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BACKGROUND: Tuberculosis (TB) is amongst the largest infectious causes of death worldwide and there is a need for a time- and resource-effective diagnostic method. In this novel and exploratory study, we show the potential of using buccal swabs to collect human DNA and investigate the DNA methylation (DNAm) signatures as a diagnostic tool for TB. METHODS: Buccal swabs were collected from pulmonary TB patients (n= 7), TB exposed (n= 7), and controls (n= 9) in Sweden. Using Illumina MethylationEPIC array the DNAm status was determined. RESULTS: We identified 5644 significant differentially methylated CpG sites between the patients and controls. Performing the analysis on a validation cohort of samples collected in Kenya and Peru (patients, n=26; exposed, n=9; control, n=10) confirmed the DNAm signature. We identified a TB consensus disease module, significantly enriched in TB-associated genes. Lastly, we used machine learning to identify a panel of seven CpG sites discriminative for TB and developed a TB classifier. In the validation cohort the classifier performed with an AUC of 0.94, sensitivity of 0.92, and specificity of 1. CONCLUSION: In summary, the result from this study shows clinical implications of using DNAm signatures from buccal swabs to explore new diagnostic strategies for TB.
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Multiple Sclerosis (MS) is a heterogeneous inflammatory and neurodegenerative disease with an unpredictable course towards progressive disability. Treating progressive MS is challenging due to limited insights into the underlying mechanisms. We examined the molecular changes associated with primary progressive MS (PPMS) using a cross-tissue (blood and post-mortem brain) and multilayered data (genetic, epigenetic, transcriptomic) from independent cohorts. In PPMS, we found hypermethylation of the 1q21.1 locus, controlled by PPMS-specific genetic variations and influencing the expression of proximal genes (CHD1L, PRKAB2) in the brain. Evidence from reporter assay and CRISPR/dCas9 experiments supports a causal link between methylation and expression and correlation network analysis further implicates these genes in PPMS brain processes. Knock-down of CHD1L in human iPSC-derived neurons and knock-out of chd1l in zebrafish led to developmental and functional deficits of neurons. Thus, several lines of evidence suggest a distinct genetic-epigenetic-transcriptional interplay in the 1q21.1 locus potentially contributing to PPMS pathogenesis.
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Encéfalo , Cromosomas Humanos Par 1 , Metilación de ADN , Proteínas de Unión al ADN , Epigénesis Genética , Pez Cebra , Humanos , Pez Cebra/genética , Animales , Metilación de ADN/genética , Cromosomas Humanos Par 1/genética , Proteínas de Unión al ADN/genética , Proteínas de Unión al ADN/metabolismo , Encéfalo/metabolismo , Encéfalo/patología , ADN Helicasas/genética , ADN Helicasas/metabolismo , Neuronas/metabolismo , Esclerosis Múltiple Crónica Progresiva/genética , Células Madre Pluripotentes Inducidas/metabolismo , Masculino , Femenino , Persona de Mediana Edad , Predisposición Genética a la Enfermedad , AdultoRESUMEN
BACKGROUND: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. METHODS: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. RESULTS: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. CONCLUSIONS: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).
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Artritis , Enfermedad de Crohn , Humanos , Medicina de Precisión , Inhibidores del Factor de Necrosis Tumoral , Perfilación de la Expresión Génica , Agentes Inmunomoduladores , Análisis de la Célula Individual , Análisis de Secuencia de ARNRESUMEN
Background: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusion: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).
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Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.
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Esclerosis Múltiple , Humanos , Proteómica , Proteínas de Neurofilamentos/líquido cefalorraquídeo , Biomarcadores , Progresión de la EnfermedadRESUMEN
Precision medicine relies on the identification of robust disease and risk factor signatures from omics data. However, current knowledge-driven approaches may overlook novel or unexpected phenomena due to the inherent biases in biological knowledge. In this study, we present a data-driven signature discovery workflow for DNA methylation analysis utilizing network-coherent autoencoders (NCAEs) with biologically relevant latent embeddings. First, we explored the architecture space of autoencoders trained on a large-scale pan-tissue compendium (n = 75 272) of human epigenome-wide association studies. We observed the emergence of co-localized patterns in the deep autoencoder latent space representations that corresponded to biological network modules. We determined the NCAE configuration with the strongest co-localization and centrality signals in the human protein interactome. Leveraging the NCAE embeddings, we then trained interpretable deep neural networks for risk factor (aging, smoking) and disease (systemic lupus erythematosus) prediction and classification tasks. Remarkably, our NCAE embedding-based models outperformed existing predictors, revealing novel DNA methylation signatures enriched in gene sets and pathways associated with the studied condition in each case. Our data-driven biomarker discovery workflow provides a generally applicable pipeline to capture relevant risk factor and disease information. By surpassing the limitations of knowledge-driven methods, our approach enhances the understanding of complex epigenetic processes, facilitating the development of more effective diagnostic and therapeutic strategies.
