Methylome and transcriptome signature of bronchoalveolar cells from multiple sclerosis patients in relation to smoking

Development of an epigenetic clock resistant to changes in immune cell composition

Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. Here, we found that human naive CD8+ T cells, which decrease in frequency during aging, exhibit an epigenetic age 15-20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created an epigenetic clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock shows a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming.

The lung-brain axis in multiple sclerosis: Mechanistic insights and future directions

Multiple sclerosis is a chronic inflammatory demyelinating disease of the central nervous system with progressive lifelong disability. Current treatments are particularly effective at the early inflammatory stage of the disease but associate with safety concerns such as increased risk of infection. While clinical and epidemiological evidence strongly support the role of a bidirectional communication between the lung and the brain in MS in influencing disease risk and severity, the exact processes underlying such relationship appear complex and not fully understood. This short review aims to summarize key findings and future perspectives that might provide new insights into the mechanisms underpinning the lung-brain axis in MS.

Waterpipe and cigarette epigenome analysis reveals markers implicated in addiction and smoking type inference

Waterpipe smoking is frequent in the Middle East and Africa with emerging prevalence worldwide. The epigenome acts as a molecular sensor to exposures and a crucial driver in several diseases. With the widespread use of waterpipe smoking, it is timely to investigate its epigenomic markers and their role in addiction, as a central player in disease prevention and therapeutic strategies. DNA methylome-wide profiling was performed on an exposure-rich population from the Middle East, constituting of 216 blood samples split equally between never, cigarette-only and waterpipe-only smokers. Waterpipe smokers showed predominantly distinct epigenetic markers from cigarette smokers, even though both smoking forms are tobacco-based. Moreover, each smoking form could be accurately (∼90 %) inferred from the DNA methylome using machine learning. Top markers showed dose-response relationship with extent of smoking and were validated using independent technologies and additional samples (total N = 284). Smoking markers were enriched in regulatory regions and several biological pathways, primarily addiction. The epigenetically altered genes were not associated with genetic etiology of tobacco use, and the methylation levels of addiction genes, in particular, were more likely to reverse after smoking cessation. In contrast, other epigenetic markers continued to feature smoking exposure after cessation, which may explain long-term health effects observed in former smokers. This study reports, for the first time, blood epigenome-wide markers of waterpipe smokers and reveals new markers of cigarette smoking, with implications in mechanisms of addiction and the capacity to discriminate between different smoking types. These markers may offer actionable targets to reverse the epigenetic memory of addiction and can guide future prevention strategies for tobacco smoking as the most preventable cause of illnesses worldwide.

Smoking affects epigenetic ageing of lung bronchoalveolar lavage cells in Multiple Sclerosis

BACKGROUND

A compelling body of evidence implicates cigarette smoking and lung inflammation in Multiple Sclerosis (MS) susceptibility and progression. Previous studies have reported epigenetic age (DNAm age) acceleration in blood immune cells and in glial cells of people with MS (pwMS) compared to healthy controls (HC).

OBJECTIVES

We aimed to examine biological ageing in lung immune cells in the context of MS and smoking.

METHODS

We analyzed age acceleration residuals in lung bronchoalveolar lavage (BAL) cells, constituted of mainly alveolar macrophages, from 17 pwMS and 22 HC in relation to smoking using eight DNA methylation-based clocks, namely AltumAge, Horvath, GrimAge, PhenoAge, Zhang, SkinBlood, Hannum, Monocyte clock as well as two RNA-based clocks, which capture different aspects of biological ageing.

RESULTS

After adjustment for covariates, five epigenetic clocks showed significant differences between the groups. Four of them, Horvath (Padj = 0.028), GrimAge (Padj = 4.28 × 10-7), SkinBlood (Padj = 0.001) and Zhang (Padj = 0.02), uncovered the sole effect of smoking on ageing estimates, irrespective of the clinical group. The Horvath, SkinBlood and Zhang clocks showed a negative impact of smoking while GrimAge detected smoking-associated age acceleration in BAL cells. On the contrary, the AltumAge clock revealed differences between pwMS and HC and indicated that, in the absence of smoking, BAL cells of pwMS were epigenetically 5.4 years older compared to HC (Padj = 0.028). Smoking further affected epigenetic ageing in BAL cells of pwMS specifically as non-smoking pwMS exhibited a 10.2-year AltumAge acceleration compared to pwMS smokers (Padj = 0.0049). Of note, blood-derived monocytes did not show any MS-specific or smoking-related AltumAge differences. The difference between BAL cells of pwMS smokers and non-smokers was attributable to the differential methylation of 114 AltumAge-CpGs (Padj < 0.05) affecting genes involved in innate immune processes such as cytokine production, defense response and cell motility. These changes functionally translated into transcriptional differences in BAL cells between pwMS smokers and non-smokers.

CONCLUSIONS

BAL cells of pwMS display inflammation-related and smoking-dependent changes associated to epigenetic ageing captured by the AltumAge clock. Future studies examining potential confounders, such as the distribution of distinct BAL myeloid cell types in pwMS compared to control individuals in relation to smoking may clarify the varying performance and DNAm age estimations among epigenetic clocks.

Evaluation of Cell-Specific Epigenetic Age Acceleration in People With Multiple Sclerosis

BACKGROUND AND OBJECTIVES

In multiple sclerosis (MS), accelerated aging of the immune system (immunosenescence) may be associated with disease onset or drive progression. DNA methylation (DNAm) is an epigenetic factor that varies among lymphocyte subtypes, and cell-specific DNAm is associated with MS. DNAm varies across the life span and can be used to accurately estimate biological age acceleration, which has been linked to a range of morbidities. The objective of this study was to test for cell-specific epigenetic age acceleration (EAA) in people with MS.

