Posts Tagged ‘nitric oxide synthase (NOS) isozymes’

Lesson 3 Cell Signaling & Motility: G Proteins, Signal Transduction: Curations and Articles of reference as supplemental information: #TUBiol3373

Curator: Stephen J. Williams, Ph.D.

Updated 7/15/2019

Lesson 3 Powerpoint (click link below):

cell signaling and motility 3 finalissima sjw

Four papers to choose from for your February 11 group presentation:

Structural studies of G protein Coupled receptor


G protein as target in neurodegerative disease

fish technique



Today’s lesson 3 explains how extracellular signals are transduced (transmitted) into the cell through receptors to produce an agonist-driven event (effect).  This lesson focused on signal transduction from agonist through G proteins (GTPases), and eventually to the effectors of the signal transduction process.  Agonists such as small molecules like neurotransmitters, hormones, nitric oxide were discussed however later lectures will discuss more in detail the large growth factor signalings which occur through receptor tyrosine kinases and the Ras family of G proteins as well as mechanosignaling through Rho and Rac family of G proteins.

Transducers: The Heterotrimeric G Proteins (GTPases)

An excellent review of heterotrimeric G Proteins found in the brain is given by

Heterotrimeric G Proteins by Eric J Nestler and Ronald S Duman.



from Seven-Transmembrane receptors – Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Examples-of-heterotrimeric-G-protein-effectors_tbl1_11180073 [accessed 4 Feb, 2019] and see references within



See below for the G Protein Cycle









<a href=”https://www.researchgate.net/figure/32-The-G-protein-cycle-In-the-absence-of-agonist-A-GPCRs-are-mainly-in-the-low_fig2_47933733″><img src=”https://www.researchgate.net/profile/Veli_Pekka_Jaakola/publication/47933733/figure/fig2/AS:669499451781133@1536632516635/32-The-G-protein-cycle-In-the-absence-of-agonist-A-GPCRs-are-mainly-in-the-low.ppm&#8221; alt=”.3.2: The G protein cycle. In the absence of agonist (A), GPCRs are mainly in the low affinity state (R). After agonist binding, the receptor is activated in the high affinity state (R*), and the agonist-GPCR-G protein complex is formed. GTP replaces GDP in Gα. After that the G protein dissociates into the Gα subunit and the Gβγ heterodimer, which then activate several effector proteins. The built-in GTPase activity of the Gα subunit cleaves the terminal phosphate group of GTP, and the GDP bound Gα subunit reassociates with Gβγ heterodimer. This results in the deactivation of both Gα and Gβγ. The G protein cycle returns to the basal state. RGS, regulator of G protein signalling.”/></a>


From Citation: Review: A. M. Preininger, H. E. Hamm, G protein signaling: Insights from new structures. Sci. STKE2004, re3 (2004)


For a tutorial on G Protein coupled receptors (GPCR) see





cyclic AMP (cAMP) signaling to the effector Protein Kinase A (PKA)

from https://courses.washington.edu/conj/gprotein/cyclicamp.htm

Cyclic AMP is an important second messenger. It forms, as shown, when the membrane enzyme adenylyl cyclase is activated (as indicated, by the alpha subunit of a G protein).


The cyclic AMP then goes on the activate specific proteins. Some ion channels, for example, are gated by cyclic AMP. But an especially important protein activated by cyclic AMP is protein kinase A, which goes on the phosphorylate certain cellular proteins. The scheme below shows how cyclic AMP activates protein kinase A.

Updated 7/15/2019

Additional New Studies on Regulation of the Beta 2 Adrenergic Receptor

We had discussed regulation of the G protein coupled beta 2 adrenergic receptor by the B-AR receptor kinase (BARK)/B arrestin system which uncouples and desensitizes the receptor from its G protein system.  In an article by Xiangyu Liu in Science in 2019, the authors describe another type of allosteric modulation (this time a POSITIVE allosteric modulation) in the intracellular loop 2.  See below:

Mechanism of β2AR regulation by an intracellular positive allosteric modulator

Xiangyu Liu1,*, Ali Masoudi2,*, Alem W. Kahsai2,*, Li-Yin Huang2, Biswaranjan Pani2Dean P. Staus2, Paul J. Shim2, Kunio Hirata3,4, Rishabh K. Simhal2, Allison M. Schwalb2, Paula K. Rambarat2, Seungkirl Ahn2, Robert J. Lefkowitz2,5,6,Brian Kobilka1

