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.

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.

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.

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.

Identification of Exosomal MicroRNA Signature by Liquid Biopsy in Hereditary Hemorrhagic Telangiectasia Patients.

Hereditary hemorrhagic telangiectasia (HHT) is a rare autosomal dominant vascular dysplasia characterized by epistaxis, mucocutaneous telangiectases, and arteriovenous malformations (AVM) in the visceral organs. The diagnosis of HHT is based on clinical Curaçao criteria, which show limited sensitivity in children and young patients. Here, we carried out a liquid biopsy by which we isolated total RNA from plasma exosome samples. A cohort of 15 HHT type 1 patients, 15 HHT type 2 patients, and 10 healthy relatives were analyzed. Upon gene expression data processing and normalization, a statistical analysis was performed to explore similarities in microRNA expression patterns among samples and detect differentially expressed microRNAs between HHT samples and the control group. We found a disease-associated molecular fingerprint of 35 miRNAs over-represented in HHT vs. controls, with eight being specific for HHT1 and 11 for HHT2; we also found 30 under-represented, including nine distinct for HHT1 and nine for HHT2. The analysis of the receiver operating characteristic (ROC) curves showed that eight miRNAs had good (AUC > 75%) or excellent (AUC > 90%) diagnosis value for HHT and even for type HHT1 and HHT2. In addition, we identified the cellular origin of these miRNAs among the cell types involved in the vascular malformations. Interestingly, we found that only some of them were incorporated into exosomes, which suggests a key functional role of these exosomal miRNAs in the pathophysiology of HHT.

Direct Detection of miR-122 in Hepatotoxicity Using Dynamic Chemical Labeling Overcomes Stability and isomiR Challenges.

Circulating microRNAs are biomarkers reported to be stable and translational across species. MicroRNA-122 (miR-122) is a hepatocyte-specific microRNA biomarker for drug-induced liver injury (DILI). We developed a single molecule, dynamic chemical labeling (DCL) assay to directly detect miR-122 in blood. The DCL assay specifically measured miR-122 directly from 10 µL of serum or plasma without any extraction steps, with a limit of detection of 1.32 pM that enabled the identification of DILI. Testing of 192 human serum samples showed that DCL accurately identified patients at risk of DILI after acetaminophen overdose (area under ROC curve 0.98 (95% CI; 0.96-1), P < 0.0001). The DCL assay also identified liver injury in rats and dogs. The use of specific captured beads had the additional benefit of stabilizing miR-122 after sample collection, with no signal loss after 14 days at room temperature, in contrast to PCR that showed significant loss of signal. RNA sequencing demonstrated the presence of multiple miR-122 isomiRs in the serum of patients with DILI that were at low concentration or not present in healthy individuals. Sample degradation over time produced more isomiRs, particularly rapidly with DILI. PCR was inaccurate when analyzing miR-122 isomiRs, whereas the DCL assay demonstrated accurate quantification. We conclude that the DCL assay can accurately measure miR-122 to diagnose liver injury in humans and other species and can overcome microRNA stability and isomiR challenges.

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.

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.

Exosomal miRNA profile as complementary tool in the diagnostic and prediction of treatment response in localized breast cancer under neoadjuvant chemotherapy.

