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Abstract To assess the impacts of man-made extremely low-frequency electromagnetic field (ELF-EMF) on soil ecosystems, the soil nematode was applied as a biological indicator to characterize ecotoxicity of ELF-EMF. In this paper, a soil-living model organism, Caenorhabditis elegans (C. elegans) was exposed to 50 Hz, 3 mT ELF-EMF. The integrated lipidome, proteome and transcriptome analysis were applied to elucidate physiological acclimations. Lipidomic analysis showed that ELF-EMF exposure induced significant alterations of 64 lipids, including significant elevation of triacylglycerols (TGs). Proteome results implied 157 changed protein expressions under ELF-EMF exposure. By transcriptomic analysis, 456 differently expressed genes were identified. Gene Ontology (GO) function and pathway analyses showed lipidomic alteration, mitochondrial dysfunction and the stress defense responses following ELF-EMF exposure in C. elegans. Conjoint analysis of proteome and transcriptome data showed that a higher expression of genes (sip-1 , mtl-1 and rpl-11.1 , etc.) were involved in stress defense responses to ELF-EMF exposure. These results indicated that ELF-EMF can induce effects on soil nematodes, mainly through disturbing lipid metabolism such as increasing TGs content, and eliciting stress defense responses. This study provided a new understanding in ELF-EMF exposure effects on soil nematodes and suggested a potential way of interpreting ELF-EMF influences on soil ecosystems. Highlights • Applied multi-omics methods to study the effects of ELF-EMF on C. elegans. • Lipid metabolic and stress-defense responses were induced by ELF-EMF exposure. • Soil nematod es were applied as a biological indicator. • Provided a potential way to assess ELF-EMF influences on soil ecosystems. [ABSTRACT FROM AUTHOR]

The availability of large-scale repositories and integrated cancer genome efforts have created unprecedented opportunities to study and describe cancer biology. In this sense, the aim of translational researchers is the integration of multiple omics data to achieve a better identification of homogeneous subgroups of patients in order to develop adequate diagnostic and treatment strategies from the personalized medicine perspective. So far, existing integrative methods have grouped together omics data information, leaving out individual omics data phenotypic interpretation. Here, we present the Massive and Integrative Gene Set Analysis (MIGSA) R package. This tool can analyze several high throughput experiments in a comprehensive way through a functional analysis strategy, relating a phenotype to its biological function counterpart defined by means of gene sets. By simultaneously querying different multiple omics data from the same or different groups of patients, common and specific functional patterns for each studied phenotype can be obtained. The usefulness of MIGSA was demonstrated by applying the package to functionally characterize the intrinsic breast cancer PAM50 subtypes. For each subtype, specific functional transcriptomic profiles and gene sets enriched by transcriptomic and proteomic data were identified. To achieve this, transcriptomic and proteomic data from 28 datasets were analyzed using MIGSA. As a result, enriched gene sets and important genes were consistently found as related to a specific subtype across experiments or data types and thus can be used as molecular signature biomarkers. [ABSTRACT FROM AUTHOR]