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larryhbern:

This is another notable post on the improvement in absorption and bioactivty of a drug, with toxicity known.

Originally posted on New Drug Approvals:

Cocrystals of telmisartan: characterization, structure elucidation, in vivo and toxicity studies

 

CrystEngComm, 2014, 16,8375-8389
DOI: 10.1039/C4CE00797B, Paper
Renu Chadha, Swati Bhandari, Jamshed Haneef, Sadhika Khullar, Sanjay Mandal
 
*Corresponding authors
aUniversity Institute of Pharmaceutical Sciences, Panjab University, Chandigarh-160014, India 
E-mail: renukchadha@rediffmail.com ;
Tel: +91 9316015096
bDepartment of Chemical Sciences, Indian Institute of Science Education and Research, Sector 81, Manauli PO, S.A.S. Nagar, Mohali, India 
E-mail: sanjaymandal@iisermohali.ac.in ;
Tel: +91 9779932606
 
 

The present study reports novel cocrystals of telmisartan (TEL) with saccharin and glutaric acid.
 
The present study reports novel cocrystals of telmisartan (TEL) with saccharin and glutaric acid. Crystal engineering approaches such as solution crystallization, solid-state grinding and slurry method have been utilized with the ultimate…

View original 184 more words


larryhbern:

Another informative post of what goes into pharmaceutical analysis.

Larry H Bernstein, MD, FCAP

Originally posted on New Drug Approvals:

Structural characterization of form I of anhydrous rifampicin

 

CrystEngComm, 2014, 16,8555-8562
DOI: 10.1039/C4CE01157K, Paper
Amanda Laura Ibiapino, Rafael Cardoso Seiceira, Altivo Pitaluga, Antonio Carlos Trindade, Fabio Furlan Ferreira
 
 
*Corresponding authors
aCenter of Natural and Human Sciences (CCNH), Federal University of ABC (UFABC), Av. dos Estados, 5001, Santo André, Brazil 
bLaboratory of Solid State Studies (LEES), Farmanguinhos, FIOCRUZ, Av. Comandante Guaranys, 447, Rio de Janeiro, Brazil
cReal Time Process and Chemical Analysis Development Center (NQTR), Chemistry Institute, Federal University of Rio de Janeiro (UFRJ), Rua Hélio de Almeida, 40, Rio de Janeiro, Brazil
dFederal Institute of São Paulo (IFSP), Av. Mogi das Cruzes, 1501, Suzano, Brazil
 

Crystal structure determination…

View original 207 more words


larryhbern:

Interesting explanation of improvement in an existing drug.

Larry H Bernstein, MD, FCAP

Originally posted on New Drug Approvals:

New cocrystals of ezetimibe with L-proline and imidazole

 

CrystEngComm, 2014, Advance Article
DOI: 10.1039/C4CE01127A, Paper
Manishkumar R. Shimpi, Scott L. Childs, Dan Bostrom, Sitaram P. Velaga
 
 *Corresponding authors
aDepartment of Health Sciences Luleå University of Technology, Luleå, Sweden 
bRenovo Research, Atlanta, USA 
cThermal Energy Conversion Laboratory, Department of Applied Physics and Electronics, Umeå University, Umeå S-90187, Sweden
 

Two new cocrystals of ezetimibe were identified and scale-up. Ezetimibe-proline cocrystal showed improved apparent solubility and physical stability.
 
 
The objectives of the study were to screen and prepare cocrystals of anti-cholesterol drug ezetimibe (EZT) with the aim of increasing its solubility and dissolution rate. Thermodynamic phase diagram based high throughput screening was performed using wet…

View original 151 more words


The Human Proteome Map Completed

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

 

Researchers Produce First Map of Human Proteome, and Reveal New
Significance in The Human Proteome

HAHNE, TECHNISCHE UNIVERSITÄT MÜNCHENTwo international teams have
independently produced the first drafts of the human proteome. These curated
catalogs of the proteins expressed in most non-diseased human tissues and
organs can be used as a baseline to better understand changes that occur in
disease states. Their findings were published today (May 29) in Nature.

Both teams uncovered new complexities of the human genome, identifying novel
proteins from regions of the genome previously thought to be non-coding.

“the real breakthrough with these two projects is the comprehensive coverage of
more than 80 percent of the expected human proteome” said Hanno Steen, director
of proteomics at Boston Children’s Hospital, who was not involved in the work.

The human proteome map provides a catalog of proteins expressed in nondiseased tissues and organs to use as baseline in understanding changes that occur in disease

Given the growing importance of proteins in medical laboratory testing,

Experts are comparing this to the first complete map of the human genome

  • and this information provides for rapid advances
  • in understanding transcriptomics and metabolomics

Map of Human Proteome Expected to Advance Medical Science

“Housekeeping genes” that are expressed in all tissues and cell types

  • have been thought to be involved in basic cellular functions.

Two teams developing a Human Proteome Map

  • detected proteins encoded by 2,350 genes
  • across all human cells and tissues.

The corresponding housekeeping proteins comprised
about 75% of total protein mass.

  •  histones,
  • ribosomal proteins,
  • metabolic enzymes, and
  • cytoskeletal proteins

The two international teams produced

  • the first drafts of the human protoeome,
  • a catalog of proteins expressed in most
  • nondiseased human issues and organs.

The evidence suggests there is translation from DNA regions

  • that were not thought to be translated—including
  • more than 400 translated long, intergenic non-coding RNAs (lincRNAs)—
    found by the Küster team—and
  • 193 new proteins—uncovered by the Pandey team.

This proteome map can be used as a baseline to understand

  • changes that occur in the disease state

These studies are part of the Human Proteome Project,

  1. an international effort by the Human Proteome Organization
  2. to revolutionize our understanding of the human proteome
  3. by coordinating research at laboratories around the world directed
  4. at mapping the entire human proteome.

This new information about the human proteome

  • is expected to trigger rapid advances in medical science
  • and a better understanding of the underlying causes of human diseases.

One Study Team Was at Johns Hopkins University

  • In one study, which was headed by Ahilesh Pandey, M.D.,
    at Johns Hopkins University in Baltimore,
  • and colleague Harsha Gowda, Ph.D.,
    of the Institute of Bioinformatics in Bangalore, India,
  • the research team used an advanced form of mass spectrometry to analyze proteins
  • to create the human proteome map,

according to a report published in NIH Research Matters.

The research team examined

  1. 30 normal human tissue and cell types:
  2. 17 adult tissues,
  3. 7 fetal tissue and
  4. 6 blood cell types.

Samples from three people per tissue type

  • were processed through several steps.

The protein fragments, or peptides, were analyzed on

The amino acid sequences were

  • then compared to known sequences.

Their results were published in the May 28, 2014, issue of Nature.

The resulting draft map of the human proteome map includes

  • proteins encoded by more than 17,000 genes,
  • noted the Research Matters article.

Among these are hundreds of proteins from regions

  • previously thought to be non-coding.

This study also provided a new understanding of

  • how genes are expressed.

For example, almost 200 genes begin in locations

  • other than those predicted based on genetic sequence.

“The fact that 193 of the proteins came from DNA sequences

  • predicted to be non-coding means that
  • we don’t fully understand how cells read DNA,
  • since the sequences code for proteins

This study also produced the Human Proteome Map,

  • an interactive online portal.

This can be accessed at this link.

The study data will soon be accessible through

German’s ProteomicsDB Analyzed a Mix of Available and New Tissue Data

The other study was conducted by a team lead by  Bernhard Küster
of the Technische Universität München in Germany.

Küster and his colleagues created a

This database contains 92% of the

  • estimated 19,629 human proteins,

noted The Scientist article.

Küster’s team also used mass spectrometry

  • to analyze human tissue samples.

This team’s approach differed from Johns Hopkins’ in that

  • it compiled about 60% of the information
  • in the ProteomicsDB database
  1. by using existing raw mass spec (MS) data
  2. from databases and colleagues’ contributions.

To fill data gaps, the Küster lab generated its own
MS data after analyzing

  1. 60 human tissues,
  2. 13 body fluids, and
  3. 147 cancer cell lines.

High-resolution public data

  • was selected and computationally processed
  • for strict quality

The database for ProteomicsDB is

  • public and searchable.

It can be accessed at this link.

German Study Added New Insights to Transcription Process

Comparing the ratio of protein to mRNA levels for every protein globally,

  • the Küster lab found that the translation rate
  • is a constant feature of each mRNA transcript. 

The proteomics community has viewed

  • transcriptome and proteome data as two sides of a coin.

But this analysis shows that at least, at steady state,

  • once the ratio for an mRNA/protein pair has been calculated,
  1. protein levels can be determined
  2. just from specific mRNA levels.

Proteomics researchers in Toronto maintaining ionic balance and in Boston commented on the
importance of the findings, even a “new paradigm” because of

  • the fixed ratio of protein to mRNA

This is quite in keeping with what we have been learning

  • with respect to homeostasis.

In 2003, the Human Genome Project created a

  • draft map of the human genome—
  • all the genes in the human body.

Genomics has since driven many advances in medical science.

This was a progress from the classic discovery of Watson and Crick -

  • the classical dogma holds that
  • DNA makes RNA makes protein.
  • no constraints are place on this

But the cell is functioning in contact with other cells,

  • immersed in interstitial fluid
  • maintaining cationic and anionic balance
  • and mitochondrial energy balance and ubiquitin systems interact
  • and protein interacts with the chromatin and transcriptional RNA

So the restriction that has been discovered has credence,

  • the classical diagram has to be redrawn

Deeper Knowledge of Proteome to Improve Diagnostics and Therapeutics

In the two projects is:

  • the comprehensive coverage of more than 80% of
  • the expected human proteome,

These studies indicate that to get to

  • a deep level of proteome coverage,
  • many different tissue types must be probed.

the  studies are  complimentary.

  1. The Hopkins group provided a survey of human proteins from a single source, which allows for easy comparisons within their data.
  2. The ProteomeDB effort connected new information with existing data

A deeper knowledge of the human proteome could help

  • fill the gap between genomes and phenotypes.

As this occurs, it has the potential to transform

  • the way diagnostics and therapeutics are developed,
  •  enhancing overall biomedical research and healthcare,

it was noted in a report presented to scientific leaders at a NIH workshop

  • on advances in proteomics and its applications.

Having completed a draft map of the human proteome—
the set of all proteins in the human body

  • It opens another window to cell function.

It has been ASSUMED -

  • genes control the most basic functions of the cell,
  • including what proteins to make and when.
  • but we have assumed for too much in assigning
    full control to the genome

Researchers have identified more than 20,000 protein- coding genes.

However, scientific understanding of the proteome has

  • lagged behind that of the genome,
  • partly because of the proteome’s complexities.

The relationship between genes and proteins isn’t a simple matter of

  • one gene coding for one protein.

Stretches of DNA can be read and translated

  • into proteins in different ways.

Proteins are also more difficult to sequence than genes.

The importance of these latest studies to pathologists and Ph.D.s working

  • in molecular diagnostics laboratories is that
  • this information will expedite further research into the human proteome.

Such research is expected to lead to

  • novel methods of diagnosis and complex
  • “multi-analyte” clinical laboratory tests that
  • look for multiple proteins in a single assay.

“The prevalent view was that information transfer was from genome to transcriptome to proteome.
What these efforts show is that it’s a two-way road— proteomics can be used to annotate the genome.
The importance is that, using these datasets, we can improve the annotation of the genome and the
algorithms that predict transcription and translation,” said Steen. “The genomics field can now hugely
benefit from proteomics data.”

Wilhelm et al., “Mass-spectrometry- based draft of the human proteome,”
Nature,  http://dx.doi.doi:/10.1038/nature13319, 2014

M.S. Kim et al. “A draft map of the human proteome,”
Nature,  http://dx.doi.org:/10.1038/nature13302, 2014.

Tags

proteomicsnoncoding RNAhuman researchhuman proteome projecthuman genetics and genomics

http://www.the-scientist.com/?articles.view/articleNo/40083/title/Human-Proteome-Mapped/

 

__Patricia Kirk

__by Harrison Wein, Ph.D.

__by Anna Azvolinsky

Related Information:

Revealing The Human Proteome

Human Proteome Mapped

The human proteome – a scientific opportunity for transforming diagnostics, therapeutics, and healthcare

Reference: A draft map of the human proteome.
Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Donahue CA, Gowda H, Pandey A.
Nature. 2014 May 29;509(7502):575-81. http://dx.doi.org:/10.1038/nature13302. PMID: 24870542

Funding: NIH’s National Institute of General Medical Sciences (NIGMS), National Cancer Institute (NCI),
and National Heart, Lung, and Blood Institute (NHLBI); the Sol Goldman Pancreatic Cancer Research Center;
India’s Council of Scientific and Industrial Research; and Wellcome Trust/DBT India Alliance.

http://nihprod.cit.nih.gov/researchmatters/june2014/06092014proteome.htm

 

 

 

 

 

 

 

 

 


New Frontiers in Gene Editing — Cambridge Healthtech Institute’s Inaugural, February 19-20, 2015 | The Inter Continental San Francisco | San Francisco, CA

Reporter: Aviva Lev-Ari, PhD, RN

Cambridge Healthtech Institute’s Inaugural

New Frontiers in Gene Editing

Transitioning From the Lab to the Clinic

February 19-20, 2015 | The InterContinental San Francisco | San Francisco, CA
Part of the 22nd International Molecular Medicine Tri-Conference

 

Gene editing is rapidly progressing from being a research/screening tool to one that promises important applications downstream in drug development and cell therapy. Cambridge Healthtech Institute’s inaugural symposium on New Frontiers in Gene Editing will bring together experts from all aspects of basic science and clinical research to talk about how and where gene editing can be best applied. What are the different tools that can be used for gene editing, and what are their strengths and limitations? How does the CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas system, compare to Transcription Activator-like Effector Nucleases (TALENs), zinc finger nucleases (ZFNs) and other systems and where are they being used? Scientists and clinicians from pharma/biotech as well as from academic and government labs will share their experiences leveraging the utility of gene editing for functional screening, creating cell lines and knock-outs for disease modeling, and for cell therapy.

