Only the combined molecular feature landscapes closely reflect the clinical implications of DN in the context of hypertension and diabetes. Omics data integration on the level of interaction networks furthermore provides a platform for identification of pathway-specific biomarkers and therapy options.

A central aim of differential gene expression profile analysis is to provide an interpretation of given data at the level of biological processes and pathways. However, traversing descriptive data into context is not straightforward. We present a gene-centric dependency graph approach supporting an interpretation of omics profiles at the level of affected networks. The core of our dependency graph comprises data objects encoding the functional categorization of a particular gene, its tissue-specific reference gene expression, as well as known interactions and subcellular location of assigned proteins. On the basis of these genome, transcriptome, and proteome data we compute pair-wise object (gene) dependencies and interpret them as weighted edges in a dependency graph. Mapping of omics profiles on this graph can be used to identify connectors linking features of the omics list, in turn providing the basis for identification of subgraphs and motifs characterizing the cellular state under analysis. We exemplify this approach by analyzing differential gene expression data characterizing B-cell lymphoma and demonstrate the identification of B-cell lymphoma associated subgraphs.

Ischemia reperfusion injury (IRI) is a choreographed process leading to delayed graft function (DGF) and reduced long-term patency of the transplanted organ. Early identification of recipients of grafts at risk would allow modification of the posttransplant management, and thereby potentially improve short- and long-term outcomes. The recently emerged "omics" technologies together with bioinformatics workup have allowed the integration and analysis of IRI-associated molecular profiles in the context of DGF. Such a systems biological approach promises qualitative information about interdependencies of complex processes such as IRI regulation, rather than offering descriptive tables of differentially regulated features on a transcriptome, proteome, or metabolome level leaking the functional, biological framework. In deceased-donor kidney transplantation as the primary causative factor resulting in IRI and DGF, a distinct signature and choreography of molecular events in the graft before harvesting seems to be associated with subsequent DGF. A systems biological assessment of these molecular changes suggests that processes along inflammation are of pivotal importance for the early stage of IRI. The causal proof of this association has been tested by a double-blinded, randomized, controlled trial of steroid or placebo infusion into deceased donors before the organs were harvested. Thorough systems biological analysis revealed a panel of biomarkers with excellent discrimination. In summary, integrated analysis of omics data has brought forward biomarker candidates and candidate panels that promise early assessment of IRI. However, the clinical utility of these markers still needs to be established in prospective trials in independent patient populations.

BackgroundThe last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders.ResultsWe introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage.ConclusionBased on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges.