific attention concerns formalin fixed and paraffin embedded (FFPE) tissues that are potential ‘‘gold mines’’ for histopathological investigations. In all, the matrix assisted laser desorption/ionization (MALDI) MS imaging, which allows for differential mapping of hundreds of compounds on a tissue section, is currently the most striking evidence of linkage and transition between ‘‘classical’’ and ‘‘molecular’’ histology. Tissue proteomics represents a veritable field of research and investment activity for modern biomarker discovery and development for the next decade.

Progressively, tissue analyses evolved towards the description of the whole molecular content of a given sample. Currently, mass spectrometry (MS) is the most versatile
analytical tool for protein identification and has proven its great potential for biological and clinical applications. ‘‘Omics’’ fields, and especially proteomics, are of particular
interest since they allow the analysis of a biomolecular picture associated with a given physiological or pathological state. Biochemical techniques were then adapted for an optimal extraction of several biocompounds classes from tissues of different natures.

Laser capture microdissection (LCM) is used to select and isolate tissue areas of interest for further analysis. The developments of MS instrumentations have then definitively transformed the scientific scene, pushing back more and more detection and identification limits. Since a few decades, new approaches of analyses appeared, involving the use of tissue sections dropped on glass slides as starting material. Two types of analyses can then be applied on tissue sections: shotgun proteomics and the very promising MS imaging (MSI) using Matrix Assisted Laser Desorption/Ionization (MALDI) sources. Also known as ‘‘molecular histology,’’ MSI is the most striking hyphen between histology and molecular analysis. In practice, this method allows visualization of the spatial distribution of proteins, peptides, drugs, or others analytes directly on tissue sections. This technique paved new ways of research, especially in the field of histopathology, since this approach appeared to be complementary to conventional histology.

Tissue processing workflows for molecular analyses. Tissues can either be processed in solution or directly on tissue sections. In solution, processing involves protein
extraction from tissue pieces in order to perform 2D gel separation and identification of proteins, shotgun proteomics, or MALDI analyses. Extracts can also be obtained from
tissues area selection and protein extraction after laser micro dissection or on-tissue processing. Imaging techniques are dedicated to the morphological characterization or molecular mapping of tissue sections. Histology can either be conducted by hematoxylin/eosin staining or by molecular mapping using antibodies with IHC. Finally, mass spectrometry imaging allows the cartography of numerous compounds in a single analysis. This approach is a modern form of ‘‘molecular histology’’ as it grafts, with the use of mathematical calculations, a molecular dimension to classical histology. (AR, antigen retrieval; FFPE, formalin fixed and paraffin embedded; fr/fr, fresh frozen; IHC, immunohistochemistry; LCM, laser capture microdissection; MALDI, matrix assisted laser desorption/ionization; MSI, mass spectrometry imaging; PTM, post translational modification.)

