Pernilla Stridh

We hypothesise that the ability of neurons to survive and function under the inflammatory conditions is, to a considerable degree, under genetic control. Notably, identifying such genes will not only give insights into the molecular causes of neurodegeneration but also provide potential therapeutic targets to halt disease progression. Specifically, we aim to: • Identify candidate genes for MS severity for mechanistic studies (WP A) • Establish the role of the hallmark genes of inflamed neurons in MS development (WP B) • Evaluate the relative contribution of immune vs. CNS reactions to MS progression (WP C) We aim to characterize molecular targets that predispose for disease outcomes in persons with MS. For that purpose, we have built the most powerful cohorts and datasets for genetic and mediation analysis. Joining forces with NeuroFlame will provide highly relevant functional context for the mechanistic characterization of risk genes. We will utilize NeuroFlame’s epigenetic, transcriptomic and pathology data to fine-map risk loci. The NeuroFlame expertise will facilitate the prioritization of candidate genes and their functional characterization. In turn, we will investigate if the genes in hallmark pathways of inflamed neurons associate with MS outcomes, thus providing a strong link with their causal involvement in disease.

Multiple Sclerosis (MS), a complex autoimmune disorder affecting mainly young adults, with a higher prevalence in women. Its diverse symptoms and inherent progression complexity hinder early detection and personalized prognostic evaluations. This proposal aspires to enhance health outcomes, elucidate MS’s intricate mechanisms, and reduce neurological disabilities through innovative, transformative methodologies. The objective is to develop advanced predictive models by harnessing extensive multimodal data, facilitating early detection, and personalized interventions. The project is organized in 6 different but interconnected tasks and will run for 5 years. We will integrate clinical, lifestyle, and 'omics' data. By targeting the prodromal phase of MS, we aspire to devise impactful intervention strategies and project disease progression, with Artificial Intelligence (AI) serving as the cornerstone for analyzing data. We will test the MS prediction among first degree relatives. We will also identify biomarkers for MS risk and severity and genetic factors associated with MS severity. A special focus will be on role of sex hormones. Employing advanced analytical techniques, network theories, autoencoders and deep learning, we will probe the multifaceted nature of MS using one of the world’s leading MS-specific databases. This will contribute to more comprehensive and individualized MS treatment approaches.

The overall aim is to characterize the biological mechanisms involved in determining disease trajectories and clinical outcomes in individuals affected by multiple sclerosis (MS). Grounded in the premise of the causal relationship between a genetic variation and an associated trait, we aim to characterize molecular and cellular mechanisms underlying the effect of genetic variants associated with MS severity. While identifying association of a trait with genomic loci genome-wide has become a routine, the detection of underlying molecular mechanisms is still notoriously challenging. The vast majority of associations reside in intergenic regions, often displaying a complex time- and tissue-specific regulatory effect on surrounding genes. We hypothesise that the ability of neurons to survive and function under inflammatory conditions is, to a considerable degree, under genetic control. Notably, identifying such genes will provide insights into the molecular causes of neurodegeneration and provide potential therapeutic targets to halt disease progression. Specifically, we aim to: Identify candidate genes for MS severity for mechanistic studies Establish the role of hallmark genes of inflamed neurons in MS development Evaluate the relative contribution of immune CNS reactions to MS progression For these aims, we have built the most powerful cohorts and datasets for genetic and mediation analysis. Genome-wide genotyping has already been completed in 14,500 MS cases from the Swedish cohort (collaboration with deCODE Genetics). In addition, 3,500 of the MS cases have also been whole genome sequenced using the Illumina HiSeq X Ten method. Through collaboration we have access to deCODE’s excellent Scandinavian reference panel for imputation of near sequence-level genotypes. The seq-imputation results in > 40 million high quality genetic variants, improving the precision of any identified associations. Moreover, we will focus on novel clinical outcomes of disability progression, such as ‘relapse-associated worsening’, ‘progression independent of relapse activity’ (PIRA), and brain atrophy, which is frequently observed in patients and appears to account for most of disability accumulation. By establishing a genetic link between MS phenotypes and pathways known to govern inflamed neurons, we can infer a causal role in MS in general, and in a particular clinical context, which is currently missing.

Multiple Sclerosis (MS), a complex autoimmune disorder affecting mainly young adults, with a higher prevalence in women. Its diverse symptoms and inherent progression complexity hinder early detection and personalized prognostic evaluations. This proposal aspires to enhance health outcomes, elucidate MS’s intricate mechanisms, and reduce neurological disabilities through innovative, transformative methodologies. The objective is to develop advanced predictive models by harnessing extensive multimodal data, facilitating early detection, and personalized interventions. The project is organized in 4 different but interconnected tasks and will run for 3 years. We will integrate clinical, lifestyle, and 'omics' data. By targeting the prodromal phase of MS, we aspire to devise impactful intervention strategies and project disease progression, with Artificial Intelligence (AI) serving as the cornerstone for analyzing data. We will test the MS prediction among first degree relatives. We will also identify biomarkers for MS risk. A special focus will be on role of sex hormones. Employing advanced analytical techniques, network theories, autoencoders and deep learning, we will probe the multifaceted nature of MS using one of the world’s leading MS-specific databases. This will contribute to earlier diagnosis of MS and opening up for reduction of the disease by personalized changes in lifestyle and possibly preventative treatments in the future.