 

KEYNOTE PRESENTATIONS:

Precise Single-Base Genome Engineering for Human Diagnostics and Therapy

Bruce R. Conklin M.D., Investigator, Roddenberry Center for Stem Cell Biology and Medicine, Gladstone Institutes and Professor, Division of Genomic Medicine, University of California, San Francisco

Genome Edited Induced Pluripotent Stem Cells for Drug Screening

Joseph C. Wu, M.D., Ph.D., Director, Stanford Cardiovascular Institute and Professor, Department of Medicine/Cardiology & Radiology, Stanford University School of Medicine

 

USING GENE EDITING FOR FUNCTIONAL SCREENS

Exploration of Cellular Stress and Trafficking Pathways Using shRNA and CRISPR/Cas9-based Systems

Michael Bassik, Ph.D., Assistant Professor, Department of Genetics, Stanford University

Gene Editing in Patient-derived Stem Cells for In Vitro Modeling of Parkinson’s Disease

Birgitt Schuele M.D., Associate Professor and Director of Gene Discovery and Stem Cell Modeling, The Parkinson’s Institute

Massively Parallel Combinatorial Genetics to Overcome Drug Resistance in Bacterial Infections and Cancer

Timothy K. Lu, M.D., Ph.D., Associate Professor, Synthetic Biology Group, Department of Electrical Engineering and Computer Science and Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology

 

TRANSLATING GENE EDITING IN VIVO

CRISPR-Cas: Tools and Applications for Genome Editing

Fei Ann Ran, Ph.D., Post-doctoral Fellow, Laboratory of Dr. Feng Zhang, Broad Institute and Junior Fellow, Harvard Society of Fellows

Anti-HIV Therapies: Genome Engineering the Virus and the Host

Paula M. Cannon Ph.D., Associate Professor, Molecular Microbiology & Immunology, Biochemistry, and Pediatrics, Keck School of Medicine, University of Southern California

Preventing Transmission of Mitochondrial Diseases by Germline Heteroplasmic Shift Using TALENs

Juan Carlos Izpisua Belmonte, Ph.D., Professor, Gene Expression Laboratory, Salk Institute

Nuclease-Based Gene Correction for Treating Single Gene Disorders

Gang Bao, Ph.D., Professor, Robert A. Milton Chair in Biomedical Engineering, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University

 

EXPLORING GENE EDITING FOR THERAPEUTIC USES

Gene Editing on the Cusp of Exciting Opportunities for Human Therapeutics

Rodger Novak, M.D., CEO, CRISPR Therapeutics

Genome Editing for Genetic Diseases of the Blood

Matthew Porteus, M.D., Ph.D., Associate Professor, Pediatrics, Stanford University School of Medicine

Genome Engineering Tools for Gene Therapy and Regenerative Medicine

Charles A. Gersbach, Ph.D., Assistant Professor, Department of Biomedical Engineering, Center for Genomic and Computational Biology, Duke University

 

INTELLECTUAL PROPERTY LANDSCAPE: OPPORTUNITIES & CONCERNS

CRISPR/Cas-9: Navigating Intellectual Property (IP) Challenges in Gene Editing

Chelsea Loughran, Associate, Litigation Group, Wolf, Greenfield and Sacks, P.C.

Suggested Event Package:

February 15 Afternoon Short Course: Best Practices in Personalized and Translational Medicine
February 15 Dinner Short Course: Regulatory Compliance in Drug-Diagnostics Co-Development
February 16 Morning Short Course: Isolation and Characterization of Cancer Stem Cells
February 16-18 Conference Program: Genome and Transcriptome Analysis

 

 

For more details on the conference, please contact: 
Tanuja Koppal, Ph.D.,
Conference Director
Cambridge Healthtech Institute
E: tkoppal@healthtech.com

For partnering and sponsorship information, please contact: 
Jon Stroup (Companies A-K)
Manager, Business Development
Cambridge Healthtech Institute
T: (+1) 781-972-5483
E: jstroup@healthtech.com

Joseph Vacca (Companies L-Z)
Manager, Business Development
Cambridge Healthtech Institute
T: (+1) 781.972.5431
E: jvacca@healthtech.com

SOURCE

http://www.triconference.com/gene-editing

From: Gene Editing <davem@healthtech.com>
Date: Wed, 27 Aug 2014 12:58:56 -0400
To: <avivalev-ari@alum.berkeley.edu>
Subject: New Frontiers in Gene Editing [preliminary agenda just released]


Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Leaders in Pharmaceutical Intelligence

This posting is the fifth in a series on metabolomics.  The first covered general principles.  Proteomics has not been covered and will be returned to.  But we have opened a door.  We have now looked at a comparison of two lymphocytic cell lines, and then the measurement of external effluxes to define internal metabolic conditions in yeast, with a view to delineating relationships between internal metabolic pathways and genetic variants under metabolic constraints.  These studies were confined to the experimental conditions, and could not measure metabolic fluxes, but I consider a study of the fluxes referred to in the comment by Dr. Jose Eduardo des Salles (JEDS) Roselino, who has brought up the concept, not yet specifically discussed – homeostasis.  It is part of a series of communications over several months.  

In the last article I might not have provided answers to some of the questions posed up front.  One of them refers to whether one finds a relationship to the Pasteur effect.  In both studies, leukemia cells and yeast, the cells are eukariotic, not prokaryotic, although the studies were preceded by other studies of bacteria.  It is important to remember that there are differences between prokaryotes and eukaryotes, and these studies encompassed aerobiuc and anaerobic glycolysis, mitochochondrial pathways, and cell death pathways, as well as energy balance, which involves ATP and a series of linked hydrogen transfers in the electron transport chain (ETC).  In the first two papers, we could infer the comparison between differences in oxidative phosphorylation and lactic aciid formation between two strains of cells, whether they be yeast or lymphocytic leukemia.  This is where the observation of Otto Warburg refers back to the work of Pasteur 60 years prior to his discovery.  

In this study we find that metabolic fluxes can be and are measured in Saccharomyces Cerevisiae, and the internal metabolites are measured extensively.  JEDS refers me to Schroedinger’s (Physics Nobel, Quantum Field Theory) classic work, “What is Life?” and his famous CATS, or Rabbits ( a Twilight Zone where objects can be two places at the same time: Schroedinger’s Rabbits: Colin Bruce, Joseph Henry Press, Washington, DC.) That is for another time.  I have also previously referred to the work of Ilya Prigogine (Chemistry Nobel; self organizing systems).  We can set up studies, but we cannot identify the initial state. These two major scientists understood the limits of our ability to study life.

