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.

A meta-analysis of pre-pregnancy maternal body mass index and placental DNA methylation identifies 27 CpG sites with implications for mother-child health

Higher maternal pre-pregnancy body mass index (ppBMI) is associated with increased neonatal morbidity, as well as with pregnancy complications and metabolic outcomes in offspring later in life. The placenta is a key organ in fetal development and has been proposed to act as a mediator between the mother and different health outcomes in children. The overall aim of the present work is to investigate the association of ppBMI with epigenome-wide placental DNA methylation (DNAm) in 10 studies from the PACE consortium, amounting to 2631 mother-child pairs. We identify 27 CpG sites at which we observe placental DNAm variations of up to 2.0% per 10 ppBMI-unit. The CpGs that are differentially methylated in placenta do not overlap with CpGs identified in previous studies in cord blood DNAm related to ppBMI. Many of the identified CpGs are located in open sea regions, are often close to obesity-related genes such as GPX1 and LGR4 and altogether, are enriched in cancer and oxidative stress pathways. Our findings suggest that placental DNAm could be one of the mechanisms by which maternal obesity is associated with metabolic health outcomes in newborns and children, although further studies will be needed in order to corroborate these findings.

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.

The SWI/SNF complex regulates the expression of miR-222, a tumor suppressor microRNA in lung adenocarcinoma

SWitch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complexes are key epigenetic regulators that are recurrently mutated in cancer. Most studies of these complexes are focused on their role in regulating protein-coding genes. However, here, we show that SWI/SNF complexes control the expression of microRNAs. We used a SMARCA4-deficient model of lung adenocarcinoma (LUAD) to track changes in the miRNome upon SMARCA4 restoration. We found that SMARCA4-SWI/SNF complexes induced significant changes in the expression of cancer-related microRNAs. The most significantly dysregulated microRNA was miR-222, whose expression was promoted by SMARCA4-SWI/SNF complexes, but not by SMARCA2-SWI/SNF complexes via their direct binding to a miR-222 enhancer region. Importantly, miR-222 expression decreased cell viability, phenocopying the tumor suppressor role of SMARCA4-SWI/SNF complexes in LUAD. Finally, we showed that the miR-222 enhancer region resides in a topologically associating domain that does not contain any cancer-related protein-coding genes, suggesting that miR-222 may be involved in exerting the tumor suppressor role of SMARCA4. Overall, this study highlights the relevant role of the SWI/SNF complex in regulating the non-coding genome, opening new insights into the pathogenesis of LUAD.

Placental DNA methylation signatures of maternal smoking during pregnancy and potential impacts on fetal growth

Maternal smoking during pregnancy (MSDP) contributes to poor birth outcomes, in part through disrupted placental functions, which may be reflected in the placental epigenome. Here we present a meta-analysis of the associations between MSDP and placental DNA methylation (DNAm) and between DNAm and birth outcomes within the Pregnancy And Childhood Epigenetics (PACE) consortium (N = 1700, 344 with MSDP). We identify 443 CpGs that are associated with MSDP, of which 142 associated with birth outcomes, 40 associated with gene expression, and 13 CpGs are associated with all three. Only two CpGs have consistent associations from a prior meta-analysis of cord blood DNAm, demonstrating substantial tissue-specific responses to MSDP. The placental MSDP-associated CpGs are enriched for environmental response genes, growth-factor signaling, and inflammation, which play important roles in placental function. We demonstrate links between placental DNAm, MSDP and poor birth outcomes, which may better inform the mechanisms through which MSDP impacts placental function and fetal growth.

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.

Detecting Differentially Methylated Promoters in Genes Related to Disease Phenotypes Using R

DNA methylation in gene promoters plays a major role in gene expression regulation, and alterations in methylation patterns have been associated with several diseases. In this context, different software suites and statistical methods have been proposed to analyze differentially methylated positions and regions. Among them, the novel statistical method implemented in the mCSEA R package proposed a new framework to detect subtle, but consistent, methylation differences. Here, we provide an easy-to-use pipeline covering all the necessary steps to detect differentially methylated promoters with mCSEA from Illumina 450K and EPIC methylation BeadChips data. This protocol covers the download of data from public repositories, quality control, data filtering and normalization, estimation of cell type proportions, and statistical analysis. In addition, we show the procedure to compare disease vs. normal phenotypes, obtaining differentially methylated regions including promoters or CpG Islands. The entire protocol is based on R programming language, which can be used in any operating system and does not require advanced programming skills.