Analysis of tissue proteomes has greatly evolved with separation methods and mass spectrometry instrumentation. The choice of the workflow strongly depends on whether a bottom-up or a top-down analysis has to be performed downstream. In-gel or off-gel proteomics principally differentiates proteomic workflows. The almost simultaneous discoveries of the MS ionization sources (Nobel Prize awarded) MALDI (Hillenkamp and Karas, 1990; Tanaka et al., 1988) and electrospray ionization (ESI) (Fenn et al., 1989) have paved the way for analysis of intact proteins and peptides. Separation methods such as two-dimension electrophoresis (2DE) (Fey and Larsen, 2001) and nanoscale reverse phase liquid chromatography (nanoRP-LC) (Deterding et al., 1991) lead to efficient preparation of proteins for respectively topdown and bottom-up strategies. A huge panel of developments was then achieved mostly for LC-MS based proteomics in order to improve ion fragmentation approaches and peptide
identification throughput relying on database interrogation. Moreover, approaches were developed to analyze post translational modifications (PTM) such as phosphorylations (Ficarro et al., 2002; Oda et al., 2001; Zhou et al., 2001) or glycosylations (Zhang et al., 2003), proposing as well different quantification procedures. Regarding instrumentation, the most cutting edge improvements are the gain of mass accuracy for an optimal detection of the eluted peptides during LC-MS runs (Mann and Kelleher, 2008; Michalski et al., 2011) and the increase in scanning speed, for example with the use of Orbitrap analyzers (Hardman and Makarov, 2003; Makarov et al., 2006; Makarov et al., 2009; Olsen et al., 2009). Ion transfer efficiency was also drastically improved with the conception of ion funnels that homogenize the ion transmission
capacities through m/z ranges (Kelly et al., 2010; Kim et al., 2000; Page et al., 2006; Shaffer et al., 1998) or by performing electrospray ionization within low vacuum (Marginean et al., 2010; Page et al., 2008; Tang et al., 2011). Beside collision induced dissociation (CID) that is proposed for many applications (Li et al., 2009; Wells and McLuckey, 2005), new fragmentation methods were investigated, such as higher-energy collisional dissociation (HCD) especially for phosphoproteomic
applications (Nagaraj et al., 2010), and electron transfer dissociation (ETD) and electron capture dissociation (ECD) that are suited for phospho- and glycoproteomics (An
et al., 2009; Boersema et al., 2009; Wiesner et al., 2008). Methods for data-independent MS2 analysis based on peptide fragmentation in given m/z windows without precursor selection neither information knowledge, also improves identification throughput (Panchaud et al., 2009; Venable et al., 2004), especially with the use of MS instruments with high resolution and high mass accuracy specifications (Panchaud et al., 2011). Gas fractionation methods such as ion mobility (IM) can also be used as a supplementary separation dimension which enable more efficient peptide identifications (Masselon et al., 2000; Shvartsburg et al., 2013; Shvartsburg et al., 2011).

Microdissection relies on a laser ablation principle. The tissue section is dropped on a plastic membrane covering a glass slide. The preparation is then placed into a microscope
equipped with a laser. A highly focused beam will then be guided by the user at the external limit of the area of interest. This area composed by the plastic membrane, and the tissue section will then be ejected from the glass slide and collected into a tube cap for further processing. This mode of microdissection is the most widely used due to its ease of handling and the large panels of devices proposed by constructors. Indeed, Leica microsystem proposed the Leica LMD system (Kolble, 2000), Molecular Machine and Industries, the MMI laser microdissection system Microcut, which was used in combination with IHC (Buckanovich et al., 2006), Applied Biosystems developed the Arcturus
microdissection System, and Carl Zeiss patented P.A.L.M. MicroBeam technology (Braakman et al., 2011; Espina et al., 2006a; Espina et al., 2006b; Liu et al., 2012; Micke
et al., 2005). LCM represents a very adequate link between classical histology and sampling methods for molecular analyses as it is a simple customized microscope. Indeed,
optical lenses of different magnification can be used and the method is compatible with classical IHC (Buckanovich et al., 2006). Only the laser and the tube holder need to be
added to the instrumentation.

After microdissection, the tissue pieces can be processed for analyses using different available MS devices and strategies. The simplest one consists in the direct analysis of the
protein profiles by MALDI-TOF-MS (MALDI-time of flight-MS). The microdissected tissues are dropped on a MALDI target and directly covered by the MALDI matrix (Palmer-Toy et al., 2000; Xu et al., 2002). This approach was already used in order to classify breast cancer tumor types (Sanders et al., 2008), identify intestinal neoplasia protein biomarkers (Xu et al., 2009), and to determine differential profiles in glomerulosclerosis (Xu et al., 2005).

Currently the most common proteomic approach for LCM tissue analysis is LC-MS/MS. Label free LC-MS approaches have been used to study several cancers like head and neck squamous cell carcinomas (Baker et al., 2005), esophageal cancer (Hatakeyama et al., 2006), dysplasic cervical cells (Gu et al., 2007), breast carcinoma tumors (Hill et al., 2011; Johann et al., 2009), tamoxifen-resistant breast cancer cells (Umar et al., 2009), ER + / – breast cancer cells (Rezaul et al., 2010), Barretts esophagus (Stingl et al., 2011), and ovarian endometrioid cancer (Alkhas et al., 2011). Different isotope labeling methods have been used in order to compare proteins expression. ICAT was first used to investigate proteomes of hepatocellular carcinoma (Li et al., 2004; 2008). The O16/O18 isotopic labeling was then used for proteomic analysis of ductal carcinoma of the breast (Zang et al., 2004).

