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Archive for the ‘Biological Networks, Gene Regulation and Evolution’ Category

Reprogramming Cell Fate

Posted in Biological Networks, Gene Regulation and Evolution, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, International Global Work in Pharmaceutical, Liver & Digestive Diseases Research, Metabolomics, Molecular Genetics & Pharmaceutical, Nutrigenomics, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Industry Competitive Intelligence, Population Health Management, Genetics & Pharmaceutical, Technology Transfer: Biotech and Pharmaceutical, tagged Cell potency, Cellular differentiation, DNA, FoxA factors, gene expression, liver cells, pioneer transcription factros, regulatory sequences, reprogramming, silent genes, Stem cell, target sites on chromatin, Transcription factor, University of Pennsylvania on March 2, 2013| 4 Comments »

Reprogramming Cell Fate

 

Reporter: Larry H.Bernstein, MD, FCAP

Kathy Liszewski: reporting Gordon Conference “Reprogramming Cell Fate” meeting

M. Azim Surani, Ph.D., Univ Cambridge

Source unknown: June 21, 2012;32(11)

They report two critical steps both of which are needed for exploring epigenetic reprogramming.  While females have two X chromosomes ,

• the inactivation of one is necessary for cell differentiation.
• Only after epigenetic reprogramming of the X chromosome can pluripotency be acquired.

Pluripotent stem cells can generate – any fetal or adult cell type but

• don’t develop into a complete organism.

Pioneer transcription factors take the lead in – facilitating cellular reprogramming – and responses to environmental cues.
Multicellular organisms consist of

• functionally distinct cellular types
• produced by differential activation of gene expression.

They seek out and bind specific regulatory sequences in DNA, even though DNA is coated with and condensed into a thick fiber of chromatin.
The pioneer factor, discovered by Prof. KS Zaret at UPenn SOM in 1996, endows the competence for gene activity,

• being among the first transcription factors to
• engage and pry open the target sites in chromatin.

FoxA factors, expressed in the foregut endoderm of the mouse,are necessary for

• induction of the liver program.

•  nearly one-third of the DNA sites bound by FoxA in the adult liver occur near silent genes.

organ regeneration example from induced pluripotent stem cells(iPS cell) (Photo credit: Wikipedia)

English: Pathway of stem cell differentiation (Photo credit: Wikipedia)

Related articles

• Neurons can be reprogrammed long after they’ve matured, study finds (rawstory.com)
• Reprogramming cells to fight diabetes (scienceblog.com)
• New Study Sheds Light On Cellular Reprogramming (medicalnewstoday.com)
• Pancreatic Cells Reprogrammed By Epigenetic Alterations To Secrete Insulin (medicalnewstoday.com)
• New light shed on reprogramming mature cells to pluripotency, to divide and differentiate into specialized cell types (sciencedaily.com)

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Unraveling Retrograde Signaling Pathways

Posted in Biological Networks, Gene Regulation and Evolution, Cell Biology, Signaling & Cell Circuits, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, International Global Work in Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Industry Competitive Intelligence, tagged Arabidopsis thaliana, Aviva Lev-Ari, Biochemistry and Molecular Biology, Biology, Cell Biology, gene expression, genome, Metabolite, Metabolomics, mutation, Signal transduction, systems biology on March 2, 2013| 2 Comments »

Unraveling Retrograde Signaling Pathways

Reporter: Larry H. Bernstein, MD, FCAP

Image Source: Created by Noam Steiner Tomer 8/10/2020

Unraveling retrograde signaling pathways: finding candidate signaling molecules via metabolomics and systems biology driven approaches
C Caldana, AR Fernie, L Willmitzer and D Steinhauser
Front. Plant Sci. 2012; 3:267.                    http://dx.doi.org/10.3389/fpls.2012.00267

http://fpls.com/Unraveling retrograde signaling pathways: finding candidate signaling molecules via
metabolomics and systems biology driven approaches

signals can be generated within organelles, such as chloroplasts and mitochondria,

• modulating the nuclear gene expression in a process called
  • retrograde signaling.

Recently, integrative genomics approaches, in which correlation analysis has been applied on transcript and metabolite profiling data
of Arabidopsis thaliana, revealed the identification of metabolites which are

• putatively acting as mediators of nuclear gene expression.

http://fpls.com/unraveling_retrograde_signaling_pathways:_finding_candidate_signaling_molecules_
via_metabolomics_and_systems_biology_driven_approaches

English: Plant Pathology in Arabidopsis thaliana (Photo credit: Wikipedia)

B0004313 Gene expression in normal and cancer cells (Photo credit: wellcome images)

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• Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes (plosone.org)
• Gene Expression and Thiopurine Metabolite Profiling in Inflammatory Bowel Disease – Novel Clues to Drug Targets and Disease Mechanisms? (plosone.org)
• Activation of the Jasmonic Acid Plant Defence Pathway Alters the Composition of Rhizosphere Bacterial Communities (plosone.org)
• Beyond the Genome (rdmag.com)
• Global Metabolomics Market Reviewed & Forecast in New In-Demand… (biotechblog.com)
• Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions (ploscompbiol.org)

