Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression.

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.

A comprehensive database for integrated analysis of omics data in autoimmune diseases.

BACKGROUND: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. RESULTS: Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. CONCLUSIONS: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

ImaGEO: integrative gene expression meta-analysis from GEO database.

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.

Immune and molecular landscape behind non-response to Mycophenolate Mofetil and Azathioprine in lupus nephritis therapy.

Lupus nephritis (LN) represents one of the most severe complications of systemic lupus erythematosus, leading to end-stage kidney disease in worst cases. Current first-line therapies for LN, including mycophenolate mofetil (MMF) and azathioprine (AZA), fail to induce long-term remission in 60-70% of the patients, evidencing the urgent need to delve into the molecular knowledge-gap behind the non-response to these therapies. A longitudinal cohort of treated LN patients including clinical, cellular and transcriptomic data, was analyzed. Gene-expression signatures behind non-response to different drugs were revealed by differential expression analysis. Drug-specific non-response mechanisms and cell proportion differences were identified. Blood cell subsets mediating non-response were described using single-cell RNASeq data. We show that AZA and MMF non-response implicates different cells and regulatory functions. Mechanistic models were used to suggest add-on therapies to improve their current performance. Our results provide new insights into the molecular mechanisms associated with treatment failures in LN.

Exploring the interplay between climate, population immunity and SARS-CoV-2 transmission dynamics in Mediterranean countries.

The relationship between SARS-CoV-2 transmission and environmental factors has been analyzed in numerous studies since the outbreak of the pandemic, resulting in heterogeneous results and conclusions. This may be due to differences in methodology, considered variables, confounding factors, studied periods and/or lack of adequate data. Furthermore, previous works have reported that the lack of population immunity is the fundamental driver in transmission dynamics and can mask the potential impact of environmental variables. In this study, we aimed to investigate the association between climate variables and COVID-19 transmission considering the influence of population immunity. We analyzed two different periods characterized by the absence of vaccination (low population immunity) and a high degree of vaccination (high level of population immunity), respectively. Although this study has some limitations, such us the restriction to a specific climatic zone and the omission of other environmental factors, our results indicate that transmission of SARS-CoV-2 may increase independently of temperature and specific humidity in periods with low levels of population immunity while a negative association is found under conditions with higher levels of population immunity in the analyzed regions.

Functional Enrichment Analysis of Regulatory Elements.

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.

DatAC: A visual analytics platform to explore climate and air quality indicators associated with the COVID-19 pandemic in Spain.

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented global health crisis, with several countries imposing lockdowns to control the coronavirus spread. Important research efforts are focused on evaluating the association of environmental factors with the survival and spread of the virus and different works have been published, with contradictory results in some cases. Data with spatial and temporal information is a key factor to get reliable results and, although there are some data repositories for monitoring the disease both globally and locally, an application that integrates and aggregates data from meteorological and air quality variables with COVID-19 information has not been described so far to the best of our knowledge. Here, we present DatAC (Data Against COVID-19), a data fusion project with an interactive web frontend that integrates COVID-19 and environmental data in Spain. DatAC is provided with powerful data analysis and statistical capabilities that allow users to explore and analyze individual trends and associations among the provided data. Using the application, we have evaluated the impact of the Spanish lockdown on the air quality, observing that NO2, CO, PM2.5, PM10 and SO2 levels decreased drastically in the entire territory, while O3 levels increased. We observed similar trends in urban and rural areas, although the impact has been more important in the former. Moreover, the application allowed us to analyze correlations among climate factors, such as ambient temperature, and the incidence of COVID-19 in Spain. Our results indicate that temperature is not the driving factor and without effective control actions, outbreaks will appear and warm weather will not substantially limit the growth of the pandemic. DatAC is available at https://covid19.genyo.es.

Transcription Factor Activity Inference in Systemic Lupus Erythematosus.

BACKGROUND: Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with diverse clinical manifestations. Although most of the SLE-associated loci are located in regulatory regions, there is a lack of global information about transcription factor (TFs) activities, the mode of regulation of the TFs, or the cell or sample-specific regulatory circuits. The aim of this work is to decipher TFs implicated in SLE. METHODS: In order to decipher regulatory mechanisms in SLE, we have inferred TF activities from transcriptomic data for almost all human TFs, defined clusters of SLE patients based on the estimated TF activities and analyzed the differential activity patterns among SLE and healthy samples in two different cohorts. The Transcription Factor activity matrix was used to stratify SLE patients and define sets of TFs with statistically significant differential activity among the disease and control samples. RESULTS: TF activities were able to identify two main subgroups of patients characterized by distinct neutrophil-to-lymphocyte ratio (NLR), with consistent patterns in two independent datasets-one from pediatric patients and other from adults. Furthermore, after contrasting all subgroups of patients and controls, we obtained a significant and robust list of 14 TFs implicated in the dysregulation of SLE by different mechanisms and pathways. Among them, well-known regulators of SLE, such as STAT or IRF, were found, but others suggest new pathways that might have important roles in SLE. CONCLUSIONS: These results provide a foundation to comprehend the regulatory mechanism underlying SLE and the established regulatory factors behind SLE heterogeneity that could be potential therapeutic targets.

Extracellular Protease ADAMTS1 Is Required at Early Stages of Human Uveal Melanoma Development by Inducing Stemness and Endothelial-Like Features on Tumor Cells.

Extracellular matrix remodeling within the tumor microenvironment has been recognized as a relevant dynamic framework during tumor growth. However, research on proteases that trigger this remodeling keeps revealing a wide range of actions including both pro- and anti-tumorigenic. The extracellular protease ADAMTS1 exemplifies this dual role. In this work, we first confirmed a positive correlation of ADAMTS1 with endothelial-like phenotype of human melanoma cells together with the finding of associated signatures, including key genes such as endothelial CDH5. Using a CRISPR-Cas9 approach, we observed that the inhibition of ADAMTS1 in an aggressive uveal melanoma model compromised its endothelial-like properties, and more importantly, caused a robust blockade on the progression of tumor xenografts. Although vasculature emerged affected in ADAMTS1-deficient tumors, the most relevant action implied the downregulation of endothelial CDH5 in tumor cells, in association with stemness markers. Indeed, melanoma sphere assays also revealed a deficient commitment to form spheres in the absence of ADAMTS1, directly correlating with stemness markers and, remarkably, also with CDH5. Finally, taking advantage of advanced bioinformatics tools and available public data of uveal melanomas, we disclosed new prognosis factors, including endothelial elements and ADAMTS proteases. Our findings support the key role of ADAMTS proteases for uveal melanoma development since earlier stages, modulating the complex crosstalk between extracellular matrix and the induction of stemness and endothelial-like features. To our knowledge, this is the first report that supports the development of therapeutic targets on the extracellular matrix to overcome uveal melanoma.

Gut Microbiota Composition Is Associated With the Global DNA Methylation Pattern in Obesity.

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. ß 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.

Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification.

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.