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Posts Tagged ‘enzyme alignments’


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
http://dx.doi.org:/10.1038/nsmb.2898

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),…
http://www.nature.com/nsmb/journal/v21/n10/carousel/nsmb.2898-F1.jpg

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…

http://www.nature.com/nsmb/journal/v21/n10/carousel/nsmb.2898-F2.jpg

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
http://dx.doi.org:/10.1038/nsmb.2881

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…

http://www.nature.com/nsmb/journal/v21/n10/carousel/nsmb.2881-F1.jpg

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…
http://www.nature.com/nsmb/journal/v21/n10/carousel/nsmb.2881-F2.jpg

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…

http://www.nature.com/nsmb/journal/v21/n10/carousel/nsmb.2881-F3.jpg

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
http://dx.doi.org:/10.1038/nbt.3018

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…
http://www.nature.com/nbt/journal/v32/n10/carousel/nbt.3018-F2.jpg

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;
http://dx.doi.org:/10.1038/nbt.3011

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…

http://www.nature.com/nbt/journal/vaop/ncurrent/carousel/nbt.3011-F1.jpg

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.
http://dx.doi.org:/10.1107/S1399004714017064

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.

KEY BENEFITS

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

Techniques

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

Application

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

Indications

  • Oncology
  • Neurology
  • Cardiology
  • Others
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