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Coordinated evolution of co-expressed gene clusters in the Drosophila transcriptome.

ABSTRACT: BACKGROUND: Co-expression of genes that physically cluster together is a common characteristic of eukaryotic transcriptomes. This organization of transcriptomes suggests that coordinated evolution of gene expression for clustered genes may also be common. Clusters where expression evolution of each gene is not independent of their neighbors are important units for understanding transcriptome evolution. RESULTS: We used a common microarray platform to measure gene expression in seven closely related species in the Drosophila melanogaster subgroup, accounting for confounding effects of sequence divergence. To summarize the correlation structure among genes in a chromosomal region, we analyzed the fraction of variation along the first principal component of the correlation matrix. We analyzed the correlation for blocks of consecutive genes to assess patterns of correlation that may be manifest at different scales of coordinated expression. We find that expression of physically clustered genes does evolve in a coordinated manner in many locations throughout the genome. Our analysis shows that relatively few of these clusters are near heterochromatin regions and that these clusters tend to be over-dispersed relative to the rest of the genome. This suggests that these clusters are not the byproduct of local gene clustering. We also analyzed the pattern of co-expression among neighboring genes within a single Drosophila species: D. simulans. For the co-expression clusters identified within this species, we find an under-representation of genes displaying a signature of recurrent adaptive amino acid evolution consistent with previous findings. However, clusters displaying co-evolution of expression among species are enriched for adaptively evolving genes. This finding points to a tie between adaptive sequence evolution and evolution of the transcriptome. CONCLUSION: Our results demonstrate that co-evolution of expression in gene clusters is relatively common among species in the D. melanogaster subgroup. We consider the possibility that local regulation of expression in gene clusters may drive the connection between adaptive sequence and coordinated gene expression evolution.

SUBMITTER: Mezey JG

PROVIDER: S-EPMC2266709 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Coordinated evolution of co-expressed gene clusters in the Drosophila transcriptome.

Mezey Jason G JG Nuzhdin Sergey V SV Ye Fangfei F Jones Corbin D CD

BMC evolutionary biology 20080107

<h4>Background</h4>Co-expression of genes that physically cluster together is a common characteristic of eukaryotic transcriptomes. This organization of transcriptomes suggests that coordinated evolution of gene expression for clustered genes may also be common. Clusters where expression evolution of each gene is not independent of their neighbors are important units for understanding transcriptome evolution.<h4>Results</h4>We used a common microarray platform to measure gene expression in seven ...[more]

PMID: 18179715

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Project description:BackgroundThe landscape of biological and biomedical research is being changed rapidly with the invention of microarrays which enables simultaneous view on the transcription levels of a huge number of genes across different experimental conditions or time points. Using microarray data sets, clustering algorithms have been actively utilized in order to identify groups of co-expressed genes. This article poses the problem of fuzzy clustering in microarray data as a multiobjective optimization problem which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. Each of these clustering solutions possesses some amount of information regarding the clustering structure of the input data. Motivated by this fact, a novel fuzzy majority voting approach is proposed to combine the clustering information from all the solutions in the resultant Pareto-optimal set. This approach first identifies the genes which are assigned to some particular cluster with high membership degree by most of the Pareto-optimal solutions. Using this set of genes as the training set, the remaining genes are classified by a supervised learning algorithm. In this work, we have used a Support Vector Machine (SVM) classifier for this purpose.ResultsThe performance of the proposed clustering technique has been demonstrated on five publicly available benchmark microarray data sets, viz., Yeast Sporulation, Yeast Cell Cycle, Arabidopsis Thaliana, Human Fibroblasts Serum and Rat Central Nervous System. Comparative studies of the use of different SVM kernels and several widely used microarray clustering techniques are reported. Moreover, statistical significance tests have been carried out to establish the statistical superiority of the proposed clustering approach. Finally, biological significance tests have been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant clusters of co-expressed genes.ConclusionThe proposed clustering method has been shown to perform better than other well-known clustering algorithms in finding clusters of co-expressed genes efficiently. The clusters of genes produced by the proposed technique are also found to be biologically significant, i.e., consist of genes which belong to the same functional groups. This indicates that the proposed clustering method can be used efficiently to identify co-expressed genes in microarray gene expression data.Supplementary Website The pre-processed and normalized data sets, the matlab code and other related materials are available at http://anirbanmukhopadhyay.50webs.com/mogasvm.html.

Project description:We describe here the Drosophila gene hydra that appears to have originated de novo in the melanogaster subgroup and subsequently evolved in both structure and expression level in Drosophila melanogaster and its sibling species. D. melanogaster hydra encodes a predicted protein of approximately 300 amino acids with no apparent similarity to any previously known proteins. The syntenic region flanking hydra on both sides is found in both D. ananassae and D. pseudoobscura, but hydra is found only in melanogaster subgroup species, suggesting that it originated less than approximately 13 million y ago. Exon 1 of hydra has undergone recurrent duplications, leading to the formation of nine tandem alternative exon 1s in D. melanogaster. Seven of these alternative exons are flanked on their 3' side by the transposon DINE-1 (Drosophila interspersed element-1). We demonstrate that at least four of the nine duplicated exon 1s can function as alternative transcription start sites. The entire hydra locus has also duplicated in D. simulans and D. sechellia. D. melanogaster hydra is expressed most intensely in the proximal testis, suggesting a role in late-stage spermatogenesis. The coding region of hydra has a relatively high Ka/Ks ratio between species, but the ratio is less than 1 in all comparisons, suggesting that hydra is subject to functional constraint. Analysis of sequence polymorphism and divergence of hydra shows that it has evolved under positive selection in the lineage leading to D. melanogaster. The dramatic structural changes surrounding the first exons do not affect the tissue specificity of gene expression: hydra is expressed predominantly in the testes in D. melanogaster, D. simulans, and D. yakuba. However, we have found that expression level changed dramatically (approximately >20-fold) between D. melanogaster and D. simulans. While hydra initially evolved in the absence of nearby transposable element insertions, we suggest that the subsequent accumulation of repetitive sequences in the hydra region may have contributed to structural and expression-level evolution by inducing rearrangements and causing local heterochromatinization. Our analysis further shows that recurrent evolution of both gene structure and expression level may be characteristics of newly evolved genes. We also suggest that late-stage spermatogenesis is the functional target for newly evolved and rapidly evolving male-specific genes.

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Coordinated evolution of co-expressed gene clusters in the Drosophila transcriptome.

Publications

Coordinated evolution of co-expressed gene clusters in the Drosophila transcriptome.

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