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Testing the ortholog conjecture with comparative functional genomic data from mammals.
PLoS Comput Biol. 2011 Jun; 7(6):e1002073
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09 Aug 2011
Confirmation
DOI: 10.3410/f.12462957.13675056
The functional divergence of genes occurring through evolution by speciation and duplication events is central in functional genomics. It is often supposed that orthologous genes retain similar functions, whereas paralogous genes are more likely to functionally diverge. In this paper, the authors investigate...Continue reading
The functional divergence of genes occurring through evolution by speciation and duplication events is central in functional genomics. It is often supposed that orthologous genes retain similar functions, whereas paralogous genes are more likely to functionally diverge. In this paper, the authors investigate this hypothesis based on current functional annotations in human and mouse and provide important new insights into the relationship between sequence divergence and functional divergence.
To test this hypothesis, the authors used curated functional annotations from Gene Ontology and homology relationships from Ensembl Compara in human and mouse. They found that paralogs have a higher functional similarity than orthologs, that in-paralogs have more similar functions than out-paralogs and that paralogs on the same chromosome have more similar functions than those on different chromosomes.
To assess the functional similarity of a gene pair, the authors used a method they previously developed. However, there are several ways to measure functional similarity and it would be interesting to confirm the results using other measures. It is good that the authors confirmed their results on independent data from microarray-based gene expression.
Altogether, their results suggest that functional similarity is mostly determined by the divergence time. To explain that proteins of the same age have the same level of functional similarity, the authors propose that the divergence time reflects the evolution of the cellular context as defined by interacting molecules. They then conclude that the functional similarity may be best reflected by the evolution of the cellular context (or network similarity) rather than sequence similarity.
To test this hypothesis, the authors used curated functional annotations from Gene Ontology and homology relationships from Ensembl Compara in human and mouse. They found that paralogs have a higher functional similarity than orthologs, that in-paralogs have more similar functions than out-paralogs and that paralogs on the same chromosome have more similar functions than those on different chromosomes.
To assess the functional similarity of a gene pair, the authors used a method they previously developed. However, there are several ways to measure functional similarity and it would be interesting to confirm the results using other measures. It is good that the authors confirmed their results on independent data from microarray-based gene expression.
Altogether, their results suggest that functional similarity is mostly determined by the divergence time. To explain that proteins of the same age have the same level of functional similarity, the authors propose that the divergence time reflects the evolution of the cellular context as defined by interacting molecules. They then conclude that the functional similarity may be best reflected by the evolution of the cellular context (or network similarity) rather than sequence similarity.
Disclosures
None declared
Add Comment
Bader G and Michaut M: F1000Prime Recommendation of [Nehrt NL et al., PLoS Comput Biol 2011, 7(6):e1002073]. In F1000Prime, 09 Aug 2011; DOI: 10.3410/f.12462957.13675056. F1000Prime.com/12462957#eval13675056
31 Aug 2011
Dissent
DOI: 10.3410/f.12462957.14132054
This is a curious paper: if its conclusions had been correct, the whole foundation of modern genome biology would have come into question. Fortunately, its conclusions are wrong, and the key principle of comparative genomics is safe for now.
The reason why I believe this paper's conclusions are wrong...Continue reading
The reason why I believe this paper's conclusions are wrong...Continue reading
This is a curious paper: if its conclusions had been correct, the whole foundation of modern genome biology would have come into question. Fortunately, its conclusions are wrong, and the key principle of comparative genomics is safe for now.
The reason why I believe this paper's conclusions are wrong is that the authors relied on an increasingly popular approach where, instead of studying the problem itself, researchers study what others say about that problem. Such 'meta-analysis' has done much damage to social and medical sciences and now appears to be creeping into bioinformatics.