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Mechanism of Variegation in Immutans

Posted in Biological Networks, Gene Regulation and Evolution, Cell Biology, Signaling & Cell Circuits, Genome Biology, International Global Work in Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, tagged biogenesis, chloroplast, color change, immutans, photosynthesis, PTOX, signaling, variegation on March 2, 2013| 1 Comment »

Mechanism of Variegation in Immutans

Reporter: Larry H. Bernstein, MD, FCAP

 

 

The mechanism of variegation in immutans provides insight into chloroplast biogenesis.

• immutans,
• PTOX,
• variegation,
• photosynthesis,
• signaling,
• leave colors,
• biogenesis

Foudree A, Putarjunan A, Kambakam S, Nolan T, et al. Front. Plant Sci. 3:260.   htp://dx.doi.org/10.3389/fpls.2012.00260 http://FrontPlantSci.com/The_mechanism_of_variegation_in_immutans_provides_insight_into_chloroplast_biogenesis/

variegated four o clock, with dew, enhanced and cropped (Photo credit: Martin LaBar (going on hiatus))

A vectorised version of File:Chloroplast-new.jpg. A diagram showing the simple structure of a chloroplast (Photo credit: Wikipedia)

Chloroplast ribosome + Predicted Location of Chloroplast-Unique Structures and Their Proximity to Functionally Important Regions of the Small Ribosomal Subunit (Photo credit: Wikipedia)

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Modeling Targeted Therapy

Posted in Advanced Drug Manufacturing Technology, Biological Networks, Gene Regulation and Evolution, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, Genomic Testing: Methodology for Diagnosis, International Global Work in Pharmaceutical, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Nutrigenomics, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, Pharmacotherapy and Cell Activity, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged Bioinformatics, Biology, Biomedical Engineering, Complex network, computational biology, Northwestern University, Technology, Therapy on March 2, 2013| 3 Comments »

Modeling Targeted Therapy

Reporter: Larry H. Bernstein, MD, FCAP
pharmaceuticalintelligence.com/2013/03/02/modeling-targeted-therapy/

Some Perspectives on Network Modeling in Therapeutic Target Prediction
R Albert, B DasGupta and N Mobasheri
Biomedical Engineering and Computational Biology Insights 2013; 5: 17–24    http://dx.doi.org/BECBI/Albert_DasGupta_ Mobasheri
Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior
http://www.la-press.com/Some_Perspectives_on_Network_Modeling_in_Therapeutic_Target_Prediction/

Journal of Computational Biology (Photo credit: Wikipedia)

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Genetic Basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes

Posted in Biological Networks, Gene Regulation and Evolution, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Proteomics, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged Biology, DNA Sequencing, Genome Institute, Illumina, Washington University in St. Louis, Washington University School of Medicine on February 20, 2013| 1 Comment »

Reporter: Aviva Lev-Ari, PhD, RN

Word Cloud by Daniel Menzin

UPDATED 3/27/2013

The Exome is Not Enough

March 27, 2013

Dan Koboldt at MassGenomics explains why exome sequencing often fails to identify causal variants, even in Mendelian disorders — “the very plausible possibility that a noncoding functional variant is responsible.”

Koboldt, the analysis manager in the human genetics group at the Genome Institute at Washington University, says that researchers shouldn’t overlook the importance of noncoding functional variants, which require a suite of technologies to detect, including RNA-seq, ChiP-seq, DNAse sequencing and footprinting, bisulfite sequencing, and chromosome conformation capture.

“These types of experiments generate a wealth of data about regulatory activity in genomes,” he says. “While studying each of these independently is certainly informative, integrative analysis will be required to elucidate how all of these different regulatory mechanisms work together.”

While this effort will require “robust statistical models, substantial computing resources, and productive collaboration among research groups, the end result “will be a far more complete understanding of how the genome works,” he says.

 

SOURCE:

http://www.genomeweb.com//node/1209591?hq_e=el&hq_m=1544811&hq_l=3&hq_v=e1df6f3681

Dan Koboldt works as a staff scientist in the Human Genetics group of the Genome Institute at Washington University in St. Louis. There, he works with scientists, physicians, programmers, and data analysts to understand the genetic basis of complex human diseases such as cancer, vision disorders, and metabolic syndromes through next-gen sequencing analysis. He received bachelor’s degrees in Computer Science and French from the University of Missouri-Columbia, and a master’s degree in Biology fromWashington University.

Dan has worked in the field of human genetics since 2003, when he joined the lab of Raymond E. Miller, which played a role in the International HapMap Project and later the genetic map of C. briggsae, a model organism related to C. elegans.

