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

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 - an inflammation-responsive gene in nerve nociceptors - was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

SH3YL1 Protein Predicts Renal Outcomes in Patients with Type 2 Diabetes

NADPH oxidase (NOX)-derived oxidative stress is an important factor in renal progression, with NOX4 being the predominant NOX in the kidney. Recently, Src homology 3 (SH3) domain-containing YSC84-like 1 (SH3YL1) was reported to be a regulator of NOX4. In this study, we tested whether the SH3YL1 protein could predict 3-year renal outcomes in patients with type 2 diabetes. A total of 131 patients with type 2 diabetes were enrolled in this study. Renal events were defined as a 15% decline in the estimated glomerular filtration rate (eGFR) from the baseline, the initiation of renal replacement therapy, or death during the 3 years. The levels of the urinary SH3YL1-to-creatinine ratio (USCR) were significantly different among the five stages of chronic kidney disease (CKD) and the three groups, based on albuminuria levels. The USCR levels showed a significant negative correlation with eGFR and a positive correlation with the urinary albumin-to-creatinine ratio (UACR). Plasma SH3YL1 levels were significantly correlated with UACR. The highest tertile group of USCR and plasma SH3YL1 had a significantly lower probability of renal event-free survival. Furthermore, the highest tertile group of USCR showed a significant association with the incidence of renal events after full adjustment: adjusted hazard ratio (4.636: 95% confidence interval, 1.416-15.181, p = 0.011). This study suggests that SH3YL1 is a new diagnostic biomarker for renal outcomes in patients with type 2 diabetes.

A Comprehensive Exploration of the Transcriptomic Landscape in Multiple Sclerosis: A Systematic Review

Multiple Sclerosis (MS) is, to date, an incurable disease of the nervous system characterized by demyelination. Several genetic mutations are associated with the disease but they are not able to explain all the diagnosticated cases. Thus, it is suggested that altered gene expression may play a role in human pathologies. In this review, we explored the role of the transcriptomic profile in MS to investigate the main altered biological processes and pathways involved in the disease. Herein, we focused our attention on RNA-seq methods that in recent years are producing a huge amount of data rapidly replacing microarrays, both with bulk and single-cells. The studies evidenced that different MS stages have specific molecular signatures and non-coding RNAs may play a key role in the disease. Sex-dependence was observed before and after treatments used to alleviate symptomatology activating different biological processes in a drug-dependent manner. New pathways, such as neddylation, were found deregulated in MS and inflammation was linked to neuron degeneration areas through spatial transcriptomics. It is evident that the use of RNA-seq in the study of complex pathologies, such as MS, is a valid strategy to shed light on new involved mechanisms.

Full spectrum of vitamin D immunomodulation in multiple sclerosis: mechanisms and therapeutic implications

Vitamin D deficiency has been associated with the risk of multiple sclerosis, disease activity and progression. Results from in vitro experiments, animal models and analysis of human samples from randomized controlled trials provide comprehensive data illustrating the pleiotropic actions of Vitamin D on the immune system. They globally result in immunomodulation by decreasing differentiation of effector T and B cells while promoting regulatory subsets. Vitamin D also modulates innate immune cells such as macrophages, monocytes and dendritic cells, and acts at the level of the blood–brain barrier reducing immune cell trafficking. Vitamin D exerts additional activity within the central nervous system reducing microglial and astrocytic activation. The immunomodulatory role of Vitamin D detected in animal models of multiple sclerosis has suggested its potential therapeutic use for treating multiple sclerosis. In this review, we focus on recent published data describing the biological effects of Vitamin D in animal models of multiple sclerosis on immune cells, blood–brain barrier function, activation of glial cells and its potential neuroprotective effects. Based on the current knowledge, we also discuss optimization of therapeutic interventions with Vitamin D in patients with multiple sclerosis, as well as new technologies allowing in-depth analysis of immune cell regulations by vitamin D.

