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.

Characterization of the Common Genetic Variation in the Spanish Population of Navarre.

Large-scale genomic studies have significantly increased our knowledge of genetic variability across populations. Regional genetic profiling is essential for distinguishing common benign variants from disease-causing ones. To this end, we conducted a comprehensive characterization of exonic variants in the population of Navarre (Spain), utilizing whole genome sequencing data from 358 unrelated individuals of Spanish origin. Our analysis revealed 61,410 biallelic single nucleotide variants (SNV) within the Navarrese cohort, with 35% classified as common (MAF > 1%). By comparing allele frequency data from 1000 Genome Project (excluding the Iberian cohort of Spain, IBS), Genome Aggregation Database, and a Spanish cohort (including IBS individuals and data from Medical Genome Project), we identified 1069 SNVs common in Navarre but rare (MAF ≤ 1%) in all other populations. We further corroborated this observation with a second regional cohort of 239 unrelated exomes, which confirmed 676 of the 1069 SNVs as common in Navarre. In conclusion, this study highlights the importance of population-specific characterization of genetic variation to improve allele frequency filtering in sequencing data analysis to identify disease-causing variants.

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.

HLA-based banking of induced pluripotent stem cells in Saudi Arabia.

BACKGROUND: Human iPSCs' derivation and use in clinical studies are transforming medicine. Yet, there is a high cost and long waiting time associated with autologous iPS-based cellular therapy, and the genetic engineering of hypo-immunogenic iPS cell lines is hampered with numerous hurdles. Therefore, it is increasingly interesting to create cell stocks based on HLA haplotype distribution in a given population. This study aimed to assess the potential of HLA-based iPS banking for the Saudi population. METHODS: In this study, we interrogated the HLA database of the Saudi Stem Cell Donor Registry (SSCDR), containing high-resolution HLA genotype data from 64,315 registered Saudi donors at the time of analysis. This database was considered to be a representative sample of the Saudi population. The most frequent HLA haplotypes in the Saudi population were determined, and an in-house developed iterative algorithm was used to identify their HLA matching percentages in the SSCDR database and cumulative coverage. Subsequently, to develop a clinically relevant protocol for iPSCs generation, and to illustrate the applicability of the concept of HLA-based banking for cell therapy purposes, the first HLA-based iPS cell line in Saudi Arabia was generated. Clinically relevant methods were employed to generate the two iPS clones from a homozygous donor for the most prevalent HLA haplotype in the Saudi population. The generated lines were then assessed for pluripotency markers, and their ability to differentiate into all three germ layers, beating cardiomyocytes, and neural progenitors was examined. Additionally, the genetic stability of the HLA-iPS cell lines was verified by comparing the mutational burden in the clones and the original blood sample, using whole-genome sequencing. The standards set by the American College of Medical Genetics and Genomics (ACMG) were used to determine the clinical significance of identified variants. RESULTS: The analysis revealed that the establishment of only 13 iPSC lines would match 30% of the Saudi population, 39 lines would attain 50% coverage, and 596 lines would be necessary for over 90% coverage. The proof-of-concept HLA-iPSCs, which cover 6.1% of the Saudi population, successfully demonstrated pluripotency and the ability to differentiate into various cell types including beating cardiomyocytes and neuronal progenitors. The comprehensive genetic analysis corroborated that all identified variants in the derived iPSCs were inherently present in the original donor sample and were classified as benign according to the standards set by the ACMG. CONCLUSIONS: Our study sets a road map for introducing iPS-based cell therapy in the Kingdom of Saudi Arabia. It underscores the pragmatic approach of HLA-based iPSC banking which circumvents the limitations of autologous iPS-based cellular therapies. The successful generation and validation of iPSC lines based on the most prevalent HLA haplotype in the Saudi population signify a promising step toward broadening the accessibility and applicability of stem cell therapies and regenerative medicine in Saudi Arabia.

Microbiome-based risk prediction in incident heart failure: a community challenge.

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.

Serum methylation of GALNT9, UPF3A, WARS, and LDB2 as noninvasive biomarkers for the early detection of colorectal cancer and advanced adenomas.