Positive reinforcement in a GPCR

Many drug discovery efforts focus on G protein–coupled receptors (GPCRs), a class of receptors that regulate many physiological processes. An exemplar is the β2-adrenergic receptor (β2AR), which is targeted by both blockers and agonists to treat cardiovascular and respiratory diseases. Most GPCR drugs target the primary (orthosteric) ligand binding site, but binding at allosteric sites can modulate activation. Because such allosteric sites are less conserved, they could possibly be targeted more specifically. Liu et al. report the crystal structure of β2AR bound to both an orthosteric agonist and a positive allosteric modulator that increases receptor activity. The structure suggests why the modulator compound is selective for β2AR over the closely related β1AR. Furthermore, the structure reveals that the modulator acts by enhancing orthosteric agonist binding and stabilizing the active conformation of the receptor.


Drugs targeting the orthosteric, primary binding site of G protein–coupled receptors are the most common therapeutics. Allosteric binding sites, elsewhere on the receptors, are less well-defined, and so less exploited clinically. We report the crystal structure of the prototypic β2-adrenergic receptor in complex with an orthosteric agonist and compound-6FA, a positive allosteric modulator of this receptor. It binds on the receptor’s inner surface in a pocket created by intracellular loop 2 and transmembrane segments 3 and 4, stabilizing the loop in an α-helical conformation required to engage the G protein. Structural comparison explains the selectivity of the compound for β2– over the β1-adrenergic receptor. Diversity in location, mechanism, and selectivity of allosteric ligands provides potential to expand the range of receptor drugs.


Recent structures of GPCRs bound to allosteric modulators have revealed that receptor surfaces are decorated with diverse cavities and crevices that may serve as allosteric modulatory sites (1). This substantiates the notion that GPCRs are structurally plastic and can be modulated by a variety of allosteric ligands through distinct mechanisms (2-7). Most of these structures have been solved with negative allosteric modulators (NAMs), which stabilize receptors in their inactive states (1). To date, only a single structure of an active GPCR bound to a small-molecule positive allosteric modulator (PAM) has been reported, namely, the M2 muscarinic acetylcholine receptor with LY2119620 (8). Thus, mechanisms of PAMs and their potential binding sites remain largely unexplored.



Fig 1. Structure of the active state T4L-B2AR in complex with the orthosteric agonist BI-167107, nanobody 689, and compound 6FA.  (A) The chemical structure of compound-6FA (Cmpd-6FA). (B) Isoproterenol (ISO) competition binding with 125I-cyanopindolol (CYP) to the β2AR reconstituted in nanodisks in the presence of vehicle (0.32% dimethylsulfoxide; DMSO), Cmpd-6, or Cmpd-6FA at 32 μM. Values were normalized to percentages of the maximal 125I-CYP binding level obtained from a one-site competition binding–log IC50 (median inhibitory concentration) curve fit. Binding curves were generated by GraphPad Prism. Points on curves represent mean ± SEM obtained from five independent experiments performed in duplicate. (C) Analysis of Cmpd-6FA interaction with the BI-167107–bound β2AR by ITC. Representative thermogram (inset) and binding isotherm, of three independent experiments, with the best titration curve fit are shown. Summary of thermodynamic parameters obtained by ITC: binding affinity (KD = 1.2 ± 0.1 μM), stoichiometry (N = 0.9 ± 0.1 sites), enthalpy (ΔH = 5.0 ± 1.2 kcal mol−1), and entropy (ΔS =13 ± 2.0 cal mol−1 deg−1). (D) Side view of T4L-β2AR bound to the orthosteric agonist BI-167107, nanobody 6B9 (Nb6B9), and Cmpd-6FA. The gray box indicates the membrane layer as defined by the OPM database. (E) Close-up view of Cmpd-6FA binding site. Covering Cmpd-6FA is 2Fo– Fc electron density contoured at 1.0 σ (green mesh).From Science  28 Jun 2019:
Vol. 364, Issue 6447, pp. 1283-1287



Fig 3. Fig. 3 Mechanism of allosteric activation of the β2AR by Cmpd-6FA.