Disclaimer: The views expressed on this site, including blog posts and static pages, do not necessarily reflect the opinions of the Genome Institute at Washington University, the Washington University School of Medicine, or Washington University in St. Louis.

Before diving in with both feet, next-generation sequencing neophytes might want to take a gander at a post by Dan Koboldt at MassGenomics where he describes his 10 commandments for good next-gen sequencing.

In his post, Koboldt breaks up his instructions into four categories: analysis, publications, data sharing and submissions, and research ethics and cost.

His list includes some oft repeated warnings. For example, he cautions against reinventing the wheel when it comes to developing analysis software, and, for pity’s sake, don’t invent any more words that end in “ome” or “omics.”

Some other no-no’s, according to Koboldt, include publishing results before they’ve been vetted properly, testing new methods on simulated data only, and taking “unfair advantage of submitted data.”

He also admonishes newcomers to think a little bit about the cost of analysis without which “your sequencing data, your $1,000 genome, is about as useful as a chocolate teapot,” and to have a care for the privacy of their study participants’ samples and data.

 http://www.genomeweb.com//node/1193731?hq_e=el&hq_m=1504568&hq_l=3&hq_v=df86f8764d

Ten Commandments for Next-Gen Sequencing

Just as the reach of next-generation sequencing has continued to grow — in both research and clinical realms — so too has the community of NGS users.  Some have been around since the early days. The days of 454 and Solexa sequencing. Since then, the field has matured at an astonishing pace. Many standards were established to help everyone make sense of this flood of data. The recent democratization of sequencing has made next-gen sequencing available to just about anyone.

And yet, there have been growing pains. With great power comes great responsibility. To help some of the newcomers into the field, I’ve drafted these ten commandments for next-gen sequencing.

NGS Analysis

1. Thou shalt not reinvent the wheel. In spite of rapid technological advances, NGS is not a new field. Most of the current “workhorse” technologies have been on the market for a couple of years or more. As such, we have a plethora of short read aligners, de novo assemblers, variant callers, and other tools already. Even so, there is a great temptation for bioinformaticians to write their own “custom scripts” to perform these tasks. There’s a new “Applications Note” every day with some tool that claims to do something new or better.

Can you really write an aligner that’s better than BWA? More importantly, do we need one? Unless you have some compelling reason to develop something new (as we did when we developed SomaticSniper and VarScan), take advantage of what’s already out there.

2. Thou shalt not coin any new term ending with “ome” or “omics”. We have enough of these already, to the point where it’s getting ridiculous. Genome, transcriptome, and proteome are obvious applications of this nomenclature. Epigenome, sure. But the metabolome, interactome, and various other “ome” words are starting to detract from the naming system. The ones we need have already been coined. Don’t give in to the temptation.

3. Thou shall follow thy field’s conventions for jargon. Technical terms, acronyms, and abbreviations are inherent to research. We need them both for precision and brevity. When we get into trouble is when people feel the need to create their own acronyms when a suitable one already exists. Is there a significant difference between next-generation sequencing (NGS), high-throughput sequencing (HTS), and massively parallel sequencing (MPS)?

Widely accepted terms provide something of a standard, and they should be used whenever possible. Insertion/deletion variants are indels, not InDels or INDELs DIPs. Structural variants are SVs, not SVars or GVs. We don’t need any more acronyms!

NGS Publications

These commandments address behaviors that get on my nerves, both as a blogger and a peer reviewer.

4. Thou shalt not publish by press release. This is a disturbing trend that seems to happen more and more frequently in our field: the announcement of “discoveries” before they have been accepted for publication. Peer review is the required vetting process for scientific research. Yes, it takes time and yes, your competitors are probably on the verge of the same discovery. That doesn’t mean you get to skip ahead and claim credit by putting out a press release.

There are already examples of how this can come back to bite you. When the reviewers trash your manuscript, or (gasp) you learn that a mistake was made, it looks bad. It reflects poorly on the researchers and the institution, both in the field and in the eyes of the public.

5. Thou shalt not rely only on simulated data. Often when I read a paper on a new method or algorithm, they showcase it using simulated data. This often serves a noble purpose, such as knowing the “correct” answer and demonstrating that your approach can find it. Even so, you’d better apply it to some real data too. Simulations simply can’t replicate the true randomness of nature and the crap-that-can-go-wrong reality of next-gen sequencing. There’s plenty of freely available data out there; go get some of it.

6. Thou shalt obtain enough samples. One consequence of the rapid growth of our field (and accompanying drop in sequencing costs) is that small sample numbers no longer impress anyone. They don’t impress me, and they certainly don’t impress the statisticians upstairs. The novelty of exome or even whole-genome sequencing has long worn off. Now, high-profile studies must back their findings with statistically significant results, and that usually means finding a cohort of hundreds (or thousands) of patients with which to extend your findings.

This new reality may not be entirely bad news, because it surely will foster collaboration between groups that might otherwise not be able to publish individually.

