Polyploidization is an important process in the evolution of eukaryotic genomes, but ensuing molecular mechanisms remain to be clarified. Autopolyploidization or whole-genome duplication events frequently are resolved in resulting lineages by the loss of single genes from most duplicated pairs, causing transient gene dosage imbalance and accelerating speciation through meiotic infertility. Allopolyploidization or formation of interspecies hybrids raises the problem of genetic incompatibility (Bateson-Dobzhansky-Muller effect) and may be resolved by the accumulation of mutational changes in resulting lineages. In this article, we show that an osmotolerant yeast species, Pichia sorbitophila, recently isolated in a concentrated sorbitol solution in industry, illustrates this last situation. Its genome is a mosaic of homologous and homeologous chromosomes, or parts thereof, that corresponds to a recently formed hybrid in the process of evolution. The respective parental contributions to this genome were characterized using existing variations in GC content. The genomic changes that occurred during the short period since hybrid formation were identified (e.g., loss of heterozygosity, unilateral loss of rDNA, reciprocal exchange) and distinguished from those undergone by the two parental genomes after separation from their common ancestor (i.e., NUMT (NUclear sequences of MiTochondrial origin) insertions, gene acquisitions, gene location movements, reciprocal translocation). We found that the physiological characteristics of this new yeast species are determined by specific but unequal contributions of its two parents, one of which could be identified as very closely related to an extant Pichia farinosa strain.

BackgroundInformation about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats.DescriptionWe have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface.ConclusionsDrug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a ‘one-stop shop’ to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com.

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