Currently, the lowest amount of collected cells for a relevant single analysis using fr/fr breast cancer tissues was 3000–4000 (Braakman et al., 2012; Liu et al., 2012; Umar et al., 2007). With a Q-Exactive (Thermo, Waltham) mass spectrometer coupled to LC, Braakman was able to identify up to 1800 proteins from 4000 cells. Processing
of FFPE microdissected tissues of limited sizes still remains an issue which is being addressed by our team.

Among direct tissue analyses modes, two categories of investigations can be done. MALDI profiling consists in the study of molecular localization of compounds and can be
combined with parallel shotgun proteomic methods. Imaging methods give less detailed molecular information, but is more focused on the accurate mapping of the detected compounds through tissue area. In 2007, a concept of direct tissue proteomics (DTP) was proposed for high-throughput examination of tissue microarray samples. However, contrary to the classical workflow, tissue section chemical treatment involved a first step of scrapping each FFPE tissue spot with a razor blade from the glass slide. The tissues were then transferred into a tube and processed with RIPA buffer and finally submitted to boiling as an AR step (Hwang et al., 2007). Afterward, several teams proved that it was possible to perform the AR directly on tissue sections. These applications were mainly dedicated to MALDI imaging analyses (Bonnel et al., 2011; Casadonte and Caprioli, 2011; Gustafsson et al., 2010). However, more recently, Longuespe´e used citric acid antigen retrieval (CAAR) before shotgun proteomics associated to global profiling proteomics (Longuespee et al., 2013).

MALDI imaging workflow. For MALDI imaging experiments, tissue sections are dropped on conductive glass slides. Sample preparations are then adapted depending on the nature of the tissue sample (FFPE or fr/fr). Then, matrix is uniformly deposited on the tissue section using dedicated devices. A laser beam subsequently irradiates the preparation following a given step length and a MALDI spectrum is acquired for each position. Using adapted software, the different detected ions are then mapped through the tissue section, in function of their differential intensities. The ‘‘molecular maps’’ are called images. (FFPE, formalin fixed and paraffin embedded; fr/fr, fresh frozen; MALDI, matrix assisted laser desorption ionization.)

Proteomics instrumentations, specific biochemical preparations, and sampling methods such as LCM altogether allow for the deep exploration and comparison of different proteomes between regions of interest in tissues with up to 104 detected proteins. MALDI MS imaging that allows for differential mapping of hundreds of compounds on a tissue section is currently the most striking illustration of association between ‘‘classical’’ and ‘‘molecular’’ histology.

Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer

L Chung, K Moore, L Phillips, FM Boyle, DJ Marsh and RC Baxter*  Breast Cancer Research 2014, 16:R63
http://breast-cancer-research.com/content/16/3/R63

Introduction: Serum profiling using proteomic techniques has great potential to detect biomarkers that might improve diagnosis and predict outcome for breast cancer patients (BC). This study used surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) to identify differentially expressed proteins in sera from BC and healthy volunteers (HV), with the goal of developing a new prognostic biomarker panel.
Methods: Training set serum samples from 99 BC and 51 HV subjects were applied to four adsorptive chip surfaces (anion-exchange, cation-exchange, hydrophobic, and metal affinity) and analyzed by time-of-flight MS. For validation, 100 independent BC serum samples and 70 HV samples were analyzed similarly. Cluster analysis of protein spectra was performed to identify protein patterns related to BC and HV groups. Univariate and multivariate statistical analyses were used to develop a protein panel to distinguish breast cancer sera from healthy sera, and its prognostic potential was evaluated.
Results: From 51 protein peaks that were significantly up- or downregulated in BC patients by univariate analysis, binary logistic regression yielded five protein peaks that together classified BC and HV with a receiver operating characteristic (ROC) area-under-the-curve value of 0.961. Validation on an independent patient cohort confirmed
the five-protein parameter (ROC value 0.939). The five-protein parameter showed positive association with large tumor size (P = 0.018) and lymph node involvement (P = 0.016). By matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, immunoprecipitation and western blotting the proteins were identified as a fragment
of apolipoprotein H (ApoH), ApoCI, complement C3a, transthyretin, and ApoAI. Kaplan-Meier analysis on 181 subjects after median follow-up of >5 years demonstrated that the panel significantly predicted disease-free survival (P = 0.005), its efficacy apparently greater in women with estrogen receptor (ER)-negative tumors (n = 50, P = 0.003) compared to ER-positive (n = 131, P = 0.161), although the influence of ER status needs to be confirmed after longer follow-up.
Conclusions: Protein mass profiling by MS has revealed five serum proteins which, in combination, can distinguish between serum from women with breast cancer and healthy control subjects with high sensitivity and specificity. The five-protein panel significantly predicts recurrence-free survival in women with ER-negative tumors and may have value in the management of these patients.

