Distinct microRNA signatures in human lymphocyte subsets and enforcement of the naive state in CD4+ T cells by the microRNA miR-125b.

MicroRNAs are small noncoding RNAs that regulate gene expression post-transcriptionally. Here we applied microRNA profiling to 17 human lymphocyte subsets to identify microRNA signatures that were distinct among various subsets and different from those of mouse lymphocytes. One of the signature microRNAs of naive CD4+ T cells, miR-125b, regulated the expression of genes encoding molecules involved in T cell differentiation, including IFNG, IL2RB, IL10RA and PRDM1. The expression of synthetic miR-125b and lentiviral vectors encoding the precursor to miR-125b in naive lymphocytes inhibited differentiation to effector cells. Our data provide an 'atlas' of microRNA expression in human lymphocytes, define subset-specific signatures and their target genes and indicate that the naive state of T cells is enforced by microRNA.

Time-resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation reveals novel regulators of FOXP3.

BACKGROUND: Regulatory T cells (Tregs) expressing the transcription factor FOXP3 are crucial mediators of self-tolerance, preventing autoimmune diseases but possibly hampering tumor rejection. Clinical manipulation of Tregs is of great interest, and first-in-man trials of Treg transfer have achieved promising outcomes. Yet, the mechanisms governing induced Treg (iTreg) differentiation and the regulation of FOXP3 are incompletely understood. RESULTS: To gain a comprehensive and unbiased molecular understanding of FOXP3 induction, we performed time-series RNA sequencing (RNA-Seq) and proteomics profiling on the same samples during human iTreg differentiation. To enable the broad analysis of universal FOXP3-inducing pathways, we used five differentiation protocols in parallel. Integrative analysis of the transcriptome and proteome confirmed involvement of specific molecular processes, as well as overlap of a novel iTreg subnetwork with known Treg regulators and autoimmunity-associated genes. Importantly, we propose 37 novel molecules putatively involved in iTreg differentiation. Their relevance was validated by a targeted shRNA screen confirming a functional role in FOXP3 induction, discriminant analyses classifying iTregs accordingly, and comparable expression in an independent novel iTreg RNA-Seq dataset. CONCLUSION: The data generated by this novel approach facilitates understanding of the molecular mechanisms underlying iTreg generation as well as of the concomitant changes in the transcriptome and proteome. Our results provide a reference map exploitable for future discovery of markers and drug candidates governing control of Tregs, which has important implications for the treatment of cancer, autoimmune, and inflammatory diseases.

Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR.

The high-throughput analysis of microRNAs (miRNAs) circulating within the blood of healthy and diseased individuals is an active area of biomarker research. Whereas quantitative real-time reverse transcription polymerase chain reaction (qPCR)-based methods are widely used, it is yet unresolved how the data should be normalized. Here, we show that a combination of different algorithms results in the identification of candidate reference miRNAs that can be exploited as normalizers, in both discovery and validation phases. Using the methodology considered here, we identify normalizers that are able to reduce nonbiological variation in the data and we present several case studies, to illustrate the relevance in the context of physiological or pathological scenarios. In conclusion, the discovery of stable reference miRNAs from high-throughput studies allows appropriate normalization of focused qPCR assays.

Network modules uncover mechanisms of skeletal muscle dysfunction in COPD patients.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) patients often show skeletal muscle dysfunction that has a prominent negative impact on prognosis. The study aims to further explore underlying mechanisms of skeletal muscle dysfunction as a characteristic systemic effect of COPD, potentially modifiable with preventive interventions (i.e. muscle training). The research analyzes network module associated pathways and evaluates the findings using independent measurements. METHODS: We characterized the transcriptionally active network modules of interacting proteins in the vastus lateralis of COPD patients (n = 15, FEV1 46 ± 12% pred, age 68 ± 7 years) and healthy sedentary controls (n = 12, age 65 ± 9  years), at rest and after an 8-week endurance training program. Network modules were functionally evaluated using experimental data derived from the same study groups. RESULTS: At baseline, we identified four COPD specific network modules indicating abnormalities in creatinine metabolism, calcium homeostasis, oxidative stress and inflammatory responses, showing statistically significant associations with exercise capacity (VO2 peak, Watts peak, BODE index and blood lactate levels) (P < 0.05 each), but not with lung function (FEV1). Training-induced network modules displayed marked differences between COPD and controls. Healthy subjects specific training adaptations were significantly associated with cell bioenergetics (P < 0.05) which, in turn, showed strong relationships with training-induced plasma metabolomic changes; whereas, effects of training in COPD were constrained to muscle remodeling. CONCLUSION: In summary, altered muscle bioenergetics appears as the most striking finding, potentially driving other abnormal skeletal muscle responses. Trial registration The study was based on a retrospectively registered trial (May 2017), ClinicalTrials.gov identifier: NCT03169270.

