An atlas of cells in the human tonsil.

Palatine tonsils are secondary lymphoid organs (SLOs) representing the first line of immunological defense against inhaled or ingested pathogens. We generated an atlas of the human tonsil composed of >556,000 cells profiled across five different data modalities, including single-cell transcriptome, epigenome, proteome, and immune repertoire sequencing, as well as spatial transcriptomics. This census identified 121 cell types and states, defined developmental trajectories, and enabled an understanding of the functional units of the tonsil. Exemplarily, we stratified myeloid slan-like subtypes, established a BCL6 enhancer as locally active in follicle-associated T and B cells, and identified SIX5 as putative transcriptional regulator of plasma cell maturation. Analyses of a validation cohort confirmed the presence, annotation, and markers of tonsillar cell types and provided evidence of age-related compositional shifts. We demonstrate the value of this resource by annotating cells from B cell-derived mantle cell lymphomas, linking transcriptional heterogeneity to normal B cell differentiation states of the human tonsil.

Global compositional and functional states of the human gut microbiome in health and disease.

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.

A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis.

BACKGROUND: Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis. METHODS: To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm's parameters (parameter-based). RESULTS: Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients' outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580). CONCLUSIONS: By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.

Gene therapy restores the transcriptional program of hematopoietic stem cells in Fanconi anemia.

Clinical trials have shown that lentiviral-mediated gene therapy can ameliorate bone marrow failure (BMF) in nonconditioned Fanconi anemia (FA) patients resulting from the proliferative advantage of corrected FA hematopoietic stem and progenitor cells (HSPC). However, it is not yet known if gene therapy can revert affected molecular pathways in diseased HSPC. Single-cell RNA sequencing was performed in chimeric populations of corrected and uncorrected HSPC co-existing in the BM of gene therapy-treated FA patients. Our study demonstrates that gene therapy reverts the transcriptional signature of FA HSPC, which then resemble the transcriptional program of healthy donor HSPC. This includes a down-regulated expression of TGF-ß and p21, typically up-regulated in FA HSPC, and upregulation of DNA damage response and telomere maintenance pathways. Our results show for the first time the potential of gene therapy to rescue defects in the HSPC transcriptional program from patients with inherited diseases; in this case, in FA characterized by BMF and cancer predisposition.

Feedforward regulation of Myc coordinates lineage-specific with housekeeping gene expression during B cell progenitor cell differentiation.

The differentiation of self-renewing progenitor cells requires not only the regulation of lineage- and developmental stage-specific genes but also the coordinated adaptation of housekeeping functions from a metabolically active, proliferative state toward quiescence. How metabolic and cell-cycle states are coordinated with the regulation of cell type-specific genes is an important question, because dissociation between differentiation, cell cycle, and metabolic states is a hallmark of cancer. Here, we use a model system to systematically identify key transcriptional regulators of Ikaros-dependent B cell-progenitor differentiation. We find that the coordinated regulation of housekeeping functions and tissue-specific gene expression requires a feedforward circuit whereby Ikaros down-regulates the expression of Myc. Our findings show how coordination between differentiation and housekeeping states can be achieved by interconnected regulators. Similar principles likely coordinate differentiation and housekeeping functions during progenitor cell differentiation in other cell lineages.

MYC as a driver of stochastic chromatin networks: implications for the fitness of cancer cells.

The relationship between stochastic transcriptional bursts and dynamic 3D chromatin states is not well understood. Using an innovated, ultra-sensitive technique, we address here enigmatic features underlying the communications between MYC and its enhancers in relation to the transcriptional process. MYC thus interacts with its flanking enhancers in a mutually exclusive manner documenting that enhancer hubs impinging on MYC detected in large cell populations likely do not exist in single cells. Dynamic encounters with pathologically activated enhancers responsive to a range of environmental cues, involved <10% of active MYC alleles at any given time in colon cancer cells. Being the most central node of the chromatin network, MYC itself likely drives its communications with flanking enhancers, rather than vice versa. We submit that these features underlie an acquired ability of MYC to become dynamically activated in response to a diverse range of environmental cues encountered by the cell during the neoplastic process.