Cellular prion protein is required for neuritogenesis: fine-tuning of multiple signaling pathways involved in focal adhesions and actin cytoskeleton dynamics

Aurélie Alleaume-Butaux, et al.   Cell Health and Cytoskeleton 2013:5 1–12

Neuritogenesis is a dynamic phenomenon associated with neuronal differentiation that allows a rather spherical neuronal stem cell to develop dendrites and axon, a prerequisite for the integration and transmission of signals. The acquisition of neuronal polarity occurs in three steps:

(1) neurite sprouting, which consists of the formation of buds emerging from the postmitotic neuronal soma;

(2) neurite outgrowth, which represents the conversion of buds into neurites, their elongation and evolution into axon or dendrites; and

(3) the stability and plasticity of neuronal polarity.

In neuronal stem cells, remodeling and activation of focal adhesions (FAs)

• associated with deep modifications of the actin cytoskeleton is
• a prerequisite for neurite sprouting and subsequent neurite outgrowth.

A multiple set of growth factors and interactors located in

• the extracellular matrix and the plasma membrane orchestrate neuritogenesis
• by acting on intracellular signaling effectors, notably small G proteins such as RhoA, Rac, and Cdc42,
• which are involved in actin turnover and the dynamics of FAs.

The cellular prion protein (PrPC), a glycosylphosphatidylinositol (GPI)-anchored membrane protein

• mainly known for its role in a group of fatal neurodegenerative diseases,
• has emerged as a central player in neuritogenesis.

Here, we review the contribution of PrPC to neuronal polarization and

• detail the current knowledge on the signaling pathways fine-tuned
• by PrPC to promote neurite sprouting, outgrowth, and maintenance.

We emphasize that PrPC-dependent neurite sprouting is a process in which

• PrPC governs the dynamics of FAs and the actin cytoskeleton via β1 integrin signaling.

The presence of PrPC is necessary to render neuronal stem cells

• competent to respond to neuronal inducers and to develop neurites.

In differentiating neurons, PrPC exerts a facilitator role towards neurite elongation.

This function relies on the interaction of PrPC with a set of diverse partners such as

1. elements of the extracellular matrix,
2. plasma membrane receptors,
3. adhesion molecules, and
4. soluble factors that control actin cytoskeleton turnover

• through Rho-GTPase signaling.

Once neurons have reached their terminal stage of differentiation and

• acquired their polarized morphology,
• PrPC also takes part in the maintenance of neurites.

By acting on tissue nonspecific alkaline phosphatase, or matrix metalloproteinase type 9,

• PrPC stabilizes interactions between neurites and the extracellular matrix.

Fusion-pore expansion during syncytium formation is restricted by an actin network

Andrew Chen et al., Journal of Cell Science 121, 3619-3628. http://dx.doi.org:/10.1242/jcs.032169

Cell-cell fusion in animal development and in pathophysiology

• involves expansion of nascent fusion pores formed by protein fusogens
• to yield an open lumen of cell-size diameter.

Here we explored the enlargement of micron-scale pores in syncytium formation,

• which was initiated by a well-characterized fusogen baculovirus gp64.

Radial expansion of a single or, more often, of multiple fusion pores

• proceeds without loss of membrane material in the tight contact zone.

