No abstract

No abstract

Patients with chronic kidney disease (CKD) show a panel of partially de-regulated serum markers indicative for bone metabolism disorders and cardiovascular diseases (CVDs). This review provides an overview of currently reported biomarker candidates at the interface of kidney disease, bone metabolism disorders and CVDs, and gives details on their functional interplay on the level of protein–protein interaction data. We retrieved 13 publications from 1999 to 2006 reporting 31 genes associated with CVDs, and 46 genes associated with bone metabolism disorders in patients with CKD. We identified these genes to be functionally involved in signal transduction processes, cell communication, immunity and defence, as well as skeletal development. On the basis of the given set of 77 genes further 276 interacting proteins were identified using reference data on known protein interactions. Their functional interplay was estimated by linking properties reflected by gene expression data characterizing CKD, gene ontology terms as provided by the gene ontology consortium and transcription factor binding site profiles. Highly connected sub-networks of proteins associated with CKD, CVDs or bone metabolism disorders were detected involving proteins like collagens (COL1A1, COL1A2), fibronectin, transforming growth factor-β1, or components of fibrinogen (FG-α, FG-β, FG-γ). A systems biology approach provides a methodological framework for linking singular biomarker candidates towards deriving functional dependencies among clinically interlinked diseases.

Cross-Omics studies aimed at characterizing a specific phenotype on multiple levels are entering the -scientific literature, and merging e.g. transcriptomics and proteomics data clearly promises to improve Omics data interpretation. Also for Systems Biology the integration of multi-level Omics profiles (also across species) is considered as central element. Due to the complexity of each specific Omics technique, specialization of experimental and bioinformatics research groups have become necessary, in turn demanding collaborative efforts for effectively implementing cross-Omics. This setting imposes specific emphasis on data sharing platforms for Omics data integration and cross-Omics data analysis and interpretation. Here we describe a software concept and methodology fostering Omics data sharing in a distributed team setting which next to the data management component also provides hypothesis generation via inference, semantic search, and community functions. Investigators are supported in data workflow management and interpretation, supporting the transition from a collection of heterogeneous Omics profiles into an integrated body of knowledge.

No abstract