Epigenetic research in multiple sclerosis: progress, challenges, and opportunities.

Multiple sclerosis (MS) is a chronic inflammatory and demyelinating disease of the central nervous system. MS likely results from a complex interplay between predisposing causal gene variants (the strongest influence coming from HLA class II locus) and environmental risk factors such as smoking, infectious mononucleosis, and lack of sun exposure/vitamin D. However, little is known about the mechanisms underlying MS development and progression. Moreover, the clinical heterogeneity and variable response to treatment represent additional challenges to a comprehensive understanding and efficient treatment of disease. Epigenetic processes, such as DNA methylation and histone posttranslational modifications, integrate influences from the genes and the environment to regulate gene expression accordingly. Studying epigenetic modifications, which are stable and reversible, may provide an alternative approach to better understand and manage disease. We here aim to review findings from epigenetic studies in MS and further discuss the challenges and clinical opportunities arising from epigenetic research, many of which apply to other diseases with similar complex etiology. A growing body of evidence supports a role of epigenetic processes in the mechanisms underlying immune pathogenesis and nervous system dysfunction in MS. However, disparities between studies shed light on the need to consider possible confounders and methodological limitations for a better interpretation of the data. Nevertheless, translational use of epigenetics might offer new opportunities in epigenetic-based diagnostics and therapeutic tools for a personalized care of MS patients.

Transcriptomics and methylomics of CD4-positive T cells in arsenic-exposed women.

Arsenic, a carcinogen with immunotoxic effects, is a common contaminant of drinking water and certain food worldwide. We hypothesized that chronic arsenic exposure alters gene expression, potentially by altering DNA methylation of genes encoding central components of the immune system. We therefore analyzed the transcriptomes (by RNA sequencing) and methylomes (by target-enrichment next-generation sequencing) of primary CD4-positive T cells from matched groups of four women each in the Argentinean Andes, with fivefold differences in urinary arsenic concentrations (median concentrations of urinary arsenic in the lower- and high-arsenic groups: 65 and 276 µg/l, respectively). Arsenic exposure was associated with genome-wide alterations of gene expression; principal component analysis indicated that the exposure explained 53% of the variance in gene expression among the top variable genes and 19% of 28,351 genes were differentially expressed (false discovery rate <0.05) between the exposure groups. Key genes regulating the immune system, such as tumor necrosis factor alpha and interferon gamma, as well as genes related to the NF-kappa-beta complex, were significantly downregulated in the high-arsenic group. Arsenic exposure was associated with genome-wide DNA methylation; the high-arsenic group had 3% points higher genome-wide full methylation (>80% methylation) than the lower-arsenic group. Differentially methylated regions that were hyper-methylated in the high-arsenic group showed enrichment for immune-related gene ontologies that constitute the basic functions of CD4-positive T cells, such as isotype switching and lymphocyte activation and differentiation. In conclusion, chronic arsenic exposure from drinking water was related to changes in the transcriptome and methylome of CD4-positive T cells, both genome wide and in specific genes, supporting the hypothesis that arsenic causes immunotoxicity by interfering with gene expression and regulation.

A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data.

MOTIVATION: The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs. RESULTS: Here we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform. AVAILABILITY: BMIQ is freely available from http://code.google.com/p/bmiq/. CONTACT: a.teschendorff@ucl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systems Medicine: from molecular features and models to the clinic in COPD.

BACKGROUND AND HYPOTHESIS: Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice. OBJECTIVE AND METHOD: Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework. RESULTS: In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice. CONCLUSIONS: The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

Identification of new autoantigens by protein array indicates a role for IL4 neutralization in autoimmune hepatitis.

Autoimmune hepatitis (AIH) is an unresolving inflammation of the liver of unknown cause. Diagnosis requires the exclusion of other conditions and the presence of characteristic features such as specific autoantibodies. Presently, these autoantibodies have relatively low sensitivity and specificity and are identified via immunostaining of cells or tissues; therefore, there is a diagnostic need for better and easy-to-assess markers. To identify new AIH-specific autoantigens, we developed a protein microarray comprising 1626 human recombinant proteins, selected in silico for being secreted or membrane associated. We screened sera from AIH patients on this microarray and compared the reactivity with that of sera from healthy donors and patients with chronic viral hepatitis C. We identified six human proteins that are specifically recognized by AIH sera. Serum reactivity to a combination of four of these autoantigens allows identification of AIH patients with high sensitivity (82%) and specificity (92%). Of the six autoantigens, the interleukin-4 (IL4) receptor fibronectin type III domain of the IL4 receptor (CD124), which is expressed on the surface of both lymphocytes and hepatocytes, showed the highest individual sensitivity and specificity for AIH. Remarkably, patients' sera inhibited STAT6 phosphorylation induced by IL4 binding to CD124, demonstrating that these autoantibodies are functional and suggesting that IL4 neutralization has a pathogenetic role in AIH.

