Dr Alexandre Xavier

Background: Pregnancy in women with multiple sclerosis (wwMS) is associated with a reduction of long-term disability progression. The mechanism that drives this effect is unknown, but converging evidence suggests a role for epigenetic mechanisms altering immune and/or central nervous system function. In this study, we aimed to identify whole blood and immune cell-specific DNA methylation patterns associated with parity in relapse-onset MS. Results: We investigated the association between whole blood and immune cell-type-specific genome-wide methylation patterns and parity in 192 women with relapse-onset MS, matched for age and disease severity. The median time from last pregnancy to blood collection was 16.7¿years (range = 1.5¿44.4¿years). We identified 2965 differentially methylated positions in whole blood, 68.5% of which were hypermethylated in parous women; together with two differentially methylated regions on Chromosomes 17 and 19 which mapped to TMC8 and ZNF577, respectively. Our findings validated 22 DMPs and 366 differentially methylated genes from existing literature on epigenetic changes associated with parity in wwMS. Differentially methylated genes in whole blood were enriched in neuronal structure and growth-related pathways. Immune cell-type-specific analysis using cell-type proportion estimates from statistical deconvolution of whole blood revealed further differential methylation in T cells specifically (four in CD4+ and eight in CD8+ T cells). We further identified reduced methylation age acceleration in parous women, demonstrating slower biological aging compared to nulligravida women. Conclusion: Differential methylation at genes related to neural plasticity offers a potential molecular mechanism driving the long-term effect of pregnancy on MS outcomes. Our results point to a potential 'CNS signature' of methylation in peripheral immune cells, as previously described in relation to MS progression, induced by parity. As the first epigenome-wide association study of parity in wwMS reported, validation studies are needed to confirm our findings.

Background: The variation in multiple sclerosis (MS) disease severity is incompletely explained by genetics, suggesting genetic and environmental interactions are involved. Moreover, the lack of prognostic biomarkers makes it difficult for clinicians to optimise care. DNA methylation is one epigenetic mechanism by which gene¿environment interactions can be assessed. Here, we aimed to identify DNA methylation patterns associated with mild and severe relapse-onset MS (RMS) and to test the utility of methylation as a predictive biomarker. Methods: We conducted an epigenome-wide association study between 235 females with mild (n = 119) or severe (n = 116) with RMS. Methylation was measured with the Illumina methylationEPIC array and analysed using logistic regression. To generate hypotheses about the functional consequence of differential methylation, we conducted gene set enrichment analysis using ToppGene. We compared the accuracy of three machine learning models in classifying disease severity: (1) clinical data available at baseline (age at onset and first symptoms) built using elastic net (EN) regression, (2) methylation data using EN regression and (3) a weighted methylation risk score of differentially methylated positions (DMPs) from the main analysis using logistic regression. We used a conservative 70:30 test:train split for classification modelling. A false discovery rate threshold of 0.05 was used to assess statistical significance. Results: Females with mild or severe RMS had 1472 DMPs in whole blood (839 hypermethylated, 633 hypomethylated in the severe group). Differential methylation was enriched in genes related to neuronal cellular compartments and processes, and B-cell receptor signalling. Whole-blood methylation levels at 1708 correlated CpG sites classified disease severity more accurately (machine learning model 2, AUC = 0.91) than clinical data (model 1, AUC = 0.74) or the wMRS (model 3, AUC = 0.77). Of the 1708 selected CpGs, 100 overlapped with DMPs from the main analysis at the gene level. These overlapping genes were enriched in neuron projection and dendrite extension, lending support to our finding that neuronal processes, rather than immune processes, are implicated in disease severity. Conclusion: RMS disease severity is associated with whole-blood methylation at genes related to neuronal structure and function. Moreover, correlated whole-blood methylation patterns can assign disease severity in females with RMS more accurately than clinical data available at diagnosis.

Next-generation sequencing continues to grow in importance for researchers. Exome sequencing became a widespread tool to further study the genomic basis of Mendelian diseases. In an effort to identify pathogenic variants, reject benign variants and better predict variant effects in downstream analysis, the American College of Medical Genetics (ACMG) published a set of criteria in 2015. While there are multiple publicly available software's available to assign the ACMG criteria, most of them do not take into account multi-sample variant calling formats. Here we present a tool for assessment and prioritisation in exome studies (TAPES, https://github.com/a-xavier/tapes), an open-source tool designed for small-scale exome studies. TAPES can quickly assign ACMG criteria using ANNOVAR or VEP annotated files and implements a model to transform the categorical ACMG criteria into a continuous probability, allowing for a more accurate classification of pathogenicity or benignity of variants. In addition, TAPES can work with cohorts sharing a common phenotype by utilising a simple enrichment analysis, requiring no controls as an input as well as providing powerful filtering and reporting options. Finally, benchmarks showed that TAPES outperforms available tools to detect both pathogenic and benign variants, while also integrating the identification of enriched variants in study cohorts compared to the general population, making it an ideal tool to evaluate a smaller cohort before using bigger scale studies.

Background: Many families with a high burden of colorectal cancer fulfil the clinical criteria for Lynch Syndrome. However, in about half of these families, no germline mutation in the mismatch repair genes known to be associated with this disease can be identified. The aim of this study was to find the genetic cause for the increased colorectal cancer risk in these unsolved cases. Materials and methods: To reach the aim, we designed a gene panel targeting 112 previously known or candidate colorectal cancer susceptibility genes to screen 274 patient samples for mutations. Mutations were validated by Sanger sequencing and, where possible, segregation analysis was performed. Results: We identified 73 interesting variants, of whom 17 were pathogenic and 19 were variants of unknown clinical significance in well-established cancer susceptibility genes. In addition, 37 potentially pathogenic variants in candidate colorectal cancer susceptibility genes were detected. Conclusion: In conclusion, we found a promising DNA variant in more than 25 % of the patients, which shows that gene panel testing is a more effective method to identify germline variants in CRC patients compared to a single gene approach.