Male-specific coordinated changes in expression of miRNA genes, but not other genes within the DLK1-DIO3 locus in multiple sclerosis

The role of miRNAs, small non-coding regulatory RNAs, in the molecular mechanisms of multiple sclerosis (MS) development has been intensively studied. MiRNAs tend to be clustered within imprinted regions, and the largest number of miRNA genes is observed in the DLK1-DIO3 locus. Earlier using RNA-seq we identified sex-specific upregulation of the set of miRNA genes from this locus in peripheral blood mononuclear cells (PBMC) of treatment-naive relapsing-remitting MS (RRMS) patients. In the present study we set up to independently investigate the expression of a vast array of genes present in the DLK1-DIO3 imprinted locus. First, we analyzed the expression of miRNA genes, which levels in RRMS were mostly inconsistent based on RNA-seq data and not previously explored using qPCR. We identified that all selected miRNAs - miR-337-3p and -665 from 14q32.2 cluster and miR-370c, -380, -494, -654-3p, -300, -539, -668, and -323b-5p - were upregulated in MS men, but not women when compared to controls, regardless of conflicting RNA-seq data. The expression of miRNAs from the DLK1-DIO3 locus was highly correlated, indicating the existence of a common regulatory mechanism(s) that controls miRNA expression, regardless of the position of their genes within this region. Second, we performed the expression analysis of non-miRNA genes within the locus. The genes encoding proteins (DLK1, DIO3, RTL1), long non-coding RNAs (MEG3, MEG8, and MEG9) and small nucleolar RNAs (SNORD112, SNORD113-5, SNORD113-7, SNORD114-3, SNORD114-8, SNORD114-19) were not dysregulated in RRMS both in men and women. DNA methylation analysis of selected CpG sites within the differentially methylated regions IG-DMR, MEG3-DMR, and MEG8-DMR of the DLK1-DIO3 imprinted locus pointed out that they were not involved in the regulation of miRNA gene expression in RRMS, at least in PBMC population. The question of whether the observed changes in expression of miRNA genes (given that there is a constant expression of other non-miRNA genes of the DLK1-DIO3 locus) are involved in the development of RRMS or are they a consequence of the disease progress, remains open and needs further investigation.

Genome-wide DNA methylation profiling identifies epigenetic changes in CD4+ and CD14+ cells of multiple sclerosis patients

Multiple sclerosis (MS) is a chronic autoimmune and degenerative disease of the central nervous system, which develops in genetically predisposed individuals upon exposure to environmental influences. Environmental triggers of MS, such as viral infections or smoking, were demonstrated to affect DNA methylation, and thus to involve this important epigenetic mechanism in the development of pathological process. To identify MS-associated DNA methylation hallmarks, we performed genome-wide DNA methylation profiling of two cell populations (CD4+ T-lymphocytes and CD14+ monocytes), collected from the same treatment-naive relapsing-remitting MS patients and healthy subjects, using Illumina 450 K methylation arrays. We revealed significant changes in DNA methylation for both cell populations in MS. In CD4+ cells of MS patients the majority of differentially methylated positions (DMPs) were shown to be hypomethylated, while in CD14+ cells - hypermethylated. Differential methylation of HLA-DRB1 gene in CD4+ and CD14+ cells was associated with carriage of DRB1*15 allele independently from the disease status. Besides, about 20% of identified DMPs were shared between two cell populations and had the same direction of methylation changes; they may be involved in basic epigenetic processes occuring in MS. These findings suggest that the epigenetic mechanism of DNA methylation in immune cells contributes to MS; further studies are now required to validate these results and understand their functional significance.

Identification of Diagnostic Markers Correlated With HIV+ Immune Non-response Based on Bioinformatics Analysis

Background: HIV-infected immunological non-responders (INRs) are characterized by their inability to reconstitute CD4⁺ T cell pools after antiretroviral therapy. The risk of non-AIDS-related diseases in INRs is increased, and the outcome and prognosis of INRs are inferior to that of immunological responders (IRs). However, few markers can be used to define INRs precisely. In this study, we aim to identify further potential diagnostic markers associated with INRs through bioinformatic analyses of public datasets.