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Algoritmos , Metilación de ADN , Humanos , Redes Neurales de la Computación , Epigénesis Genética , Factores de RiesgoRESUMEN
Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70-90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.
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Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/metabolismo , Transducción de Señal/fisiología , Insulina , Adipocitos/metabolismo , Lipólisis/fisiologíaRESUMEN
BACKGROUND: Multiple sclerosis (MS) is a neuroinflammatory disease in which pregnancy leads to a temporary amelioration in disease activity as indicated by the profound decrease in relapses rate during the 3rd trimester of pregnancy. CD4+ and CD8+ T cells are implicated in MS pathogenesis as being key regulators of inflammation and brain lesion formation. Although Tcells are prime candidates for the pregnancy-associated improvement of MS, the precise mechanisms are yet unclear, and in particular, a deep characterization of the epigenetic and transcriptomic events that occur in peripheral T cells during pregnancy in MS is lacking. METHODS: Women with MS and healthy controls were longitudinally sampled before, during (1st, 2nd and 3rd trimesters) and after pregnancy. DNA methylation array and RNA sequencing were performed on paired CD4+ and CD8+ T cells samples. Differential analysis and network-based approaches were used to analyze the global dynamics of epigenetic and transcriptomic changes. RESULTS: Both DNA methylation and RNA sequencing revealed a prominent regulation, mostly peaking in the 3rd trimester and reversing post-partum, thus mirroring the clinical course with improvement followed by a worsening in disease activity. This rebound pattern was found to represent a general adaptation of the maternal immune system, with only minor differences between MS and controls. By using a network-based approach, we highlighted several genes at the core of this pregnancy-induced regulation, which were found to be enriched for genes and pathways previously reported to be involved in MS. Moreover, these pathways were enriched for in vitro stimulated genes and pregnancy hormones targets. CONCLUSION: This study represents, to our knowledge, the first in-depth investigation of the methylation and expression changes in peripheral CD4+ and CD8+ T cells during pregnancy in MS. Our findings indicate that pregnancy induces profound changes in peripheral T cells, in both MS and healthy controls, which are associated with the modulation of inflammation and MS activity.
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Esclerosis Múltiple , Embarazo , Humanos , Femenino , Esclerosis Múltiple/patología , Linfocitos T CD8-positivos , Transcriptoma , Linfocitos T CD4-Positivos , Epigénesis Genética , Inflamación/metabolismoRESUMEN
Endothelial dysfunction and impaired vasodilation are linked with adverse cardiovascular events. T lymphocytes expressing choline acetyltransferase (ChAT), the enzyme catalyzing biosynthesis of the vasorelaxant acetylcholine (ACh), regulate vasodilation and are integral to the cholinergic antiinflammatory pathway in an inflammatory reflex in mice. Here, we found that human T cell ChAT mRNA expression was induced by T cell activation involving the PI3K signaling cascade. Mechanistically, we identified that ChAT mRNA expression was induced following the attenuation of RE-1 Silencing Transcription factor REST-mediated methylation of the ChAT promoter, and that ChAT mRNA expression levels were up-regulated by GATA3 in human T cells. In functional experiments, T cell-derived ACh increased endothelial nitric oxide-synthase activity, promoted vasorelaxation, and reduced vascular endothelial activation and promoted barrier integrity by a cholinergic mechanism. Further, we observed that survival in a cohort of patients with severe circulatory failure correlated with their relative frequency of ChAT +CD4+ T cells in blood. These findings on ChAT+ human T cells provide a mechanism for cholinergic immune regulation of vascular endothelial function in human inflammation.
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Colina O-Acetiltransferasa , Linfocitos T , Humanos , Ratones , Animales , Linfocitos T/metabolismo , Colina O-Acetiltransferasa/genética , Colina O-Acetiltransferasa/metabolismo , Fosfatidilinositol 3-Quinasas/metabolismo , Colinérgicos , Acetilcolina/metabolismo , ARN Mensajero/metabolismoRESUMEN
Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4+ T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma.
