Pathway Analysis unter Verwendung des Gene Set Enrichment Analysis (GSEA) Werkzeug

Gene Set Enrichment Analysis ist einer von vielen Ansätzen, um die Analyse der Genexpression Profildaten und ist unter a Papiervon Arbeitern am Broad Institute.

Das grundlegende Konzept wurde durch die Beobachtung, dass das Studium aufgefordert einzelne Gene zeigt die bedeutendste Unterschied in der Expression-Ebene zwischen zwei Staaten oder Phänotypen ist fehlt in mechanistische Einblicke. Stattdessen, macht es mehr Sinn, ein zu nehmen Satz von Genen sie einige biologische Verbindung, und die Frage stellen - nicht das ganze Set eine statistisch zeigen signifikante Anreicherung in jenen Genen, die differentielle Expression haben?

A Gen-Set gewählt werden kann, VON VORNHEREIN, für eine Anzahl von Gründen, z.B.. der Satz von Genen bekannt, um über beeinflussbar- oder Unterexpression eines Mikro-RNA, oder vielleicht ein Satz basierend auf chromosomale Standort gewählt, oder Gene für die molekulare Funktion, Zellkomponente und / oder biologischen Prozess zugewiesen wurden über die kontrollierten Vokabulare der Gene Ontology.

Ein Vorteil der GSEA Ansatz ist, dass es möglich ist, zu übernehmen Ihre vollständigen Datensatz, nicht nur diejenigen Transkripte mit einer willkürlich gewählten differentiellen Expression Schwelle. Ich bin mir sicher, dass viele Leute dies lesen werden denken - "Wie kann es sein OK, um den kompletten Datensatz zu verwenden? Normalerweise würde ich berücksichtigen nur Gene mit >2 (Oder andere Lieblings-Wert)-Falte unterschiedliche Expression. "Der Grund ist der Ansatz gültig ist, dass die Gene auf einem niedrigen Niveau oder mit einer großen Varianz zwischen repliziert ausgedrückt nicht von dem Haupt-Metrik durch GSEA verwendet beitragen, die 'Bereicherung des Gastes" (ES).

GSEA funktioniert durch die erste Rang der Wert des Ausdrucks für jedes Gen durch Signal-Rausch ratio - Berechnen der Differenz zwischen den Mittelwerten für die Proben, welche jedes Phänotyp und skaliert werden, damit durch die Summe der Standardabweichungen. Dies bedeutet, dass Gene, die mit großen Unterschiede in der Expression Ebene zwischen verschiedenen Staaten und wenig Variation zwischen biologischen repliziert werden hoch bewertet.

Der nächste Schritt ist, dass die ES, die primäre Statistik durch GSEA generiert, wird für jedes Gen Satz berechnet - im GSEA manuelle, Welche Dokumente die Software hervorragend, heißt es:

"Alle Gene werden zunächst durch ihr Signal-Rausch-Verhältnis sortiert, dann die ES durch "Fuß" down der Rangliste von Genen berechnet zunehmend ein Einlaufschicht Summe statistische wenn ein Gen in der Gen-Set ist und abnehmend es, wenn es nicht. Die Größenordnung der Inkrement abhängig von der Korrelation des Gens mit einem Phänotyp. Die ES ist die maximale Abweichung von Null zu Fuß die Liste gestoßen. A positiv ES gibt Gen-Set Bereicherung auf dem Top der Rangliste; ein negativ ES gibt Gen-Set Bereicherung auf dem Boden der Rangliste. "

Die ES-Werte sind normalisiert basierend auf Gen-Set-Größe und dann eine false discovery rate berechnet wird, um eine geschätzte Wahrscheinlichkeit von Fehlalarmen zu geben. GSEA nutzt eine sehr entspannte Standardwert 25%, welche geeignet ist Hypothesenerzeugung mit einer relativ großen Anzahl biologischer repliziert.