Single-Cell RNA Sequencing Analysis Reveals a Crucial Role for CTHRC1 (Collagen Triple Helix Repeat Containing 1) Cardiac Fibroblasts After Myocardial Infarction.

BACKGROUND: Cardiac fibroblasts (CFs) have a central role in the ventricular remodeling process associated with different types of fibrosis. Recent studies have shown that fibroblasts do not respond homogeneously to heart injury. Because of the limited set of bona fide fibroblast markers, a proper characterization of fibroblast population heterogeneity in response to cardiac damage is lacking. The purpose of this study was to define CF heterogeneity during ventricular remodeling and the underlying mechanisms that regulate CF function. METHODS: Collagen1α1-GFP (green fluorescent protein)-positive CFs were characterized after myocardial infarction (MI) by single-cell and bulk RNA sequencing, assay for transposase-accessible chromatin sequencing, and functional assays. Swine and patient samples were studied using bulk RNA sequencing. RESULTS: We identified and characterized a unique CF subpopulation that emerges after MI in mice. These activated fibroblasts exhibit a clear profibrotic signature, express high levels of Cthrc1 (collagen triple helix repeat containing 1), and localize into the scar. Noncanonical transforming growth factor-ß signaling and different transcription factors including SOX9 are important regulators mediating their response to cardiac injury. Absence of CTHRC1 results in pronounced lethality attributable to ventricular rupture. A population of CFs with a similar transcriptome was identified in a swine model of MI and in heart tissue from patients with MI and dilated cardiomyopathy. CONCLUSIONS: We report CF heterogeneity and their dynamics during the course of MI and redefine the CFs that respond to cardiac injury and participate in myocardial remodeling. Our study identifies CTHRC1 as a novel regulator of the healing scar process and a target for future translational studies.

The salivary proteome reflects some traits of dietary habits in diabetic and non-diabetic older adults.

PURPOSE: Objective markers of usual diet are of interest as alternative or validating tools in nutritional epidemiology research. The main purpose of the work was to assess whether saliva protein composition can reflect dietary habits in older adults, and how type 2 diabetes impacted on the saliva-diet correlates. METHODS: 214 participants were selected from 2 European cohorts of community-dwelling older adults (3C-Bordeaux and Seniors-ENRICA-2), using a case-control design nested in each cohort. Cases were individuals with type 2 diabetes. Dietary information was obtained using the Mediterranean Diet Adherence Screener (MEDAS). Saliva was successfully obtained from 211 subjects, and its proteome analyzed by liquid chromatography-tandem mass spectrometry. RESULTS: The relative abundance of 246 saliva proteins was obtained across all participants. The salivary proteome differed depending on the intake level of some food groups (especially vegetables, fruits, sweet snacks and red meat), in a diabetic status- and cohort-specific manner. Gene Set Enrichment Analysis suggested that some biological processes were consistently affected by diet across cohorts, for example enhanced platelet degranulation in high consumers of sweet snacks. Minimal models were then fitted to predict dietary variables by sociodemographic, clinical and salivary proteome variables. For the food group «sweet snacks¼, selected salivary proteins contributed to the predictive model and improved its performance in the Seniors-ENRICA-2 cohort and when both cohorts were combined. CONCLUSION: Saliva proteome composition of elderly individuals can reflect some aspects of dietary patterns.

Higher sRAGE Levels Predict Mortality in Frail Older Adults with Cardiovascular Disease.