(A) Superposition of the inactive β2AR bound to the antagonist carazolol (PDB code: 2RH1) and the active β2AR bound to the agonist BI-167107, Cmpd-6FA, and Nb6B9. Close-up view of the Cmpd-6FA binding site is shown. The residues of the inactive (yellow) and active (blue) β2AR are depicted, and the hydrogen bond formed between Asp1303.49and Tyr141ICL2 in the active state is indicated by a black dashed line. (B) Topography of Cmpd-6FA binding surface on the active β2AR (left, blue) and the corresponding surface of the inactive β2AR (right, yellow) with Cmpd-6FA (orange sticks) docked on top. Molecular surfaces are of only those residues involved in interaction with Cmpd-6FA. Steric clash between Cmpd-6FA and the surface of inactive β2AR is represented by a purple asterisk. (C) Overlay of the β2AR bound to BI-167107, Nb6B9, and Cmpd-6FA with the β2AR–Gscomplex (PDB code: 3SN6). The inset shows the position of Phe139ICL2 relative to the α subunit of Gs. (D) Superposition of the active β2AR bound to the agonist BI-167107, Nb6B9, and Cmpd-6FA (blue) with the inactive β2AR bound to carazolol (yellow) (PDB code: 2RH1) as viewed from the cytoplasm. For clarity, Nb6B9 and the orthosteric ligands are omitted. The arrows indicate shifts in the intracellular ends of the TM helices 3, 5, and 6 upon activation and their relative distances.





Allosteric sites may not face the same evolutionary pressure as do orthosteric sites, and thus are more divergent across subtypes within a receptor family (2426). Therefore, allosteric sites may provide a greater source of specificity for targeting GPCRs.



  1. D. M. Thal, A. Glukhova, P. M. Sexton, A. Christopoulos, Structural insights into G-protein-coupled receptor allostery. Nature 559, 45–53 (2018). doi:10.1038/s41586-018-0259-zpmid:29973731CrossRefPubMedGoogle Scholar


  1. D. Wacker, R. C. Stevens, B. L. Roth, How Ligands Illuminate GPCR Molecular Pharmacology. Cell 170, 414–427 (2017).

doi:10.1016/j.cell.2017.07.009pmid:28753422CrossRefPubMedGoogle Scholar


  1. D. P. Staus, R. T. Strachan, A. Manglik, B. Pani, A. W. Kahsai, T. H. Kim, L. M. Wingler, S. Ahn, A. Chatterjee, A. Masoudi, A. C. Kruse, E. Pardon, J. Steyaert, W. I. Weis, R. S. Prosser, B. K. Kobilka, T. Costa, R. J. Lefkowitz, Allosteric nanobodies reveal the dynamic range and diverse mechanisms of G-protein-coupled receptor activation. Nature 535, 448–452 (2016). doi:10.1038/nature18636pmid:27409812CrossRefPubMedGoogle Scholar


  1. A. Manglik, T. H. Kim, M. Masureel, C. Altenbach, Z. Yang, D. Hilger, M. T. Lerch, T. S. Kobilka, F. S. Thian, W. L. Hubbell, R. S. Prosser, B. K. Kobilka, Structural Insights into the Dynamic Process of β2-Adrenergic Receptor Signaling. Cell 161, 1101–1111 (2015). doi:10.1016/j.cell.2015.04.043pmid:25981665CrossRefPubMedGoogle Scholar


5,   L. Ye, N. Van Eps, M. Zimmer, O. P. Ernst, R. S. Prosser, Activation of the A2A adenosine G-protein-coupled receptor by conformational selection. Nature 533, 265–268 (2016). doi:10.1038/nature17668pmid:27144352CrossRefPubMedGoogle Scholar


  1. N. Van Eps, L. N. Caro, T. Morizumi, A. K. Kusnetzow, M. Szczepek, K. P. Hofmann, T. H. Bayburt, S. G. Sligar, O. P. Ernst, W. L. Hubbell, Conformational equilibria of light-activated rhodopsin in nanodiscs. Proc. Natl. Acad. Sci. U.S.A. 114, E3268–E3275 (2017). doi:10.1073/pnas.1620405114pmid:28373559Abstract/FREE Full TextGoogle Scholar