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Multiple sclerosis (MS) is a chronic autoimmune neuroinflammatory and neurodegenerative disorder of the central nervous system. Pregnancy represents a natural modulation of the disease course, where the relapse rate decreases, especially in the 3rd trimester, followed by a transient exacerbation after delivery. Although the exact mechanisms behind the pregnancy-induced modulation are yet to be deciphered, it is likely that the immune tolerance established during pregnancy is involved. In this study, we used the highly sensitive and specific proximity extension assay technology to perform protein profiling analysis of 92 inflammation-related proteins in MS patients (n=15) and healthy controls (n=10), longitudinally sampled before, during, and after pregnancy. Differential expression analysis was performed using linear models and p-values were adjusted for false discovery rate due to multiple comparisons. Our findings reveal gradual dynamic changes in plasma proteins that are most prominent during the 3rd trimester while reverting post-partum. Thus, this pattern reflects the disease activity of MS during pregnancy. Among the differentially expressed proteins in pregnancy, several proteins with known immunoregulatory properties were upregulated, such as PD-L1, LIF-R, TGF-ß1, and CCL28. On the other hand, inflammatory chemokines such as CCL8, CCL13, and CXCL5, as well as members of the tumor necrosis factor family, TRANCE and TWEAK, were downregulated. Further in-depth studies will reveal if these proteins can serve as biomarkers in MS and whether they are mechanistically involved in the disease amelioration and worsening. A deeper understanding of the mechanisms involved may identify new treatment strategies mimicking the pregnancy milieu.
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Esclerosis Múltiple , Complicaciones del Embarazo , Proteínas Sanguíneas , Femenino , Humanos , Inmunomodulación , Embarazo , Trimestres del EmbarazoRESUMEN
In this article we use high-throughput epigenomics, transcriptomics, and proteomics data to construct fine-graded models of the "protein-coding units" gathering all transcript isoforms and chromatin accessibility peaks associated with more than 4000 genes in humans. Each protein-coding unit has the structure of a directed acyclic graph (DAG) and can be represented as a Bayesian network. The factorization of the joint probability distribution induced by the DAGs imposes a number of conditional independence relationships among the variables forming a protein-coding unit, corresponding to the missing edges in the DAGs. We show that a large fraction of these conditional independencies are indeed verified by the data. Factors driving this verification appear to be the structural and functional annotation of the transcript isoforms, as well as a notion of structural balance (or frustration-free) of the corresponding sample correlation graph, which naturally leads to reduction of correlation (and hence to independence) upon conditioning.
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Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). We find that the expression of 1600 TFs can explain >95% of the variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an over-representation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the dysregulation of the target genes (rho = 0.61, P < 10-216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. We demonstrate a methodology for constructing an interpretable neural network predictor, where analyses of the predictors identified key TFs that were inducing transcriptional changes during disease.
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Genoma , Transcriptoma , Humanos , Redes Neurales de la Computación , Unión Proteica , Factores de Transcripción/genética , Factores de Transcripción/metabolismo , Transcriptoma/genéticaRESUMEN
TH1-mediated diseases such as multiple sclerosis (MS) and rheumatoid arthritis (RA) improve during pregnancy, coinciding with increasing levels of the pregnancy hormone progesterone (P4), highlighting P4 as a potential mediator of this immunomodulation. Here, we performed detailed characterization of how P4 affects the chromatin and transcriptomic landscape during early human TH1 differentiation, utilizing both ATAC-seq and RNA-seq. Time series analysis of the earlier events (0.5-24 hrs) during TH1 differentiation revealed that P4 counteracted many of the changes induced during normal differentiation, mainly by downregulating key regulatory genes and their upstream transcription factors (TFs) involved in the initial T-cell activation. Members of the AP-1 complex such as FOSL1, FOSL2, JUN and JUNB were particularly affected, in both in promoters and in distal regulatory elements. Moreover, the changes induced by P4 were significantly enriched for disease-associated changes related to both MS and RA, revealing several shared upstream TFs, where again JUN was highlighted to be of central importance. Our findings support an immune regulatory role for P4 during pregnancy by impeding T-cell activation, a crucial checkpoint during pregnancy and in T-cell mediated diseases, and a central event prior to T-cell lineage commitment. Indeed, P4 is emerging as a likely candidate involved in disease modulation during pregnancy and further studies evaluating P4 as a potential treatment option are needed.