Nehrt and colleagues are correct in noting that the current genome annotation implicitly relies on what they call the 'ortholog conjecture', the assumption that orthologs (genes with a common origin that were vertically inherited from the same gene in the last common ancestor of the host organisms) typically retain the same function or have closely related ones. When checked on model organisms, such as Escherichia coli, Bacillus subtilis, yeast, worm, and mouse, this assumption mostly held true, although there were certain cases of function loss, non-orthologous gene displacement, promiscuous enzyme activities, moonlighting, and so on (please see refs {1-3} and ref {4}, a book, where I am a co-author). Accordingly, this assumption underlies gene annotations in non-model organisms, whose functions we’ll never be able to check experimentally and, therefore, have to deduce from sequence comparisons. A thorough, genome-wide check of the 'ortholog conjecture' is a potentially useful task; it would be interesting to learn what fraction of genome annotations actually proves to be incorrect. Unfortunately, Nehrt and colleagues used the meta-analysis approach to this problem: instead of painstakingly comparing functions of orthologs in two reasonably well characterized genomes, they merely compared the Gene Ontology (GO) annotations {5} for orthologous and paralogous genes in human and mouse. GO is a valuable resource that uses structured, controlled vocabularies for functional annotation of genes and gene products {6}. However, GO annotations for human and mouse are not independent, as experimentally characterized functions are being propagated to homologous (orthologous and paralogous) genes in other organisms. The results presented by Nehrt and colleagues are stunning indeed: orthologous genes had fewer overlapping GO terms (e.g. in the Biological Process and Molecular Function categories) than paralogous genes. However, instead of concluding that there might be systematic bias in the GO assignments (which would be interesting but not terribly exciting), they questioned the whole 'ortholog conjecture' (which resulted in the above paper). I am not sure this was an appropriate use of the GO system. Anyway, the key question, as I see it, is whether these results reflect some kind of biological reality. I don’t think so: I was unable to find a single example where orthologous genes in human and mouse have been both experimentally characterized and shown to have distinct functions. I could see that the examples that Nehrt et al. cite in their paper all suffer from the same drawback: when the GO assignments for orthologous genes are indeed different, the actual function of both genes either has never been tested or, if tested, proved to be identical. This is not to say that no orthologous genes in human and mouse would have different functions, the authors have just not used the right approach to identify such genes.
In conclusion, I see no reasons to believe that the 'ortholog conjecture' is seriously threatened by the article of Nehrt and colleagues; we can continue annotating new genomes the same way we did before. However, this key proposition of genome biology certainly deserves further scrutiny, and this could be a positive effect of the publication by Nehrt and colleagues, even if its conclusions are not convincing.
References:
{1} Sonnhammer and Koonin, Trends Genet 2002, 18:619-20 [PMID:12446146].
{2} Koonin EV, Annu Rev Genet 2005, 39:309-38 [PMID:16285863].
{3} Jeffery CJ, Mol Biosyst 2009, 5:345-50 [PMID:19396370].
{4} Koonin and Galperin, "Sequence-Evolution-Function: Computational Approaches in Comparative Genomics" Boston: Kluwer Academic, 2003 ISBN:978-1402072741 [PMID:21089240].
{5} Hill et al. BMC Bioinformatics 2008, 9 Suppl 5:S2 [PMID:18460184].
{6} Gene Ontology Consortium, Nucleic Acids Res 2010, 38:D331-5 [PMID:19920128].
The reason why I believe this paper's conclusions are wrong is that the authors relied on an increasingly popular approach where, instead of studying the problem itself, researchers study what others say about that problem. Such 'meta-analysis' has done much damage to social and medical sciences and now appears to be creeping into bioinformatics.