  1. R. O. Dror, H. F. Green, C. Valant, D. W. Borhani, J. R. Valcourt, A. C. Pan, D. H. Arlow, M. Canals, J. R. Lane, R. Rahmani, J. B. Baell, P. M. Sexton, A. Christopoulos, D. E. Shaw, Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs. Nature 503, 295–299 (2013). doi:10.1038/nature12595pmid:24121438CrossRefPubMedWeb of ScienceGoogle Scholar


  1. A. C. Kruse, A. M. Ring, A. Manglik, J. Hu, K. Hu, K. Eitel, H. Hübner, E. Pardon, C. Valant, P. M. Sexton, A. Christopoulos, C. C. Felder, P. Gmeiner, J. Steyaert, W. I. Weis, K. C. Garcia, J. Wess, B. K. Kobilka, Activation and allosteric modulation of a muscarinic acetylcholine receptor. Nature 504, 101–106 (2013). doi:10.1038/nature12735pmid:24256733



Additional information on Nitric Oxide as a Cellular Signal

Nitric oxide is actually a free radical and can react with other free radicals, resulting in a very short half life (only a few seconds) and so in the body is produced locally to its site of action (i.e. in endothelial cells surrounding the vascular smooth muscle, in nerve cells). In the late 1970s, Dr. Robert Furchgott observed that acetylcholine released a substance that produced vascular relaxation, but only when the endothelium was intact. This observation opened this field of research and eventually led to his receiving a Nobel prize. Initially, Furchgott called this substance endothelium-derived relaxing factor (EDRF), but by the mid-1980s he and others identified this substance as being NO.

Nitric oxide is produced from metabolism of endogenous substances like L-arginine, catalyzed by one of three isoforms of nitric oxide synthase (for link to a good article see here) or release from exogenous compounds like drugs used to treat angina pectoris like amyl nitrate or drugs used for hypertension such as sodium nitroprusside.

The following articles are a great reference to the chemistry, and physiological and pathological Roles of Nitric Oxide:

46. The Molecular Biology of Renal Disorders: Nitric Oxide – Part III

Curator and Author: Larry H Bernstein, MD, FACP


47. Nitric Oxide Function in Coagulation – Part II

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


48. Nitric Oxide, Platelets, Endothelium and Hemostasis

Curator and Author: Larry H Bernstein, MD, FACP


49. Interaction of Nitric Oxide and Prostacyclin in Vascular Endothelium

Curator and Author: Larry H Bernstein, MD, FACP


50. Nitric Oxide and Immune Responses: Part 1

Curator and Author:  Aviral Vatsa PhD, MBBS


51. Nitric Oxide and Immune Responses: Part 2

Curator and Author:  Aviral Vatsa PhD, MBBS


56. Nitric Oxide and iNOS have Key Roles in Kidney Diseases – Part II

Curator and Author: Larry H Bernstein, MD, FACP


57. New Insights on Nitric Oxide donors – Part IV

Curator and Author: Larry H Bernstein, MD, FACP


59. Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function

Curator and Author: Larry H Bernstein, MD, FACP

https://pharmaceuticalintelligence.com/2012/09/16/nitric-oxide-has-a-ubiquitous-role-in-the-regulation-of-glycolysis-with-         a-concomitant-influence-on-mitochondrial-function/

Biochemistry of the Coagulation Cascade and Platelet Aggregation: Nitric Oxide: Platelets, Circulatory Disorders, and Coagulation Effects

Nitric Oxide Function in Coagulation – Part II

Nitric oxide is implicated in many pathologic processes as well.  Nitric oxide post translational modifications have been attributed to nitric oxide’s role in pathology however, although the general mechanism by which nitric oxide exerts its physiological effects is by stimulation of soluble guanylate cyclase to produce cGMP, these post translational modifications can act as a cellular signal as well.  For more information of NO pathologic effects and how NO induced post translational modifications can act as a cellular signal see the following:

Nitric Oxide Covalent Modifications: A Putative Therapeutic Target?

58. Crucial role of Nitric Oxide in Cancer

Curator and Author: Ritu Saxena, Ph.D.


