Publications by authors named "David Gomez-Cabrero"

87 Publications

Gene Regulatory Network of Human GM-CSF-Secreting T Helper Cells.

J Immunol Res 2021 3;2021:8880585. Epub 2021 Jul 3.

Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institutet, ki.se Karolinska University Hospital & Science for Life Laboratory, 17176 Solna, Stockholm, Sweden.

GM-CSF produced by autoreactive CD4-positive T helper cells is involved in the pathogenesis of autoimmune diseases, such as multiple sclerosis. However, the molecular regulators that establish and maintain the features of GM-CSF-positive CD4 T cells are unknown. In order to identify these regulators, we isolated human GM-CSF-producing CD4 T cells from human peripheral blood by using a cytokine capture assay. We compared these cells to the corresponding GM-CSF-negative fraction, and furthermore, we studied naïve CD4 T cells, memory CD4 T cells, and bulk CD4 T cells from the same individuals as additional control cell populations. As a result, we provide a rich resource of integrated chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) data from these primary human CD4 T cell subsets and we show that the identified signatures are associated with human autoimmune diseases, especially multiple sclerosis. By combining information about mRNA expression, DNA accessibility, and predicted transcription factor binding, we reconstructed directed gene regulatory networks connecting transcription factors to their targets, which comprise putative key regulators of human GM-CSF-positive CD4 T cells as well as memory CD4 T cells. Our results suggest potential therapeutic targets to be investigated in the future in human autoimmune disease.
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http://dx.doi.org/10.1155/2021/8880585DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275380PMC
July 2021

Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution.

Front Cell Dev Biol 2021 12;9:645276. Epub 2021 May 12.

Department of Animal Biology, Faculty of Sciences, Instituto Malagueño de Biomedicina, University of Málaga, Málaga, Spain.

During the last decade, extensive efforts have been made to comprehend cardiac cell genetic and functional diversity. Such knowledge allows for the definition of the cardiac cellular interactome as a reasonable strategy to increase our understanding of the normal and pathologic heart. Previous experimental approaches including cell lineage tracing, flow cytometry, and bulk RNA-Seq have often tackled the analysis of cardiac cell diversity as based on the assumption that cell types can be identified by the expression of a single gene. More recently, however, the emergence of single-cell RNA-Seq technology has led us to explore the diversity of individual cells, enabling the cardiovascular research community to redefine cardiac cell subpopulations and identify relevant ones, and even novel cell types, through their cell-specific transcriptomic signatures in an unbiased manner. These findings are changing our understanding of cell composition and in consequence the identification of potential therapeutic targets for different cardiac diseases. In this review, we provide an overview of the continuously changing cardiac cellular landscape, traveling from the pre-single-cell RNA-Seq times to the single cell-RNA-Seq revolution, and discuss the utilities and limitations of this technology.
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http://dx.doi.org/10.3389/fcell.2021.645276DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8149764PMC
May 2021

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

Eur J Nutr 2021 May 26. Epub 2021 May 26.

Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université de Bourgogne Franche-Comté, Dijon, France.

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 snacks», 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.
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http://dx.doi.org/10.1007/s00394-021-02584-2DOI Listing
May 2021

Predicting anti-PD-1 responders in malignant melanoma from the frequency of S100A9+ monocytes in the blood.

J Immunother Cancer 2021 05;9(5)

Department of Medicine, Centre for Molecular Medicine, Karolinska Institutet, Stockholm, Stockholm, Sweden

Background: While programmed cell death receptor 1 (PD-1) blockade treatment has revolutionized treatment of patients with melanoma, clinical outcomes are highly variable, and only a fraction of patients show durable responses. Therefore, there is a clear need for predictive biomarkers to select patients who will benefit from the treatment.