Wissenschaftler auf Daten aus nichtmenschlichen Proben können weiterhin GSEA, sondern müssensich hüten - Die Gensymbole verwendet von GSEA sind "übersetzt"Von ihren menschlichen Äquivalenten d.h.. Kennungen für Gene von eurer Spezies von Interesse auf dem Microarray vertreten eingesetzt werden zu Symbolen für ihre konvertierten Menschen Orthologe, dann in der Analyse verwendeten. Subramanian und Kollegen behaupten dass diese Umwandlung hat wenig oder kein Effekt über den Nutzen der GSEA; es wurde erfolgreich in mehreren nicht-menschlichen Spezies verwendet, aber natürlich muss im Auge behalten werden, in denen Ergebnisse im Detail.

Für eine exzellente, eingehend, Überprüfung der Weg Werkzeugen, konsultieren:

Khatri, P., Sirota, M., & Butte, A. J. (2012). Zehn Jahre Pathway Analysis: Aktuelle Ansätze und anstehenden Herausforderungen. PLoS Computational Biology, 8(2), e1002375. zwei:10.1371/journal.pcbi.1002375

Eine weitere gute Quelle Erfahrungsberichte zu Pfadanalyse, vor allem für diejenigen, die mit der R Statistik-Paket ist hier.

Weiterführende Literatur

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene Set Bereicherung Analyse: eine wissensbasierte Ansatz zur Interpretation genomweite Expressions-Profilen. Proc Natl Acad Sci U S A 102:15545-15550

Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ES, Kellis M (2005) Systematische Entdeckung regulatorische Motive im menschlichen Promotoren und 3[Primzahl] UTRs durch Vergleich mehrerer Säugetieren. Natur 434:338-345

Veröffentlicht in Pathway-Analyse | 1 Antwort

Joys of bearbeiten akademischen Wissenschaft Pfund

Image courtesy of ningmilo / FreeDigitalPhotos.net

Oder:"Ein Leitfaden für Anfänger zur Herding Cats".

Betrachten Sie dieses Szenario: Sie sind eine akademische Wissenschaftler, in einem langen Forschungsinstitut und Ihr Chef wird aufgefordert, ein Buch bearbeiten, aber lehnt aufgrund des Drucks der Arbeit; dann legt nahe, dass es aussehen würde in Ihrem Lebenslauf gut. Sie stimmen, es würde auf Ihrem Lebenslauf gut, so you commit yourself to editing your first multi-author academic science book.

So why is that a problem?

Getting authors on board

You want the best people to write the chapters. You Google some big-name experts and invite them to contribute a chapter to your book. They almost all decline, or fail to reply to your email. But, somewhat to your amazement, one agrees. Jedoch, this paragon of science then never, ever replies to any future contacts. So, you lower your sights and aim for good scientists, but not Nobel Prize winners. Schließlich, you get enough authors together to write the chapters around the topic that the publishers have given you – phew!

Getting authors to agree a deadline

Assuming it’s not unreasonable, everyone is usually relaxed about the deadline set. Jedoch, the real challenge is:

Getting them to meet the deadline

  1. This should be easy, right? Scientists are grown-up, professional people. Aren’t they? Nun, sort of. In reality, academics typically over-commit themselves, doing not only research and teaching, but also writing grant funding applications, papers, reviews, book chapters, etc, etc. After all, the scientific mission statement is “publish OR be damned.”
  2. As the deadlines go past – “wooshh”, like passing cars, half your authors have submitted their chapters, the rest not. Now another sticky moment arrives – these are meant to be cutting edge reviews. State-of-the-Art. But this delay now means that the ‘good’ authors work is rapidly reaching its sell-by date. You may have to go crawling back to them to ask for updates. Which they are usually not too unhappy about, but you hate the loss of face.
  3. One more thing that I forgot to mention; as the editor, you have to READ these chapters. Worse still, you are expected to produce cogent critiques – what the author needs to add, remove, expand or contract. Even if the topic is on the fringe of your main expertise.

What happens if authors go AWOL?

What do you do when one of your authors decides that they are NOT going to write their chapter? Not simply procrastinate, fail to meet deadlines, but stop all communication. Disappear off the map. So, now you’re stuck – find another aSohor(s) – more delay – write the chapter yourself? – but it’s too far outside your own area of expertise. So, eventually, you find someone else. Which means yet more delay.