BACKGROUND: Early detection has proven to be the most effective strategy to reduce the incidence and mortality of colorectal cancer (CRC). Nevertheless, most current screening programs suffer from low participation rates. A blood test may improve both the adherence to screening and the selection to colonoscopy. In this study, we conducted a serum-based discovery and validation of cfDNA methylation biomarkers for CRC screening in a multicenter cohort of 433 serum samples including healthy controls, benign pathologies, advanced adenomas (AA), and CRC. RESULTS: First, we performed an epigenome-wide methylation analysis with the MethylationEPIC array using a sample pooling approach, followed by a robust prioritization of candidate biomarkers for the detection of advanced neoplasia (AN: AA and CRC). Then, candidate biomarkers were validated by pyrosequencing in independent individual cfDNA samples. We report GALNT9, UPF3A, WARS, and LDB2 as new noninvasive biomarkers for the early detection of AN. The combination of GALNT9/UPF3A by logistic regression discriminated AN with 78.8% sensitivity and 100% specificity, outperforming the commonly used fecal immunochemical test and the methylated SEPT9 blood test. CONCLUSIONS: Overall, this study highlights the utility of cfDNA methylation for CRC screening. Our results suggest that the combination methylated GALNT9/UPF3A has the potential to serve as a highly specific and sensitive blood-based test for screening and early detection of CRC.

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.

Preclinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma.

The historical lack of preclinical models reflecting the genetic heterogeneity of multiple myeloma (MM) hampers the advance of therapeutic discoveries. To circumvent this limitation, we screened mice engineered to carry eight MM lesions (NF-κB, KRAS, MYC, TP53, BCL2, cyclin D1, MMSET/NSD2 and c-MAF) combinatorially activated in B lymphocytes following T cell-driven immunization. Fifteen genetically diverse models developed bone marrow (BM) tumors fulfilling MM pathogenesis. Integrative analyses of ∼500 mice and ∼1,000 patients revealed a common MAPK-MYC genetic pathway that accelerated time to progression from precursor states across genetically heterogeneous MM. MYC-dependent time to progression conditioned immune evasion mechanisms that remodeled the BM microenvironment differently. Rapid MYC-driven progressors exhibited a high number of activated/exhausted CD8+ T cells with reduced immunosuppressive regulatory T (Treg) cells, while late MYC acquisition in slow progressors was associated with lower CD8+ T cell infiltration and more abundant Treg cells. Single-cell transcriptomics and functional assays defined a high ratio of CD8+ T cells versus Treg cells as a predictor of response to immune checkpoint blockade (ICB). In clinical series, high CD8+ T/Treg cell ratios underlie early progression in untreated smoldering MM, and correlated with early relapse in newly diagnosed patients with MM under Len/Dex therapy. In ICB-refractory MM models, increasing CD8+ T cell cytotoxicity or depleting Treg cells reversed immunotherapy resistance and yielded prolonged MM control. Our experimental models enable the correlation of MM genetic and immunological traits with preclinical therapy responses, which may inform the next-generation immunotherapy trials.

Palidis: fast discovery of novel insertion sequences.

The diversity of microbial insertion sequences, crucial mobile genetic elements in generating diversity in microbial genomes, needs to be better represented in current microbial databases. Identification of these sequences in microbiome communities presents some significant problems that have led to their underrepresentation. Here, we present a bioinformatics pipeline called Palidis that recognizes insertion sequences in metagenomic sequence data rapidly by identifying inverted terminal repeat regions from mixed microbial community genomes. Applying Palidis to 264 human metagenomes identifies 879 unique insertion sequences, with 519 being novel and not previously characterized. Querying this catalogue against a large database of isolate genomes reveals evidence of horizontal gene transfer events across bacterial classes. We will continue to apply this tool more widely, building the Insertion Sequence Catalogue, a valuable resource for researchers wishing to query their microbial genomes for insertion sequences.

scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences.

Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.

Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution.

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize an enriched population of human HSPCs obtained from young and elderly healthy individuals.Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain aging-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks (GRNs) and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRNs regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2, and RUNX1 suggesting a role of these transcription factors (TFs) in the pathogenesis of the disease.In summary, we demonstrate that the combination of single-cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

Our blood contains many different types of cells; red blood cells carry oxygen through the body, platelets help to stop bleeding and a variety of white blood cells fight infections. All of these critical components come from a pool of immature cells in bone marrow, which can develop and specialise into any of these. However, as we get older, these immature cells can accumulate damage, including mutations in specific genes. This increases the risk of diseases such as myelodysplastic syndromes (MDS), a type of cancer in which the cells cannot develop and the patient does not have enough healthy mature blood cells. The changes in gene activity in the immature cells have previously been studied using samples from young and elderly people, as well as individuals with MDS. These studies examined large numbers of cells together, revealing differences between young and elderly people, and individuals with MDS. However, this does not describe how the different types alter their behaviour. To address this, Ainciburu, Ezponda et al. used a technique called single-cell RNA sequencing to study the gene activity in individual immature blood cells. This revealed changes associated with maturation that may account for the different combinations of cell populations in younger and older people. The results confirmed findings from previous studies and suggested new genes involved in ageing or MDS. Ainciburu, Ezponda et al. used these results to create an analytical system that highlights gene activity differences in individual MDS patients that are independent of age-related changes. These results provide new insights that could help further research into the development of MDS and the ageing process. In addition, scientists could study other diseases using this approach of analysing individual patients' gene activity. In future, this could help to personalise clinical decisions on diagnosis and treatment.

The Regulators of Peroxisomal Acyl-Carnitine Shuttle CROT and CRAT Promote Metastasis in Melanoma.

Circulating tumor cells are the key link between a primary tumor and distant metastases, but once in the bloodstream, loss of adhesion induces cell death. To identify the mechanisms relevant for melanoma circulating tumor cell survival, we performed RNA sequencing and discovered that detached melanoma cells and isolated melanoma circulating tumor cells rewire lipid metabolism by upregulating fatty acid (FA) transport and FA beta-oxidation‒related genes. In patients with melanoma, high expression of FA transporters and FA beta-oxidation enzymes significantly correlates with reduced progression-free and overall survival. Among the highest expressed regulators in melanoma circulating tumor cells were the carnitine transferases carnitine O-octanoyltransferase and carnitine acetyltransferase, which control the shuttle of peroxisome-derived medium-chain FAs toward mitochondria to fuel mitochondrial FA beta-oxidation. Knockdown of carnitine O-octanoyltransferase or carnitine acetyltransferase and short-term treatment with peroxisomal or mitochondrial FA beta-oxidation inhibitors thioridazine or ranolazine suppressed melanoma metastasis in mice. Carnitine O-octanoyltransferase and carnitine acetyltransferase depletion could be rescued by medium-chain FA supplementation, indicating that the peroxisomal supply of FAs is crucial for the survival of nonadherent melanoma cells. Our study identifies targeting the FA-based cross-talk between peroxisomes and mitochondria as a potential therapeutic opportunity to challenge melanoma progression. Moreover, the discovery of the antimetastatic activity of the Food and Drug Administration‒approved drug ranolazine carries translational potential.

RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases.

Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4+ T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma.

Epigenetic clock indicates accelerated aging in glial cells of progressive multiple sclerosis patients.

Background: Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disease of the central nervous system (CNS) characterized by irreversible disability at later progressive stages. A growing body of evidence suggests that disease progression depends on age and inflammation within the CNS. We aimed to investigate epigenetic aging in bulk brain tissue and sorted nuclei from MS patients using DNA methylation-based epigenetic clocks. Methods: We applied Horvath's multi-tissue and Shireby's brain-specific Cortical clock on bulk brain tissue (n = 46), sorted neuronal (n = 54), and glial nuclei (n = 66) from post-mortem brain tissue of progressive MS patients and controls. Results: We found a significant increase in age acceleration residuals, corresponding to 3.6 years, in glial cells of MS patients compared to controls (P = 0.0024) using the Cortical clock, which held after adjustment for covariates (P adj = 0.0263). The 4.8-year age acceleration found in MS neurons (P = 0.0054) did not withstand adjustment for covariates and no significant difference in age acceleration residuals was observed in bulk brain tissue between MS patients and controls. Conclusion: While the findings warrant replication in larger cohorts, our study suggests that glial cells of progressive MS patients exhibit accelerated biological aging.

Alterations in the Oral Microbiome Associated With Diabetes, Overweight, and Dietary Components.

The Mediterranean diet (MedDiet) represents the traditional food consumption patterns of people living in countries bordering the Mediterranean Sea and is associated with a reduced incidence of obesity and type-2 diabetes mellitus (T2DM). The objective of this study was to examine differences in the composition of the oral microbiome in older adults with T2DM and/or high body mass index (BMI) and whether the microbiome was influenced by elements of a MedDiet. Using a nested case-control design individuals affected by T2DM were selected from the Seniors-ENRICA-2 cohort concurrently with non-diabetic controls. BMI was measured, a validated dietary history taken, and adherence to a Mediterranean diet calculated using the MEDAS (Mediterranean Diet Adherence Screener) index. Oral health status was assessed by questionnaire and unstimulated whole mouth saliva was collected, and salivary flow rate calculated. Richness and diversity of the salivary microbiome were reduced in participants with T2DM compared to those without diabetes. The bacterial community structure in saliva showed distinct "signatures" or "salivatypes," characterized by predominance of particular bacterial genera. Salivatype 1 was more represented in subjects with T2DM, whilst those with obesity (BMI ≥ 30 kg/m2) had a predominance of salivatype 2, and control participants without T2DM or obesity had an increased presence of salivatype 3. There was an association of salivatype 1 with increased consumption of sugary snacks combined with reduced consumption of fish/shellfish and nuts. It can be concluded that the microbial community structure of saliva is altered in T2DM and obesity and is associated with altered consumption of particular food items. In order to further substantiate these observations a prospective study should be undertaken to assess the impact of diets aimed at modifying diabetic status and reducing weight.