Method: To identify potential predictive markers for response to PD-1 checkpoint blockade immunotherapy, we conducted single-cell RNA sequencing analyses of peripheral blood mononuclear cells (PBMC) (n=8), as well as an in-depth immune monitoring study (n=20) by flow cytometry in patients with advanced melanoma undergoing treatment with nivolumab at Karolinska University Hospital. Blood samples were collected before the start of treatment and at the time of the second dose.

Results: Unbiased single-cell RNA sequencing of PBMC in patients with melanoma uncovered that a higher frequency of monocytes and a lower ratio of CD4+ T cells to monocyte were inversely associated with overall survival. Similarly, S100A9 expression in the monocytic subset was correlated inversely with overall survival. These results were confirmed by a flow cytometry-based analysis in an independent patient cohort.

Conclusion: Our results suggest that monocytic cell populations can critically determine the outcome of PD-1 blockade, particularly the subset expressing S100A9, which should be further explored as a possible predictive biomarker. Detailed knowledge of the biological role of S100A9+ monocytes is of high translational relevance.
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http://dx.doi.org/10.1136/jitc-2020-002171DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108662PMC
May 2021

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline.

Front Genet 2021 4;12:620453. Epub 2021 Mar 4.

Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain.

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATega Bioconductor package.
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http://dx.doi.org/10.3389/fgene.2021.620453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7970106PMC
March 2021

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

Front Microbiol 2021 22;12:635781. Epub 2021 Feb 22.

Molecular Nutrition and Proteomics Lab, Faculty of the Food Science and Technology, Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
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http://dx.doi.org/10.3389/fmicb.2021.635781DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937616PMC
February 2021

A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts.

Geroscience 2021 06 18;43(3):1317-1329. Epub 2021 Feb 18.

German Institute for Human Nutrition, Potsdam, Germany.

Phenotype-specific omic expression patterns in people with frailty could provide invaluable insight into the underlying multi-systemic pathological processes and targets for intervention. Classical approaches to frailty have not considered the potential for different frailty phenotypes. We characterized associations between frailty (with/without disability) and sets of omic factors (genomic, proteomic, and metabolomic) plus markers measured in routine geriatric care. This study was a prevalent case control using stored biospecimens (urine, whole blood, cells, plasma, and serum) from 1522 individuals (identified as robust (R), pre-frail (P), or frail (F)] from the Toledo Study of Healthy Aging (R=178/P=184/F=109), 3 City Bordeaux (111/269/100), Aging Multidisciplinary Investigation (157/79/54) and InCHIANTI (106/98/77) cohorts. The analysis included over 35,000 omic and routine laboratory variables from robust and frail or pre-frail (with/without disability) individuals using a machine learning framework. We identified three protective biomarkers, vitamin D3 (OR: 0.81 [95% CI: 0.68-0.98]), lutein zeaxanthin (OR: 0.82 [95% CI: 0.70-0.97]), and miRNA125b-5p (OR: 0.73, [95% CI: 0.56-0.97]) and one risk biomarker, cardiac troponin T (OR: 1.25 [95% CI: 1.23-1.27]). Excluding individuals with a disability, one protective biomarker was identified, miR125b-5p (OR: 0.85, [95% CI: 0.81-0.88]). Three risks of frailty biomarkers were detected: pro-BNP (OR: 1.47 [95% CI: 1.27-1.7]), cardiac troponin T (OR: 1.29 [95% CI: 1.21-1.38]), and sRAGE (OR: 1.26 [95% CI: 1.01-1.57]). Three key frailty biomarkers demonstrated a statistical association with frailty (oxidative stress, vitamin D, and cardiovascular system) with relationship patterns differing depending on the presence or absence of a disability.
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http://dx.doi.org/10.1007/s11357-021-00334-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190217PMC
June 2021

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

Gerontology 2021 21;67(2):202-210. Epub 2021 Jan 21.

The Cellular Senescence and Pathophysiology Group, Cardiff Metropolitan University, Cardiff, United Kingdom,

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.
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http://dx.doi.org/10.1159/000512287DOI Listing
January 2021

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

Nucleic Acids Res 2020 11;48(19):10867-10876

Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden.