Writing your own chapter

Oh, yes, you forgot that you agreed to write one of the chapters yourself. Oops. Oh well, not a problem. Offer co-authorship to one of your PhD students – they’ll be falling over themselves to get another publication on their CV. Or maybe not: no, they are not interested after all; obviously suspecting (correctly) that your aim is to let them write the whole thing, then submit the chapter to you for a little light editorial polishing.

Pleading with the publishers for more time

  1. You now hold the dubious record for the longest gestation period of a multi-author academic book in human history, excluding the Bible.
  2. ‘Please, sir, I want some more.’
  3. The publishers are not impressed, but quietly resigned, telling you to go away and come back when you meet a new deadline.

Losing your marbles and giving up completely

It’s all taking SO LONG – too few authors have submitted first drafts of their chapters. You start to get desperate – the original deadline was so long ago that you’ve forgotten it – the “new” deadline is also now history. You consider giving the whole thing up – apologise to the authors and the publishers and say the book can’t be finished. But your co-editor and the authors who have delivered on time are indignant – naturally enough they don’t want to see their work wastedand insist that you go back to the recalcitrant scientists with a big stick. How do you threaten authors with a stick by email? Or by phone? Jedoch, a combination of the metaphorical big stick, pleas for mercy and piling on the guilt eventually work and all the chapters are delivered! Hooray.

Hooray!

So, now, you’re on the last lap. Or the last dregs – the soul-destroying process of assembling the index and proofreading. Once, a sub-editor with a scientific background might have written an index, but not now. Academic publishers want their pound of flesh, so this task is delegated to authors and editors. Authors select keywords from their chapters, with varying degrees of enthusiasm or accuracy, then the editor attempts to assemble them into something useful to the reader. Schließlich, a draft proof arrives by email. You are now heartily sick of every word, but a final spurt of enthusiasm drives you on and the book is finished.

One more thing – did I forget? – you don’t get paid – but you are given a few free copies of your own book. Such fun!

 

Veröffentlicht in Licht Erleichterung | 1 Antwort

Wie funktioniert eine Mutation in einem Transkriptionsfaktor Ursache Leim Ohr?

Die akute Otitis media, sometimes known as “glue ear”, is the most common bacterial infection in children and by 1 Lebensjahr über 60% der Kinder haben eine Episode. In einigen Fällen, children develop a chronic condition, welche, trotz der Infektion geheilt, die “Kleber” doesn’t go away and causes deafness.  In an inherited Maus model of chronic glue ear the causative mutation has been shown to be in a gene encoding a Transkriptionsfaktor, Evi1.

The EVI1 protein has multiple domains, can repress or enhance expression of target genes and interact with many other proteins. Tatsächlich, the multiplicity of known and potential interactions is a challenge to determining the role of the mutation.  There were clues, jedoch, as to how this mutation might lead to disease from differences in phenotype e.g. mutant mice raised in a “clean” SPF animal facility were less likely to become deaf than those kept in the older, “dirty” animal house.

Did this mean that gene-environment interactions e.g. between immune system and microbes, influence disease susceptibility? It was also known that mutant mice showed high levels of influx of neutrophils into their middle ear cavities (inflammation), but it was unclear whether EVI1 was acting directly or indirectly in this process. Possible answers to these questions came recently from studies in cultured cells, showing that EVI1 can act as an inhibitor of one of the key proteins regulating inflammation, another transcription factor, nuclear factor kappa B (NFkB).  EVI1 binds to to one of the subunits of NFkB and interferes with a critical protein modification, acetylation.  However, EVI1 does not acetylate proteins directly, so other factors must be involved. What were those other factors?

I combined public and unpublished data using literature searches und open source software e.g. iRefWeb in order to identify steps in the NFkB signalling pathways that might be disturbed by the mutation in EVI1.  The novel target proteins and starting points for drug development I discovered are suitable for testing in this preclinical model of chronic otitis media.

Read our testimonial from Dr Michael Cheeseman.

