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We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information., QC 20220407

We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses.

Despite advancements in genetic studies, it is difficult to understand and characterize the functional relevance of disease-associated genetic variants, especially in the context of a complex multifactorial disease such as multiple sclerosis (MS). As a large proportion of expression quantitative trait loci (eQTLs) are context-specific, we performed RNA-Seq in peripheral blood mononuclear cells from MS patients (n = 145) to identify eQTLs in regions centered on 109 MS risk single nucleotide polymorphisms and 7 associated human leukocyte antigen variants. We identified 77 statistically significant eQTL associations, including pseudogenes and non-coding RNAs. Thirty-eight out of 40 testable eQTL effects were colocalized with the disease association signal. As many eQTLs are tissue specific, we aimed to detail their significance in different cell types. Approximately 70% of the eQTLs were replicated and characterized in at least one major peripheral blood mononuclear cell-derived cell type. Furthermore, 40% of eQTLs were found to be more pronounced in MS patients compared with non-inflammatory neurological diseases patients. In addition, we found two single nucleotide polymorphisms to be significantly associated with the proportions of three different cell types. Mapping to enhancer histone marks and predicted transcription factor binding sites added additional functional evidence for eight eQTL regions. As an example, we found that rs71624119, shared with three other autoimmune diseases and located in a primed enhancer (H3K4me1) with potential binding for STAT transcription factors, significantly associates with ANKRD55 expression. This study provides many novel and validated targets for future functional characterization of MS and other diseases.

The human leukocyte antigen (HLA) haplotype DRB1 15:01 is the major risk factor for multiple sclerosis (MS). Here, we find that DRB1 15:01 is hypomethylated and predominantly expressed in monocytes among carriers of DRB1 15:01. A differentially methylated region (DMR) encompassing HLA-DRB1 exon 2 is particularly affected and displays methylation-sensitive regulatory properties in vitro. Causal inference and Mendelian randomization provide evidence that HLA variants mediate risk for MS via changes in the HLA-DRB1 DMR that modify HLA-DRB1 expression. Meta-analysis of 14,259 cases and 171,347 controls confirms that these variants confer risk from DRB1 15:01 and also identifies a protective variant (rs9267649, p < 3.32 × 10 -8 , odds ratio = 0.86) after conditioning for all MS-associated variants in the region. rs9267649 is associated with increased DNA methylation at the HLA-DRB1 DMR and reduced expression of HLA-DRB1, suggesting a modulation of the DRB1 15:01 effect. Our integrative approach provides insights into the molecular mechanisms of MS susceptibility and suggests putative therapeutic strategies targeting a methylation-mediated regulation of the major risk gene.

The main objective of the research studies presented in this thesis is to study the genetic variants and the expression of genes that relate to Multiple Sclerosis (MS). MS is a polygenic disease with HLA-DRB1*15:01 allele as a strong risk factor. Currently there are more than 200 non-HLA regions identified for MS. However, most of the risk loci identified in those studies are primarily driven by the relapsing-remitting form of MS (RRMS). To identify risk factors specific for the primary progressive form of MS (PPMS) which is a smaller group of MS patients, we have examined the exomes of PPMS and RRMS patients matching to population based controls in a case-control study setting and reported risk variants and mutations that are associated to PPMS and RRMS. The context of this study is during the ‘post-GWAS’ era, when researchers are primarily focused to understand the functional consequences of the genetic risk factors. Using the possibilities of transcriptomic and genotyping data, genes that correlate to the risk loci are identified in relevant cell types of MS. Several statistical methods are implemented to characterize the risk loci and replicate the findings in the context of disease. MicroRNAs (miRNAs), small non-coding RNAs which regulate gene expression at post-transcriptional level, have been identified dysregulated in autoimmune diseases, including MS. We used experimental autoimmune encephalomyelitis (EAE), a commonly used animal model for MS to understand the role of miRNA in the immune activation of EAE. Next generation sequencing (NGS) methods were widely applied in all of these studies specifically at transcriptomic and genomic level of the disease. NGS methods are data intensive but have higher reliability. To test the reliability, we compared reported gene expression measurements for ostensibly similar tissue samples collected from different RNA-seq studies. We found an overall consistency on expression data obtained from different studies and identif

MicroRNAs (miRNAs) are known to regulate most biological processes and have been found dysregulated in a variety of diseases, including multiple sclerosis (MS). In this study, we characterized miRNAs that associate with susceptibility to develop experimental autoimmune encephalomyelitis (EAE) in rats, a well-established animal model of MS. Using Illumina next-generation sequencing, we detected 544 miRNAs in the lymph nodes of EAE-susceptible Dark Agouti and EAE-resistant Piebald Virol Glaxo rats during immune activation. Forty-three miRNAs were found differentially expressed between the two strains, with 81% (35 out of 43) showing higher expression in the susceptible strain. Only 33% of tested miRNAs displayed differential expression in naive lymph nodes, suggesting that a majority of regulated miRNAs are EAE dependent. Further investigation of a selected six miRNAs indicates differences in cellular source and kinetics of expression. Several of the miRNAs, including miR-146a, miR-21, miR-181a, miR-223, and let-7, have previously been implicated in immune system regulation. Moreover, 77% (33 out of 43) of the miRNAs were associated with MS and other autoimmune diseases. Target genes likely regulated by the miRNAs were identified using computational predictions combined with whole-genome expression data. Differentially expressed miRNAs and their targets involve functions important for MS and EAE, such as immune cell migration through targeting genes like Cxcr3 and cellular maintenance and signaling by regulation of Prkcd and Stat1. In addition, we demonstrated that these three genes are direct targets of miR-181a. Our study highlights the impact of multiple miRNAs, displaying diverse kinetics and cellular sources, on development of pathogenic autoimmune inflammation.