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.
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http://dx.doi.org/10.1093/nar/gkaa817DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641766PMC
November 2020

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

BMC Bioinformatics 2020 Oct 7;21(1):443. Epub 2020 Oct 7.

Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain.

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.
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http://dx.doi.org/10.1186/s12859-020-03784-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542881PMC
October 2020

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

Circulation 2020 11 25;142(19):1831-1847. Epub 2020 Sep 25.

Advanced Genomics Laboratory (J.P.R., A.V.-Z., L.C.-L., P.S.M.-U., E.L.-V., D.L.-A.), Program of Hemato-Oncology, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.

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-β 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 as a novel regulator of the healing scar process and a target for future translational studies.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.119.044557DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730974PMC
November 2020

The role of overweight in the association between the Mediterranean diet and the risk of type 2 diabetes mellitus: a mediation analysis among 21 585 UK biobank participants.

Int J Epidemiol 2020 10;49(5):1582-1590

Team Lifelong Exposures, Health and Aging, Inserm, Bordeaux Population Health Research Center, University Bordeaux, Bordeaux, France.

Background: There is growing evidence that the Mediterranean (Medi) diet may lower the risk of type 2 diabetes mellitus (T2DM). Whether this association is due to the Medi diet by itself or is mediated by a diet-associated lower rate of overweight is uncertain. Our aim was to disentangle these relationships among UK adults.

Methods: Based on 21 585 participants from the UK Biobank cohort, the adherence to the Medi diet (high fruits, vegetables, legumes, cereals, fish, olive oil; low meat, dairy products; and intermediate alcohol intakes) was assessed (range 0-18). Data on diabetes were self-reported, and overweight was defined as a body mass index (BMI) ≥ 25 kg/m². A mediation analysis was implemented to disentangle the role of overweight in the Medi diet-T2DM relationship.

Results: The average baseline Medi diet score was 8.8 [standard deviation (SD) 2.6]. During a mean follow-up of 6.1 years, 473 individuals developed T2DM. A higher adherence to a Medi diet (+1 point) was associated with 14% decreased risk of T2DM [hazard ratio (HR): 0.86, 95% confidence interval (CI): 0.82-0.90]. This association split into an indirect effect of 10%, mediated by lower odds of overweight (HR: 0.90, 95% CI: 0.87-0.92), and a direct effect of the Medi diet of 4% (HR: 0.96, 95% CI: 0.93-0.99), regardless of the effect mediated by overweight.

Conclusions: Considered as a single mediator, reduced overweight mainly contributes to the association between greater Medi diet adherence and lower risk of T2DM on this British subsample. However, the direct effect of the diet on the risk of T2DM, even weaker, should not be overlooked.
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http://dx.doi.org/10.1093/ije/dyaa103DOI Listing
October 2020

Harmonization of quality metrics and power calculation in multi-omic studies.

Nat Commun 2020 06 18;11(1):3092. Epub 2020 Jun 18.

Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida, Gainesville, FL, USA.

Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.
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http://dx.doi.org/10.1038/s41467-020-16937-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303201PMC
June 2020

Genome-wide investigation of DNA methylation in congenital adrenal hyperplasia.

J Steroid Biochem Mol Biol 2020 07 17;201:105699. Epub 2020 May 17.

Department of Women's and Children's Health, Karolinska Institutet, Paediatric Endocrinology Unit (QB83), Karolinska University Hospital, SE-171 76 Stockholm, Sweden.

