Integrating Plasma Proteomics and Genetics to Identify Novel Targets for Multiple Sclerosis
Multiple sclerosis (MS) is a complex immune-mediated disease of the central nervous system characterized by immune-mediated demyelination, neuroinflammation, and progressive neurological disability. Although genome-wide association studies have identified many MS susceptibility loci, translating these loci into clinically actionable drug targets remains difficult because many risk variants lie in non-coding genomic regions. The article addresses this translational gap by integrating genetic association data with plasma proteomic information, aiming to identify proteins that may not only correlate with MS risk but also participate causally in disease pathogenesis.
Rationale for a Proteome-Wide Association Strategy
The authors emphasize that protein abundance provides biological information that cannot always be inferred from transcriptomic data alone. Because proteins are direct mediators of cellular signaling, immune regulation, and tissue injury, they are highly relevant for drug discovery. In this study, the researchers used proteome-wide association studies, or PWAS, to connect genetically predicted plasma protein levels with MS susceptibility. This approach is especially valuable because it moves beyond identifying risk loci and instead prioritizes functional molecular entities that could be modulated therapeutically.
Study Design and Integrated Analytical Pipeline
The study combined MS genome-wide association summary statistics from 115,803 individuals, including 47,429 cases and 68,374 controls, with plasma protein quantitative trait locus data from 7,213 participants in the ARIC cohort. The authors applied the FUSION framework to conduct PWAS, followed by Mendelian randomization to test putative causal relationships between protein abundance and MS risk. Bayesian colocalization was then used to determine whether the same genetic variants influenced both protein abundance and MS susceptibility, reducing the likelihood that observed associations were due to linkage disequilibrium rather than shared causal biology.
Principal Findings from PWAS and Mendelian Randomization
The PWAS identified 25 statistically significant cis-regulated plasma proteins associated with MS. The Manhattan plot on page 3 highlights the strongest signals, including ATF6B, C2, AIF1, HLA-DQA2, TNXB, CFB, NCR3, MICB, TYMP, and MAPK3, while Table 1 on page 4 provides the corresponding z-scores, P values, and false discovery rate-adjusted values. Subsequent Mendelian randomization narrowed these candidates to seven proteins with evidence of causal association: PLEK, TNXB, CASP3, CD59, CR1, TAPBPL, and ATXN3. Higher genetically predicted levels of TNXB and CD59 were associated with reduced MS risk, whereas higher levels of PLEK, CASP3, CR1, TAPBPL, and ATXN3 were associated with increased risk.
Colocalization Prioritizes PLEK, CR1, and CD59
Bayesian colocalization further refined the list of candidate proteins by identifying PLEK, CR1, and CD59 as proteins whose genetic regulation likely shares causal variants with MS risk. Figure 4 on page 6 illustrates colocalization at the PLEK, CR1, and CD59 loci, supporting the interpretation that these associations are not merely statistical artifacts. This step is important because it strengthens the evidence that modulation of these proteins could influence MS biology, rather than simply marking nearby genetic variation. The authors therefore prioritize these three proteins as especially promising therapeutic targets.
Biological Interpretation and Drug-Discovery Implications
The biological relevance of the prioritized proteins is particularly notable. PLEK is involved in immune-cell signaling and inflammatory cytokine secretion, suggesting a possible role in amplifying inflammatory pathways relevant to MS. CR1 and CD59 are both linked to complement regulation, a pathway increasingly recognized as important in neuroinflammation, demyelination, and tissue injury. CD59 appears protective, consistent with its role in inhibiting membrane attack complex formation, whereas CR1 was associated with increased MS risk. Using the Drug-Gene Interaction Database, the authors identified Eculizumab as a drug interacting with CR1, raising the possibility of therapeutic repositioning or complement-pathway modulation in MS.
Limitations and Future Directions
Despite its strengths, the study has important limitations. The analysis focused on plasma proteins, which may not fully capture protein dynamics within the central nervous system, where MS pathology occurs. The datasets were also restricted to individuals of European ancestry, limiting immediate generalizability to other populations. Moreover, genetic evidence can prioritize targets but cannot substitute for experimental validation in cellular systems, animal models, or clinical trials. Nevertheless, the article provides a rigorous framework for moving from genetic association to therapeutic hypothesis, and it identifies PLEK, CR1, and CD59 as compelling candidates for future mechanistic and pharmacological investigation in MS.
Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.
References:
Liu, Y., Wang, Q., Zhao, Y., Liu, L., Hu, J., Qiao, Y., ... & Qin, C. (2024). Identification of novel drug targets for multiple sclerosis by integrating plasma genetics and proteomes. Experimental Gerontology, 194, 112505.