Data Sharing and Submissions

7. Thou shalt withhold no data. With some exceptions, sequencing datasets are meant to be shared. Certain institutions, such as large-scale sequencing centers in the U.S., are mandated by their funding agencies to deposit data generated using public funds on a timely basis following its generation. Since the usual deposition site is dbGaP, this means that IRB approvals and dbGaP certification letters must be in hand before sequencing can begin.

Any researchers who plan to publish their findings based on sequencing datasets will have to submit them to public datasets before publication.This is not optional. It is not “something we should do when we get around to it after the paper goes out.” It is required to reproduce the work, so it should really be done before a manuscript is submitted. Consider this excerpt from Nature‘s publication guidelines:

Data sets must be made freely available to readers from the date of publication, and must be provided to editors and peer-reviewers at submission, for the purposes of evaluating the manuscript.

For the following types of data set, submission to a community-endorsed, public repository is mandatory. Accession numbers must be provided in the paper.

The policies go on to list various types of sequencing data:

• DNA and RNA sequences
• DNA sequencing data (traces for capillary electrophoresis and short reads for next-generation sequencing)
• Deep sequencing data
• Epitopes, functional domains, genetic markers, or haplotypes.

Every journal should have a similar policy; most top-tier journals already do. Editors and referees need to enforce this submission requirement by rejecting any manuscripts that do not include the submission accession numbers.

8. Thou shalt not take unfair advantage of submitted data. Many investigators are concerned about data sharing (especially when mandated upon generation, not publication) from fear of being scooped. This is a valid concern. When you submit your data to a public repository, others can find it and (if they meet the requirements) use it. Personally, I think most of these fears are not justified — I mean, have you ever tried to get data out of dbGaP? The time it takes for someone to find, request, obtain, and use submitted data should allow the producers of the data to write it up.

Large-scale efforts to which substantial resources have been devoted — such as the Cancer Genome Atlas — have additional safeguards in place. Their data use policy states that, for a given cancer type, submitted data can’t be used until the “marker paper” has been published. This is a good rule of thumb for the NGS community, and something that journal editors (and referees) haven’t always enforced.

Just because you can scoop someone doesn’t mean that you should. It’s not only bad karma, but bad for your reputation. Scientists have long memories. They will likely review your manuscript or grant proposal sometime in the future. When that happens, you want to be the person who took the high road.

Research Ethics and Cost

9. Thou shalt not discount the cost of analysis. It’s true that since the advent of NGS technology, the cost of sequencing has plummeted. The cost of analysis, however, has not. And making sense of genomic data — alignment, quality control, variant calling, annotation, interpretation — is a daunting task indeed. It takes computational resources as well as expertise. This infrastructure is not free; in fact, it can be more expensive than the sequencing itself. 

Without analysis, your sequencing data, your $1,000 genome, is about as useful as a chocolate teapot.

10. Thou shalt honor thy patients and their samples. Earlier this month, I wrote about how supposedly anonymous individuals from the CEPH collection were identified using a combination of genetic markers and online databases. It is a simple fact that we can no longer guarantee a sequenced sample’s anonymity. That simple fact, combined with our growing ability to interpret the possible consequences of an individual genome, means a great deal of risk for study volunteers.

We must safeguard the privacy of study participants — and find ways to protect them from privacy violations and/or discrimination — if we want their continued cooperation.

This means obtaining good consent documents and ensuring that they’re all correct before sequencing begins. It also means adhering to the data use policies those consents specify. As I’ve written before, samples are the new commodity in our field. Anyone can rent time on a sequencer. If you don’t make an effort to treat your samples right, someone else will.

Related Posts:

• Science and Science Fiction It occurred to me recently, while refereeing a manuscript, that publication in research is a lot more forgiving than publication in fiction. I’d just read …
• Identifying Samples from Genomic Data In last week’s issue of Science, Melissa Gymrek and colleagues from the lab of Yaniv Erlich (Whitehead) report a method for the triangulation the identity …
• Human Genetics Challenges in an Era of Cheap Sequencing Next-generation sequencing promises to reach unprecedented levels of throughput this year, driving down the cost of sequencing dramatically. Somewhere between the GridION, the Ion Proton, …
• Ethical Challenges for Clinical Genome Sequencing The past decade has been a turbulent one for the ream of genetic and genomic research. Since the completion of the human genome and beginning …

SOURCE:

http://massgenomics.org/2013/02/ngs-commandments.html

Dan Koboldt’s Publications

Bose R, Kavuri SM, Searleman AC, Shen W, Shen D, Koboldt DC, Monsey J, Goel N, Aronson AB, Li S, Ma CX, Ding L, Mardis ER, & Ellis MJ (2013).Activating HER2 mtations in HER2 gene amplification negative breast cancer. Cancer discovery PMID: 23220880

The 1000 Genomes Project Consortium (2012). An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56-65. DOI: 10.1038/nature11632