Note:  A more comprehensive ebook on Nitric Oxide and Disease Perspectives is found at

Cardiovascular Diseases, Volume One: Perspectives on Nitric Oxide in Disease Mechanisms

available on Kindle Store @ Amazon.com


Read Full Post »

Metabolomics is about Metabolic Systems Integration

Author and Curator: Larry H Bernstein, MD, FCAP 


This is an exploration of biological thoughts in the series on metabolomics, putting enzymatic reactions, proteins and protein conformation, and subanatomic structure into a more complete perspective in order to realize normal and dysfunctional states

of eukaryoticcells and organ systems and prokaryotic organisms.  There are structures and functions that have evolved in evolution that have concordance, even
if we find variation on themes.  Moreover, these have to be understood in a systems oriented view to have any clarity, which is currently an ongoing proposal.
It is perhaps relevant to quote Radoslav Bosov on his observation:

“After finishing her portion of the work on DNA, Franklin led pioneering work on the tobacco mosaic virus and the polio virus. She died in 1958 at the age of 37 of ovarian cancer.”  My job is to illuminate what is cancer, but serving structural identity issues.

DNA is not DNA, as RNA is not RNA as proteins are not Proteins, there is only time – interference of particles/strings/waves within ever emerging discrete relative spaces where energy transforms from one absolute form into another!

He adds the following: “A 2005 study showed methionine restriction without energy restriction extends mouse lifespan.” BUT balancing energy is not as same as balancing matter due quantum electrodynamics interference and transfromability – http://en.wikipedia.org/wiki/Methionine

I have made the following calculations!

1 – methyl groups = i Ln (1 – Lactate )/Ln (Oxygen) – K (O) =

i Ln (1/(Sqrt (1 – Acetate^2)) /Ln(Oxygen) – K(O) = i Ln (Glyoxylate)/Ln (Oxygen) – K(O)

where K(O) – mechanical electro magnetics pressure, with increase of T, increase of S (entropy), and 1-S = negative entropy

But don’t try to realize the path of derivation, it would get you in dark matter issues – water!

The problem seems to be:

  1. Methionine is necessary to provide S for acetyl CoA
  2. Insufficiency of this amino acid has consequences, which leads to increased homocysteine
  3. This imbalance is also associated with a decrease in lewan body mass
  4. Of course, the reality is that geographic location, proximity to volcanic ash, and temperate zone have relevance, as does food source, and they are relevant variables

JEDS Rosalino has referred to the important conclusion in Erwin Schroedinger’s “What is Life?”, and Schroedinger’s cat.  It is impossible to come up with a predictive equation to explain life.
It had to come from a founder of “Quantum Mechanics” because, unlike economics, physics is a science based on experimental validation.  In entering biology from Physics to make it more rigorous, as was the case for  Max Delbruck, who was preceded by the Cori’s, Beadle and Tatum, Herschey, Luria, Dubecco, Kornberg and Ochoa, Lipmann, Watson and Crick, a discipline called “Molecular Biology and Biochemistry” emerged that would open the secrets of life.  Beadle and Tatum gave us “one gene – one enzyme”, a formulation that led in medical teaching from William Osler’s edict to “Inherited Metabolic Disorders” – gene related disruption of the chemical reactions taking place in the body to convert or use energy. Physiological chemistry taught:

  1. Breaking down the carbohydrates, proteins, and fats in food to release energy.
  2. Transforming excess nitrogen into waste products excreted in urine.
  3. Breaking down or converting chemicals into other substances and transporting them inside cells.

Metabolism is an organized but chaotic chemical assembly line. Raw materials, half-finished products, and waste materials are constantly being used, produced, transported, and excreted. The “workers” on the assembly line are enzymes and other proteins that make chemical reactions happen. – http://www.webmd.com/a-to-z-guides/inherited-metabolic-disorder-types-and-treatments

The original cause of most genetic metabolic disorders is a gene mutation that occurred many, many generations ago. Each inherited metabolic disorder is quite rare in the general population, affecting about 1 in 1,000 to 2,500 newborns. But the developments now refocused an emphasis on HOW – a gene mutation occurs that is passed on through generations.  This had to be derived initially from methods developed in prokaryotes in order to relieve the complexity.  However, complexity came from evolutionary events over a long time span.

Part I. Transcription regulation

The timing is right

R Magnus N Friis  & Michael C Schultz
Affiliations  Corresponding author

Nature Structural & Molecular Biology 07 Oct 2014; 21: 846–847

Yeast cells display synchronized oscillation between

  • phases of high and low oxygen consumption
  • accompanied by a program of cyclical gene expression.