The focus here is on homeostasis.  Homeostasis (Wikipedia), also spelled homoeostasis (from Greek: ὅμοιος, “hómoios”, “similar”,[1] and στάσις, stásis, “standing still”[2]), is the property of a system in which variables are regulated so that internal conditions remain stable and relatively constant. Examples of homeostasis include the regulation of temperature and the balance between acidity and alkalinity (pH). It is a process that maintains the stability of the human body’s internal environment in response to changes in external conditions. The concept was described by Claude Bernard in 1865.  The term was originally used to refer to processes within living organisms, it is frequently applied to automatic control systems.

Buffering of deoxyribonucleotide pool homeostasis by threonine metabolism

John L. Hartman IV *

Author Affiliations   

 Communicated by Leland H. Hartwell, Fred Hutchinson Cancer Research Center, Seattle, WA, June 4, 2007 (received for review January 21, 2007)

Synergistically interacting gene mutations reveal buffering relationships that provide growth homeostasis through their compensation of one another.

Abstract

This analysis in Saccharomyces cerevisiae revealed genetic modules involved in

  • tricarboxylic acid cycle regulation (RTG1RTG2RTG3),
  • threonine biosynthesis (HOM3HOM2HOM6THR1,THR4),
  • amino acid permease trafficking (LST4LST7), and
  • threonine catabolism (GLY1).

These modules contribute to a molecular circuit that

  • regulates threonine metabolism and
  • buffers deficiency in deoxyribonucleotide biosynthesis.

Phenotypic, genetic, and biochemical evidence for this buffering circuit was obtained

  • through analysis of deletion mutants,
  • titratable alleles of ribonucleotide reductase genes, and
  • measurements of intracellular deoxyribonucleotide pool concentrations.

This circuit provides experimental evidence, in eukaryotes, for the presence of a

  • high-flux backbone of metabolism,

which was previously predicted from 

  • in silicomodeling of global metabolism in bacteria.

This part of the high-flux backbone appears to

  • buffer deficiency in ribonucleotide reductase
  • by enabling a compensatory increase in
  • de novopurine biosynthesis
  • that provides additional rate-limiting substrates for
  • dNTP production and DNA synthesis.

Hypotheses regarding unexpected connections

  • between these metabolic pathways
  • were facilitated by genome-wide, and
  • quantitative phenotypic assessment of interactions.

Validation of these hypotheses substantiates

  • the added benefit of quantitative phenotyping
  • for identifying subtleties in gene interactionnetworks
  • that modulate cellular phenotypes.

Keywords: genetic buffering, high-flux backbone of metabolism, protein trafficking, ribonucleotide reductase, mitochondria-to-nucleus retrograde signaling pathway

Introduction 

Cells are complex genetic systems, having evolved

  • compensatory molecular networks
  • that provide growth homeostasis (robustness).

Conceptually, gene interactions

  • underlie robustness by buffering
  • environmental or genetic perturbations (13).

Synergistic effects on the phenotype resulting from

  • two genetic deficiencies or chemical inhibition
  • in combination with a genetic deficiency
  1. reveal buffering relationships
  2. when the double limitation
  3. is more severe than either single limitation.

Genome-wide phenotypic analysis,

  • as possible with RNAi or
  • use of the complete set of
    yeast gene deletion mutants,
  • has enabled new approaches
  • to investigate buffering relationships
    systematically (245).

It has been shown that

  • quantitative (strength) and
  • qualitative (pattern) aspects of
    gene interaction profiles reveal
    • how genes organize
    • in a pathway or cellular process (46).

such sets of genes represent genetic modules that

  • contribute buffering capacity to the cell,
    • providing insight into
    • how molecular circuitry

is arranged to achieve robustness (78).

Comprehensive and quantitative methods

  • for genotype–phenotype analysis are available
    • to gain a more global and precise understanding
    • of buffering networks (469).

These methods permit unbiased

  • investigation of growth homeostasis,
  • systematically revealing
    • how combinations of genetic and
    • environmental variables

result in phenotypic complexity.

High-throughput genotype–phenotype data

  • offer an opportunity to use
  • the extensive and growing genome annotations

to discover new connections between

  • previously annotated genes and pathways,
  • with respect to physiological homeostasis.

Systematic, experimentally derived understanding of

  • genetic interaction networks

will advance efforts to

  • map natural phenotypic variation,
  • thereby aiding the dissection

of genetic disease complexity (10).

This work tests a model constructed after

  • finding threonine biosynthesis
  • to play a role in
  • buffering growth inhibition with

the deoxyribonucleotide (dNTP) biosynthesis
inhibitor, hydroxyurea (HU) (4).

HU is a chemotherapy agent that

  • limits cell proliferation by inhibition of
    ribonucleotide reductase (RNR)
    • leading to dNTP pool deficiency and
    • slow DNA synthesis (11).

The results provide

  • genetic,
  • biochemical, and
  • phenotypic evidence

that growth homeostasis

  • is maintained by
  • a molecular circuit
    • that regulates threonine metabolism
    • to buffer depletion of dNTP pools.

These findings shed light on

  • systems-level observations about
  • cellular metabolism, including
    • function of a high-flux backbone of metabolism (12)
    • and gating of DNA synthesis by
      • oscillation of global transcription
      • and redox metabolism (1314).

FUNCTIONAL INTERACTIONS BETWEEN DNTP AND THREONINE METABOLISM.

This work focused on understanding genetic modules

  • found to buffer RNR deficiency (4).

Synergistic interactions between HU and

  • threonine biosynthesis genes, were uncovered ( 1a).
  • but not genes that function in the synthesis of other amino acids

Deletion ofAAT2 (aspartate aminotransferase) was also synergistic,

  • suggesting buffering by tricarboxylic acid (TCA) cycle flux
  • as AAT2converts the TCA cycle intermediate,
    • oxaloacetate è aspartate
    • the substrate for synthesis of homoserine
    • and ultimately threonine (yeastgenome.org). 

RTG1RTG2, and RTG3, transcription factors

  • regulating transcription of TCA cycle genes
  • in response to mitochondrial stress
    • were also synergistic with hydroxyurea growth limitation
  • further implicating TCA cycle involvement ( 1b).

The RTG and threonine biosynthesis modules were

  • independently confirmed to buffer HU-induced stress
    by Panet al. (17).

Synergistic interaction between HU and

  • deletion alleles ofLST4 and LST7 
  • implicated extracellular uptake of threonine
  • as an alternative mechanism
  • to augment threonine flux ( 1cand ​and22a)
    • becauseLST4 and LST7 regulate
    • delivery of amino acid permeases
      • between the vacuole and
      • plasma membrane compartments of the cell (18).

To confirm that chemical–genetic

  • interactions with HU
  • were caused by its known inhibitory effect
  • on dNTP biosynthesis,
    • a more specific method was used.

Integrating plasmids were

  • introduced into mutant strains
    • to placeRNR1 or RNR2 
  • under transcriptional control by doxycycline (19).