Genetic variation in the interleukin-28B gene is not associated with fibrosis progression in patients with chronic hepatitis C and known date of infection.

UNLABELLED: Polymorphisms in the interleukin-28B (IL28B) region are associated with spontaneous and treatment-induced viral clearance in hepatitis C virus (HCV) infection. Nevertheless, it is unknown whether genetic variation at the IL28B locus influences the natural history of chronic HCV infection. Thus, we asked whether an association between IL28B polymorphisms and liver fibrosis progression existed. We studied 247 consecutive patients with chronic HCV, an accurate estimate of the date of infection, and a liver biopsy performed before any treatment. No patient had a history of alcohol abuse or coinfection with other viruses. We assessed the role of rs8099917 and rs12979860 polymorphisms and the effect of host and environmental factors on fibrosis progression. Blood transfusion (75%) was the main modality of infection. Median age at infection was 21 years, and median interval between infection and liver biopsy was 25 years. One hundred twenty-nine patients (52%) were infected by HCV-1, 74 (30%) by HCV-2, 34 (14%) by HCV-3, and 10 (4%) by HCV-4. Bridging fibrosis/cirrhosis (Ishak ≥ 4) was detected in 24% of patients. Age at infection had a marked effect on fibrosis progression by both a linear model and Cox proportional-hazard regression (P < 2E-16). A 12.1% increase in the hazard of advanced fibrosis was estimated for each additional year at infection, suggesting that this was the major explanatory variable in this cohort. Male gender (P < 0.05), HCV genotype 3 (P < 0.001) and steatosis (P < 0.05) were also associated with faster fibrosis progression. Conversely, the two IL28B polymorphisms had no impact on fibrosis progression. CONCLUSION: In HCV patients with a known date of infection, IL28B genotype was not associated with fibrosis progression rate or with the risk of developing advanced liver fibrosis.

Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health.

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

Different epigenetic clocks reflect distinct pathophysiological features of multiple sclerosis.

Aim: Accumulating evidence links epigenetic age to diseases and age-related conditions, but little is known about its association with multiple sclerosis (MS). Materials & methods: We estimated epigenetic age acceleration measures using DNA methylation from blood or sorted cells of MS patients and controls. Results: In blood, sex (p = 4.39E-05) and MS (p = 2.99E-03) explained the variation in age acceleration, and isolated blood cell types showed different epigenetic age. Intrinsic epigenetic age acceleration and extrinsic epigenetic age acceleration were only associated with sex (p = 2.52E-03 and p = 1.58E-04, respectively), while PhenoAge Acceleration displayed positive association with MS (p = 3.40E-02). Conclusion: Different age acceleration measures are distinctly influenced by phenotypic factors, and they might measure separate pathophysiological aspects of MS. Data deposition: DNA methylation data can be accessed at Gene Expression Omnibus database under accession number GSE35069, GSE43976, GSE106648, GSE130029, GSE130030.

An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems.

We introduce and develop a method that demonstrates that the algorithmic information content of a system can be used as a steering handle in the dynamical phase space, thus affording an avenue for controlling and reprogramming systems. The method consists of applying a series of controlled interventions to a networked system while estimating how the algorithmic information content is affected. We demonstrate the method by reconstructing the phase space and their generative rules of some discrete dynamical systems (cellular automata) serving as controlled case studies. Next, the model-based interventional or causal calculus is evaluated and validated using (1) a huge large set of small graphs, (2) a number of larger networks with different topologies, and finally (3) biological networks derived from a widely studied and validated genetic network (E. coli) as well as on a significant number of differentiating (Th17) and differentiated human cells from a curated biological network data.

STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse.

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.

Author Correction: Smoking induces DNA methylation changes in Multiple Sclerosis patients with exposure-response relationship.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

DNA methylation as a mediator of HLA-DRB1*15:01 and a protective variant in multiple sclerosis.

The human leukocyte antigen (HLA) haplotype DRB1*15:01 is the major risk factor for multiple sclerosis (MS). Here, we find that DRB1*15:01 is hypomethylated and predominantly expressed in monocytes among carriers of DRB1*15:01. A differentially methylated region (DMR) encompassing HLA-DRB1 exon 2 is particularly affected and displays methylation-sensitive regulatory properties in vitro. Causal inference and Mendelian randomization provide evidence that HLA variants mediate risk for MS via changes in the HLA-DRB1 DMR that modify HLA-DRB1 expression. Meta-analysis of 14,259 cases and 171,347 controls confirms that these variants confer risk from DRB1*15:01 and also identifies a protective variant (rs9267649, p < 3.32 × 10-8, odds ratio = 0.86) after conditioning for all MS-associated variants in the region. rs9267649 is associated with increased DNA methylation at the HLA-DRB1 DMR and reduced expression of HLA-DRB1, suggesting a modulation of the DRB1*15:01 effect. Our integrative approach provides insights into the molecular mechanisms of MS susceptibility and suggests putative therapeutic strategies targeting a methylation-mediated regulation of the major risk gene.