Pore growth requires cell metabolism and is

• accompanied by a local disassembly of the actin cortex under the pores.

Effects of actin-modifying agents indicate that

• the actin cortex slows down pore expansion.

We propose that the growth of the strongly bent fusion-pore rim

1. is restricted by a dynamic resistance of the actin network and
2. driven by membrane-bending proteins that are involved in
3. the generation of highly curved intracellular membrane compartments.

Pak1 Is Required to Maintain Ventricular Ca2+ Homeostasis and Electrophysiological Stability Through SERCA2a Regulation in Mice

Yanwen Wang, et al.  Circ Arrhythm Electrophysiol. 2014;7:00-00.

Impaired sarcoplasmic reticular Ca2+ uptake resulting from

• decreased sarcoplasmic reticulum Ca2+-ATPase type 2a (SERCA2a) expression or activity
• is a characteristic of heart failure with its associated ventricular arrhythmias.

Recent attempts at gene therapy of these conditions explored strategies

• enhancing SERCA2a expression and the activity as novel approaches to heart failure management.

We here explore the role of Pak1 in maintaining ventricular Ca2+ homeostasis and electrophysiological stability

• under both normal physiological and acute and chronic β-adrenergic stress conditions.

Methods and Results—Mice with a cardiomyocyte-specific Pak1 deletion (Pak1cko), but not controls (Pak1f/f), showed

• high incidences of ventricular arrhythmias and electrophysiological instability
• during either acute β-adrenergic or chronic β-adrenergic stress leading to hypertrophy,
• induced by isoproterenol.

Isolated Pak1cko ventricular myocytes correspondingly showed

• aberrant cellular Ca2+ homeostasis.

Pak1cko hearts showed an associated impairment of SERCA2a function and

• downregulation of SERCA2a mRNA and protein expression.

Further explorations of the mechanisms underlying the altered transcriptional regulation

• demonstrated that exposure to control Ad-shC2 virus infection
• increased SERCA2a protein and mRNA levels after
• phenylephrine stress in cultured neonatal rat cardiomyocytes.

This was abolished by the

• Pak1-knockdown in Ad-shPak1–infected neonatal rat cardiomyocytes and
• increased by constitutive overexpression of active Pak1 (Ad-CAPak1).

We then implicated activation of serum response factor, a transcriptional factor well known for

• its vital role in the regulation of cardiogenesis genes in the Pak1-dependent regulation of SERCA2a.

Conclusions—These findings indicate that

Pak1 is required to maintain ventricular Ca2+ homeostasis and electrophysiological stability

• and implicate Pak1 as a novel regulator of cardiac SERCA2a through
• a transcriptional mechanism

fusion in animal development and in pathophysiology involves expansion of nascent fusion pores

• formed by protein fusogens to yield an open lumen of cell-size diameter.

Here we explored the enlargement of micron-scale pores in syncytium formation,

• which was initiated by a well-characterized fusogen baculovirus gp64.

Radial expansion of a single or, more often, of multiple fusion pores proceeds

• without loss of membrane material in the tight contact zone.

Pore growth requires cell metabolism and is accompanied by

• a local disassembly of the actin cortex under the pores.

Effects of actin-modifying agents indicate that the actin cortex slows down pore expansion.

We propose that the growth of the strongly bent fusion-pore rim is restricted

• by a dynamic resistance of the actin network and driven by
• membrane-bending proteins that are involved in the generation of
• highly curved intracellular membrane compartments.

Role of forkhead box protein A3 in age-associated metabolic decline

Xinran Maa,1, Lingyan Xua,1, Oksana Gavrilovab, and Elisabetta Muellera,2
PNAS Sep 30, 2014 | 111 | 39 | 14289–14294  http://pnas.org/cgi/doi/10.1073/pnas.1407640111

Significance
This paper reports that the transcription factor forkhead box protein A3 (Foxa3) is

• directly involved in the development of age-associated obesity and insulin resistance.

Mice that lack the Foxa3 gene

1. remodel their fat tissues,
2. store less fat, and
3. burn more energy as they age.

These mice also live significantly longer.

We show that Foxa3 suppresses a key metabolic cofactor, PGC1α,

• which is involved in the gene programs that turn on energy expenditure in adipose tissues.