A study monitoring

  • mRNA levels,
  • histone modifications and
  • chromatin occupancy of histone modifiers

during the yeast metabolic cycle (YMC) at high temporal resolution reveals both

  • ‘just-in-time’ supply of YMC gene products and
  • new patterns of chromatin reconfiguration

associated with transcriptional regulation.

Figure 1: The yeast metabolic cycle.

yeast metabolic cycle.

The YMC is divided into metabolic phases that correspond to periods of high and low oxygen concentration in the culture medium. The program of gene (mRNA) expression during the YMC is composed of successive reductive-charging (RC),…

Figure 2: Modes of transcriptional regulation during the YMC.

Modes of transcriptional regulation during the YMC

Modes of transcriptional regulation during the YMC

(a) Previous work on cycling cells in batch culture revealed that H3K4me3 is typically limited to the promoter region of active genes (MET16 shown here 9, 10). (b) During the YMC, however, the OX gene RMT2 is marked by H3K4me3 regardles…


High-temporal-resolution view of transcription and chromatin states across distinct metabolic states in budding yeast

Z Kuang, L Cai, X Zhang, H Ji, BP Tu  & JD Boeke
Affiliations Contributions Corresponding authors

Nature Structural & Molecular Biology 31 Aug,2014; 21: 854–863

Under continuous, ​glucose-limited conditions, budding yeast exhibit

  1. robust metabolic cycles
  2. associated with major oscillations of gene expression.

We examine the correlated

  1. genome-wide transcription and chromatin states
  2. across the yeast metabolic cycle
  3. at unprecedented temporal resolution,
  4. revealing a ‘just-in-time supply chain’

by which components from specific cellular processes such as ribosome biogenesis become available in a highly coordinated manner. We identify

  1. distinct chromatin and splicing patterns
  2. associated with different gene categories and
  3. determine the relative timing of chromatin modifications
  4. relative to maximal transcription.

There is unexpected variation in the chromatin modification and expression relationship, with

  1. histone acetylation peaks occurring with
  2. varying timing and ‘sharpness’ relative to RNA expression
  3. both within and between cycle phases.

Chromatin-modifier occupancy reveals subtly distinct spatial and temporal patterns compared to those of the modifications themselves.

Figure 1: High-temporal-resolution analysis of gene expression reveals meticulous temporal compartmentalization in yeast.

High-temporal-resolution analysis of gene expression

High-temporal-resolution analysis of gene expression

Oscillation of ​oxygen (dO2) in the YMC. The 16 time points of one cycle for RNA-seq are labeled. Metabolic phases are color coded throughout figures: magenta, OX phase; green, RB phase; blue, RC phase. (b–d) Subtly distinct tempor…


Figure 2: RNA-seq analysis at introns reveals transient accumulation of pre-mRNAs during OX phase.

RNA-seq analysis at introns reveals transient accumulation of pre-mRNAs

RNA-seq analysis at introns reveals transient accumulation of pre-mRNAs

Relative RNA signals at intron-containing genes. Each track represents relative RNA levels at one of 16 time points, ordered sequentially from top to bottom. Signals are displayed as a percentage of the maximum value of the 16 time…

Figure 3: Dynamic chromatin states across the YMC.

Dynamic chromatin states across the YMC

Dynamic chromatin states across the YMC

(a)Oscillation of ​oxygen in one YMC. Cycling cells were collected at 16 intentionally uneven time points over one cycle for ChIP-seq. (b,c) Temporal relationship between RNA level and histone modifications at the ​RMT2 locus. (b) RNA…


Part 2. Structure of metabolic channeling

Enzyme clustering accelerates processing of intermediates through metabolic channeling

Michele Castellana, Maxwell Z Wilson, Yifan Xu, Preeti Joshi, Ileana M Cristea, Joshua D Rabinowitz, Zemer Gitai & Ned S Wingreen
Affiliations Contributions Corresponding authors

Nature Biotechnology (2014)32, 1011–1018

We present a quantitative model to demonstrate that

  • coclustering multiple enzymes into compact agglomerates
  • accelerates the processing of intermediates,
  • yielding the same efficiency benefits as direct channeling,

a well-known mechanism in which enzymes are funneled between enzyme active sites through a physical tunnel. The model predicts

  • the separation and size of coclusters that maximize metabolic efficiency,
  • and this prediction is in agreement with previously reported spacings between coclusters in mammalian cells.