Expression of Transient Receptor Potential Channels in the Purified Human Pancreatic ß-Cells.

OBJECTIVE: Members of the transient receptor potential (TRP) channels are involved in mediating the electrical excitability and stimulus-secretion coupling in the pancreatic ß-cells. The expression and the relative abundance of different TRP channels in the human ß-cells are unknown. The objective of this study was to examine the expression of the TRP channels and their relative abundance in the human ß-cell. METHODS: RNA sequencing data obtained from human islets, fluorescence-activated cell sorting-purified human ß-cell and human pancreatic acinar cells were analyzed. Gene counts and fragments per kilobase per million mapped reads were obtained. RESULTS: Among the TRPC family only the TRPC1 was expressed in the human ß-cell. TRPV1 channels were not expressed in the human ß-cells. Among the TRPM family, TRPM4, TRPM7, TRPM2, and TRPM3 were expressed in the human ß-cell. Of the remaining TRP channels, TRPP2, TRPML1, and TRPML3 were expressed in these cells. CONCLUSIONS: By analyzing the RNA sequencing data, we have detected for the first time the TRP channels that are expressed in the purified human ß-cells, in comparison to the other relevant pancreatic cell types. Our study provides an opportunity to focus on these TRP channels for a better understanding of the electrophysiology and stimulus-secretion coupling in these cells.

Guidelines for Developing Successful Short Advanced Courses in Systems Medicine and Systems Biology.

Systems medicine and systems biology have inherent educational challenges. These have largely been addressed either by providing new masters programs or by redesigning undergraduate programs. In contrast, short courses can respond to a different need: they can provide condensed updates for professionals across academia, the clinic, and industry. These courses have received less attention. Here, we share our experiences in developing and providing such courses to current and future leaders in systems biology and systems medicine. We present guidelines for how to reproduce our courses, and we offer suggestions for how to select students who will nurture an interdisciplinary learning environment and thrive there.

Phosphoproteomics Reveals Regulatory T Cell-Mediated DEF6 Dephosphorylation That Affects Cytokine Expression in Human Conventional T Cells.

Regulatory T cells (Tregs) control key events of immune tolerance, primarily by suppression of effector T cells. We previously revealed that Tregs rapidly suppress T cell receptor (TCR)-induced calcium store depletion in conventional CD4+CD25- T cells (Tcons) independently of IP3 levels, consequently inhibiting NFAT signaling and effector cytokine expression. Here, we study Treg suppression mechanisms through unbiased phosphoproteomics of primary human Tcons upon TCR stimulation and Treg-mediated suppression, respectively. Tregs induced a state of overall decreased phosphorylation as opposed to TCR stimulation. We discovered novel phosphosites (T595_S597) in the DEF6 (SLAT) protein that were phosphorylated upon TCR stimulation and conversely dephosphorylated upon coculture with Tregs. Mutation of these DEF6 phosphosites abrogated interaction of DEF6 with the IP3 receptor and affected NFAT activation and cytokine transcription in primary Tcons. This novel mechanism and phosphoproteomics data resource may aid in modifying sensitivity of Tcons to Treg-mediated suppression in autoimmune disease or cancer.

Smoking induces DNA methylation changes in Multiple Sclerosis patients with exposure-response relationship.

Cigarette smoking is an established environmental risk factor for Multiple Sclerosis (MS), a chronic inflammatory and neurodegenerative disease, although a mechanistic basis remains largely unknown. We aimed at investigating how smoking affects blood DNA methylation in MS patients, by assaying genome-wide DNA methylation and comparing smokers, former smokers and never smokers in two Swedish cohorts, differing for known MS risk factors. Smoking affects DNA methylation genome-wide significantly, an exposure-response relationship exists and the time since smoking cessation affects methylation levels. The results also show that the changes were larger in the cohort bearing the major genetic risk factors for MS (female sex and HLA risk haplotypes). Furthermore, CpG sites mapping to genes with known genetic or functional role in the disease are differentially methylated by smoking. Modeling of the methylation levels for a CpG site in the AHRR gene indicates that MS modifies the effect of smoking on methylation changes, by significantly interacting with the effect of smoking load. Alongside, we report that the gene expression of AHRR increased in MS patients after smoking. Our results suggest that epigenetic modifications may reveal the link between a modifiable risk factor and the pathogenetic mechanisms.