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Gene family level comparative analysis of gene expression in mammals validates the ortholog conjecture.

ABSTRACT: The ortholog conjecture (OC), which is central to functional annotation of genomes, posits that orthologous genes are functionally more similar than paralogous genes at the same level of sequence divergence. However, a recent study challenged the OC by reporting a greater functional similarity, in terms of Gene Ontology (GO) annotations and expression profiles, among within-species paralogs compared with orthologs. These findings were taken to indicate that functional similarity of homologous genes is primarily determined by the cellular context of the genes, rather than evolutionary history. However, several subsequent studies suggest that GO annotations and microarray data could artificially inflate functional similarity between paralogs from the same organism. We sought to test the OC using approaches distinct from those used in previous studies. Analysis of a large RNAseq data set from multiple human and mouse tissues shows that expression similarity (correlations coefficients, rank's, or Z-scores) between orthologs is substantially greater than that for between-species paralogs with the same sequence divergence, in agreement with the OC and the results of recent detailed analyses. These findings are further corroborated by a fine-grain analysis in which expression profiles of orthologs and paralogs were compared separately for individual gene families. Expression profiles of within-species paralogs are more strongly correlated than profiles of orthologs but it is shown that this is caused by high background noise, that is, correlation between profiles of unrelated genes in the same organism. Z-scores and rank scores show a nonmonotonic dependence of expression profile similarity on sequence divergence. This complexity of gene expression evolution after duplication might be at least partially caused by selection for protein dosage rebalancing following gene duplication.

SUBMITTER: Rogozin IB

PROVIDER: S-EPMC4007545 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Gene family level comparative analysis of gene expression in mammals validates the ortholog conjecture.

Rogozin Igor B IB Managadze David D Shabalina Svetlana A SA Koonin Eugene V EV

Genome biology and evolution 20140401 4

The ortholog conjecture (OC), which is central to functional annotation of genomes, posits that orthologous genes are functionally more similar than paralogous genes at the same level of sequence divergence. However, a recent study challenged the OC by reporting a greater functional similarity, in terms of Gene Ontology (GO) annotations and expression profiles, among within-species paralogs compared with orthologs. These findings were taken to indicate that functional similarity of homologous ge ...[more]

PMID: 24610837

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Project description:The ortholog conjecture posits that orthologous genes are functionally more similar than paralogous genes. This conjecture is a cornerstone of phylogenomics and is used daily by both computational and experimental biologists in predicting, interpreting, and understanding gene functions. A recent study, however, challenged the ortholog conjecture on the basis of experimentally derived Gene Ontology (GO) annotations and microarray gene expression data in human and mouse. It instead proposed that the functional similarity of homologous genes is primarily determined by the cellular context in which the genes act, explaining why a greater functional similarity of (within-species) paralogs than (between-species) orthologs was observed. Here we show that GO-based functional similarity between human and mouse orthologs, relative to that between paralogs, has been increasing in the last five years. Further, compared with paralogs, orthologs are less likely to be included in the same study, causing an underestimation in their functional similarity. A close examination of functional studies of homologs with identical protein sequences reveals experimental biases, annotation errors, and homology-based functional inferences that are labeled in GO as experimental. These problems and the temporary nature of the GO-based finding make the current GO inappropriate for testing the ortholog conjecture. RNA sequencing (RNA-Seq) is known to be superior to microarray for comparing the expressions of different genes or in different species. Our analysis of a large RNA-Seq dataset of multiple tissues from eight mammals and the chicken shows that the expression similarity between orthologs is significantly higher than that between within-species paralogs, supporting the ortholog conjecture and refuting the cellular context hypothesis for gene expression. We conclude that the ortholog conjecture remains largely valid to the extent that it has been tested, but further scrutiny using more and better functional data is needed.

Project description:BackgroundMammalian gonadal sex is determined by the presence or absence of a Y chromosome and the subsequent production of sex hormones contributes to secondary sexual differentiation. However, sex chromosome-linked genes encoding dosage-sensitive transcription and epigenetic factors are expressed well before gonad formation and have the potential to establish sex-biased expression that persists beyond the appearance of gonadal hormones. Here, we apply a comparative bioinformatics analysis on a pair of published single-cell datasets from mouse and human during very early embryogenesis-from two-cell to pre-implantation stages-to characterize sex-specific signals and to assess the degree of conservation among early acting sex-specific genes and pathways.ResultsClustering and regression analyses of gene expression across samples reveal that sex initially plays a significant role in overall gene expression patterns at the earliest stages of embryogenesis which potentially may be the byproduct of signals from male and female gametes during fertilization. Although these transcriptional sex effects rapidly diminish, sex-biased genes appear to form sex-specific protein-protein interaction networks across pre-implantation stages in both mammals providing evidence that sex-biased expression of epigenetic enzymes may establish sex-specific patterns that persist beyond pre-implantation. Non-negative matrix factorization (NMF) on male and female transcriptomes generated clusters of genes with similar expression patterns across sex and developmental stages, including post-fertilization, epigenetic, and pre-implantation ontologies conserved between mouse and human. While the fraction of sex-differentially expressed genes (sexDEGs) in early embryonic stages is similar and functional ontologies are conserved, the genes involved are generally different in mouse and human.ConclusionsThis comparative study uncovers much earlier than expected sex-specific signals in mouse and human embryos that pre-date hormonal signaling from the gonads. These early signals are diverged with respect to orthologs yet conserved in terms of function with important implications in the use of genetic models for sex-specific disease.

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Project description:Plant genome-scale resources are being generated at an increasing rate as sequencing technologies continue to improve and raw data costs continue to fall; however, the cost of downstream analyses remains large. This has resulted in a considerable range of genome assembly and annotation qualities across plant genomes due to their varying sizes, complexity, and the technology used for the assembly and annotation. To effectively work across genomes, researchers increasingly rely on comparative genomic approaches that integrate across plant community resources and data types. Such efforts have aided the genome annotation process and yielded novel insights into the evolutionary history of genomes and gene families, including complex non-model organisms. The essential tools to achieve these insights rely on gene family analysis at a genome-scale, but they are not well integrated for rapid analysis of new data, and the learning curve can be steep. Here we present PlantTribes2, a scalable, easily accessible, highly customizable, and broadly applicable gene family analysis framework with multiple entry points including user provided data. It uses objective classifications of annotated protein sequences from existing, high-quality plant genomes for comparative and evolutionary studies. PlantTribes2 can improve transcript models and then sort them, either genome-scale annotations or individual gene coding sequences, into pre-computed orthologous gene family clusters with rich functional annotation information. Then, for gene families of interest, PlantTribes2 performs downstream analyses and customizable visualizations including, (1) multiple sequence alignment, (2) gene family phylogeny, (3) estimation of synonymous and non-synonymous substitution rates among homologous sequences, and (4) inference of large-scale duplication events. We give examples of PlantTribes2 applications in functional genomic studies of economically important plant families, namely transcriptomics in the weedy Orobanchaceae and a core orthogroup analysis (CROG) in Rosaceae. PlantTribes2 is freely available for use within the main public Galaxy instance and can be downloaded from GitHub or Bioconda. Importantly, PlantTribes2 can be readily adapted for use with genomic and transcriptomic data from any kind of organism.

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Gene family level comparative analysis of gene expression in mammals validates the ortholog conjecture.

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Gene family level comparative analysis of gene expression in mammals validates the ortholog conjecture.

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