For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model prediction that enzyme agglomerates can

  • accelerate the processing of a shared intermediate by one branch, and thus
  • regulate steady-state flux division.

Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling

Figure 1: Different types of intermediate channeling in a two-step metabolic pathway, where a substrate is processed by enzyme E1 and turned into intermediate, which is then processed by enzyme E2 and turned into product.

two-step metabolic pathway

two-step metabolic pathway

Direct channeling. The intermediate is funneled from enzyme E1 to enzyme E2 by means of a protein tunnel that connects the active sites of E1 and E2, thus preventing the intermediate from diffusing away. (b) Proximity channeling. http://www.nature.com/nbt/journal/v32/n10/carousel/nbt.3018-F1.jpg

Figure 2: Two-step metabolic pathway with an unstable intermediate.

Two-step metabolic pathway with an unstable intermediate

Two-step metabolic pathway with an unstable intermediate

(a) The two-step metabolic pathway. Substrate S0 is processed by enzyme E1 and turned into intermediate S1, which is then processed by enzyme E2 and turned into product P. (b) Enzyme configurations in the two-step metabolic pathway. Le…

Part 3. Antibiotics directed at specific DNA sequences

Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases

Robert J Citorik, Mark Mimee & Timothy K Lu
Affiliations Contributions Corresponding author

Nature Biotechnology 21 Sep 2014;

Current antibiotics tend to be broad spectrum, leading to

  • indiscriminate killing of commensal bacteria and
  • accelerated evolution of drug resistance.

Here, we use CRISPR-Cas technology to create antimicrobials

  • whose spectrum of activity is chosen by design.

RNA-guided nucleases (RGNs) targeting specific DNA sequences are delivered efficiently to microbial populations using bacteriophage or bacteria carrying plasmids transmissible by conjugation. The DNA targets of RGNs can be

  • undesirable genes or polymorphisms,
  • including antibiotic resistance and virulence determinants in
  1. carbapenem-resistant Enterobacteriaceae and
  2. enterohemorrhagic Escherichia coli.

Delivery of RGNs significantly improves survival in a Galleria mellonella infection model. We also show that

  • RGNs enable modulation of complex bacterial populations
  • by selective knockdown of targeted strains
  • based on genetic signatures.

RGNs constitute a class of highly discriminatory, customizable antimicrobials that enact

  • selective pressure at the DNA level to
  1. reduce the prevalence of undesired genes,
  2. minimize off-target effects and
  3. enable programmable remodeling of microbiota.

Figure 1: RGN constructs delivered by bacteriophage particles (ΦRGN) exhibit efficient and specific antimicrobial effects against strains harboring plasmid or chromosomal target sequences

RGN constructs delivered by bacteriophage particles

RGN constructs delivered by bacteriophage particles

(a) Bacteriophage-delivered RGN constructs differentially affect host cell physiology in a sequence-dependent manner. If the target sequence is: (i) absent, the RGN exerts no effect; (ii) chromosomal, RGN activity is cytotoxic; (iii) e…


Figure 3: ΦRGN particles elicit sequence-specific toxicity against enterohemorrhagic E. coli in vitroand in vivo.

ΦRGN particles elicit sequence-specific toxicity against enterohemorrhagic E. coli in vitro and in vivo.

ΦRGN particles elicit sequence-specific toxicity against enterohemorrhagic E. coli in vitro and in vivo.

(a) E. coli EMG2 wild-type (WT) cells or ATCC 43888 F′ (EHEC) cells were treated with SM buffer, ΦRGNndm-1 orΦRGNeae at a multiplicity of infection (MOI) ~100 and plated onto LB agar to enumerate total cell number or LB+kanamycin (Km)…  http://www.nature.com/nbt/journal/vaop/ncurrent/carousel/nbt.3011-F3.jpg

Part 4. Structure and Isoform functions

Structures of human constitutive nitric oxide synthases.

