Decoding the Immune Genetics of Multiple Sclerosis: How Subtle DNA Changes Shape Disease Risk
Multiple sclerosis (MS) has long puzzled scientists with its complex interplay between genetics and the immune system. Although we’ve known for decades that MS runs in families, pinpointing how genes influence disease risk has been much harder. Now, a groundbreaking study by Melissa Gresle and colleagues has taken a major step forward — revealing how specific genetic variants alter gene expression in the immune cells that drive MS.
The Genetic Puzzle of MS
MS is an autoimmune disease in which the body’s own immune cells attack the central nervous system, damaging myelin and nerve fibers. It’s known that both environment (such as viral infections and vitamin D levels) and genetics shape a person’s risk.
Genome-wide association studies (GWAS) have identified more than 200 single-nucleotide polymorphisms (SNPs) — tiny DNA changes — associated with MS. But most of these SNPs don’t alter protein-coding genes directly. Instead, they lie in noncoding regions of the genome, suggesting they may affect how genes are regulated.
This is where expression quantitative trait loci (eQTL) mapping comes in — a method that links specific DNA variants to differences in gene activity.
The Study: A Deep Dive into Immune Cells
To investigate how MS risk variants influence immune function, Gresle et al. collected blood samples from 73 untreated MS patients and 97 healthy controls. They purified five key immune cell types — monocytes, natural killer (NK) cells, B cells, CD4 T cells, and CD8 T cells — and used microarrays to measure gene expression.
They then analyzed 172 known MS risk SNPs and searched for nearby genes (within ±500 kb) whose expression correlated with each variant.
The results were impressive: they identified 129 genes whose expression was linked to MS-associated SNPs across these immune cells. Many of these regulatory effects were cell-type specific, revealing how different immune cell populations may contribute uniquely to MS risk.
Not Just CD4 T Cells: Both Innate and Adaptive Immunity Matter
Previous studies, such as Raj et al. (2014), suggested that most MS risk variants act mainly in CD4 T cells, a key player in adaptive immunity. However, Gresle’s team found that these genetic effects were spread across all immune cell types, including innate immune cells like monocytes and NK cells.
This finding challenges the notion that MS is purely a T-cell–driven disease and underscores the importance of the innate immune system in shaping susceptibility.
When Genes Behave Differently in MS Patients
Perhaps the most fascinating discovery was that certain SNPs affected gene expression differently in MS patients versus healthy controls — suggesting that disease status modifies genetic regulation.
For example:
rs703842, a well-known MS risk allele, was linked to lower expression of METTL21B (a lysine methyltransferase) in CD8 T cells, but the effect was stronger in MS patients.
METTL21B modifies the protein eEF1A, which is essential for protein synthesis and cellular stress responses.
Reduced METTL21B activity could impact how CD8 T cells function during immune activation.
rs2256814 showed opposing effects on MYT1 expression in CD4 T cells — lowering it in MS cases but raising it in controls.
MYT1 is a transcription factor linked to myelin gene regulation, making this finding particularly intriguing for a disease marked by demyelination.
rs12087340 influenced expression of the small RNA gene RF00136 differently in monocytes from MS patients versus controls.
These “genotype-by-disease” interactions imply that MS risk variants don’t act in isolation — their effects can change depending on the immune environment of the individual.
A Subtle Immune Landscape in Untreated MS
Interestingly, when the researchers looked at overall gene expression differences between cases and controls (regardless of genotype), they found only a handful of significant changes.
One standout was SOCS1, which was upregulated by 16% in B cells of MS patients. This gene is a negative regulator of cytokine signaling — possibly reflecting a compensatory response to chronic inflammation.
Other notable differences included:
SESN1, upregulated in B cells (41% higher in MS).
FKBP5, upregulated in CD4 T cells (22% higher in MS).
These subtle shifts suggest that, in untreated MS, the circulating immune cells are not overtly activated — the disease’s molecular fingerprints may only appear under certain inflammatory conditions.
Why This Matters
This work provides one of the most comprehensive maps yet linking MS genetics to immune cell gene regulation. It supports the idea that:
MS risk variants shape immune diversity, influencing how genes respond to signals even in a resting state.
These effects can differ between healthy and disease states, reflecting interactions between genetic and environmental factors.
Both innate and adaptive immunity play central roles in MS susceptibility.
The study also highlights the value of investigating context-dependent eQTLs — genetic effects that become visible only under specific conditions, such as inflammation or disease.
Looking Forward
While the findings are powerful, the authors note that larger and more context-specific studies — for example, using single-cell sequencing or stimulated immune cells — will be crucial to fully understand how these regulatory mechanisms drive MS.
Ultimately, such research could pave the way for precision immunology in MS — identifying patients whose disease is driven by specific genetic and regulatory networks, and tailoring therapies accordingly.
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:
Gresle, M. M., Jordan, M. A., Stankovich, J., Spelman, T., Johnson, L. J., Laverick, L., ... & Butzkueven, H. (2020). Multiple sclerosis risk variants regulate gene expression in innate and adaptive immune cells. Life science alliance, 3(7).
