Statistical methods for enrichment analysis are important tools to extract biological information from omics experiments. Although these methods have been widely used for the analysis of gene and protein lists, the development of high-throughput technologies for regulatory elements demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical significance is determined via a power weighting function for target genes and tested by the Wallenius noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of this tool to extract biological information from a list of regulatory elements. These new methods are available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that allows the integration of heterogeneous information.

Summary: The Gene Expression Omnibus (GEO) database provides an invaluable resource of publicly available gene expression data that can be integrated and analyzed to derive new hypothesis and knowledge. In this context, gene expression meta-analysis (geMAs) is increasingly used in several fields to improve study reproducibility and discovering robust biomarkers. Nevertheless, integrating data is not straightforward without bioinformatics expertise. Here, we present ImaGEO, a web tool for geMAs that implements a complete and comprehensive meta-analysis workflow starting from GEO dataset identifiers. The application integrates GEO datasets, applies different meta-analysis techniques and provides functional analysis results in an easy-to-use environment. ImaGEO is a powerful and useful resource that allows researchers to integrate and perform meta-analysis of GEO datasets to lead robust findings for biomarker discovery studies.Availability and implementation: ImaGEO is accessible at http://bioinfo.genyo.es/imageo/.Supplementary information: Supplementary data are available at Bioinformatics online.

Objective: Obesity and obesity-related metabolic diseases are characterized by gut microbiota and epigenetic alterations. Recent insight has suggested the existence of a crosstalk between the gut microbiome and the epigenome. However, the possible link between alterations in gut microbiome composition and epigenetic marks in obesity has been not explored yet. The aim of this work is to establish a link between the gut microbiota and the global DNA methylation profile in a group of obese subjects and to report potential candidate genes that could be epigenetically regulated by gut microbiota in adipose tissue. Methods: Gut microbiota composition was analyzed in DNA stool samples from 45 obese subjects by 16S ribosomal RNA (rRNA) gene sequencing. Twenty patients were selected based on their Bacteroidetes-to-Firmicutes ratio (BFR): HighBFR group (BFR > 2.5, n = 10) and LowBFR group (BFR < 1.2, n = 10). Genome-wide analysis of DNA methylation pattern in both whole blood and visceral adipose tissue of these selected patients was performed with an Infinium EPIC BeadChip array-based platform. Gene expression analysis of candidate genes was done in adipose tissue by real-time quantitative PCR. Results: Genome-wide analysis of DNA methylation revealed a completely different DNA methylome pattern in both blood and adipose tissue in the low BFR group vs. the high BFR group. Two hundred fifty-eight genes were differentially methylated in both blood and adipose tissue, of which several potential candidates were selected for gene expression analysis. We found that in adipose tissue, both HDAC7 and IGF2BP2 were hypomethylated and overexpressed in the low BFR group compared with the high BFR group. β values of both genes significantly correlated with the BFR ratio and the relative abundance of Bacteroidetes and/or Firmicutes. Conclusions: In this study, we demonstrate that the DNA methylation status is associated with gut microbiota composition in obese subjects and that the expression levels of candidate genes implicated in glucose and energy homeostasis (e.g., HDAC7 and IGF2BP2) could be epigenetically regulated by gut bacterial populations in adipose tissue.

Objectives: Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions. Methods: Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores. Results: MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes. Conclusions: MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.