INTRODUCTION: The evidence that blood levels of the soluble receptor for advanced glycation end products (sRAGE) predict mortality in people with cardiovascular diseases (CVD) is inconsistent. To clarify this matter, we investigated if frailty status influences this association. METHODS: We analysed data of 1,016 individuals (median age, 75 years) from 3 population-based European cohorts, enrolled in the FRAILOMIC project. Participants were stratified by history of CVD and frailty status. Mortality was recorded during 8 years of follow-up. RESULTS: In adjusted Cox regression models, baseline serum sRAGE was positively associated with an increased risk of mortality in participants with CVD (HR 1.64, 95% CI 1.09-2.49, p = 0.019) but not in non-CVD. Within the CVD group, the risk of death was markedly enhanced in the frail subgroup (CVD-F, HR 1.97, 95% CI 1.18-3.29, p = 0.009), compared to the non-frail subgroup (CVD-NF, HR 1.50, 95% CI 0.71-3.15, p = 0.287). Kaplan-Meier analysis showed that the median survival time of CVD-F with high sRAGE (>1,554 pg/mL) was 2.9 years shorter than that of CVD-F with low sRAGE, whereas no survival difference was seen for CVD-NF. Area under the ROC curve analysis demonstrated that for CVD-F, addition of sRAGE to the prediction model increased its prognostic value. CONCLUSIONS: Frailty status influences the relationship between sRAGE and mortality in older adults with CVD. sRAGE could be used as a prognostic marker of mortality for these individuals, particularly if they are also frail.

GeneSetCluster: a tool for summarizing and integrating gene-set analysis results.

BACKGROUND: Gene-set analysis tools, which make use of curated sets of molecules grouped based on their shared functions, aim to identify which gene-sets are over-represented in the set of features that have been associated with a given trait of interest. Such tools are frequently used in gene-centric approaches derived from RNA-sequencing or microarrays such as Ingenuity or GSEA, but they have also been adapted for interval-based analysis derived from DNA methylation or ChIP/ATAC-sequencing. Gene-set analysis tools return, as a result, a list of significant gene-sets. However, while these results are useful for the researcher in the identification of major biological insights, they may be complex to interpret because many gene-sets have largely overlapping gene contents. Additionally, in many cases the result of gene-set analysis consists of a large number of gene-sets making it complicated to identify the major biological insights. RESULTS: We present GeneSetCluster, a novel approach which allows clustering of identified gene-sets, from one or multiple experiments and/or tools, based on shared genes. GeneSetCluster calculates a distance score based on overlapping gene content, which is then used to cluster them together and as a result, GeneSetCluster identifies groups of gene-sets with similar gene-set definitions (i.e. gene content). These groups of gene-sets can aid the researcher to focus on such groups for biological interpretations. CONCLUSIONS: GeneSetCluster is a novel approach for grouping together post gene-set analysis results based on overlapping gene content. GeneSetCluster is implemented as a package in R. The package and the vignette can be downloaded at https://github.com/TranslationalBioinformaticsUnit.

Impact of genetic risk loci for multiple sclerosis on expression of proximal genes in patients.

Despite advancements in genetic studies, it is difficult to understand and characterize the functional relevance of disease-associated genetic variants, especially in the context of a complex multifactorial disease such as multiple sclerosis (MS). As a large proportion of expression quantitative trait loci (eQTLs) are context-specific, we performed RNA-Seq in peripheral blood mononuclear cells from MS patients (n = 145) to identify eQTLs in regions centered on 109 MS risk single nucleotide polymorphisms and 7 associated human leukocyte antigen variants. We identified 77 statistically significant eQTL associations, including pseudogenes and non-coding RNAs. Thirty-eight out of 40 testable eQTL effects were colocalized with the disease association signal. As many eQTLs are tissue specific, we aimed to detail their significance in different cell types. Approximately 70% of the eQTLs were replicated and characterized in at least one major peripheral blood mononuclear cell-derived cell type. Furthermore, 40% of eQTLs were found to be more pronounced in MS patients compared with non-inflammatory neurological diseases patients. In addition, we found two single nucleotide polymorphisms to be significantly associated with the proportions of three different cell types. Mapping to enhancer histone marks and predicted transcription factor binding sites added additional functional evidence for eight eQTL regions. As an example, we found that rs71624119, shared with three other autoimmune diseases and located in a primed enhancer (H3K4me1) with potential binding for STAT transcription factors, significantly associates with ANKRD55 expression. This study provides many novel and validated targets for future functional characterization of MS and other diseases.