H Li, J Jamal, C Plaza, SH Pineda, G Chreifi, Q Jing, MA Cinelli, RB Silverman, TLPoulos, [more]
Acta Crystallographica Section D Biological Crystallography (Impact Factor: 12.67). 10/2014; 70(Pt 10):2667-74.

Mammals produce three isoforms of nitric oxide synthase (NOS):

  1. neuronal NOS (nNOS),
  2. inducible NOS (iNOS) and
  3. endothelial NOS (eNOS).

The overproduction of NO by nNOS is associated with a number of neurodegenerative disorders; therefore, a desirable therapeutic goal is

  • the design of drugs that target nNOS
  • but not the other isoforms.

Crystallography, coupled with computational approaches and medicinal chemistry, has played a critical role in developing highly

  • selective nNOS inhibitors that
  • exhibit exceptional neuroprotective properties.

For historic reasons, crystallography has focused on rat nNOS and bovine eNOS because these were available in high quality; thus, their structures have been used in

  • structure-activity-relationship studies.

Although these constitutive NOSs share more than 90% sequence identity across mammalian species for each NOS isoform,

  • inhibitor-binding studies revealed that subtle differences near the heme active site
  • in the same NOS isoform across species still impact enzyme-inhibitor interactions.

Therefore, structures of the human constitutive NOSs are indispensible. Here, the first structure of human neuronal NOS at 2.03 Å resolution is reported and a different crystal form of human endothelial NOS is reported at 1.73 Å resolution.

“We are learning more about less and less” – PJ Russell. 1973.

Part 5.  Global Metabolomics

Global Metabolomics Market (Technique, Application, Indication and Geography) – Size, Application Analysis, Regional Outlook, Competitive Strategies and Forecasts, 2014 – 2020

Metabolomics is

  • the study of chemical processes which involve metabolites.

Metabolites are small molecules present in the blood, tissues and urine. Metabolomics pertains to the study of the

  • unique chemical fingerprints left behind by cellular processes.

These metabolite fingerprints could be used to learn about the health of an organism. It is an upcoming technology in the field of analytical biochemistry. Metabolomics has become an experimental technique that can be applied in medicine, biology and environmental science. The incorporation of computers has enabled

the creation of computational metabolomics that has application in life sciences.

Metabolics finds application in other areas as well; for instance, it is used to identify the quality, taste and nutritional value of food in the food science field.

The metabolomics market is segmented based on its application in different fields such as

  • biomarkers discovery,
  • drug discovery,
  • toxicology testing,
  • nutrigenomics,
  • clinical studies etc.

The drug discovery segment holds the dominant share in the metabolomics market due to its crucial role in

  • drug target identification & validation and
  • optimization & prioritization of diagnostic approaches for oncology research.

The metabolomics market is expected to grow at a rapid rate due to the rise in the number of

  • pre clinical & clinical trials,
  • advancements in toxicological studies and
  • growing awareness about nutritional products.

The stellar growth of data analysis software & solutions in metabolomics and its use in the biomarker screening of diseases would fuel the growth of the metabolomics market. The metabolomics market is also segmented based on techniques into

  • gas chromatography,
  • high performance liquid chromatography (HPLC),
  • ultra performance liquid chromatography, and
  • capillary electrophoresis.

HPLC holds the dominant share in the metabolomics market.


In-depth analysis of various regions would enable a clear understanding of current and future trends so that companies can make region specific plans

Comprehensive analysis of the factors that drive and restrict the growth of the metabolomics market

Key regulatory guidelines in various regions which impact the metabolomics market

Quantitative analysis of the current market

Deep dive analysis of various regions

Value chain analysis enables a clear understanding of the roles of the stakeholders involved in the supply chain of the metabolomics market

Market Segmentation

The metabolomics market is segmented based on techniques, applications, indication and geography


Separation Method

  • Gas Chromatography
  • Capillary Electrophoresis
  • High Performance Liquid Chromatography
  • Ultra Performance Liquid Chromatography

Detection Methods

  • Nuclear Magnetic Resonance
  • Mass Spectrometry
  • Surface Base Mass Analysis


  • Biomarkers Discovery
  • Drug Discovery
  • Toxicology Testing
  • Nutrigenomics
  • Clinical & Preclinical Studies


  • Oncology
  • Neurology
  • Cardiology
  • Others

Read Full Post »

%d bloggers like this: