Complex autoimmune diseases, such as multiple sclerosis (MS), develop as a result of perturbations in the regulatory system controlling the function of immune cells. The disease course of MS is heterogeneous but is characterised by chronic inflammation in the central nervous system causing neurodegeneration resulting in gradual disability worsening. Disease biomarkers which are present at early stages of a disease can help clinicians to tailor treatment strategies to the expected disease course of individual persons. Gene products, i.e. RNA and proteins, serve as promising disease biomarkers due to the possibility to detect changes in abundance at early stages of a disease. Putative biomarkers can be identified by modelling different levels of gene regulation from high-throughput measurements of gene product abundance. Extracting information of disease relevance from high-throughput data is a complex problem which requires the use of efficient and targeted computational algorithms. 

The aim of this thesis was to develop and refine methods for identifying key biomarkers involved in the development and progression of complex diseases, with the main focus on MS. In Paper I, we used a machine learning approach to identify a combination of protein biomarkers, present in the cerebrospinal fluid, which could predict the disease trajectory of persons in the early stages of MS. The abundance of proteins is a result of an intricate network of multiple regulatory factors controlling the expression of genes. A large part of the expression of genes is controlled by a few key regulators, which are believed to be crucial for the development of diseases. In addition, disease-associated genes are believed to colocalise in these networks forming so called disease modules. In Paper II, we developed a method, named ComHub, for extracting the key regulators of gene expression. In Paper III, we combined ComHub with the tool MODifieR, for disease module predictions, in a network analysis pipeline for identifying a limited set of disease-associated genes. Using this network analysis pipeline we identified a set of MS-associated genes, as well as a promising key regulator of MS. 

The work performed in this doctoral thesis covers development of new and refined methods for modelling complex diseases, while simultaneously utilising these methods to identify disease biomarkers important for the development and progression of MS. The identified biomarkers can be used for understanding the pathology of MS, as candidate drug targets, and as promising biomarkers to aid clinicians in tailoring treatment strategies to individual persons. 

Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease driven by complex pathophysiological mechanisms that contribute to neurologic impairment and disability progression. This thesis explores protein and metabolite biomarkers and employs machine learning models to predict disease trajectory in MS, aiming to improve diagnosis, prognosis, and treatment response.

To address this objective, comprehensive proteomic profiling was conducted on cerebrospinal fluid (CSF) and plasma from individuals with early-stage MS and healthy controls. Differentially expressed CSF proteins were enriched in pathways related to B cell activation, and a linear regression model incorporating 11 of these proteins and age effectively predicted long-term disability progression for up to 13 years. Additionally, logistic regression models based on CSF proteins could distinguish MS from controls and predict short-term disease activity.

Since disease progression in MS is influenced not only by baseline pathology but also by therapeutic interventions, further focus was placed on how dimethyl fumarate (DMF), a common oral treatment in MS, affects plasma and CSF proteomic profiles related to pathological mechanisms in MS. Longitudinal analysis revealed DMF-induced reductions in inflammatory proteins associated with T-helper 1 immunity, underscoring the drug’s ability to modulate this key pathologic pathway. Importantly, baseline levels of specific axonal, glial and myelination-related proteins differentiated responders from non-responders, suggesting a potential role for these biomarkers in guiding treatment selection and optimizing therapeutic strategies.

Expanding the focus beyond proteomics, metabolic dysregulation in MS was examined through the analysis of CSF and normal-appearing white matter (NAWM) metabolites across different disease stages. Metabolites that were most strongly associated with clinical factors in MS were linked to mitochondrial dysfunction, axonal integrity, astrogliosis and demyelination. CSF biomarkers in linear regression models could distinguish MS from unspecific but similar neurological symptoms and differentiate between subtypes of the disease. A random forest model incorporating NAWM metabolites demonstrated high predictive power for long-term disability progression for up to 16 years, offering a promising non-invasive tool for MS prognosis.

Together, these studies provide a comprehensive perspective on MS pathophysiology, presenting protein- and metabolite-based models for enhanced diagnosis, treatment response monitoring, and long-term disease progression assessment. The biomarkers suggested in this thesis lay the groundwork for future translational applications in clinical practice.