Using DNA microarrays to study gene expression in closely related species

MOTIVATION: Comparisons of gene expression levels within and between species have become a central tool in the study of the genetic basis for phenotypic variation, as well as in the study of the evolution of gene regulation. DNA microarrays are a key technology that enables these studies. Currently, however, microarrays are only available for a small number of species. Thus, in order to study gene expression levels in species for which microarrays are not available, researchers face three sets of choices: (i) use a microarray designed for another species, but only compare gene expression levels within species, (ii) construct a new microarray for every species whose gene expression profiles will be compared or (iii) build a multi-species microarray with probes from each species of interest. Here, we use data collected using a multi-primate cDNA array to evaluate the reliability of each approach. RESULTS: We find that, for inter-species comparisons, estimates of expression differences based on multi-species microarrays are more accurate than those based on multiple species-specific arrays. We also demonstrate that within-species expression differences can be estimated using a microarray for a closely related species, without discernible loss of information. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Exploring the genomes: From Arabidopsis to crops

Model systems have played a crucial role for understanding biological processes at genetic, molecular and systems levels. Arabidopsis thaliana is one of the best studied model species for higher plants. Large genomic resources and mutant collections made Arabidopsis an excellent source for functional and comparative genomics. Rice and Brachypodium have a great potential to become model systems for grasses. Given the agronomic importance of grass crops, it is an attractive strategy to apply knowledge from Arabidopsis to grasses. Despite many efforts successful reports are sparse. Knowledge transfer should generally work best between orthologous genes that share functionality and a common ancestor. In higher plants, however, recent genome projects revealed an active and rapid evolution of genome structure, which challenges the concept of one-to-one orthologous mates between two species. In this study, we estimated on the example of protein families that are involved in redox related processes, the impact of gene expansions on the success rate for a knowledge transfer from Arabidopsis to the grass species rice, sorghum and Brachypodium. The sparse synteny between dicot and monocot plants due to frequent rearrangements, translocations and gene losses strongly impairs and reduces the number of orthologs detectable by positional conservation. To address the limitations of sparse synteny and expanded gene families, we applied for the detection of orthologs in this study orthoMCL, a sequence-based approach that allows to group closely related paralogs into one orthologous gene cluster. For a total of 49 out of 170 Arabidopsis genes we could identify conserved copy numbers between the dicot model and the grass annotations whereas approximately one third (34.7%, 59 genes) of the selected Arabidopsis genes lack an assignment to any of the grass genome annotations. The remaining 62 Arabidopsis genes represent groups that are considerably biased in their copy numbers between Arabidopsis and all or most of the three grass genomes.     

Evolutionary conservation of expression profiles between human and mouse orthologous genes

Mouse models are often used to study human genes because it is believed that the expression and function are similar for the majority of orthologous genes between the two species. However, recent comparisons of microarray data from thousands of orthologous human and mouse genes suggested rapid evolution of gene expression profiles under minimal or no selective constraint. These findings appear to contradict non-array-based observations from many individual genes and imply the uselessness of mouse models for studying human genes. Because absolute levels of gene expression are not comparable between species when the data are generated by species-specific microarrays, use of relative mRNA abundance among tissues (RA) is preferred to that of absolute expression signals. We thus reanalyze human and mouse genome-wide gene expression data generated by oligonucleotide microarrays. We show that the mean correlation coefficient among expression profiles detected by different probe sets of the same gene is only 0.38 for humans and 0.28 for mice, indicating that current measures of expression divergence are flawed because the large estimation error (discrepancy in expression signal detected by different probe sets of the same gene) is mistakenly included in the between-species divergence. When this error is subtracted, 84% of human-mouse orthologous gene pairs show significantly lower expression divergence than that of random gene pairs. In contrast to a previous finding, but consistent with the common sense, expression profiles of orthologous tissues between species are more similar to each other than to those of nonorthologous tissues. Furthermore, the evolutionary rate of expression divergence and that of coding sequence divergence are found to be weakly, but significantly positively correlated, when RA and the Euclidean distance are used to measure expression-profile divergence. These results highlight the importance of proper consideration of various estimation errors in comparing the microarray data between species.     

CLOE: Identification of putative functional relationships among genes by comparison of expression profiles between two species

Background  

Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation.