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BackgroundOver the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks.ResultsWe present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape.ConclusionWith KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpi-inf.mpg.de.

The tumor microenvironment is composed of many immune cell subpopulations and is an important factor in the malignant progression of neoplasms, particularly breast cancer (BC). However, the cytokine networks that coordinate various regulatory events within the BC interstitium remain largely uncharacterized. Moreover, the data obtained regarding the origin of cytokine secretions, the levels of secretion associated with tumor development, and the possible clinical relevance of cytokines remain controversial. Therefore, we profiled 27 cytokines in 78 breast tumor interstitial fluid (TIF) samples, 43 normal interstitial fluid (NIF) samples, and 25 matched serum samples obtained from BC patients with Luminex xMAP multiplex technology. Eleven cytokines exhibited significantly higher levels in the TIF samples compared with the NIF samples: interleukin (IL)-7, IL-10, fibroblast growth factor-2, IL-13, interferon (IFN)γ-inducible protein (IP-10), IL-1 receptor antagonist (IL-1RA), platelet-derived growth factor (PDGF)-β, IL-1β, chemokine ligand 5 (RANTES), vascular endothelial growth factor, and IL-12. An immunohistochemical analysis further demonstrated that IL-1RA, IP-10, IL-10, PDGF-β, RANTES, and VEGF are widely expressed by both cancer cells and tumor-infiltrating lymphocytes (TILs), whereas IP-10 and RANTES were preferentially abundant in triple-negative breast cancers (TNBCs) compared to Luminal A subtype cancers. The latter observation corresponds with the high level of TILs in the TNBC samples. IL-1β, IL-7, IL-10, and PDGFβ also exhibited a correlation between the TIF samples and matched sera. In a survival analysis, high levels of IL-5, a hallmark T2 cytokine, in the TIF samples were associated with a worse prognosis. These findings have important implications for BC immunotherapy research.

De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art.

Nonsense-mediated RNA decay (NMD) is an RNA-based quality control mechanism that eliminates transcripts bearing premature translation termination codons (PTC). Approximately, one-third of all inherited disorders and some forms of cancer are caused by nonsense or frame shift mutations that introduce PTCs, and NMD can modulate the clinical phenotype of these diseases. 5-azacytidine is an analogue of the naturally occurring pyrimidine nucleoside cytidine, which is approved for the treatment of myelodysplastic syndrome and myeloid leukemia. Here, we reveal that 5-azacytidine inhibits NMD in a dose-dependent fashion specifically upregulating the expression of both PTC-containing mutant and cellular NMD targets. Moreover, this activity of 5-azacytidine depends on the induction of MYC expression, thus providing a link between the effect of this drug and one of the key cellular pathways that are known to affect NMD activity. Furthermore, the effective concentration of 5-azacytidine in cells corresponds to drug levels used in patients, qualifying 5-azacytidine as a candidate drug that could potentially be repurposed for the treatment of Mendelian and acquired genetic diseases that are caused by PTC mutations.

The number of pregnancies complicated by gestational diabetes (GDM) is increasing worldwide. To identify novel characteristics of GDM, we studied miRNA profiles of maternal and fetal whole blood cells (WBCs) from GDM and normal glucose tolerant (NGT) pregnant women matched for body mass index and maternal age. After adjustment for maternal weight gain and pregnancy week, we identified 29 mature micro-RNAs (miRNAs) up-regulated in GDM, one of which, i.e., miRNA-340, was validated by qPCR. mRNA and protein expression of PAIP1, a miRNA-340 target gene, was found down-regulated in GDM women, accordingly. In lymphocytes derived from the mothers’ blood and treated in vitro, insulin increased and glucose reduced miRNA-340 expression. In fetal cord blood samples, no associations of miRNA-340 with maternal GDM were observed. Our results provide evidence for insulin-induced epigenetic, i.e., miRNA-dependent, programming of maternal WBCs in GDM.

In human subjects imiquimod induces contact dermatitis with the distinctive feature that pDCs are the primary sensors, leading to an IL-23/T17 deviation. Despite these shortcomings, the human imiquimod model might be useful to investigate early pathogenic events and prove molecular concepts in patients with psoriasis.

Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.