Deletion of homoserine or threonine biosynthesis genes

  • was found to be synergistic with
  • repression of RNR activity by
  • using doxycycline in these mutants ( 1d),
    • confirming that interactions with HU
    • were caused by its inhibitory effect on RNR.

 Media supplementation with amino acids

  • tested whether uptake of extracellular threonine
    • suppresses the growth limitation of mutations
  • in threonine biosynthesis in the presence of HU.

Threonine was found to selectively suppress

  • interaction between HU and
  • disruption of threonine biosynthesis,
  • in a concentration-dependent manner ( 2b–d).

This finding led to the prediction that

  • disabling both threonine biosynthesis and
  • threonine uptake
    • would be synergistic
  • in the presence of HU growth limitation.

 Double deletion mutants of

  • the four possible combinations of
  • thr1or thr4 and lst4 or lst7 were created.

 All combinations were synthetic lethal

  • even in the absence of HU

This is consistent with the hypothesis that lst4 and lst7 

  • compensate threonine biosynthetic deficiency
  • through regulation of extracellular uptake ( 3).

A slow-growth phenotype observed

  • for thehom6 deletion mutant
    • was exacerbated by extracellular threonine,
  • even in the absence of HU (Fig. 2b).

 The hom6deletion mutant is unique

  • among threonine biosynthesis mutants
  • in that the resulting intermediate metabolite is toxic (20)
    (aspartate β-semialdehyde)

Whether this toxicity could be related to its different phenotype

  • in the context of HU perturbation is unexplained.

BIOSYNTHESIS AND EXTRACELLULAR UPTAKE OF THREONINE CONTRIBUTE TO DNTP POOL HOMEOSTASIS.

To test the effect of threonine metabolism on dNTP pools

  • pools were measured in threonine metabolism deletion mutants
  • perturbed by doxycycline-conditional repression
    • of RNR2transcription ( 4).

Although rnr2 deletion is lethal in a haploid,

  • repression of RNR2transcription
  • only reduced the growth rate

when an otherwise non-growth-inhibitory concentration (5 mM) of HU was present (data not shown).

In contrast, RNR1repression was

  • growth-limiting without HU (see  1d) and
  • did not sensitize growth to 5 mM HU
    (data not shown).

The specificity of low-dose HU for

  • growth inhibition in combination with
  • repression of RNR2
  • is explained by the mechanism of action of HU. 

HU scavenges a tyrosyl radical

  • that is present on the Rnr2p and
  • that is required as a cofactor
  • for ribonucleotide reduction (21).

 Non-growth-inhibitory concentrations of HU

  • paradoxically induced increased steady-state
  • dNTP pool concentrations.

The increase in pools was sustained over time

  • and additive with the effect of modulating RNR
    transcriptional levels ( 4a).
  • As a result, growth inhibition,
  • caused byRNR2 repression
  • combined with low-dose HU treatment,
  • occurred with dNTP levels slightly higher than
  • those in the untreated wild-type (WT) control strain
    (endogenousRNR2 promoter).

A possible explanation is that increases in dNTP pools

  • are required for growth fitness
  • in the setting of DNA damage, which is
  • known to involve RNR regulation (22).

 Fig. 1.

HU chemical–genetic interactions. Interactions for three genetic modules are depicted.

HU chemical–genetic interactions. Interactions for three genetic modules are depicted.

HU chemical–genetic interactions. Interactions for three genetic modules are depicted.
The WT control is compared with deletion strains representative of each module,
with respect to their area under the growth curve (AUGC) vs. perturbing drug …

Fig. 2.

Extracellular threonine suppresses interactions between HU and threonine biosynthesis zpq0290770000002

Extracellular threonine suppresses interactions between HU and threonine biosynthesis.
(a) A model explaining interactions between HU and genes involved in threonine metabolism.
In the context of dNTP pool deficiency, threonine metabolism is up-regulated …

To confirm that chemical–genetic interactions with HU

  • were caused by its known inhibitory effect on dNTP biosynthesis,
  • a more specific method was used.

Integrating plasmids were introduced

  • into a variety of mutant strains to place 
  • RNR1or RNR2 under transcriptional control
    by doxycycline (19).

Deletion of homoserine or threonine biosynthesis

  • genes was found to be synergistic with
  • repression of RNR activity by using doxycycline
  • in these mutants ( 1d), confirming that
    • interactions with HU were caused by
      its inhibitory effect on RNR.

Media supplementation with amino acids was used

  • to test whether uptake of extracellular threonine
  • suppresses the growth limitation of mutations
  • in threonine biosynthesis in the presence of HU.

Threonine selectively

  • suppresses interaction between HU and
  • disruption of threonine biosynthesis,
    • in a concentration-dependent manner
      ( 2b–d).

This finding led to the prediction that disabling

  • both threonine biosynthesis and threonine uptake
  • would be synergistic with HU growth limitation.

To test this hypothesis, double deletion mutants of

  • the four possible combinations of
    • thr1or thr4 and lst4 or lst7 were created.

All combinations were synthetic lethal

  • even in the absence of HU,
  • consistent with the hypothesis that
    • lst4andlst7 compensate threonine
      biosynthetic deficiency

through regulation of extracellular uptake (Fig. 3).

A slow-growth phenotype observed for

  • the hom6deletion mutant was exacerbated
  • by extracellular threonine, even
  • in the absence of HU ( 2b).

The hom6deletion mutant is unique among

  • threonine biosynthesis mutants in that
  • the resulting intermediate metabolite is toxic (20)
    (aspartate β-semialdehyde), although
    • whether this toxicity could be related
      to its different phenotype
  • in the context of HU perturbation is unexplained.

Fig. 3.

Deficiency in amino acid permease trafficking (LST4 or LST7) is synthetic lethal with deletion of threonine biosynthesis (THR1 or THR4).

Deficiency in amino acid permease trafficking (LST4 or LST7) is synthetic lethal with deletion of threonine biosynthesis (THR1 or THR4).

Deficiency in amino acid permease trafficking (LST4 or LST7) is synthetic lethal
with deletion of threonine biosynthesis (THR1 or THR4). Interactions between
permease trafficking (LST4 and LST7) and threonine biosynthesis (THR1 and THR4)
were assessed …

BIOSYNTHESIS AND EXTRACELLULAR UPTAKE OF
THREONINE CONTRIBUTE TO DNTP POOL HOMEOSTASIS
.

To test the effect of threonine metabolism on dNTP pools,

  • pools were measured in threonine metabolism deletion mutants
  • perturbed by doxycycline-conditional repression of RNR2transcription ( 4).

Although rnr2 deletion is lethal in a haploid,

  • repression of RNR2transcription only
  • reduced the growth rate when an otherwise
    • non-growth-inhibitory concentration (5 mM)
  • of HU was present (data not shown).

In contrast, RNR1repression was

  • growth-limiting without HU (see  1d)
    • and did not sensitize growth
    • to 5 mM HU (data not shown).