Targeted lipidomics reveals extensive changes in circulating lipid mediators in patients with acutely decompensated cirrhosis.

BACKGROUND & AIMS: Acute-on-chronic liver failure (ACLF) is a newly described syndrome, which develops in patients with acute decompensation of cirrhosis, and is characterized by intense systemic inflammation, multiple organ failures and high short-term mortality. The profile of circulating lipid mediators, which are endogenous signaling molecules that play a major role in inflammation and immunity, is poorly characterized in ACLF. METHODS: In the current study, we assessed the profile of lipid mediators by liquid chromatography coupled to tandem mass spectrometry in plasma from patients with acute decompensation of cirrhosis, with (n = 119) and without (n = 127) ACLF, and from healthy controls (n = 18). Measurements were prospectively repeated in 191 patients with acute decompensation of cirrhosis during a 28-day follow-up period. RESULTS: Fifty-nine lipid mediators (out of 100) were detected in plasma from cirrhotic patients, of which 16 were significantly associated with disease status. Among these, 11 lipid mediators distinguished patients at any stage from healthy controls, whereas 2 lipid mediators (LTE4 and 12-HHT, both derived from arachidonic acid) shaped a minimal plasma fingerprint that discriminated patients with ACLF from those without. Levels of LTE4 distinguished ACLF grade 3 from ACLF grades 1 and 2, followed the clinical course of the disease (increased with worsening and decreased with improvement) and positively correlated with markers of inflammation and non-apoptotic cell death. Moreover, LTE4 together with LXA5 (derived from eicosapentaenoic acid) and EKODE (derived from linoleic acid) were associated with short-term mortality. LXA5 and EKODE formed a signature associated with coagulation and liver failures. CONCLUSION: Taken together, these findings uncover specific lipid mediator profiles associated with disease severity and prognosis in patients with acute decompensation of cirrhosis. LAY SUMMARY: Acute-on-chronic liver failure (ACLF) is characterized by intense systemic inflammation, multiple organ failures and high short-term mortality. In the current study, we assessed the plasma lipid profile of 100 bioactive lipid mediators in healthy controls, patients with decompensated cirrhosis, and those who had developed ACLF. We identified lipid mediator signatures associated with inflammation and non-apoptotic cell death that discriminate disease severity and evolution, short-term mortality and organ failures.

EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases.

BACKGROUND: Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs). METHODS: A multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated. RESULTS: Three overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice. CONCLUSION: These EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps.

Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.

Functional genomics analysis of vitamin D effects on CD4+ T cells in vivo in experimental autoimmune encephalomyelitis ‬.

Vitamin D exerts multiple immunomodulatory functions and has been implicated in the etiology and treatment of several autoimmune diseases, including multiple sclerosis (MS). We have previously reported that in juvenile/adolescent rats, vitamin D supplementation protects from experimental autoimmune encephalomyelitis (EAE), a model of MS. Here we demonstrate that this protective effect associates with decreased proliferation of CD4+ T cells and lower frequency of pathogenic T helper (Th) 17 cells. Using transcriptome, methylome, and pathway analyses in CD4+ T cells, we show that vitamin D affects multiple signaling and metabolic pathways critical for T-cell activation and differentiation into Th1 and Th17 subsets in vivo. Namely, Jak/Stat, Erk/Mapk, and Pi3K/Akt/mTor signaling pathway genes were down-regulated upon vitamin D supplementation. The protective effect associated with epigenetic mechanisms, such as (i) changed levels of enzymes involved in establishment and maintenance of epigenetic marks, i.e., DNA methylation and histone modifications; (ii) genome-wide reduction of DNA methylation, and (iii) up-regulation of noncoding RNAs, including microRNAs, with concomitant down-regulation of their protein-coding target RNAs involved in T-cell activation and differentiation. We further demonstrate that treatment of myelin-specific T cells with vitamin D reduces frequency of Th1 and Th17 cells, down-regulates genes in key signaling pathways and epigenetic machinery, and impairs their ability to transfer EAE. Finally, orthologs of nearly 50% of candidate MS risk genes and 40% of signature genes of myelin-reactive T cells in MS changed their expression in vivo in EAE upon supplementation, supporting the hypothesis that vitamin D may modulate risk for developing MS.