Patients with congenital adrenal hyperplasia (CAH) are at risk of long-term cognitive and metabolic sequelae with some of the effects being attributed to the chronic glucocorticoid treatment that they receive. Our pilot study investigates genome-wide DNA methylation in patients with CAH to determine whether there is preliminary evidence for epigenomic reprogramming as well as any relationship to patient outcome. Here, we analysed CD4 + T cell DNA from 28 patients with CAH (mean age = 18.5 ± 6.5 years [y]) and 37 population controls (mean age = 17.0 ± 6.1 y) with the Infinium-HumanMethylation450 BeadChip array to measure genome-wide locus-specific DNA methylation levels. Effects of CAH, phenotype and CYP21A2 genotype on methylation were investigated as well as the association between differentially methylated CpGs and glucose homeostasis, blood lipid profile, and cognitive functions. In addition, we report data on a small cohort of 11 patients (mean age = 19.1, ±6.0 y) with CAH who were treated prenatally with dexamethasone (DEX) in addition to postnatal glucocorticoid treatment. We identified two CpGs to be associated with patient phenotype: cg18486102 (located in the FAIM2 gene; rho = 0.58, adjusted p = 0.027) and cg02404636 (located in the SFI1 gene; rho = 0.58, adjusted p = 0.038). cg02404636 was also associated with genotype (rho = 0.59, adjusted p = 0.024). Higher levels of serum C-peptide was also observed in patients with CAH (p = 0.044). Additionally, levels of C-peptide and HbA1c were positively correlated with patient phenotype (p = 0.044 and p = 0.034) and genotype (p = 0.044 and p = 0.033), respectively. No significant association was found between FAIM2 methylation and cognitive or metabolic outcome. However, SFI1 TSS methylation was associated with fasting plasma HDL cholesterol levels (p = 0.035). In conclusion, in this pilot study, higher methylation levels in CpG sites covering FAIM2 and SFI1 were associated with disease severity. Hypermethylation in these genes may have implications for long-term cognitive and metabolic outcome in patients with CAH, although the data must be interpreted with caution due to the small sample size. Additional studies in larger cohorts are therefore warranted.
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http://dx.doi.org/10.1016/j.jsbmb.2020.105699DOI Listing
July 2020

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

J Hepatol 2020 10 12;73(4):817-828. Epub 2020 Apr 12.

European Foundation for the Study of Chronic Liver Failure (EF-Clif) and Grifols Chair, Barcelona, Spain; Biochemistry and Molecular Genetics Service, Hospital Clínic-IDIBAPS and CIBERehd, Barcelona, Spain; Department of Biomedical Sciences, University of Barcelona, Barcelona, Spain. Electronic address:

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 (LTE and 12-HHT, both derived from arachidonic acid) shaped a minimal plasma fingerprint that discriminated patients with ACLF from those without. Levels of LTE 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, LTE together with LXA (derived from eicosapentaenoic acid) and EKODE (derived from linoleic acid) were associated with short-term mortality. LXA 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.
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http://dx.doi.org/10.1016/j.jhep.2020.03.046DOI Listing
October 2020

Functional and transcriptomic analysis of extracellular vesicles identifies calprotectin as a new prognostic marker in peripheral arterial disease (PAD).

J Extracell Vesicles 2020 19;9(1):1729646. Epub 2020 Feb 19.

Laboratory of Atherothrombosis, Program of Cardiovascular Diseases, Cima Universidad de Navarra, Pamplona, Spain.