The specificity of low-dose HU for growth inhibition

  • in combination with repression of RNR2
  • is explained by the mechanism of action of HU.

HU scavenges a tyrosyl radical

  • present on the Rnr2p
  • that is required as a cofactor
    • for ribonucleotide reduction (21).

Fig. 4.

Effect of threonine metabolism on dNTP pool homeostasis

Effect of threonine metabolism on dNTP pool homeostasis

Effect of threonine metabolism on dNTP pool homeostasis.
(a) Intracellular dNTP pool concentrations are depicted 90 (black)
and 360 (gray) min after exposure to the perturbations  indicated
by each block. Block 1 is the unperturbed WT (BY4741) strain in …

Non-growth-inhibitory concentrations of HU

  • paradoxically induced increased steady-state
    dNTP pool concentrations.

The increase in pools was sustained over time

  • and additive with the effect of modulating
    RNR transcriptional levels ( 4a).

As a result, growth inhibition, caused by RNR2 repression

  • combined with low-dose HU treatment, occurred
    • with dNTP levels slightly higher than those
    • in the untreated wild-type (WT) control strain
      (endogenous RNR2promoter).

A possible explanation is that increases in dNTP pools are

  • required for growth fitness in the setting of DNA damage,
  • which is known to involve RNR regulation (22).

However, production of DNA damage
(requiring increased dNTP pools for DNA repair)

  • would have been expected only at HU concentrations
  • high enough to arrest DNA synthesis in the first place (23).

The observation that low concentrations of HU

  • led to increased pools could be explained
  • if DNA damage occurs by a mechanism
    • independent of the effect of HU
    • on cytoplasmic pools.

A possible mechanism could involve

  • dNTP pool concentrations at replication forks
  • being affected differentially from
    • cytoplasmic pools;
  • this is not thought to occur
    in eukaryotic cells (24).

Thus, the paradoxical effect of

  • low HU concentrations
  • on increasing dNTP pools
  • remains unexplained.

dNTP pools were increased

  • by expression ofRNR2  from the Tet promoter
    (in the absence of repression with doxycycline),
  • presumably because of overexpression
  • relative to the endogenousRNR2 

Dox-conditional repression of RNR2 

  • reduced dNTP pools in a
  • concentration-dependent fashion ( 4a)
    • so that synergism from deletion of
      threonine metabolism genes could be tested.

The rtg2hom2thr1, and lst4 deletion mutants

  • all exacerbated the reduction in dNTP pools
    • afterRNR2 repression ( 4b).

The contribution of RTG2 for

  • dNTP pool maintenance
  • was less than that of HOM2,THR1, or LST4,
  • consistent with their effects on growth ( 1).

 Consistent with low dNTP pools

  • causing cell cycle arrest in each of the mutants,
    • median cell size increased
    • the relative number of cells and
    • total cell volume
      (median cell size × median cell volume)
    • decreased as pools became depleted ( 4c).

The scs7 (functions in sphingolipid metabolism) deletion strain

  • maintained dNTP pools comparable with WT,
  • despite a greater fitness defect ( 4b and c),
    • indicating a specific role of threonine metabolic genes
    • in homeostatic regulation of dNTP pools.

THREONINE ALDOLASE IS RATE-LIMITING FOR DNTP METABOLISM IN SACCHAROMYCES CEREVISIAE.

The genetic, phenotypic, and biochemical results

  • are consistent with a model whereby
    • TCA cycle regulation (RTG genes),
    • threonine biosynthesis (HOM and THR genes), and
    • permease trafficking (LST genes) pathways
    • coordinately buffer dNTP pool depletion by
      • up-regulating threonine metabolism.

The model postulates that threonine catabolism

  • contributes glycine to augment
  • de novopurine synthesis ( 2a).

HU has been shown to preferentially deplete

  • dATP pools in mammalian cells (2526), and
  • there was a tendency for purine pools to fluctuate
    (particularly dATP)
    • acutely whenever threonine metabolism and
    • RNR activity were perturbed in combination
      ( 4aand b).

However, allosteric regulation of RNR would

  • distribute this effect across all pools (21).

Threonine aldolase, encoded by GLY1 (EC 4.1.2.5),

  • cleaves threonine into glycine and acetaldehyde (27).

Notably, the gly1 deletion mutant exhibited slow growth
(data not shown)

  • even with glycine supplementation.

This phenotype was found to be the result of

  • limitation of dNTP metabolism.

Basal dNTP pools were reduced

  • in the gly1deletion mutant,

Pools fell dramatically

  • after treatment with 10 mM HU,

and normal homeostatic increases in dNTP concentrations

  • after treatment with 50 mM HU were delayed,
  • particularly dATP pools (Fig. 5). 

CHA1 (EC 4.2.1.13) and ILV1 (EC 4.3.1.19) are

  • deaminases that convert threonine to 2-oxybutanoate
  • or other metabolic intermediates such as
    • homoserine, cystathionine, or propionyl-CoA.

However, deletion of neither CHA1 nor ILV1 

  • modified the growth response to HU (4).

Fig. 5.

Threonine aldolase contributes to normal dNTP metabolism

Threonine aldolase contributes to normal dNTP metabolism

Threonine aldolase contributes to normal dNTP metabolism. Intracellular dNTP pools are shown for the WT control strain (BY4741) and gly1 (threonine aldolase) deletion mutant before (Left) and 120 or 360 min after perturbation with 10 mM (Center) or 50 

DISCUSSION

Computational analysis of global metabolism

  • inEscherichia coli has suggested
  • that threonine flux is of particular importance.

These studies propose that

  • threonine synthesis and
  • its degradation to glycine
    • for purine biosynthesis
  • are part of a high-flux backbone (HFB)
  • of metabolism (12).

The HFB was defined by a

  • subset of all metabolic reactions
  • found to have sufficient flux for
    • providing growth homeostasis
  • in response to growth-limiting perturbations
    (such shifting to a poor carbon source).

Utilization of threonine for buffering

  • dNTP metabolism and growth homeostasis
  • provides experimental evidence for
  • the presence of the HFB in eukaryotes.

Discovery of new connections between

  • dNTP and threonine metabolism
  1. demonstrates the value of quantitative high-throughput
    cellular phenotyping for identifying 
  2. functional redundancies in gene networks
    • by measuring interactions between
    • genetic module metabolism.

The ability to detect relatively small effects

  • of individual modules and
  • to order their relative quantitative impact
    • aided hypotheses about how
    • these modules might relate to one another (4).

By this approach, genes involved in

  1. TCA cycle regulation,
  2. threonine biosynthesis,
  3. amino acid permease trafficking,
  4. threonine catabolism, and
  5. ribonucleotide reduction
  • were found to function as a modular circuit
  • to maintain robust dNTP pools
  • for DNA synthesis
    • even though these modules
    • appear to function independently
      in other contexts (1835).

In natural (outbred) populations,

  • compensatory networks also buffer
  • genetic and chemical growth perturbations;

however, the amount of genotypic and phenotypic variation

  • renders dissection of interactions relatively intractable.