Nehrt and colleagues are correct in noting that the current genome annotation implicitly relies on what they call the 'ortholog conjecture', the assumption that orthologs (genes with a common origin that were vertically inherited from the same gene in the last common ancestor of the host organisms) typically retain the same function or have closely related ones. When checked on model organisms, such as Escherichia coli, Bacillus subtilis, yeast, worm, and mouse, this assumption mostly held true, although there were certain cases of function loss, non-orthologous gene displacement, promiscuous enzyme activities, moonlighting, and so on (please see refs {1-3} and ref {4}, a book, where I am a co-author). Accordingly, this assumption underlies gene annotations in non-model organisms, whose functions we’ll never be able to check experimentally and, therefore, have to deduce from sequence comparisons. A thorough, genome-wide check of the 'ortholog conjecture' is a potentially useful task; it would be interesting to learn what fraction of genome annotations actually proves to be incorrect. Unfortunately, Nehrt and colleagues used the meta-analysis approach to this problem: instead of painstakingly comparing functions of orthologs in two reasonably well characterized genomes, they merely compared the Gene Ontology (GO) annotations {5} for orthologous and paralogous genes in human and mouse. GO is a valuable resource that uses structured, controlled vocabularies for functional annotation of genes and gene products {6}. However, GO annotations for human and mouse are not independent, as experimentally characterized functions are being propagated to homologous (orthologous and paralogous) genes in other organisms. The results presented by Nehrt and colleagues are stunning indeed: orthologous genes had fewer overlapping GO terms (e.g. in the Biological Process and Molecular Function categories) than paralogous genes. However, instead of concluding that there might be systematic bias in the GO assignments (which would be interesting but not terribly exciting), they questioned the whole 'ortholog conjecture' (which resulted in the above paper). I am not sure this was an appropriate use of the GO system. Anyway, the key question, as I see it, is whether these results reflect some kind of biological reality. I don’t think so: I was unable to find a single example where orthologous genes in human and mouse have been both experimentally characterized and shown to have distinct functions. I could see that the examples that Nehrt et al. cite in their paper all suffer from the same drawback: when the GO assignments for orthologous genes are indeed different, the actual function of both genes either has never been tested or, if tested, proved to be identical. This is not to say that no orthologous genes in human and mouse would have different functions, the authors have just not used the right approach to identify such genes.
In conclusion, I see no reasons to believe that the 'ortholog conjecture' is seriously threatened by the article of Nehrt and colleagues; we can continue annotating new genomes the same way we did before. However, this key proposition of genome biology certainly deserves further scrutiny, and this could be a positive effect of the publication by Nehrt and colleagues, even if its conclusions are not convincing.
References:
{1} Sonnhammer and Koonin, Trends Genet 2002, 18:619-20 [PMID:12446146].
{2} Koonin EV, Annu Rev Genet 2005, 39:309-38 [PMID:16285863].
{3} Jeffery CJ, Mol Biosyst 2009, 5:345-50 [PMID:19396370].
{4} Koonin and Galperin, "Sequence-Evolution-Function: Computational Approaches in Comparative Genomics" Boston: Kluwer Academic, 2003 ISBN:978-1402072741 [PMID:21089240].
{5} Hill et al. BMC Bioinformatics 2008, 9 Suppl 5:S2 [PMID:18460184].
{6} Gene Ontology Consortium, Nucleic Acids Res 2010, 38:D331-5 [PMID:19920128].
Disclosures
Michael Galperin uses the 'ortholog conjecture' in his everyday work and receives money from the U.S. Government for doing so.
Galperin M: F1000Prime Dissenting Opinion on [Nehrt NL et al., PLoS Comput Biol 2011, 7(6):e1002073]. In F1000Prime, 31 Aug 2011; DOI: 10.3410/f.12462957.14132054. F1000Prime.com/12462957#eval14132054
F1000Prime Recommendations, Dissents and Comments for [Nehrt NL et al., PLoS Comput Biol 2011, 7(6):e1002073]. In F1000Prime, 19 May 2013; F1000Prime.com/12462957
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A common assumption in comparative genomics is that orthologous genes share greater functional similarity than do paralogous genes (the "ortholog conjecture"). Many methods used to computationally predict protein function are based on this assumption, even though it is largely untested. Here we present the first large-scale test of the ortholog conjecture using comparative functional genomic data from human and mouse. We use the experimentally derived functions of more than 8,900 genes, as well as an independent microarray dataset, to directly assess our ability to predict function using both orthologs and paralogs. Both datasets show that paralogs are often a much better predictor of function than are orthologs, even at lower sequence identities. Among paralogs, those found within the same species are consistently more functionally similar than those found in a different species. We also find that paralogous pairs residing on the same chromosome are more functionally similar than those on different chromosomes, perhaps due to higher levels of interlocus gene conversion between these pairs. In addition to offering implications for the computational prediction of protein function, our results shed light on the relationship between sequence divergence and functional divergence. We conclude that the most important factor in the evolution of function is not amino acid sequence, but rather the cellular context in which proteins act.
© 2011 Nehrt et al.
DOI: 10.1371/journal.pcbi.1002073
PMID: 21695233
