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Genome-scale human protein–protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein–protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

Structured elements of RNA molecules are essential in, e.g., RNA stabilization, localization, and protein interaction, and their conservation across species suggests a common functional role. We computationally screened vertebrate genomes for conserved RNA structures (CRSs), leveraging structure-based, rather than sequence-based, alignments. After careful correction for sequence identity and GC content, we predict ∼516,000 human genomic regions containing CRSs. We find that a substantial fraction of human–mouse CRS regions (1) colocalize consistently with binding sites of the same RNA binding proteins (RBPs) or (2) are transcribed in corresponding tissues. Additionally, a CaptureSeq experiment revealed expression of many of our CRS regions in human fetal brain, including 662 novel ones. For selected human and mouse candidate pairs, qRT-PCR and in vitro RNA structure probing supported both shared expression and shared structure despite low abundance and low sequence identity. About 30,000 CRS regions are located near coding or long noncoding RNA genes or within enhancers. Structured (CRS overlapping) enhancer RNAs and extended 3′ ends have significantly increased expression levels over their nonstructured counterparts. Our findings of transcribed uncharacterized regulatory regions that contain CRSs support their RNA-mediated functionality.

Recent studies show that mantle cell lymphoma (MCL) express aberrant microRNA (miRNA) profiles; however, the clinical effect of miRNA expression has not previously been examined and validated in large prospective homogenously treated cohorts. We performed genome-wide miRNA microarray profiling of 74 diagnostic MCL samples from the Nordic MCL2 trial (screening cohort). Prognostic miRNAs were validated in diagnostic MCL samples from 94 patients of the independent Nordic MCL3 trial (validation cohort). Three miRNAs (miR-18b, miR-92a, and miR-378d) were significantly differentially expressed in patients who died of MCL in both cohorts. MiR-18b was superior to miR-92a and miR-378d in predicting high risk. Thus, we generated a new biological MCL International Prognostic Index (MIPI-B)-miR prognosticator, combining expression levels of miR-18b with MIPI-B data. Compared to the MIPI-B, this prognosticator improved identification of high-risk patients with regard to cause-specific, overall, and progression-free survival. Transfection of 2 MCL cell lines with miR-18b decreased their proliferation rate without inducing apoptosis, suggesting that miR-18b may render MCL cells resistant to chemotherapy by decelerating cell proliferation. We conclude that overexpression of miR-18b identifies patients with poor prognosis in 2 large prospective MCL cohorts and adds prognostic information to the MIPI-B. MiR-18b may reduce the proliferation rate of MCL cells as a mechanism of chemoresistance.

Recent studies show that mantle cell lymphoma (MCL) express aberrant microRNA (miRNA) profiles; however, the clinical effect of miRNA expression has not previously been examined and validated in large prospective homogenously treated cohorts. We performed genome-wide miRNA microarray profiling of 74 diagnostic MCL samples from the Nordic MCL2 trial (screening cohort). Prognostic miRNAs were validated in diagnostic MCL samples from 94 patients of the independent Nordic MCL3 trial (validation cohort). Three miRNAs (miR-18b, miR-92a, and miR-378d) were significantly differentially expressed in patients who died of MCL in both cohorts. MiR-18b was superior to miR-92a and miR-378d in predicting high risk. Thus, we generated a new biological MCL International Prognostic Index (MIPI-B)-miR prognosticator, combining expression levels of miR-18b with MIPI-B data. Compared to the MIPI-B, this prognosticator improved identification of high-risk patients with regard to cause-specific, overall, and progression-free survival. Transfection of 2 MCL cell lines with miR-18b decreased their proliferation rate without inducing apoptosis, suggesting that miR-18b may render MCL cells resistant to chemotherapy by decelerating cell proliferation. We conclude that overexpression of miR-18b identifies patients with poor prognosis in 2 large prospective MCL cohorts and adds prognostic information to the MIPI-B. MiR-18b may reduce the proliferation rate of MCL cells as a mechanism of chemoresistance.

Members of the SH2 domain family modulate signal transduction by binding to short peptides containing phosphorylated tyrosines. Each domain displays a distinct preference for the sequence context of the phosphorylated residue. We have developed a high-density peptide chip technology that allows for probing of the affinity of most SH2 domains for a large fraction of the entire complement of tyrosine phosphopeptides in the human proteome. Using this technique, we have experimentally identified thousands of putative SH2-peptide interactions for more than 70 different SH2 domains. By integrating this rich data set with orthogonal context-specific information, we have assembled an SH2-mediated probabilistic interaction network, which we make available as a community resource in the PepspotDB database. A predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the extracellular signal-regulated kinase activation loop was validated by experiments in living cells.

Ayurveda, the traditional Indian medicine is one of the most ancient, yet living medicinal traditions. In the present work, we developed an in silico library of natural products from Ayurveda medicine, coupled with structural information, plant origin and traditional therapeutic use. Following this, we compared their structures with those of drugs from DrugBank and we constructed a structural similarity network. Information on the traditional therapeutic use of the plants was integrated in the network in order to provide further evidence for the predicted biologically active natural compounds. We hereby present a number of examples where the traditional medicinal use of the plant matches with the medicinal use of the drug that is structurally similar to a plant component. With this approach, we have brought to light a number of obscure compounds of natural origin (e.g. kanugin, norruffscine, isoazadirolide) that could provide the basis and inspiration for further lead development. Apart from the identification of novel natural leads in drug discovery, we envisage that this integrated in silico ethnopharmacology approach could find applications in the elucidation of the molecular basis of Ayurveda medicine and in drug repurposing.