Peripheral arterial disease (PAD) is associated with a high risk of cardiovascular events and death and is postulated to be a critical socioeconomic cost in the future. Extracellular vesicles (EVs) have emerged as potential candidates for new biomarker discovery related to their protein and nucleic acid cargo. In search of new prognostic and therapeutic targets in PAD, we determined the prothrombotic activity, the cellular origin and the transcriptomic profile of circulating EVs. This prospective study included control and PAD patients. Coagulation time (Procoag-PPL kit), EVs cellular origin and phosphatidylserine exposure were determined by flow cytometry in platelet-free plasma (n = 45 PAD). Transcriptomic profiles of medium/large EVs were generated using the MARS-Seq RNA-Seq protocol (n = 12/group). The serum concentration of the differentially expressed gene S100A9, in serum calprotectin (S100A8/A9), was validated by ELISA in control (n = 100) and PAD patients (n = 317). S100A9 was also determined in EVs and tissues of human atherosclerotic plaques (n = 3). Circulating EVs of PAD patients were mainly of platelet origin, predominantly Annexin V positive and were associated with the procoagulant activity of platelet-free plasma. Transcriptomic analysis of EVs identified 15 differentially expressed genes. Among them, serum calprotectin was elevated in PAD patients ( < 0.05) and associated with increased amputation risk before and after covariate adjustment (mean follow-up 3.6 years,  < 0.01). The combination of calprotectin with hs-CRP in the multivariate analysis further improved risk stratification ( < 0.01). Furthermore, S100A9 was also expressed in femoral plaque derived EVs and tissues. In summary, we found that PAD patients release EVs, mainly of platelet origin, highly positive for AnnexinV and rich in transcripts related to platelet biology and immune responses. Amputation risk prediction improved with calprotectin and was significantly higher when combined with hs-CRP. Our results suggest that EVs can be a promising component of liquid biopsy to identify the molecular signature of PAD patients.
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http://dx.doi.org/10.1080/20013078.2020.1729646DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7048174PMC
February 2020

Immunometabolic Network Interactions of the Kynurenine Pathway in Cutaneous Malignant Melanoma.

Front Oncol 2020 3;10:51. Epub 2020 Feb 3.

Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden.

Dysregulation of the kynurenine pathway has been regarded as a mechanism of tumor immune escape by the enzymatic activity of indoleamine 2, 3 dioxygenase and kynurenine production. However, the immune-modulatory properties of other kynurenine metabolites such as kynurenic acid, 3-hydroxykynurenine, and anthranilic acid are poorly understood. In this study, plasma from patients diagnosed with metastatic cutaneous malignant melanoma (CMM) was obtained before (PRE) and during treatment (TRM) with inhibitors of mitogen-activated protein kinase pathway (MAPKIs). Immuno-oncology related protein profile and kynurenine metabolites were analyzed by proximity extension assay (PEA) and LC/MS-MS, respectively. Correlation network analyses of the data derived from PEA and LC/MS-MS identified a set of proteins that modulate the differentiation of Th1 cells, which is linked to 3-hydroxykynurenine levels. Moreover, MAPKIs treatments are associated with alteration of 3-hydroxykynurenine and 3hydroxyanthranilic acid (3HAA) concentrations and led to higher "CXCL11," and "KLRD1" expression that are involved in T and NK cells activation. These findings imply that the kynurenine pathway is pathologically relevant in patients with CMM.
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http://dx.doi.org/10.3389/fonc.2020.00051DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017805PMC
February 2020

Abundance and diversity of resistomes differ between healthy human oral cavities and gut.

Nat Commun 2020 02 4;11(1):693. Epub 2020 Feb 4.

Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.

The global threat of antimicrobial resistance has driven the use of high-throughput sequencing techniques to monitor the profile of resistance genes, known as the resistome, in microbial populations. The human oral cavity contains a poorly explored reservoir of these genes. Here we analyse and compare the resistome profiles of 788 oral cavities worldwide with paired stool metagenomes. We find country and body site-specific differences in the prevalence of antimicrobial resistance genes, classes and mechanisms in oral and stool samples. Within individuals, the highest abundances of antimicrobial resistance genes are found in the oral cavity, but the oral cavity contains a lower diversity of resistance genes compared to the gut. Additionally, co-occurrence analysis shows contrasting ARG-species associations between saliva and stool samples. Maintenance and persistence of antimicrobial resistance is likely to vary across different body sites. Thus, we highlight the importance of characterising the resistome across body sites to uncover the antimicrobial resistance potential in the human body.
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http://dx.doi.org/10.1038/s41467-020-14422-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7000725PMC
February 2020

Challenges in the Multivariate Analysis of Mass Cytometry Data: The Effect of Randomization.