 

Veröffentlicht in Pathway-Analyse, Target-Entdeckung | Kommentieren

Target-Erkennung in der Kindheit beginnende Asthma

Asthma is caused by a combination of environmental und genetic influences, aber die spezifischen Faktoren werden kaum verstanden. Ein signifikanter "Hit" in einer genomweiten Assoziationsstudien Scans erkannt (GWAS) for childhood asthma led a client to believe that one gene might be partially responsible. Der Beweis, dass diese genetische Assoziation wirklich verursacht wurde Asthma war, jedoch, schwierig. Firstly, no one knew the function of the protein made by the gene and secondly, changing genes in humans to test a hypothesis, rather than as therapy, is technically challenging & ethically questionable, especially in children. Fortunately, mice share about 90% of their genes with humans, so scientists “knocked-out” the equivalent gene, then tested whether these animals behaved like children with asthma. The short answer is – they didn’t. In lung-function tests that would have had asthmatics reaching for their inhalers, die knock-out mice were completely normalen. So, what was going on? Were mice not enough like humans? Was this the wrong gene?

For this project, I went back to first principles – what was the evidence supporting the idea that this gene was responsible for increased asthma risk? Digging through the online literature, in particular papers from other groups studying the same gene and supplementary material not available in print, there were suggestions that the genetic effects were more complex. I found evidence that two other genes nearby were either more or less transcriptionally active in asthmatics and so might play a role in susceptibility to asthma. Furthermore, using data from the ENCODE project, I found that the regulatory element predicted to control these genes was conserved in mice, so it would be possible to test the predictions experimentally.

This suggested a novel therapeutic target – altering the activity of a cluster of genes, rather than just one, might alter disease risk.

Testimonial

Veröffentlicht in Target-Entdeckung | Kommentieren

Pathway-Analyse von Genexpressionsdaten – male reduzierte Fertilität / Sterilität

A group of animals that can breed and produce fertile offspring is one of the definitions of a species.

This means that the biological mechanisms of fertility and infertility are of interest not only to evolutionary biologists, but also to clinicians and of course to the wider public. At the Institute of Molecular Genetics in Prague, Prof. Jiri Forejt is studying what controls fertility in the hybrid offspring produced by the mating of mouse sub-species. He wanted to know why some male mice were infertile – he knew that genes in one particular genome region were important, but not how those genes influenced the expression of the rest of the genome.

This is where I was recruited into the team, to help with identifying the classes of genes disrupted in mice with reduced fertility. Scientists in his group had produced Affymetrix gene expression results from the testes of fertile, sub-fertile and infertile mice and I analysed these data genome-wide for differentially-expressed transcripts. Using the Broad Institute’s marvellous GSEA tool, I assessed the statistical evidence that specific Gene Ontology terms and pathways were over-represented and also whether differential genes were localised to particular genome regions. This analysis uncovered evidence that specific, functionally related sets of genes were over-represented in the expression data and helped to develop novel hypotheses about the causes of reduced fertility.

Veröffentlicht in Pathway-Analyse, Target-Entdeckung | 1 Antwort

Target-Entdeckung in geerbten Muskelschwäche

Muscle weakness can be caused by a rare inherited disease called myofibrillar myopathy. Gonzalo Blanco’s team found a mouse model of this disease and wanted to identify the underlying cause of the severe muscle weakness. Their aim was to discover potential therapeutic targets to translate into pre-clinical and clinical studies.

Before I became involved, the disease had been mapped to a large region of one chromosome and Dr Blanco’s team were planning to use conventional positional cloning methods to find the mutation. I proposed that a faster approach would be to use next-generation sequencing targeted at genes in the region. I designed a set of probes to enrich specific DNA fragments and I worked with a bioinformatician, Dr. Michelle Simon, to design a software pipeline to find and characterise mutations.

At the end of the design process, the pipeline was used to identify mutations in the muscle weakness mutants and predict that they altered the coding sequences of two genes; Myh4 und Pmp22. Two lines of evidence suggested that the mutation in Myh4, which codes for a muscle myosin protein, was the most likely cause of the weakness. Firstly, our colleagues found that mice carrying only the myosin mutation still had the trait and secondly, abnormal protein aggregates from affected mice contained large amounts of the myosin.

Scientists at the MRC’s Mammalian Genetics Unit have used the same approach, that Michelle Simon and I pioneered, to find mutations in other disease models.

Publication in Human Molecular Genetics

Testimonial from Dr. Gonzalo Blanco

Veröffentlicht in Target-Entdeckung | Kommentieren
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