BACKGROUND: Breast cancer patients under neoadjuvant chemotherapy includes a heterogeneous group of patients who eventually develop distal disease, not detectable by current methods. We propose the use of exosomal miRNAs and circulating tumor cells as diagnostic and predictive biomarkers in these patients. METHODS: Fifty-three breast cancer women initially diagnosed with localized breast cancer under neoadjuvant chemotherapy were prospectively enrolled in this study. However, six of them were later re-evaluated and diagnosed as metastatic breast cancer patients by PET-CT scan. Additionally, eight healthy donors were included. Circulating tumor cells and serum exosomal miRNAs were isolated from blood samples before and at the middle of neoadjuvant therapy and exosomal miRNA levels analyzed by qPCR. RESULTS: Before neoadjuvant therapy, exosomal miRNA-21 and 105 expression levels were higher in metastatic versus non-metastatic patients and healthy donors. Likewise, higher levels of miRNA-222 were observed in basal-like (p = 0.037) and in luminal B versus luminal A (p = 0.0145) tumor subtypes. Exosomal miRNA-222 levels correlated with clinical and pathological variables such as progesterone receptor status (p = 0.017) and Ki67 (p = 0.05). During neoadjuvant treatment, exosomal miRNA-21 expression levels directly correlated with tumor size (p = 0.039) and inversely with Ki67 expression (p = 0.031). Finally, higher levels of exosomal miRNA-21, miRNA-222, and miRNA-155 were significantly associated with the presence of circulating tumor cells. CONCLUSION: Liquid biopsies based on exosomal miRNAs and circulating tumor cells can be a complementary clinical tool for improving breast cancer diagnosis and prognosis.

Identification and visualization of differential isoform expression in RNA-seq time series.

Motivation: As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Results: Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. Availability and implementation: The package is freely available under the LGPL license from the Bioconductor web site. Contact: mj.nueda@ua.es or aconesa@ufl.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Metagene projection characterizes GEN2.2 and CAL-1 as relevant human plasmacytoid dendritic cell models.

MOTIVATION: Plasmacytoid dendritic cells (pDC) play a major role in the regulation of adaptive and innate immunity. Human pDC are difficult to isolate from peripheral blood and do not survive in culture making the study of their biology challenging. Recently, two leukemic counterparts of pDC, CAL-1 and GEN2.2, have been proposed as representative models of human pDC. Nevertheless, their relationship with pDC has been established only by means of particular functional and phenotypic similarities. With the aim of characterizing GEN2.2 and CAL-1 in the context of the main circulating immune cell populations we have performed microarray gene expression profiling of GEN2.2 and carried out an integrated analysis using publicly available gene expression datasets of CAL-1 and the main circulating primary leukocyte lineages. RESULTS: Our results show that GEN2.2 and CAL-1 share common gene expression programs with primary pDC, clustering apart from the rest of circulating hematopoietic lineages. We have also identified common differentially expressed genes that can be relevant in pDC biology. In addition, we have revealed the common and differential pathways activated in primary pDC and cell lines upon CpG stimulatio. AVAILABILITY AND IMPLEMENTATION: R code and data are available in the supplementary material. CONTACT: pedro.carmona@genyo.es or concepcion.maranon@genyo.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MetaGenyo: a web tool for meta-analysis of genetic association studies.

BACKGROUND: Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise. RESULTS: We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results. CONCLUSIONS: MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/ .

MiRNA expression as outcome predictor in pediatric AML: systematic evaluation of a new model.

Accurately predicting patient outcomes is essential for optimizing treatment and improving outcomes in pediatric acute myeloid leukemia (AML). In recent years, microRNAs have emerged as a promising prognostic marker, with a growing body of evidence supporting their potential predictive value. We systematically reviewed all previous studies that have analyzed the expression of microRNAs as predictors of survival in pediatric AML and found 16 microRNAs and 4 microRNA signatures previously proposed as predictors of survival. We then used a public access cohort of 1414 pediatric AML patients from the TARGET project to develop a new predictive model using penalized lasso Cox regression based on microRNA expression. Here we propose a new score based on a 37-microRNA signature that is associated with AML and is able to predict survival more accurately than previous microRNA-based methods.

Differential diagnosis of systemic lupus erythematosus and Sjögren's syndrome using machine learning and multi-omics data.

Systemic lupus erythematosus and primary Sjogren's syndrome are complex systemic autoimmune diseases that are often misdiagnosed. In this article, we demonstrate the potential of machine learning to perform differential diagnosis of these similar pathologies using gene expression and methylation data from 651 individuals. Furthermore, we analyzed the impact of the heterogeneity of these diseases on the performance of the predictive models, discovering that patients assigned to a specific molecular cluster are misclassified more often and affect to the overall performance of the predictive models. In addition, we found that the samples characterized by a high interferon activity are the ones predicted with more accuracy, followed by the samples with high inflammatory activity. Finally, we identified a group of biomarkers that improve the predictions compared to using the whole data and we validated them with external studies from other tissues and technological platforms.

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.

Changes in PRC1 activity during interphase modulate lineage transition in pluripotent cells.

The potential of pluripotent cells to respond to developmental cues and trigger cell differentiation is enhanced during the G1 phase of the cell cycle, but the molecular mechanisms involved are poorly understood. Variations in polycomb activity during interphase progression have been hypothesized to regulate the cell-cycle-phase-dependent transcriptional activation of differentiation genes during lineage transition in pluripotent cells. Here, we show that recruitment of Polycomb Repressive Complex 1 (PRC1) and associated molecular functions, ubiquitination of H2AK119 and three-dimensional chromatin interactions, are enhanced during S and G2 phases compared to the G1 phase. In agreement with the accumulation of PRC1 at target promoters upon G1 phase exit, cells in S and G2 phases show firmer transcriptional repression of developmental regulator genes that is drastically perturbed upon genetic ablation of the PRC1 catalytic subunit RING1B. Importantly, depletion of RING1B during retinoic acid stimulation interferes with the preference of mouse embryonic stem cells (mESCs) to induce the transcriptional activation of differentiation genes in G1 phase. We propose that incremental enrolment of polycomb repressive activity during interphase progression reduces the tendency of cells to respond to developmental cues during S and G2 phases, facilitating activation of cell differentiation in the G1 phase of the pluripotent cell cycle.

EZH2 endorses cell plasticity to non-small cell lung cancer cells facilitating mesenchymal to epithelial transition and tumour colonization.

Reversible transition between the epithelial and mesenchymal states are key aspects of carcinoma cell dissemination and the metastatic disease, and thus, characterizing the molecular basis of the epithelial to mesenchymal transition (EMT) is crucial to find druggable targets and more effective therapeutic approaches in cancer. Emerging studies suggest that epigenetic regulators might endorse cancer cells with the cell plasticity required to conduct dynamic changes in cell state during EMT. However, epigenetic mechanisms involved remain mostly unknown. Polycomb Repressive Complexes (PRCs) proteins are well-established epigenetic regulators of development and stem cell differentiation, but their role in different cancer systems is inconsistent and sometimes paradoxical. In this study, we have analysed the role of the PRC2 protein EZH2 in lung carcinoma cells. We found that besides its described role in CDKN2A-dependent cell proliferation, EZH2 upholds the epithelial state of cancer cells by repressing the transcription of hundreds of mesenchymal genes. Chemical inhibition or genetic removal of EZH2 promotes the residence of cancer cells in the mesenchymal state during reversible epithelial-mesenchymal transition. In fitting, analysis of human patient samples and tumour xenograft models indicate that EZH2 is required to efficiently repress mesenchymal genes and facilitate tumour colonization in vivo. Overall, this study discloses a novel role of PRC2 as a master regulator of EMT in carcinoma cells. This finding has important implications for the design of therapies based on EZH2 inhibitors in human cancer patients.

Protocol for large scale whole blood immune monitoring by mass cytometry and Cyto Quality Pipeline.

Mass cytometry (MC) is a powerful large-scale immune monitoring technology. To maximize MC data quality, we present a protocol for whole blood analysis together with an R package, Cyto Quality Pipeline (CytoQP), which minimizes the experimental artifacts and batch effects to ensure data reproducibility. We describe the steps to stimulate, fix, and freeze blood samples before acquisition to make them suitable for retrospective studies. We then detail the use of barcoding and reference samples to facilitate multicenter and multi-batch experiments. For complete details on the use and execution of this protocol, please refer to Rybakowska et al. (2021a) and (2021b).