Researchers at the SIB Swiss Institute of Bioinformatics and the EMBL-European Bioinformatics Institute have confirmed the long-held conjecture that studying the genes we share with other animals is a viable means of extrapolating information about human biology. The study, published in the open access journal PLoS Computational Biology, shows how bioinformatics makes it possible to test the conjecture.
Scientists have long looked to model species - mice, for example - to understand human biology. This is at the root of what is called the 'ortholog conjecture': the idea that studying genes which are separated by a speciation event, but retain the same evolutionary ancestor, is useful.
In genetics, scientists must address these issues in order to pinpoint the best genes to study. For example, it may be better to compare genes in mice and humans that directly descend from a common ancestor (orthologs), than to compare copies of genes with a different function (paralogs).
Consider hemoglobins, for example, which are protein complexes used to carry oxygen across diverse animals. Normal hemoglobin in humans is made up of two subunits - alpha globin and beta globin. These proteins are related to each other by a duplication event that happened long ago in animal evolution. If one were interested in the human beta globin, would the best model be to study beta globin in mouse (this is the ortholog of human beta globin) or would it be to study alpha globin in humans (alpha and beta globins are paralogs)?
For the past 40 years, scientists have used orthologs by studying genes in model species, and this has provided invaluable insights in all areas of biology. Until now, there hasn't been enough data to use orthologs with empirical authority. However, with advances in biotechnology producing vast quantities of data, there is finally enough data to settle the debate.
Using advanced computational techniques on data derived from tens of thousands of scientific articles, the researchers analysed 400,000 pairs of genes (orthologs and paralogs) from 13 different species. The team compared the two approaches and observed only a weak decrease in functional similarity between orthologs.
"We have the data to prove that the study of orthologs is indeed useful, but we are only at the beginning," says Prof. Marc Robinson-Rechavi of SIB and the University of Lausanne. "This is at the heart of all of comparative genomics, in which we try to extrapolate knowledge from a handful of organisms and apply it to all of life."
"We found that current experimental annotations do support the standard model," explains Christophe Dessimoz of EMBL-EBI. "Our work corroborates the assumption that studying the genes of other species - whether mice, yeast, or even bacteria - can elucidate aspects of human biology."
The same question has recently been addressed by Nehrt and colleagues, whose different conclusion sparked some debate. The new research suggests that these controversial results were due to overlooked biases in the collective knowledge of gene function. Controlling for these, the new study strongly supports the ortholog conjecture and the fact that studying species we are only distantly related to - even worms, flies, yeasts or bacteria - is relevant and useful.
FINANCIAL DISCLOSURE: RAS acknowledges funding from the Fondation du 450e`me anniversaire de l'Universite´ de Lausanne and Swiss National Science Foundation grants 132476 and 136477. MR-R acknowledges funding from Etat de Vaud and Swiss National Science Foundation grant 133011. CD is supported by a fellowship from the Swiss National Science Foundation (grant 136461). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
COMPETING INTERESTS: The authors have declared that no competing interests exist.
CITATION: Altenhoff AM, Studer RA, Robinson-Rechavi M, Dessimoz C (2012) Resolving the Ortholog Conjecture: Orthologs Tend to Be Weakly, but Significantly, More Similar in Function than Paralogs. PLoS Comput Biol 8(5): e1002514. doi:10.1371/journal.pcbi.1002514
PLEASE ADD THIS LINK TO THE FREELY AVAILABLE ARTICLE IN ONLINE VERSIONS OF YOUR REPORT (the link will go live when the embargo ends): http://www.
EMBL-European Bioinformatics Institute
Hinxton, Cambridgeshire CB10 1SD
Phone: +44 1223 494 695
This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by journal staff, or by the article authors and/or their institutions. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.
PLoS Journals publish under a Creative Commons Attribution License (http://creativecommons.
About PLoS Computational Biology
PLoS Computational Biology (www.ploscompbiol.org) features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained.
About the Public Library of Science
The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource. For more information, visit http://www.