Cancer Genome Atlas Network (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490 (7418), 61-70 PMID:23000897

Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, Van Tine BA, Hoog J, Goiffon RJ, Goldstein TC, Ng S, Lin L, Crowder R, Snider J, Ballman K, Weber J, Chen K, Koboldt DC, Kandoth C, Schierding WS, McMichael JF, Miller CA, Lu C, Harris CC, McLellan MD, Wendl MC, DeSchryver K, Allred DC, Esserman L, Unzeitig G, Margenthaler J, Babiera GV, Marcom PK, Guenther JM, Leitch M, Hunt K, Olson J, Tao Y, Maher CA, Fulton LL, Fulton RS, Harrison M, Oberkfell B, Du F, Demeter R, Vickery TL, Elhammali A, Piwnica-Worms H, McDonald S, Watson M, Dooling DJ, Ota D, Chang LW, Bose R, Ley TJ, Piwnica-Worms D, Stuart JM, Wilson RK, & Mardis ER (2012). Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature, 486 (7403), 353-60 PMID: 22722193

Welch JS, Ley TJ, Link DC, Miller CA, Larson DE, Koboldt DC, Wartman LD, Lamprecht TL, Liu F, Xia J, Kandoth C, Fulton RS, McLellan MD, Dooling DJ, Wallis JW, Chen K, Harris CC, Schmidt HK, Kalicki-Veizer JM, Lu C, Zhang Q, Lin L, O’Laughlin MD, McMichael JF, Delehaunty KD, Fulton LA, Magrini VJ, McGrath SD, Demeter RT, Vickery TL, Hundal J, Cook LL, Swift GW, Reed JP, Alldredge PA, Wylie TN, Walker JR, Watson MA, Heath SE, Shannon WD, Varghese N, Nagarajan R, Payton JE, Baty JD, Kulkarni S, Klco JM, Tomasson MH, Westervelt P, Walter MJ, Graubert TA, DiPersio JF, Ding L, Mardis ER, & Wilson RK (2012). The origin and evolution of mutations in acute myeloid leukemia. Cell, 150 (2), 264-78 PMID: 22817890

Cancer Genome Atlas Network (2012). Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487(7407), 330-7 PMID: 22810696

Dees ND, Zhang Q, Kandoth C, Wendl MC, Schierding W, Koboldt DC, Mooney TB, Callaway MB, Dooling D, Mardis ER, Wilson RK, & Ding L (2012). MuSiC: identifying mutational significance in cancer genomes.Genome research, 22 (8), 1589-98 PMID: 22759861

Walter MJ, Shen D, Ding L, Shao J, Koboldt DC, Chen K, Larson DE, McLellan MD, Dooling D, Abbott R, Fulton R, Magrini V, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Fan X, Grillot M, Witowski S, Heath S, Frater JL, Eades W, Tomasson M, Westervelt P, DiPersio JF, Link DC, Mardis ER, Ley TJ, Wilson RK, & Graubert TA (2012). Clonal architecture of secondary acute myeloid leukemia. The New England journal of medicine, 366(12), 1090-8 PMID: 22417201

Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, Arthur CD, White JM, Chen YS, Shea LK, Hundal J, Wendl MC, Demeter R, Wylie T, Allison JP, Smyth MJ, Old LJ, Mardis ER, & Schreiber RD (2012).Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature, 482 (7385), 400-4 PMID: 22318521

Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L, & Wilson RK (2012). VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research PMID: 22300766

Koboldt DC, Larson DE, Chen K, Ding L, & Wilson RK (2012). Massively parallel sequencing approaches for characterization of structural variation. Methods in molecular biology (Clifton, N.J.), 838, 369-84 PMID:22228022

Graubert TA, Shen D, Ding L, Okeyo-Owuor T, Lunn CL, Shao J, Krysiak K, Harris CC, Koboldt DC, Larson DE, McLellan MD, Dooling DJ, Abbott RM, Fulton RS, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Grillot M, Baty J, Heath S, Frater JL, Nasim T, Link DC, Tomasson MH, Westervelt P, DiPersio JF, Mardis ER, Ley TJ, Wilson RK, & Walter MJ (2011). Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nature genetics, 44 (1), 53-7 PMID: 22158538

Larson DE, Harris CC, Chen K, Koboldt DC, Abbott TE, Dooling DJ, Ley TJ, Mardis ER, Wilson RK, & Ding L. (2011). SomaticSniper: Identification of Somatic Point Mutations in Whole Genome Sequencing Data.Bioinformatics, Online : doi: 10.1093/bioinformatics/btr665

Cancer Genome Atlas Research Network (2011). Integrated genomic analyses of ovarian carcinoma. Nature, 474 (7353), 609-15 PMID:21720365

Marth GT, Yu F, Indap AR, Garimella K, et al & the 1000 Genomes Project (2011). The functional spectrum of low-frequency coding variation.Genome biology, 12 (9) PMID: 21917140