Cytometry A 2019 11 6;95(11):1178-1190. Epub 2019 Nov 6.

Computer Science Department, University of Crete, Heraklion, Greece.

Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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http://dx.doi.org/10.1002/cyto.a.23908DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027760PMC
November 2019

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

PLoS Comput Biol 2019 11 4;15(11):e1006555. Epub 2019 Nov 4.

Developmental and Cell Biology, University of California Irvine, Irvine, California, United States of America.

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.
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http://dx.doi.org/10.1371/journal.pcbi.1006555DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6855564PMC
November 2019

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

Sci Data 2019 10 31;6(1):256. Epub 2019 Oct 31.

Microbiology and Cell Science Department, Institute for Food and Agricultural Research, Genetics Institute, University of Florida, Gainesville, Florida, USA.

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.
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http://dx.doi.org/10.1038/s41597-019-0202-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823427PMC
October 2019

Associations of fat-soluble micronutrients and redox biomarkers with frailty status in the FRAILOMIC initiative.

J Cachexia Sarcopenia Muscle 2019 12 21;10(6):1339-1346. Epub 2019 Aug 21.

Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany.

Background: A poor fat-soluble micronutrient (FMN) and a high oxidative stress status are associated with frailty. Our aim was to determine the cross-sectional association of FMNs and oxidative stress biomarkers [protein carbonyls (PrCarb) and 3-nitrotyrosine] with the frailty status in participants older than 65 years.

Methods: Plasma levels of vitamins A (retinol), D , E (α-tocopherol and γ-tocopherol) and carotenoids (α-carotene and β-carotene, lycopene, lutein/zeaxanthin, and β-cryptoxanthin), PrCarb, and 3-nitrotyrosine were measured in 1450 individuals of the FRAILOMIC initiative. Participants were classified into robust, pre-frail, and frail using Fried's frailty criteria. Associations between biomarkers and frailty status were assessed by general linear and logistic regression models, both adjusted for cohort, season of blood sampling, gender, age, height, weight, and smoking.

Results: Robust participants had significantly higher vitamin D and lutein/zeaxanthin concentrations than pre-frail and frail subjects; had significantly higher γ-tocopherol, α-carotene, β-carotene, lycopene, and β-cryptoxanthin concentrations than frail subjects, and had significantly lower PrCarb concentrations than frail participants in multivariate linear models. Frail subjects were more likely to be in the lowest than in the highest tertile for vitamin D (adjusted odds ratio: 2.15; 95% confidence interval: 1.42-3.26), α-tocopherol (2.12; 1.39-3.24), α-carotene (1.69; 1.00-2.88), β-carotene (1.84; 1.13-2.99), lycopene (1.94; 1.24-3.05), lutein/zeaxanthin (3.60; 2.34-5.53), and β-cryptoxanthin (3.02; 1.95-4.69) and were more likely to be in the highest than in the lowest tertile for PrCarb (2.86; 1.82-4.49) than robust subjects in multivariate regression models.

Conclusions: Our study indicates that both low FMN and high PrCarb concentrations are associated with pre-frailty and frailty.
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http://dx.doi.org/10.1002/jcsm.12479DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6903435PMC
December 2019

Non-parametric combination analysis of multiple data types enables detection of novel regulatory mechanisms in T cells of multiple sclerosis patients.

Sci Rep 2019 08 19;9(1):11996. Epub 2019 Aug 19.

Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.

