Understanding the functions of proteins requires information about their protein-protein interactions (PPI). The collective effort of the scientific community generates far more data on any given protein than individual experimental approaches. The latter are often too limited to reveal an interactome comprehensively. We developed a workflow for parallel mining of all major PPI databases, containing data from several model organisms, and to integrate data from the literature for a protein of interest. We applied this novel approach to build the PPI network of the human Hsp90 molecular chaperone machine (Hsp90Int) for which previous efforts have yielded limited and poorly overlapping sets of interactors. We demonstrate the power of the Hsp90Int database as a discovery tool by validating the prediction that the Hsp90 co-chaperone Aha1 is involved in nucleocytoplasmic transport. Thus, we both describe how to build a custom database and introduce a powerful new resource for the scientific community.

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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.

We established co-cultures of invasive or non-invasive NSCLC cell lines and various types of fibroblasts (FBs) to more precisely characterize the molecular mechanism of tumor-stroma crosstalk in lung cancer. The HGF-MET-ERK1/2-CREB-axis was shown to contribute to the onset of the invasive phenotype of Calu-1 with HGF being secreted by FBs. Differential expression analysis of the respective mono- and co-cultures revealed an upregulation of NFκB-related genes exclusively in co-cultures with Calu-1. Cytokine Array- and ELISA-based characterization of the “cytokine fingerprints” identified CSF2 (GM-CSF), CXCL1, CXCL6, VEGF, IL6, RANTES and IL8 as being specifically upregulated in various co-cultures. Whilst CXCL6 exhibited a strictly FB-type-specific induction profile regardless of the invasiveness of the tumor cell line, CSF2 was only induced in co-cultures of invasive cell lines regardless of the partnered FB type. These cultures revealed a clear link between the induction of CSF2 and the EMT signature of the cancer cell line. The canonical NFκB signaling in FBs, but not in tumor cells, was shown to be responsible for the induced and constitutive CSF2 expression. In addition to CSF2, cytokine IL6, IL8 and IL1B, and chemokine CXCL1 and CXCL6 transcripts were also shown to be increased in co-cultured FBs. In contrast, their induction was not strictly dependent on the invasiveness of the co-cultured tumor cell. In a multi-reporter assay, additional signaling pathways (AP-1, HIF1-α, KLF4, SP-1 and ELK-1) were found to be induced in FBs co-cultured with Calu-1. Most importantly, no difference was observed in the level of inducibility of these six signaling pathways with regard to the type of FBs used. Finally, upon tumor fibroblast interaction the massive induction of chemokines such as CXCL1 and CXCL6 in FBs might be responsible for increased recruitment of a monocytic cell line (THP-1) in a transwell assay.

Background: Autoantigens have been reported in a variety of tumors, providing insight into the interplay between malignancies and the immune response, and also giving rise to novel diagnostic and therapeutic concepts. Why certain tumor-associated proteins induce an immune response remains largely elusive.