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Diferenciación Celular/efectos de los fármacos , Cromatina/efectos de los fármacos , Inmunomodulación/efectos de los fármacos , Activación de Linfocitos/inmunología , Progesterona/farmacología , Artritis Reumatoide/inmunología , Células Cultivadas , Secuenciación de Inmunoprecipitación de Cromatina , Femenino , Regulación de la Expresión Génica/efectos de los fármacos , Regulación de la Expresión Génica/inmunología , Humanos , Activación de Linfocitos/efectos de los fármacos , Esclerosis Múltiple/inmunología , Embarazo , RNA-Seq , Linfocitos T/efectos de los fármacos , Linfocitos T/inmunologíaRESUMEN
Motivation: Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators. Results: We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data. Availability and implementation: MODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.
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Epigenetics may play a central, yet unexplored, role in the profound changes that the maternal immune system undergoes during pregnancy and could be involved in the pregnancy-induced modulation of several autoimmune diseases. We investigated changes in the methylome in isolated circulating CD4+ T-cells in non-pregnant and pregnant women, during the 1st and 2nd trimester, using the Illumina Infinium Human Methylation 450K array, and explored how these changes were related to autoimmune diseases that are known to be affected during pregnancy. Pregnancy was associated with several hundreds of methylation differences, particularly during the 2nd trimester. A network-based modular approach identified several genes, e.g., CD28, FYN, VAV1 and pathways related to T-cell signalling and activation, highlighting T-cell regulation as a central component of the observed methylation alterations. The identified pregnancy module was significantly enriched for disease-associated methylation changes related to multiple sclerosis, rheumatoid arthritis and systemic lupus erythematosus. A negative correlation between pregnancy-associated methylation changes and disease-associated changes was found for multiple sclerosis and rheumatoid arthritis, diseases that are known to improve during pregnancy whereas a positive correlation was found for systemic lupus erythematosus, a disease that instead worsens during pregnancy. Thus, the directionality of the observed changes is in line with the previously observed effect of pregnancy on disease activity. Our systems medicine approach supports the importance of the methylome in immune regulation of T-cells during pregnancy. Our findings highlight the relevance of using pregnancy as a model for understanding and identifying disease-related mechanisms involved in the modulation of autoimmune diseases.Abbreviations: BMIQ: beta-mixture quantile dilation; DMGs: differentially methylated genes; DMPs: differentially methylated probes; FE: fold enrichment; FDR: false discovery rate; GO: gene ontology; GWAS: genome-wide association studies; MDS: multidimensional scaling; MS: multiple sclerosis; PBMC: peripheral blood mononuclear cells; PBS: phosphate buffered saline; PPI; protein-protein interaction; RA: rheumatoid arthritis; SD: standard deviation; SLE: systemic lupus erythematosus; SNP: single nucleotide polymorphism; TH: CD4+ T helper cell; VIStA: diVIsive Shuffling Approach.
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Artritis Reumatoide , Enfermedades Autoinmunes , Lupus Eritematoso Sistémico , Esclerosis Múltiple , Enfermedades Autoinmunes/genética , Antígenos CD28/genética , Linfocitos T CD4-Positivos , Metilación de ADN , Femenino , Estudio de Asociación del Genoma Completo , Humanos , Leucocitos Mononucleares , Lupus Eritematoso Sistémico/genética , Esclerosis Múltiple/genética , Fosfatos , Embarazo , Linfocitos TRESUMEN
BACKGROUND: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. RESULT: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10- 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. CONCLUSIONS: We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.