Multiple Sclerosis (MS) is an autoimmune disease of the central nervous system with prominent neurodegenerative components. The triggering and progression of MS is associated with transcriptional and epigenetic alterations in several tissues, including peripheral blood. The combined influence of transcriptional and epigenetic changes associated with MS has not been assessed in the same individuals. Here we generated paired transcriptomic (RNA-seq) and DNA methylation (Illumina 450 K array) profiles of CD4+ and CD8+ T cells (CD4, CD8), using clinically accessible blood from healthy donors and MS patients in the initial relapsing-remitting and subsequent secondary-progressive stage. By integrating the output of a differential expression test with a permutation-based non-parametric combination methodology, we identified 149 differentially expressed (DE) genes in both CD4 and CD8 cells collected from MS patients. Moreover, by leveraging the methylation-dependent regulation of gene expression, we identified the gene SH3YL1, which displayed significant correlated expression and methylation changes in MS patients. Importantly, silencing of SH3YL1 in primary human CD4 cells demonstrated its influence on T cell activation. Collectively, our strategy based on paired sampling of several cell-types provides a novel approach to increase sensitivity for identifying shared mechanisms altered in CD4 and CD8 cells of relevance in MS in small sized clinical materials.
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http://dx.doi.org/10.1038/s41598-019-48493-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6700160PMC
August 2019

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.

RMD Open 2019 18;5(2):e001004. Epub 2019 Jul 18.

Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), UMR S 1136, Sorbonne Universite, Paris, France.

Objective: To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).

Methods: A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.

Results: Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000-5 billion) in RMDs, and 9.1 billion (range 100 000-200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).

Conclusions: Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.
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http://dx.doi.org/10.1136/rmdopen-2019-001004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668041PMC
April 2020

Therapeutic efficacy of dimethyl fumarate in relapsing-remitting multiple sclerosis associates with ROS pathway in monocytes.

Nat Commun 2019 07 12;10(1):3081. Epub 2019 Jul 12.

Department of Clinical Neurosciences, Section of Neurology, Karolinska Institutet, Stockholm, Sweden.

Dimethyl fumarate (DMF) is a first-line-treatment for relapsing-remitting multiple sclerosis (RRMS). The redox master regulator Nrf2, essential for redox balance, is a target of DMF, but its precise therapeutic mechanisms of action remain elusive. Here we show impact of DMF on circulating monocytes and T cells in a prospective longitudinal RRMS patient cohort. DMF increases the level of oxidized isoprostanes in peripheral blood. Other observed changes, including methylome and transcriptome profiles, occur in monocytes prior to T cells. Importantly, monocyte counts and monocytic ROS increase following DMF and distinguish patients with beneficial treatment-response from non-responders. A single nucleotide polymorphism in the ROS-generating NOX3 gene is associated with beneficial DMF treatment-response. Our data implicate monocyte-derived oxidative processes in autoimmune diseases and their treatment, and identify NOX3 genetic variant, monocyte counts and redox state as parameters potentially useful to inform clinical decisions on DMF therapy of RRMS.
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http://dx.doi.org/10.1038/s41467-019-11139-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626021PMC
July 2019

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

Ann Rheum Dis 2020 01 22;79(1):69-76. Epub 2019 Jun 22.

Dept of Rheumatology, Clinical Immunology and Laboratory of Translational Immunology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands.

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.
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http://dx.doi.org/10.1136/annrheumdis-2019-215694DOI Listing
January 2020

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

Age Ageing 2019 09;48(5):696-702

Department of Biomedical Sciences, Cardiff Metropolitan University, Cardiff, UK.

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.
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http://dx.doi.org/10.1093/ageing/afz073DOI Listing
September 2019

Neuronal methylome reveals CREB-associated neuro-axonal impairment in multiple sclerosis.

Clin Epigenetics 2019 05 30;11(1):86. Epub 2019 May 30.

Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.

Background: Due to limited access to brain tissue, the precise mechanisms underlying neuro-axonal dysfunction in neurological disorders such as multiple sclerosis (MS) are largely unknown. In that context, profiling DNA methylation, which is a stable and cell type-specific regulatory epigenetic mark of genome activity, offers a unique opportunity to characterize the molecular mechanisms underpinning brain pathology in situ. We examined DNA methylation patterns of neuronal nuclei isolated from post-mortem brain tissue to infer processes that occur in neurons of MS patients.

