Cristian I. Castillo-Davis

Selection for short introns in highly expressed genes.

Transcription is a slow and expensive process: in eukaryotes, approximately 20 nucleotides can be transcribed per second at the expense of at least two ATP molecules per nucleotide. Thus, at least for highly expressed genes, transcription of long introns, which are particularly common in mammals, is costly. Using data on the expression of genes that encode proteins in Caenorhabditis elegans and Homo sapiens, we show that introns in highly expressed genes are substantially shorter than those in genes that are expressed at low levels. This difference is greater in humans, such that introns are, on average, 14 times shorter in highly expressed genes than in genes with low expression, whereas in C. elegans the difference in intron length is only twofold. In contrast, the density of introns in a gene does not strongly depend on the level of gene expression. Thus, natural selection appears to favor short introns in highly expressed genes to minimize the cost of transcription and other molecular processes, such as splicing.

GeneMerge—post-genomic analysis, data mining, and hypothesis testing

SUMMARY GeneMerge is a web-based and standalone program written in PERL that returns a range of functional and genomic data for a given set of study genes and provides statistical rank scores for over-representation of particular functions or categories in the data set. Functional or categorical data of all kinds can be analyzed with GeneMerge, facilitating regulatory and metabolic pathway analysis, tests of population genetic hypotheses, cross-experiment comparisons, and tests of chromosomal clustering, among others. GeneMerge can perform analyses on a wide variety of genomic data quickly and easily and facilitates both data mining and hypothesis testing. AVAILABILITY GeneMerge is available free of charge for academic use over the web and for download from: http://www.oeb.harvard.edu/hartl/lab/publications/GeneMerge.html.

Systems-level analysis and evolution of the phototransduction network in Drosophila

Networks of interacting genes are responsible for generating lifes complexity and for mediating how organisms respond to their environment. Thus, a basic understanding of genetic variation in gene networks in natural populations is important for elucidating how changes at the genetic level map to higher levels of biological organization. Here, using the well-characterized phototransduction network in Drosophila, we analyze variation in gene expression within and between two closely related species, Drosophila melanogaster and Drosophila simulans, under different environmental conditions. Gene expression levels in the pathway are largely conserved between these two sibling species. For most genes in the network, differences in level of gene expression between species are correlated with degree of polymorphism within species. However, one gene encoding the light-induced ion channel TRPL (transient receptor potential-like) shows an excess of expression divergence relative to polymorphism, suggesting a possible role for natural selection in shaping this expression difference between species. Finally, this difference in TRPL expression likely has significant functional consequences, because it is known that a high level of rhabdomeral TRPL leads to increased sensitivity to dim background light and an increased response to a wider range of light intensities. These results provide a preliminary quantification of variation and divergence of gene expression between species in a known gene network and provide a foundation for a system-level understanding of functional and evolutionary change.