Ross JA, Koboldt DC, Staisch JE, Chamberlin HM, Gupta BP, Miller RD, Baird SE, & Haag ES (2011). Caenorhabditis briggsae recombinant inbred line genotypes reveal inter-strain incompatibility and the evolution of recombination. PLoS genetics, 7 (7) PMID: 21779179

Bowne SJ, Humphries MM, Sullivan LS, Kenna PF, Tam LC, Kiang AS, Campbell M, Weinstock GM, Koboldt DC, Ding L, Fulton RS, Sodergren EJ, et al (2011). A dominant mutation in RPE65 identified by whole-exome sequencing causes retinitis pigmentosa with choroidal involvement. European journal of human genetics : EJHG, 19 (10) PMID:21938004

Link DC, Schuettpelz LG, Shen D, Wang J, Walter MJ, Kulkarni S, Payton JE, Ivanovich J, Goodfellow PJ, Le Beau M, Koboldt DC, Dooling DJ, Fulton RS, et al (2011). Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA : the journal of the American Medical Association, 305 (15), 1568-76 PMID: 21505135

Ley T, Ding L, Walter M, McLellan M, Lamprecht T, Larson D, Kandoth C, Payton J, Baty J, Welch J, Harris C, Lichti C, Townsend R, Fulton R, Dooling D, Koboldt D, et al. (2010). DNMT3A Mutations in Acute Myeloid Leukemia
New England Journal of Medicine DOI: 10.1056/NEJMoa1005143

Ding L, Wendl MC, Koboldt DC, & Mardis ER (2010). Analysis of next-generation genomic data in cancer: accomplishments and challenges. Human Molecular Genetics, 19 (R2):R188-96. PMID:20843826

Sudmant PH, Kitzman JO, Antonacci F, Alkan C, Malig M, Tsalenko A, Sampas N, Bruhn L, Shendure J, 1000 Genomes Project, & Eichler EE (2010). Diversity of human copy number variation and multicopy genes. Science (New York, N.Y.), 330 (6004), 641-6 PMID: 21030649

The 1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing. Nature, 467(7319), 1061-1073 DOI: 10.1038/nature09534

Bowne SJ, Sullivan LS, Koboldt DC, Ding L, Fulton R, Abbott RM, Sodergren EJ, Birch DG, Wheaton DH, Heckenlively JR, Liu Q, Pierce EA, Weinstock GM, & Daiger SP (2010). Identification of Disease-Causing Mutations in Autosomal Dominant Retinitis Pigmentosa (adRP) Using Next-Generation DNA Sequencing. Investigative ophthalmology & visual science PMID: 20861475

Fehniger, T., Wylie, T., Germino, E., Leong, J., Magrini, V., Koul, S., Keppel, C., Schneider, S., Koboldt, D., Sullivan, R., Heinz, M., Crosby, S., Nagarajan, R., Ramsingh, G., Link, D., Ley, T., & Mardis, E. (2010). Next-generation sequencing identifies the natural killer cell microRNA transcriptome Genome Research DOI: 10.1101/gr.107995.110

Ramsingh G, Koboldt DC, Trissal M, Chiappinelli KB, Wylie T, Koul S, Chang LW, Nagarajan R, Fehniger TA, Goodfellow P, Magrini V, Wilson RK, Ding L, Ley TJ, Mardis ER, & Link DC (2010). Complete characterization of the microRNAome in a patient with acute myeloid leukemia. BloodPMID: 20876853

Koboldt DC, Ding L, Mardis ER & Wilson RK. (2010). Challenges of sequencing human genomes. Briefings in Bioinformatics DOI:10.1093/bib/bbq016

Ding L, Ellis MJ, Li S, Larson DE, Chen K, Wallis JW, Harris CC, McLellan MD, Fulton RS, Fulton LL, Abbott RM, Hoog J, Dooling DJ, Koboldt DC, et al. (2010). Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature, 464 (7291), 999-1005 PMID:20393555

Koboldt DC and Miller RD (2010). Identification of polymorphic markers for genetic mapping. Genomics: Essential Methods, In Press.

Koboldt DC, Staisch J, Thillainathan B, Haines K, Baird SE, Chamberlin HM, Haag ES, Miller RD, & Gupta BP (2010). A toolkit for rapid gene mapping in the nematode Caenorhabditis briggsae. BMC genomics, 11 (1) PMID: 20385026

Voora D, Koboldt DC, King CR, Lenzini PA, Eby CS, Porche-Sorbet R, Deych E, Crankshaw M, Milligan PE, McLeod HL, Patel SR, Cavallari LH, Ridker PM, Grice GR, Miller RD, & Gage BF (2010). A polymorphism in the VKORC1 regulator calumenin predicts higher warfarin dose requirements in African Americans. Clinical pharmacology and therapeutics, 87 (4), 445-51 PMID: 20200517