Results: We isolated subcortical neuronal nuclei from post-mortem white matter tissue of MS patients and non-neurological controls using flow cytometry. We examined bulk DNA methylation changes (total n = 29) and further disentangled true DNA methylation (5mC) from neuron-specific DNA hydroxymethylation (5hmC) (n = 17), using Illumina Infinium 450K arrays. We performed neuronal sub-type deconvolution using glutamate and GABA methylation profiles to further reduce neuronal sample heterogeneity. In total, we identified 2811 and 1534 significant (genome-wide adjusted P value < 0.05) differentially methylated and hydroxymethylated positions between MS patients and controls. We found striking hypo-5mC and hyper-5hmC changes occurring mainly within gene bodies, which correlated with reduced transcriptional activity, assessed using published RNAseq data from bulk brain tissue of MS patients and controls. Pathway analyses of the two cohorts implicated dysregulation of genes involved in axonal guidance and synaptic plasticity, with meta-analysis confirming CREB signalling as the most highly enriched pathway underlying these processes. We functionally investigated DNA methylation changes of CREB signalling-related genes by immunohistofluoresence of phosphorylated CREB in neurons from brain sections of a subcohort of MS patients and controls (n = 15). Notably, DNA methylation changes associated with a reduction of CREB activity in white matter neurons of MS patients compared to controls.

Conclusions: Our data demonstrate that investigating 5mC and 5hmC modifications separately allows the discovery of a substantial fraction of changes occurring in neurons, which can escape traditional bisulfite-based DNA methylation analysis. Collectively, our findings indicate that neurons of MS patients acquire sustained hypo-5mC and hyper-5hmC, which may impair CREB-mediated neuro-axonal integrity, in turn relating to clinical symptoms.
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http://dx.doi.org/10.1186/s13148-019-0678-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543588PMC
May 2019

Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression.

EBioMedicine 2019 May 30;43:411-423. Epub 2019 Apr 30.

Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm 17177, Sweden. Electronic address:

Background: Multiple Sclerosis (MS) is a chronic inflammatory disease and a leading cause of progressive neurological disability among young adults. DNA methylation, which intersects genes and environment to control cellular functions on a molecular level, may provide insights into MS pathogenesis.

Methods: We measured DNA methylation in CD4 T cells (n = 31), CD8 T cells (n = 28), CD14 monocytes (n = 35) and CD19 B cells (n = 27) from relapsing-remitting (RRMS), secondary progressive (SPMS) patients and healthy controls (HC) using Infinium HumanMethylation450 arrays. Monocyte (n = 25) and whole blood (n = 275) cohorts were used for validations.

Findings: B cells from MS patients displayed most significant differentially methylated positions (DMPs), followed by monocytes, while only few DMPs were detected in T cells. We implemented a non-parametric combination framework (omicsNPC) to increase discovery power by combining evidence from all four cell types. Identified shared DMPs co-localized at MS risk loci and clustered into distinct groups. Functional exploration of changes discriminating RRMS and SPMS from HC implicated lymphocyte signaling, T cell activation and migration. SPMS-specific changes, on the other hand, implicated myeloid cell functions and metabolism. Interestingly, neuronal and neurodegenerative genes and pathways were also specifically enriched in the SPMS cluster.

Interpretation: We utilized a statistical framework (omicsNPC) that combines multiple layers of evidence to identify DNA methylation changes that provide new insights into MS pathogenesis in general, and disease progression, in particular. FUND: This work was supported by the Swedish Research Council, Stockholm County Council, AstraZeneca, European Research Council, Karolinska Institutet and Margaretha af Ugglas Foundation.
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http://dx.doi.org/10.1016/j.ebiom.2019.04.042DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6558224PMC
May 2019

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

PLoS Biol 2019 04 12;17(4):e2006506. Epub 2019 Apr 12.

Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.

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.
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http://dx.doi.org/10.1371/journal.pbio.2006506DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481923PMC
April 2019
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