Patterns of circulating fat-soluble vitamins and carotenoids and risk of frailty in four European cohorts of older adults.

PURPOSE: To investigate the cross-sectional and prospective associations between patterns of serum fat-soluble micronutrients and frailty in four European cohorts of older adults 65 years of age and older. METHODS: Participants from the Three-City (Bordeaux, France), AMI (Gironde, France), TSHA (Toledo, Spain) and InCHIANTI (Tuscany, Italy) cohorts with available data on serum α-carotene, ß-carotene, lycopene, cryptoxanthin, lutein + zeaxanthin, retinol, α-tocopherol, γ-tocopherol and 25-hydroxyvitamin D3 (25(OH)D) were included. A principal component (PC) analysis was used to derive micronutrient patterns. Frailty was defined using Fried's criteria. Multivariate logistic regression models adjusted for socio-demographic and health-related covariates were performed to assess the association between micronutrient patterns and prevalent frailty in 1324 participants, and the risk of frailty in 915 initially non-frail participants. RESULTS: Three different patterns were identified: the first pattern was characterized by higher serum carotenoids and α-tocopherol levels; the second was characterized by high loadings for serum vitamins A and E levels and low loadings for carotenes level; the third one had the highest loading for serum 25(OH)D and cryptoxanthin level and the lowest loading for vitamin A and E. A significant cross-sectional association was only observed between the seconnd PC and prevalent frailty (p = 0.02). Compared to the highest quartile, participants in the lowest quartile-i.e., high carotenes and low vitamins E and A levels-had higher odds of frailty (Odds ratio = 2.2; 95% confidence interval 1.3-3.8). No association with the risk of frailty was observed. CONCLUSIONS: These findings suggest that some specific micronutrient patterns are markers but not predictors of frailty in these European cohorts of older adults.

Increased levels of soluble Receptor for Advanced Glycation End-products (RAGE) are associated with a higher risk of mortality in frail older adults.

OBJECTIVE: to evaluate the relationship between serum levels of the soluble Receptor for Advanced Glycation End-products (sRAGE) and mortality in frail and non-frail older adults. METHODS: we studied 691 subjects (141 frail and 550 non-frail) with a median age of 75 years from two population-based cohorts, the Toledo Study of Healthy Aging and the AMI study, who were enrolled to the FRAILOMIC initiative. Multivariate Cox proportional hazards regression and Kaplan-Meier survival analysis were used to assess the relationship between baseline sRAGE and mortality. RESULTS: during 6 years of follow-up 101 participants died (50 frail and 51 non-frail). Frail individuals who died had significantly higher sRAGE levels than those who survived (median [IQR]: 1563 [1015-2248] vs 1184 [870-1657] pg/ml, P = 0.006), whilst no differences were observed in the non-frail group (1262 [1056-1554] vs 1186 [919-1551] pg/ml, P = 0.19). Among frail individuals higher sRAGE levels were associated with an increased risk of death after adjustment for relevant covariates (HR = 2.72 per unit increment in ln-sRAGE, 95%CI 1.48-4.99, P = 0.001). In contrast, in non-frail individuals sRAGE showed no association with mortality. Survival curves demonstrated that among frail individuals the incidence of death was significantly higher in the top sRAGE quartile compared to the three lower quartiles (P = 0.002). Area under the ROC curve analysis demonstrated that for frail individuals, inclusion of sRAGE in the hazard model increased its predictive accuracy by ~3%. CONCLUSIONS: sRAGE is an independent predictor of mortality among frail individuals. Determination of sRAGE in frail subjects could be useful for prognostic assessment and treatment stratification.

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.

Public data and open source tools for multi-assay genomic investigation of disease.

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.

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.