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Summary: Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps., Availability and Implementation: INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM., Supplementary Information: Supplementary data are available at Bioinformatics online.

Understanding the complex molecular alterations related to engineered nanomaterial (ENM) exposure is essential for carrying out toxicity assessment. Current experimental paradigms rely on both in vitro and in vivo exposure setups that often are difficult to compare, resulting in questioning the real efficacy of cell models to mimic more complex exposure scenarios at the organism level. Here, we have systematically investigated transcriptomic responses of the THP-1 macrophage cell line and lung tissues of mice, after exposure to several carbon nanomaterials (CNMs). Under the assumption that the CNM exposure related molecular alterations are mixtures of signals related to their intrinsic properties, we inferred networks of responding genes, whose expression levels are coordinately altered in response to specific CNM intrinsic properties. We observed only a minute overlap between the sets of intrinsic property-correlated genes at different exposure scenarios, suggesting specific transcriptional programs working in different exposure scenarios. However, when the effects of the CNM were investigated at the level of significantly altered molecular functions, a broader picture of substantial commonality emerged. Our results imply that in vitro exposures can efficiently recapitulate the complex molecular functions altered in vivo. In this study, altered molecular pathways in response to specific CNM intrinsic properties have been systematically characterized from transcriptomic data generated from multiple exposure setups. Our computational approach to the analysis of network response modules further revealed similarities between in vitro and in vivo exposures that could not be detected by traditional analysis of transcriptomics data. Our analytical strategy also opens a possibility to look for pathways of toxicity and understanding the molecular and cellular responses identified across predefined biological themes.

Background: OMICs technologies allow to assay the state of a large number of different features (e.g., mRNA expression, miRNA expression, copy number variation, DNA methylation, etc.) from the same samples. The objective of these experiments is usually to find a reduced set of significant features, which can be used to differentiate the conditions assayed. In terms of development of novel feature selection computational methods, this task is challenging for the lack of fully annotated biological datasets to be used for benchmarking. A possible way to tackle this problem is generating appropriate synthetic datasets, whose composition and behaviour are fully controlled and known a priori., Results: Here we propose a novel method centred on the generation of networks of interactions among different biological molecules, especially involved in regulating gene expression. Synthetic datasets are obtained from ordinary differential equations based models with known parameters. Our results show that the generated datasets are well mimicking the behaviour of real data, for popular data analysis methods are able to selectively identify existing interactions., Conclusions: The proposed method can be used in conjunction to real biological datasets in the assessment of data mining techniques. The main strength of this method consists in the full control on the simulated data while retaining coherence with the real biological processes. The R package MVBioDataSim is freely available to the scientific community at http://neuronelab.unisa.it/?p=1722.

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers. [ABSTRACT FROM AUTHOR]

Background: After the Second World War, the population living in the Karelian region was strictly divided by the "iron curtain" between Finland and Russia. This resulted in different lifestyle, standard of living, and exposure to the environment. Allergic manifestations and sensitization to common allergens have been much more common on the Finnish compared to the Russian side. Objective: The remarkable allergy disparity in the Finnish and Russian Karelia calls for immunological explanations. Methods: Young people, aged 15‐20 years, in the Finnish (n = 69) and Russian (n = 75) Karelia were studied. The impact of genetic variation on the phenotype was studied by a genome‐wide association analysis. Differences in gene expression (transcriptome) were explored from the blood mononuclear cells (PBMC) and related to skin and nasal epithelium microbiota and sensitization. Results: The genotype differences between the Finnish and Russian populations did not explain the allergy gap. The network of gene expression and skin and nasal microbiota was richer and more diverse in the Russian subjects. When the function of 261 differentially expressed genes was explored, innate immunity pathways were suppressed among Russians compared to Finns. Differences in the gene expression paralleled the microbiota disparity. High Acinetobacter abundance in Russians correlated with suppression of innate immune response. High‐total IgE was associated with enhanced anti‐viral response in the Finnish but not in the Russian subjects. Conclusions and clinical relevance: Young populations living in the Finnish and Russian Karelia show marked differences in genome‐wide gene expression and host contrasting skin and nasal epithelium microbiota. The rich gene‐microbe network in Russians seems to result in a better‐balanced innate immunity and associates with low allergy prevalence. [ABSTRACT FROM AUTHOR]