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Estudio de Asociación del Genoma Completo , Esclerosis Múltiple , Epigenómica , Redes Reguladoras de Genes , Humanos , Esclerosis Múltiple/genética , Factores de RiesgoRESUMEN
Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive malignancy resulting from overproduction of immature T-cells in the thymus and is typified by widespread alterations in DNA methylation. As survival rates for relapsed T-ALL remain dismal (10 to 25%), development of targeted therapies to prevent relapse is key to improving prognosis. Whereas mutations in the DNA demethylating enzyme TET2 are frequent in adult T-cell malignancies, TET2 mutations in T-ALL are rare. Here, we analyzed RNA-sequencing data of 321 primary T-ALLs, 20 T-ALL cell lines, and 25 normal human tissues, revealing that TET2 is transcriptionally repressed or silenced in 71% and 17% of T-ALL, respectively. Furthermore, we show that TET2 silencing is often associated with hypermethylation of the TET2 promoter in primary T-ALL. Importantly, treatment with the DNA demethylating agent, 5-azacytidine (5-aza), was significantly more toxic to TET2-silenced T-ALL cells and resulted in stable re-expression of the TET2 gene. Additionally, 5-aza led to up-regulation of methylated genes and human endogenous retroviruses (HERVs), which was further enhanced by the addition of physiological levels of vitamin C, a potent enhancer of TET activity. Together, our results clearly identify 5-aza as a potential targeted therapy for TET2-silenced T-ALL.
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Ácido Ascórbico/farmacología , Azacitidina/farmacología , Biomarcadores de Tumor/metabolismo , Metilación de ADN , Proteínas de Unión al ADN/antagonistas & inhibidores , Dioxigenasas/antagonistas & inhibidores , Regulación Neoplásica de la Expresión Génica , Leucemia-Linfoma Linfoblástico de Células T Precursoras/tratamiento farmacológico , Antimetabolitos Antineoplásicos/farmacología , Antioxidantes/farmacología , Apoptosis , Biomarcadores de Tumor/genética , Proliferación Celular , Proteínas de Unión al ADN/genética , Proteínas de Unión al ADN/metabolismo , Dioxigenasas/genética , Dioxigenasas/metabolismo , Quimioterapia Combinada , Humanos , Leucemia-Linfoma Linfoblástico de Células T Precursoras/metabolismo , Leucemia-Linfoma Linfoblástico de Células T Precursoras/patología , Regiones Promotoras Genéticas , RNA-Seq , Células Tumorales CultivadasRESUMEN
MOTIVATION: The simultaneous availability of ATAC-seq and RNA-seq experiments allows to obtain a more in-depth knowledge on the regulatory mechanisms occurring in gene regulatory networks. In this article, we highlight and analyze two novel aspects that leverage on the possibility of pairing RNA-seq and ATAC-seq data. Namely we investigate the causality of the relationships between transcription factors, chromatin and target genes and the internal consistency between the two omics, here measured in terms of structural balance in the sample correlations along elementary length-3 cycles. RESULTS: We propose a framework that uses the a priori knowledge on the data to infer elementary causal regulatory motifs (namely chains and forks) in the network. It is based on the notions of conditional independence and partial correlation, and can be applied to both longitudinal and non-longitudinal data. Our analysis highlights a strong connection between the causal regulatory motifs that are selected by the data and the structural balance of the underlying sample correlation graphs: strikingly, >97% of the selected regulatory motifs belong to a balanced subgraph. This result shows that internal consistency, as measured by structural balance, is close to a necessary condition for 3-node regulatory motifs to satisfy causality rules. AVAILABILITY AND IMPLEMENTATION: The analysis was carried out in MATLAB and the code can be found at https://github.com/albertozenere/Multi-omics-elementary-regulatory-motifs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Redes Reguladoras de Genes , Multiómica , Cromatina , Factores de Transcripción/genética , Secuenciación de Inmunoprecipitación de CromatinaRESUMEN
Estradiol (E2) and progesterone (P4) are steroid hormones important for the regulation of immune responses during pregnancy. Their increasing levels coincide with an improvement of T cell-mediated diseases such as multiple sclerosis (MS). Although immune-endocrine interactions are involved in this phenomenon, the relative contribution of hormones is not known. We here report a direct comparison of E2- and P4-mediated effects on human CD4+ T cells, key cells in immune regulation. T cells were stimulated to obtain different activation levels and exposed to a broad range of hormone concentrations. Activation level was assessed by CD69/CD25 expression by flow cytometry, and secreted proteins (n = 196) were measured in culture supernatants using proximity extension assay and electrochemiluminescence immunoassay. We found that in low activated cells, pregnancy-relevant E2 concentrations increased activation and the secretion of several immune- and inflammation-related proteins. P4, on the other hand, showed a biphasic pattern, where serum-related concentrations upregulated activation and protein secretion while placenta-relevant concentrations induced a prominent dampening irrespective of the initial activation level. Our results demonstrate the importance of P4 as a major hormone in the immune modulation of T cells during pregnancy and emphasize the need to further evaluate its potency in the treatment of diseases like MS.