METHODS

This was a case-control study of EAA using existing DNAm data from several independent previously published studies. Data were included if .idat files from Illumina 450K or EPIC arrays were available for both a case with MS and an age-matched and sex-matched control, from the same study. Multifactor statistical modeling was performed to assess the primary outcome of EAA. We explored the relationship of EAA and MS, including interaction terms to identify immune cell-specific effects. Cell-sorted DNA methylation data from 3 independent datasets were used to validate findings.

RESULTS

We used whole blood DNA methylation data from 583 cases with MS and 643 non-MS controls to calculate EAA using the GrimAge algorithm. The MS group exhibited an increased EAA compared with controls (approximately 9 mths, 95% CI 3.6-14.4), p = 0.001). Statistical deconvolution showed that EAA is associated with MS in a B cell-dependent manner (β int = 1.7, 95% CI 0.3-2.8), p = 0.002), irrespective of B-cell proportions. Validation analysis using 3 independent datasets enriched for B cells showed an EAA increase of 5.1 years in cases with MS compared with that in controls (95% CI 2.8-7.4, p = 5.5 × 10-5). By comparison, there was no EAA difference in MS in a T cell-enriched dataset. We found that EAA was attributed to the DNAm surrogates for Beta-2-microglobulin (difference = 47,546, 95% CI 10,067-85,026; p = 7.2 × 10-5), and smoking pack-years (difference = 8.1, 95% CI 1.9-14.2, p = 0.002).

DISCUSSION

This study provides compelling evidence that B cells exhibit marked EAA in MS and supports the hypothesis that premature B-cell immune senescence plays a role in MS. Future MS studies should focus on age-related molecular mechanisms in B cells.

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.

Integration of small RNAs from plasma and cerebrospinal fluid for classification of multiple sclerosis

Multiple Sclerosis (MS) is an autoimmune, neurological disease, commonly presenting with a relapsing-remitting form, that later converts to a secondary progressive stage, referred to as RRMS and SPMS, respectively. Early treatment slows disease progression, hence, accurate and early diagnosis is crucial. Recent advances in large-scale data processing and analysis have progressed molecular biomarker development. Here, we focus on small RNA data derived from cell-free cerebrospinal fluid (CSF), cerebrospinal fluid cells, plasma and peripheral blood mononuclear cells as well as CSF cell methylome data, from people with RRMS (n = 20), clinically/radiologically isolated syndrome (CIS/RIS, n = 2) and neurological disease controls (n = 14). We applied multiple co-inertia analysis (MCIA), an unsupervised and thereby unbiased, multivariate method for simultaneous data integration and found that the top latent variable classifies RRMS status with an Area Under the Receiver Operating Characteristics (AUROC) score of 0.82. Variable selection based on Lasso regression reduced features to 44, derived from the small RNAs from plasma (20), CSF cells (8) and cell-free CSF (16), with a marginal reduction in AUROC to 0.79. Samples from SPMS patients (n = 6) were subsequently projected on the latent space and differed significantly from RRMS and controls. On contrary, we found no differences between relapse and remission or between inflammatory and non-inflammatory disease controls, suggesting that the latent variable is not prone to inflammatory signals alone, but could be MS-specific. Hence, we here showcase that integration of small RNAs from plasma and CSF can be utilized to distinguish RRMS from SPMS and neurological disease controls.

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.

Trans-omics analyses revealed key epigenetic genes associated with overall survival in secondary progressive multiple sclerosis

BACKGROUND

Secondary progressive multiple sclerosis (SPMS) is the second most common presentation of multiple sclerosis (MS) and is characterized by a gradually deteriorating disease with or without relapses. Approximately 80% of patients with relapsing-remitting MS (RRMS) develop SPMS within 20 years. Epidemiological investigations have revealed an average 7-year life expectancy decrease (more severe in progressive subtypes) in patients with MS. Studies have focused on the neurodegenerative pathogenesis of SPMS; and epigenetic changes have been associated with disease progression in neurodegenerative disorders. However, the evidence for the association between epigenetic changes and SPMS is scarce. Thus, in this study we aimed to identify the key epigenetic genes in SPMS.

METHODS

We downloaded DNA methylation and gene expression matrices from the Gene Expression Omnibus (GEO) database. We used bioinformatic analyses to identify key epigenetic genes associated with overall survival (OS) in patients with SPMS.

RESULTS

We found 49 differentially methylated positions (DMPs) between the SPMS and control GSE40360 datasets. We used the wANNOVAR server to obtain 64 methylated genes. We merged the gene expression datasets (GSE131282 and GSE135511) in the NetworkAnalyst platform and found 12,442 differentially-expressed genes (DEGs) between SPMS and controls using the Fisher's method, fixed effect model, Vote counting, and direct merging methods. Moreover, we identified 21 epigenetic genes (all hyper-methylated) after an integrating analysis of DMPs and DEGs of patients with SPMS. We established an epigenetic gene signature associated with the OS of patients with SPMS including six hyper-methylated genes (ITGA6, PPP1R16B, RNF126, ABHD8, FOXK1, and SLC6A19) based on the LASSO-Cox method. The calculated individual risk scores were associated with Oss, and we divided patients into high- and low-risk groups on the basis of the mean cut-off value. The six key epigenetic genes were significantly associated with gender, disease duration, and age at death via Spearman correlation analyses. In addition, survival analyses revealed a significant OS difference between high- and low-risk groups. The ROC curves indicated good performance for this predictive model.

CONCLUSION

We identified 21 hyper-methylated genes in patients with SPMS via an integrated analysis of DNA methylation and gene expression datasets. We identified a six-epigenetic gene signature that predicts the individual OS with good accuracy. These results indicated that epigenetic modifications play a vital role in the disease progression of SPMS.

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.