By contrast, systematic analysis of yeast deletion mutants

  • exposes interactions on a fixed genetic background
    • but does not survey natural variation.

Recently, segregants from a cross of S288C
(the background used for systematic gene deletion)

  • and a natural isolate
  • have been genotyped at high resolution (3637).

Quantitative high-throughput cellular phenotyping,

  • applied in parallel to these strains and
  • the comprehensive collection of
  • yeast gene deletion mutants, would
    • provide a dual strategy to
    • deconstruct gene networks
    • that buffer growth perturbations, by
    • systematic analysis of all deletion mutants
    • in parallel with surveying for natural occurrence.

Quantitative genetic dissection of

  • buffering networks in yeast thus
  • provides a way to model genotype–phenotype variation
  • on a genomic scale, providing insight into
    • functional interactions between conserved pathways
    • that potentially modulate human disease.

Cell Proliferation Measurements. 

Experiments represented in Figs. 1 and ​and22 were performed in Hartwell complete agar medium. High-throughput kinetic phenotyping (by imaging and image analysis) and area under the growth curve (AUGC) calculations were performed as described previously (4). AUGC encapsulates the overall growth phenotype of a strain with respect to time under a particular condition. AUGC is affected by initial population size (no. of cells transferred in a spot culture), lag time (delay before log-linear growth), maximum specific rate (actual log-linear rate), total efficiency (saturation density), and duration of the assay. For assessing the strength of a genetic interaction, the change in the AUGC conferred by a particular deletion allele relative to its WT control allele is considered with respect to perturbation intensity, e.g., concentration of HU, as depicted in Fig. 1. AUGC values for all mutants perturbed with 0, 50, and 150 mM HU are available at (http://genomebiology.com/2004/5/7/R49/additional).

dNTP Pool Sample Collections. 

Strains were grown overnight in liquid medium at 30°C to a concentration of ≈3 × 106 cells/ml and diluted to prewarmed medium with HU or doxycycline to achieve the desired cell and drug concentrations in a final volume of 30 ml. Each time point was grown separately and harvested when the cell concentration was ≈3 × 106 cells per ml. Twenty milliliters of culture was collected by vacuum filtration and immediately washed with ice-cold medium, and filters were transferred to 2 ml of ice-cold medium (dNTP concentrations remain stable in iced medium for several hours). Cells were removed from the filter by vortexing, the sample was divided in half for duplicate readings, cells were pelleted, medium removed by aspiration, and cells were lysed with 40 μl of 0.1 M perchloric acid and then snap-frozen.

Cell Volume Measurements. 

Cell volumes were measured by size analysis with a Coulter Counter (Beckman–Coulter, Fullerton, CA). The total cell volume of each culture (median cell size × total cell number) was used for calculating intracellular dNTP pool concentrations. Before vacuum filtration and lysis of each culture for mass spectrometry analysis, 200 μl was collected into 10 ml of ice-cold isoton (Beckman–Coulter). Samples were sonicated at low power to separate nonspecifically adherent cells. To calculate relative changes in total cell volume (Fig. 4c), values for each strain were first normalized against self at time zero and then divided by the corresponding normalized WT (BY4741) values.

HPLC. 

Samples were thawed by microcentrifugation (18,000 × g) for 15 min at 4°C. Sixteen microliters of lysate was added to 8 μl of 3× mobile-phase buffer [60 mM acetic acid/0.075% dimethylhydroxylamine (Sigma, St. Louis, MO)/pH adjusted to 7 with ammonium hydroxide], and 10 μl was injected onto an Agilent C-8 Zorbax column (part 883700-906) with a linear 5–30% methanol gradient from 2 to 11 min, 30–50% from 11 to 12 min, with final reequilibration for 5 min in 5% methanol (flow rate of 0.3 ml/min). Retention times of 4.5 (dTTP), 7.5 (dGTP and dTTP), and 9.5 min (dATP) were observed. dNTP-depleted lysate was obtained by lysis of saturation-density cultures after 30-min incubation in room temperature water. Dilution of standards in this lysate improved dCTP chromatography. Trace amounts of dNTPs remaining in the diluent were subtracted for standard curve calculations.

Mass Spectrometry. 

Mass spectrometry was performed with electron spray ionization in negative ion mode. Two instruments were used: (i) an Agilent 1100 MSD [dNTPs were monitored as single ions at m/z 466 (dCTP)], 481 (dTTP), 490 (dTTP), and 506 (dGTP). The drying gas was N2 at 340°C at 10 liters/min, and nebulizing pressure 25 psi (1 psi = 6.89 kPa). The fragmentor was set at 90 eV and capillary voltage 3500. (ii) An ABI API-4000 Q-trap triple quadrupole instrument was used [mass transition to a 189 fragment was monitored for each of the dNTP species, as described previously (39); N2 gas was used for nebulization, drying, and collision and the ionization chamber temperature was 250°C]. New standard curves were created for every assay.

Calculation of Intracellular dNTP Concentrations. 

Sample concentrations were determined from standard curves and adjusted to account for dilution by lysis and total cell volume [volume added for lysis + 2(tcv)] μL /tcv (μL). Standard curves showed high linear correlation (R2 > 0.998), and variation from duplicate mass spec measurements was generally <10%.

ABBREVIATIONS: AUGC, area under the growth curve; dNTP, deoxyribonucleotide; HU, hydroxyurea; RNR, ribonucleotide reductase; TCA, tricarboxylic acid..

The requirement reported here of mitochondrial-to-nucleus retrograde signaling for dNTP pool homoeostasis in yeast may be of importance to a recent report that mutations in p53R2 cause human mitochondrial depletion syndromes (MDS) (4041). If compensatory/buffering relationships between RNR and retrograde signaling in yeast are evolutionarily conserved, then genetic variation in retrograde signaling may modulate MDS disease phenotypes resulting from deficiency in p53R2 activity.

ARTICLE INFORMATION

Proc Natl Acad Sci U S A. Jul 10, 2007; 104(28): 11700–11705.

Published online Jul 2, 2007. doi:  10.1073/pnas.0705212104

PMCID: PMC1913885

Genetics

John L. Hartman, IV*

Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35294

*To whom correspondence should be addressed. E-mail: ude.bau@namtrahj

Communicated by Leland H. Hartwell, Fred Hutchinson Cancer Research Center, Seattle, WA, June 4, 2007.

Author contributions: J.L.H. designed research, performed research, contributed new reagents/analytic tools,
analyzed data, and wrote the paper.

Received January 21, 2007

Buffering of deoxyribonucleotide pool homeostasis by threonine metabolism

John L. Hartman, IV

Additional article information

NOTE ADDED IN PROOF.

Note Added in Proof.

The requirement reported here of mitochondrial-to-nucleus retrograde signaling for dNTP pool homoeostasis in yeast may be of importance
to a recent report that mutations in p53R2 cause human mitochondrial depletion syndromes (MDS) (4041). If compensatory/buffering
relationships between RNR and retrograde signaling in yeast are evolutionarily conserved, then genetic variation in retrograde signaling
may modulate MDS disease phenotypes resulting from deficiency in p53R2 activity.