Zhang Q, Ding L, Larson DE, Koboldt DC, McLellan MD, Chen K, Shi X, Kraja A, et al (2009). CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. Bioinformatics (Oxford, England) PMID: 20031968

Mardis ER, Ding L, Dooling DJ, Larson DE, McLellan MD, Chen K, Koboldt DC, et al (2009). Recurring mutations found by sequencing an acute myeloid leukemia genome. The New England journal of medicine, 361(11), 1058-66 PMID: 19657110

Koboldt DC, Chen K, Wylie T, Larson DE, McLellan MD, Mardis ER, Weinstock GM, Wilson RK, & Ding L (2009). VarScan: variant detection in massively parallel sequencing of individual and pooled samples.Bioinformatics (Oxford, England), 25 (17), 2283-5 PMID: 19542151

Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, Dooling D, Dunford-Shore BH, McGrath S, Hickenbotham M, Cook L, Abbott R, Larson DE, Koboldt DC, et al (2008). DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature, 456 (7218), 66-72 PMID: 18987736

Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, et al (2008). Somatic mutations affect key pathways in lung adenocarcinoma. Nature, 455 (7216), 1069-75 PMID: 18948947

Cancer Genome Atlas Research Network (2008). Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455 (7216), 1061-8 PMID: 18772890

International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature, 449 (7164), 851-61 PMID: 17943122

Sabeti PC, Varilly P, Fry B, et al (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449 (7164), 913-8 PMID: 17943131

Hillier LW, Miller RD, Baird SE, Chinwalla A, Fulton LA, Koboldt DC, & Waterston RH (2007). Comparison of C. elegans and C. briggsaegenome sequences reveals extensive conservation of chromosome organization and synteny. PLoS biology, 5 (7) PMID: 17608563

Stanley SL Jr, Frey SE, Taillon-Miller P, Guo J, Miller RD, Koboldt DC, Elashoff M, Christensen R, Saccone NL, & Belshe RB (2007). The immunogenetics of smallpox vaccination. The Journal of infectious diseases, 196 (2), 212-9 PMID: 17570108

Koboldt DC, Miller RD, & Kwok PY (2006). Distribution of human SNPs and its effect on high-throughput genotyping. Human mutation, 27(3), 249-54 PMID: 16425292

The International HapMap Consortium (2005). A haplotype map of the human genome. Nature, 437 (7063), 1299-1320 PMID: 16255080

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Other Writing by Dan Koboldt

Dan Koboldt is also the author of Get Your Baby to Sleep, a resource to help new parents whose baby won’t sleep with advice on establishing healthy baby sleep habits and handling baby sleep problems. He contributes to The Best of Twins and In Search of Whitetails blogs as well.

How would you like to start your own blog? See this guide to building a blog or website in 20 minutes. It walks you through setting up a site with open-source WordPress software, which happens to be what runs Massgenomics.

SOURCE:

 http://massgenomics.org/about

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Mechanosensation and Mechanotransduction: Our Connection to the Outside World

Posted in Biological Networks, Gene Regulation and Evolution, Bone Disease and Musculoskeletal Disease, Cell Biology, Signaling & Cell Circuits, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Human Sensation and Cellular Transduction: Physiology and Therapeutics, Uncategorized, tagged actin, biomechanics, Biophysics, Cellular differentiation, cellular mechanics, Cytoskeleton, forces, hydrostatic pressure, Julius Wolff, mechanical, mechanosensation, mechanotransduction, osteocytes, primary osteocyte, shear stress, Signal transduction, signalling, Vatsa, wollf's law on February 20, 2013| 2 Comments »

Author: Aviral Vatsa PhD MBBS

This is the first post in a series of posts on mechanosensation and mechanotransduction and their role in physiology and disease.

Future posts in this category will focus on various aspects of role of mechanosensation and mechanotransduction in human physiology. These aspects will include among others: gene modulation, cellular mechanosensation, tissue regeneration, stem cell differentiation, cancer, disease models, nanomodulation, material science and therapeutics etc.

Based on Zhang et al [1]

Multicellular organisms such as humans require intricate orchestration of signals between cells to achieve global morphogenesis and organ function and thus maintain haemostasis. Three major ‘signalling modalities’ work in unison intracellularly and/or exrtacellularly to regulate harmonious functioning of the physiological milieu. These ‘modalities’ namely biochemical molecules, electrical currents or fields and mechanical forces (external or internal) cohesively direct the downstream regulation of physiological processes.

Traditionally most of the biological studies have focused on biochemical or electrical signalling events and relatively lesser resources have been dedicated towards exploring the role of mechanical forces in human health and disease. Despite early theories proposed by scientists such as Julius Wolff (Wolff’s law [2]) in the late nineteenth century “ that bone in a healthy person or animal will adapt to the loads under which it is placed”, relatively little has been studied about the role of external mechanical forces in maintaining haemostasis. However, recent important developments such as

• identification of external force dependent regulation of signalling pathways [3]
• determination of mechanosensing elements of cellular cytoskeleton [4]
• manipulation of single molecules [5]

have reinstated the importance of external mechanical forces in physiology. As a result more recent investigations have demonstrated that external mechanical forces are major coordinators of development and haemostasis of organisms [6], [7] [8].