Methods: This study retrieved the microarray data sets of GSE106792 and GSE77939 from the Gene Expression Omnibus (GEO) database. After merging two microarray data and adjusting the batch effect, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) resource and Kyoto Encyclopedia of Genes and Genomes (KEGG) resource were conducted to analyze the biological process and functional enrichment. We performed receiver operating characteristic (ROC) curves to filtrate potential diagnostic markers for INRs. Gene Set Enrichment Analysis (GSEA) was conducted to perform the pathway enrichment analysis of individual genes. Single sample GSEA (ssGSEA) was performed to assess scores of immune cells within INRs and IRs. The correlations between the diagnostic markers and differential immune cells were examined by conducting Spearman’s rank correlation analysis. Subsequently, miRNA-mRNA-TF interaction networks in accordance with the potential diagnostic markers were built with Cytoscape. We finally verified the mRNA expression of the diagnostic markers in clinical samples of INRs and IRs by performing RT-qPCR.

Results: We identified 52 DEGs in the samples of peripheral blood mononuclear cells (PBMC) between INRs and IRs. A few inflammatory and immune-related pathways, including chronic inflammatory response, T cell receptor signaling pathway, were enriched. FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1 were considered as potential diagnostic markers. ssGSEA results showed that the IRs had significantly higher enrichment scores of seven immune cells compared with IRs. The miRNA-mRNA-TF network was constructed with 97 miRNAs, 6 diagnostic markers, and 26 TFs, which implied a possible regulatory relationship.

Conclusion: The six potential crucial genes, FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1, may be associated with clinical diagnosis in INRs. Our study provided new insights into diagnostic and therapeutic targets.

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.

Non-parametric combination of multimodal MRI for lesion detection in focal epilepsy

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Multivariate voxel-based analysis useful for lesion detection in focal epilepsy.

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Non-parametric combination algorithm used to combine data from various MR sequences.

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Successful lesion detection demonstrated in MRI-positive and MRI-negative patients.

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Multimodal analysis detected abnormalities from diverse epileptogenic pathologies.

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Sensitivity of multivariate analysis notably higher than univariate analyses.

One third of patients with medically refractory focal epilepsy have normal-appearing MRI scans. This poses a problem as identification of the epileptogenic region is required for surgical treatment. This study performs a multimodal voxel-based analysis (VBA) to identify brain abnormalities in MRI-negative focal epilepsy. Data was collected from 69 focal epilepsy patients (42 with discrete lesions on MRI scans, 27 with no visible findings on scans), and 62 healthy controls. MR images comprised T1-weighted, fluid-attenuated inversion recovery (FLAIR), fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging, and neurite density index (NDI) from neurite orientation dispersion and density imaging. These multimodal images were coregistered to T1-weighted scans, normalized to a standard space, and smoothed with 8 mm FWHM. Initial analysis performed voxel-wise one-tailed t-tests separately on grey matter concentration (GMC), FLAIR, FA, MD, and NDI, comparing patients with epilepsy to controls. A multimodal non-parametric combination (NPC) analysis was also performed simultaneously on FLAIR, FA, MD, and NDI. Resulting p-maps were family-wise error rate corrected, threshold-free cluster enhanced, and thresholded at p < 0.05. Sensitivity was established through visual comparison of results to manually drawn lesion masks or seizure onset zone (SOZ) from stereoelectroencephalography. A leave-one-out cross-validation with the same analysis protocols was performed on controls to determine specificity. NDI was the best performing individual modality, detecting focal abnormalities in 38% of patients with normal MRI and conclusive SOZ. GMC demonstrated the lowest sensitivity at 19%. NPC provided superior performance to univariate analyses with 50% sensitivity. Specificity in controls ranged between 96 and 100% for all analyses. This study demonstrated the utility of a multimodal VBA utilizing NPC for detecting epileptogenic lesions in MRI-negative focal epilepsy. Future work will apply this approach to datasets from other centres and will experiment with different combinations of MR sequences.

N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis

Background

Progressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing–remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF).

Methods

Gene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction.

Results

The 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples.

Conclusions

The dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02981-5.

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

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 STATegRa Bioconductor package.