Overall, these findings suggest that Foxa3 contributes to the increased adiposity observed during aging,

• and that it can be a possible target for the treatment of metabolic disorders.

Aging is associated with increased adiposity and diminished thermogenesis, but

• the critical transcription factors influencing these metabolic changes late in life are poorly understood.

We recently demonstrated that the winged helix factor forkhead box protein A3 (Foxa3)

• regulates the expansion of visceral adipose tissue in high-fat diet regimens; however,
• whether Foxa3 also contributes to the increase in adiposity and the decrease in brown fat activity
• observed during the normal aging process is currently unknown.

Here we report that during aging, levels of Foxa3 are significantly and selectively

• up-regulated in brown and inguinal white fat depots, and that
• midage Foxa3-null mice have increased white fat browning and thermogenic capacity,

1. decreased adipose tissue expansion,
2. improved insulin sensitivity, and
3. increased longevity.

Foxa3 gain-of-function and loss-of-function studies in inguinal adipose depots demonstrated

• a cell-autonomous function for Foxa3 in white fat tissue browning.

The mechanisms of Foxa3 modulation of brown fat gene programs involve

• the suppression of peroxisome proliferator activated receptor γ coactivtor 1 α (PGC1α) levels
• through interference with cAMP responsive element binding protein 1-mediated
• transcriptional regulation of the PGC1α promoter.

Our data demonstrate a role for Foxa3 in energy expenditure and in age-associated metabolic disorders.

Control of Mitochondrial pH by Uncoupling Protein 4 in Astrocytes Promotes Neuronal Survival

HP Lambert, M Zenger, G Azarias, Jean-Yves Chatton, PJ. Magistretti,§, S Lengacher
JBC (in press) M114.570879  http://www.jbc.org/cgi/doi/10.1074/jbc.M114.570879

Background: Role of uncoupling proteins (UCP) in the brain is unclear.
Results: UCP, present in astrocytes, mediate the intra-mitochondrial acidification leading to a decrease in mitochondrial ATP production.
Conclusion: Astrocyte pH regulation promotes ATP synthesis by glycolysis whose final product, lactate, increases neuronal survival.
Significance: We describe a new role for a brain uncoupling protein.

Brain activity is energetically costly and requires a steady and

• highly regulated flow of energy equivalents between neural cells.

It is believed that a substantial share of cerebral glucose, the major source of energy of the brain,

• will preferentially be metabolized in astrocytes via aerobic glycolysis.

The aim of this study was to evaluate whether uncoupling proteins (UCPs),

• located in the inner membrane of mitochondria,
• play a role in setting up the metabolic response pattern of astrocytes.

UCPs are believed to mediate the transmembrane transfer of protons

• resulting in the uncoupling of oxidative phosphorylation from ATP production.

UCPs are therefore potentially important regulators of energy fluxes. The main UCP isoforms

• expressed in the brain are UCP2, UCP4, and UCP5.

We examined in particular the role of UCP4 in neuron-astrocyte metabolic coupling

• and measured a range of functional metabolic parameters
• including mitochondrial electrical potential and pH,

1. reactive oxygen species production,
2. NAD/NADH ratio,
3. ATP/ADP ratio,
4. CO2 and lactate production, and
5. oxygen consumption rate (OCR).

In brief, we found that UCP4 regulates the intra-mitochondrial pH of astrocytes

• which acidifies as a consequence of glutamate uptake,
• with the main consequence of reducing efficiency of mitochondrial ATP production.
• the diminished ATP production is effectively compensated by enhancement of glycolysis.
• this non-oxidative production of energy is not associated with deleterious H2O2 production.

We show that astrocytes expressing more UCP4 produced more lactate,

• used as energy source by neurons, and had the ability to enhance neuronal survival.

Jose Eduardo des Salles Roselino

The problem with genomics was it was set as explanation for everything. In fact, when something is genetic in nature the genomic reasoning works fine. However, this means whenever an inborn error is found and only in this case the genomic knowledge afterwards may indicate what is wrong and not the completely way to put biology upside down by reading everything in the DNA genetic as well as non-genetic problems.

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