‘Mechanotransduction’ has been traditionally defined as the conversion of mechanical stimulus into chemical cues for the cells and thus altering downstream signalling e.g conformational changes in ion channels might lead to initiation of downstream signalling. However, with the accumulation of new knowledge pertaining to the effects of external mechanical loads on extracellular matrix or a cell or on subcellular structures, it is being widely accepted that mechanotransduction is more than merely a physical switch. Rather it entails the whole spectrum of cell-cell , cell-ECM, and intracellular interactions that can directly or indirectly modulate the functioning of cellular mechanisms involved in haemostasis. This modulation can function at various levels such as organism level, tissue level, cellular level and subcellular level.

Forces in cells and organisms

From biological point of view mechanical forces can be grouped into three categories

• intracellular forces
• intercellular forces
• inter-tissue forces

In the eukaryotic cells these forces are generally generated by the the contractile cytoskeletal machinery of the cell that is comprised of

• microfilaments : Diameter-6 nm; example- actin
• intermediate filaments: Diameter-10 nm; example- vimentin, keratin
• microtubules: Diameter-23 nm; example- alpha and beta tubulin

 

Actin labeling in single Osteocyte in situ in mouse bone. Source: Aviral Vatsa

Actin (cytoskeleton) staining of single osteocyte in situ in mouse calvaria (source: Aviral Vatsa)

There are a range of forces generated in the biological milieu (adopted from Mammoto et al [8]): 

• Hydrostatic pressure: mechanical force applied by fluids or gases (e.g. blood or air) that perfuse or infuse living organs (e.g. blood vessels or lung).
• Shear stress: frictional force of fluid flow on the surface of cells. The shear stress generated by the heart pumping blood through the systemic circulation has a key role in the determination of the cell fate of cardiomyocytes, endothelial cells and hematopoietic cells.
• Compressive force: pushing force that shortens the material in the direction of the applied force. Tensional force: pulling force that lengthens materials in the direction of the applied force.
• Cell traction force: is exerted on the adhesion to the ECM and other cells as a result of the shortening of the contractile cytoskeletal actomyosin filaments, which transmit tensional forces across cell surface adhesion receptors (e.g. integrins, cadherins).
• Cell prestress: stabilizing isometric tension in the cell that is generated by the establishment of a mechanical force balance within the cytoskeleton through a tensegrity mechanism. Pulling forces generated within contractile microfilaments are resisted by external tethers of the cell (e.g. to the ECM or neighboring cells) and by internal load-bearing structures that resist compression (e.g. microtubules, filipodia). Prestress controls signal transduction and regulates cell fate.

It is the interplay of these forces generated by the cellular cytoskeleton and the ECM that regulate physiological functions. Disruption in mechanotransduction has been implicated in a variety of diseases such as hypertension, muscular dystrophies, cardiomyopathies, loss of hearing, cancer progression and metastasis. Ongoing attempts at unravelling the finer details of mechanosensation hold promising potential for new therapeutic approaches.

 

References

[1] H. Zhang and M. Labouesse, “Signalling through mechanical inputs – a coordinated process,” Journal of Cell Science, vol. 125, no. 17, pp. 4172–4172, Oct. 2012.

[2] R. A. Brand, “Biographical Sketch: Julius Wolff, 1836–1902,” Clin Orthop Relat Res, vol. 468, no. 4, pp. 1047–1049, Apr. 2010.

[3] A. J. Hudspeth, “The cellular basis of hearing: the biophysics of hair cells,” Science, vol. 230, no. 4727, pp. 745–752, Nov. 1985.

[4] N. Wang, J. P. Butler, and D. E. Ingber, “Mechanotransduction across the cell surface and through the cytoskeleton,” Science, vol. 260, no. 5111, pp. 1124–1127, May 1993.

[5] J. T. Finer, R. M. Simmons, and J. A. Spudich, “Single myosin molecule mechanics: piconewton forces and nanometre steps,” , Published online: 10 March 1994; | doi:10.1038/368113a0, vol. 368, no. 6467, pp. 113–119, Mar. 1994.

[6] P. A. Janmey and R. T. Miller, “Mechanisms of mechanical signaling in development and disease,” J Cell Sci, vol. 124, no. 1, pp. 9–18, Jan. 2011.

[7] R. Keller, L. A. Davidson, and D. R. Shook, “How we are shaped: The biomechanics of gastrulation,” Differentiation, vol. 71, no. 3, pp. 171–205, Apr. 2003.

[8] T. Mammoto and D. E. Ingber, “Mechanical control of tissue and organ development,” Development, vol. 137, no. 9, pp. 1407–1420, May 2010.

 

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