Deconvolution of the hematopoietic stem cell microenvironment reveals a high degree of specialization and conservation.

Understanding the regulation of normal and malignant human hematopoiesis requires comprehensive cell atlas of the hematopoietic stem cell (HSC) regulatory microenvironment. Here, we develop a tailored bioinformatic pipeline to integrate public and proprietary single-cell RNA sequencing (scRNA-seq) datasets. As a result, we robustly identify for the first time 14 intermediate cell states and 11 stages of differentiation in the endothelial and mesenchymal BM compartments, respectively. Our data provide the most comprehensive description to date of the murine HSC-regulatory microenvironment and suggest a higher level of specialization of the cellular circuits than previously anticipated. Furthermore, this deep characterization allows inferring conserved features in human, suggesting that the layers of microenvironmental regulation of hematopoiesis may also be shared between species. Our resource and methodology is a stepping-stone toward a comprehensive cell atlas of the BM microenvironment.

Data-driven bioinformatics to disentangle cells within a tissue microenvironment.

Molecular profiling of clinical tissue samples is at the core of precision medicine. Yet, to elucidate the contribution of mixed cell types and detect changes in cell populations in response to infections or drugs is challenging. Recent advances using machine learning promise to learn explanatory models directly from data.

DNA methylation changes in glial cells of the normal-appearing white matter in Multiple Sclerosis patients.

Multiple Sclerosis (MS), the leading cause of non-traumatic neurological disability in young adults, is a chronic inflammatory and neurodegenerative disease of the central nervous system (CNS). Due to the poor accessibility to the target organ, CNS-confined processes underpinning the later progressive form of MS remain elusive thereby limiting treatment options. We aimed to examine DNA methylation, a stable epigenetic mark of genome activity, in glial cells to capture relevant molecular changes underlying MS neuropathology. We profiled DNA methylation in nuclei of non-neuronal cells, isolated from 38 post-mortem normal-appearing white matter (NAWM) specimens of MS patients (n = 8) in comparison to white matter of control individuals (n = 14), using Infinium MethylationEPIC BeadChip. We identified 1,226 significant (genome-wide adjusted P-value < 0.05) differentially methylated positions (DMPs) between MS patients and controls. Functional annotation of the altered DMP-genes uncovered alterations of processes related to cellular motility, cytoskeleton dynamics, metabolic processes, synaptic support, neuroinflammation and signaling, such as Wnt and TGF-ß pathways. A fraction of the affected genes displayed transcriptional differences in the brain of MS patients, as reported by publically available transcriptomic data. Cell type-restricted annotation of DMP-genes attributed alterations of cytoskeleton rearrangement and extracellular matrix remodelling to all glial cell types, while some processes, including ion transport, Wnt/TGF-ß signaling and immune processes were more specifically linked to oligodendrocytes, astrocytes and microglial cells, respectively. Our findings strongly suggest that NAWM glial cells are highly altered, even in the absence of lesional insult, collectively exhibiting a multicellular reaction in response to diffuse inflammation.

Deep characterization of paired chromatin and transcriptomes in four immune cell types from multiple sclerosis patients.

Background: The putative involvement of chromatin states in multiple sclerosis (MS) is thus far unclear. Here we determined the association of chromatin-accessibility with concurrent genetic, epigenetic and transcriptional events. Material & methods: We generated paired assay for transposase-accessible chromatin sequencing and RNA-sequencing profiles from sorted blood immune CD4+ and CD8+ T cells, CD14+ monocytes and CD19+ B cells from healthy controls (HCs) and MS patients. Results: We identified differentially accessible regions between MS patients and HCs, primarily in CD4+ and CD19+. CD4+ regions were enriched for MS-associated single nucleotide polymorphisms and differentially methylated loci. In the vicinity of differentially accessible regions of CD4+ cells, 42 differentially expressed genes were identified. The top two dysregulated genes identified in this multilayer analysis were CCDC114 and SERTAD1. Conclusion: These findings provide new insight into the primary role of CD4+ and CD19+ cells in MS.

Lay abstract Multiple sclerosis (MS) is a devastating disease that affects individuals at a young age and gradually worsens over their lifespan. Currently, treatment for MS is broad, meaning it treats the symptoms but not the cause of the disease. Treating symptoms means that patients may feel better, but their general quality of life is not normal. In addition, treating symptoms can lead to the underlying cause still being present, which can come back once treatment is stopped. What we are striving to do in this article is to better understand the cause. If we can do that, we can have targeted treatment that will get rid of the disease without the fear of it coming back and drastically improve quality of life and life span. Here, we have identified the complex nature of MS and made an effort to identify certain genes that are different in MS patients and present a way to better understand MS using advanced genome study methodologies.