DREIMT: a drug repositioning database and prioritization tool for immunomodulation

MOTIVATION

Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases.

RESULTS

DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables.

AVAILABILITYAND IMPLEMENTATION

http://www.dreimt.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

OBJECTIVE

Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis.

METHODS

Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time.

RESULTS

Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient.

CONCLUSION

Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases.

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, PM_2.5, 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.

mCSEA: detecting subtle differentially methylated regions

MOTIVATION

The identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.

RESULTS

We present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify DMRs from Illumina450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify DMRs not detected by other methods.

AVAILABILITY AND IMPLEMENTATION

mCSEA is freely available from the Bioconductor repository.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The molecular clock protein Bmal1 regulates cell differentiation in mouse embryonic stem cells

Mammals optimize their physiology to the light-dark cycle by synchronization of the master circadian clock in the brain with peripheral clocks in the rest of the tissues of the body. Circadian oscillations rely on a negative feedback loop exerted by the molecular clock that is composed by transcriptional activators Bmal1 and Clock, and their negative regulators Period and Cryptochrome. Components of the molecular clock are expressed during early development, but onset of robust circadian oscillations is only detected later during embryogenesis. Here, we have used naïve pluripotent mouse embryonic stem cells (mESCs) to study the role of Bmal1 during early development. We found that, compared to wild-type cells, Bmal1-/- mESCs express higher levels of Nanog protein and altered expression of pluripotency-associated signalling pathways. Importantly, Bmal1-/- mESCs display deficient multi-lineage cell differentiation capacity during the formation of teratomas and gastrula-like organoids. Overall, we reveal that Bmal1 regulates pluripotent cell differentiation and propose that the molecular clock is an hitherto unrecognized regulator of mammalian development.

Polycomb regulation is coupled to cell cycle transition in pluripotent stem cells

When self-renewing pluripotent cells receive a differentiation signal, ongoing cell duplication needs to be coordinated with entry into a differentiation program. Accordingly, transcriptional activation of lineage specifier genes and cell differentiation is confined to the G₁ phase of the cell cycle by unknown mechanisms. We found that Polycomb repressive complex 2 (PRC2) subunits are differentially recruited to lineage specifier gene promoters across cell cycle in mouse embryonic stem cells (mESCs). Jarid2 and the catalytic subunit Ezh2 are markedly accumulated at target promoters during S and G₂ phases, while the transcriptionally activating subunits EPOP and EloB are enriched during G₁ phase. Fluctuations in the recruitment of PRC2 subunits promote changes in RNA synthesis and RNA polymerase II binding that are compromised in Jarid2 -/- mESCs. Overall, we show that differential recruitment of PRC2 subunits across cell cycle enables the establishment of a chromatin state that facilitates the induction of cell differentiation in G₁ phase.

A survey of gene expression meta-analysis: methods and applications

The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.

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.

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.

Stratification of Systemic Lupus Erythematosus Patients Into Three Groups of Disease Activity Progression According to Longitudinal Gene Expression

OBJECTIVE

The highly heterogeneous clinical presentation of systemic lupus erythematosus (SLE) is characterized by the unpredictable occurrence of disease flares and organ damage. Attempts to stratify lupus patients have been limited to classification based on clinical information, leading to unsuccessful clinical trials and controversial research results. This study was undertaken to develop and validate a robust method to stratify patients with lupus according to longitudinal disease activity and whole-genome gene expression data in order to establish subgroups of patients who share disease progression mechanisms.

METHODS

We used a cluster-based approach to stratify SLE patients based on the correlation between disease activity scores and longitudinal gene expression information. Clustering robustness was evaluated by the bootstrap method, and the clusters were characterized in terms of clinical and functional features.

RESULTS

We observed a clear partition into 3 different disease clusters in 2 independent sets of patients, one pediatric and one adult, which was not influenced by treatment, race, or other source of bias. Two of the clusters differentiated into a group showing a correlation between the percentage of neutrophils and disease activity progression and a group showing a correlation between the percentage of lymphocytes and disease activity progression. The third cluster, in which the percentage of neutrophils correlated to a lesser degree with disease activity, was functionally more heterogeneous. Patients in the neutrophil-driven clusters had an increased risk of developing proliferative nephritis.

CONCLUSION

Our findings indicate that SLE patients can be stratified into 3 subgroups of patients who show different mechanisms of disease progression and are clinically differentiated. Our results have important implications for treatment options, the design of clinical trials, our understanding of the etiology of the disease, and the prediction of severe glomerulonephritis.