Phenotypes are an important subject of biomedical research for which many repositories have already been created. Most of these databases are either dedicated to a single species or to a single disease of interest. With the advent of technologies to generate phenotypes in a high-throughput manner, not only is the volume of phenotype data growing fast but also the need to organize these data in more useful ways. We have created PhenomicDB (freely available at ), a multi-species genotype/phenotype database, which shows phenotypes associated with their corresponding genes and grouped by gene orthologies across a variety of species. We have enhanced PhenomicDB recently by additionally incorporating quantitative and descriptive RNA interference (RNAi) screening data, by enabling the usage of phenotype ontology terms and by providing information on assays and cell lines. We envision that integration of classical phenotypes with high-throughput data will bring new momentum and insights to our understanding. Modern analysis tools under development may help exploiting this wealth of information to transform it into knowledge and, eventually, into novel therapeutic approaches.

Background: Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships.

Molecular mechanisms underlying the development of resistance to platinum-based treatment in patients with ovarian cancer remain poorly understood. This is mainly due to the lack of appropriate in vivo models allowing the identification of resistance-related factors. In this study, we used human whole-genome microarrays and linear model analysis to identify potential resistance-related genes by comparing the expression profiles of the parental human ovarian cancer model A2780 and its platinum-resistant variant A2780cis before and after carboplatin treatment in vivo. Growth differentiation factor 15 (GDF15) was identified as one of five potential resistance-related genes in the A2780cis tumor model. Although A2780-bearing mice showed a strong carboplatin-induced increase of GDF15 plasma levels, the basal higher GDF15 plasma levels of A2780cis-bearing mice showed no further increase after short-term or long-term carboplatin treatment. This correlated with a decreased DNA damage response, enhanced AKT survival signaling and abrogated cell cycle arrest in the carboplatin-treated A2780cis tumors. Furthermore, knockdown of GDF15 in A2780cis cells did not alter cell proliferation but enhanced cell migration and colony size in vitro. Interestingly, in vivo knockdown of GDF15 in the A2780cis model led to a basal-enhanced tumor growth, but increased sensitivity to carboplatin treatment as compared to the control-transduced A2780cis tumors. This was associated with larger necrotic areas, a lobular tumor structure and increased p53 and p16 expression of the carboplatin-treated shGDF15-A2780cis tumors. Furthermore, shRNA-mediated GDF15 knockdown abrogated p27 expression as compared to control-transduced A2780cis tumors. In conclusion, these data show that GDF15 may contribute to carboplatin resistance by suppressing tumor growth through p27. These data show that GDF15 might serve as a novel treatment target in women with platinum-resistant ovarian cancer.

Summary: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional analysis. This system is part of PhenomicDB, providing the world's largest cross-species phenotype data collection with a tool to mine its wealth of information.Availability: Freely available at http://www.phenomicdb.deContact: bertram.weiss@bayerhealthcare.comSupplementary information: Supplementary data are available at Bioinformatics online.