FOOTNOTES

The author declares no conflict of interest.

ARTICLE INFORMATION

Proc Natl Acad Sci U S A. Jul 10, 2007; 104(28): 11700–11705.

Published online Jul 2, 2007. http://dx.doi.org:/10.1073/pnas.0705212104

PMCID: PMC1913885

Genetics

John L. Hartman, IV*

Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35294

*To whom correspondence should be addressed. E-mail: ude.bau@namtrahj

Communicated by Leland H. Hartwell, Fred Hutchinson Cancer Research Center, Seattle, WA, June 4, 2007.

Author contributions: J.L.H. designed research, performed research, contributed new reagents/analytic tools, analyzed data, and wrote the paper.

Received January 21, 2007  Copyright © 2007 by The National Academy of Sciences of the USA

This article has been cited by other articles in PMC.

Articles from Proceedings of the National Academy of Sciences of the United States of America are provided here courtesy of National Academy of Sciences

 REFERENCES

  1. Hartman JL, IV, Garvik B, Hartwell L. Science. 2001;291:1001–1004. [PubMed]
  2. Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, et al. Science. 2004;303:808–813. [PubMed]
  3. Lehner B, Crombie C, Tischler J, Fortunato A, Fraser AG. Nat Genet. 2006;38:896–903.[PubMed]
  4. Hartman JL, IV, Tippery NP. Genome Biol. 2004;5:R49. [PMC free article] [PubMed]
  5. Parsons AB, Brost RL, Ding H, Li Z, Zhang C, Sheikh B, Brown GW, Kane PM, Hughes TR, Boone C. Nat Biotechnol. 2004;22:62–69. [PubMed]
  6. Collins SR, Schuldiner M, Krogan NJ, Weissman JS. Genome Biol. 2006;7:R63.[PMC free article] [PubMed]
  7. Csete ME, Doyle JC. Science. 2002;295:1664–1669. [PubMed]
  8. Hartwell LH, Hopfield JJ, Leibler S, Murray AW. Nature. 1999;402:C47–C52. [PubMed]
  9. Shah NA, Laws RJ, Wardman B, Zhao LP, Hartman JL., IV BMC Syst Biol. 2007;1:3.[PMC free article] [PubMed]
  10. Badano JL, Katsanis N. Nat Rev Genet. 2002;3:779–789. [PubMed]
  11. Krakoff IH, Brown NC, Reichard P. Cancer Res. 1968;28:1559–1565. [PubMed]
  12. Almaas E, Kovacs B, Vicsek T, Oltvai ZN, Barabasi AL. Nature. 2004;427:839–843.[PubMed]

 …..


Cell Research News – What’s to Follow?

Larry H. Bernstein, MD, FCAP, Reporter

Leaders in Pharmaceutical Intelligence

http://pharmaceuticalintelligence.com/2014/08/26/larryhbern/Cell_Research_News_-_What’s_to_Follow?

 

Stem Cell Research ‘Holy Grail’ Uncovered, Thanks to Zebrafish

By Estel Grace Masangkay

With help from the zebrafish, a team of Australian researchers has uncovered how
hematopoietic stem cells (HSC) renew themselves.

HSCs refers to stem cells present in the blood and bone marrow that are used 
for  the replenishment of the body’s supply of blood and immune cells – 

  • in transplants for leukemia and myeloma.
  • Stem cells have the potential to transform into vital cells

    including muscle, bone, and blood vessels.

Understanding how HSCs form and renew themselves has potential application in the
treatment of

  • spinal cord injuries
  • degenerative disorders
  • diabetes.

Professor Peter Currie, of the Australian Regen Med Institute at Victoria’s Monash
University, led a research team to discover a crucial part of HSC’s development. Using 
a high-resolution microscopy, Prof. Curie’s team 

  • caught zebrafish embyonic SCs on film as they formed. 
  • the researchers were studying muscle mutations in the aquatic animal.

“Zebrafish make ESCs in exactly the same way as humans do, but their embryos and
larvae develop free living, but the larvae are both free swimming and transparent, so one could see every cell in the body forming, including ESCs,” explained Prof. Currie.

The researchers noticed in films that a

  •  ‘buddy cell’ came along to help the ESCs form.

Called endotome cells, 

  • they aided pre-ESCs to turn into ESCs.  

Prof. Currie said that endotome cells act as helper cells for pre-ESCs , 

  • helping them progress to become fully fledged stem cells.

The team not only

  • identified some of the cells and signals 
  • required for ESC formation, but also 
  • pinpointed the genes required 
  • for endotome formation in the first place.

The next step for the researchers is to 

  • locate the signals present in the endotome cells 
  • that trigger ESC formation in the embryo. 

This may provide clues for developing

  • specific blood cells on demand for blood-related disorders. 

Professor Currie also pointed out the discovery’s potential for 

  • correcting genetic defects in the cell and 
  • transplanting them back in the body to treat disorders.

The team’s work was published in the international journal Nature.

 

Jell-O Like Biomaterial Could Hold Key to Cancer Cell Destruction

by Estel Grace Masangkay

Scientists from Penn State University reported that a biomaterial made of tiny 
molecules was able to attract and destroy cancer cells.

Professor Yong Wang and bioengineering faculty at Penn State, built the 
tissue-like biomaterial to accomplish what chemotherapy could not -

  • kill every cancer cell without leaving
  • the possibility of a recurrence.

Prof. Wang and team built polymers 

  • from tiny molecules called monomers. They
  • then wove the polymers into 3D networks 

called hydrogels. Hydrogel is soft and flexible, 
like Jell-O, and it contains a lot of water, and

  • can be safely put into the body, unlike 

other implants that the body often tries 

  • to get rid of through the immune response.

“We want to make sure the materials we are using are compatible in the body.”

The researchers 

  • attached aptamers to the hydrogels, 
  • which release bio-chemical signal-only molecules 
  • that draw in cancer cells. 

Once attracted, the cancer cells are entrapped in the Jell-O-like substance. 

What happens next is 

  • an oligonucleotide binds to the protein-binding site of the aptamer 
  • and triggers the release of anticancer drugs at the proper time.

“Once we trap the cancer cells, we can deliver anticancer drugs 

  • to that specific location to kill them. 

This technique would help avoid the need for systemic medications that kill not only cancer cells, but normal cells as well. Systemic chemotherapy drugs

  • make patients devastatingly sick and possibly 
  • leave behind cancer cells to wreak havoc another day

If our new technique has any side effects at all, it would be only local side 
effects and not whole-body systemic side effects,” explained Prof. Wang.

The initial results of the research were published by Prof. Wang in the 
Journal of the American Chemical Society in 2012. Prof. Wang also shared 
the latest results of his work at the Society for Biomaterials Meeting &
 Exposition in April this year.

 

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