Multiple Sclerosis (MS) is a life-altering autoimmune disease that affects one million Americans. It arises from a complex interplay of genetics, environmental factors, and immune system dysfunction.
To combat MS, the immune system relies on specialized defenders known as Natural Killer (NK) cells. These cells play a crucial role in protecting the body by identifying and attacking harmful cells, including those infected by viruses, cancerous cells, and cells involved in autoimmune diseases like MS. NK cells can distinguish between healthy and abnormal cells, enabling them to target and eliminate those that pose a threat, such as infected or tumor cells. In the context of MS, they are involved in regulating the immune response. However, their actions can sometimes be a double-edged sword, either exacerbating the disease through inflammation or helping to control the damage caused by an overactive immune system.
Suraju Sadeeq, a research assistant in the Norman Lab at the University of Colorado School of Medicine (SOM), is delving into the co-evolution of HLA molecules and KIR receptors, which play a critical role in immune regulation. KIRs, found on Natural Killer (NK) cells, interact with HLA molecules to modulate immune activity. Using population genetics and molecular analyses, Sadeeq aims to uncover how genetic variation in these systems influences immune function. These insights guide functional experiments, helping us understand the precise mechanisms by which genetic diversity shapes immunity. The study is led by Paul Norman, PhD, professor of biomedical informatics, in collaboration with Jill Hollenbach, PhD, MPH, professor in the Department of Neurology and the Department of Epidemiology and Biostatistics at the University of California San Francisco.
Sadeeq is conducting his research under the mentorship of Norman, with co-mentorship from Chris Gignoux, PhD. He shared, “With their invaluable guidance and support, I’ve had the opportunity to attend conferences, workshops, and seminars, which have enriched my academic and professional development.” Sadeeq’s current work in the Norman Lab is funded by grants NIAID 1R01AI169070 from the NIH, and 2004262 from the NH&MRC of Australia. He added that this experience has been incredibly exciting and rewarding.
We sat down with Sadeeq to learn more about the study, surprises and challenges, and the impact of this research.
How are you using advanced computational techniques to investigate the associations between KIR and HLA in MS, and what are the potential benefits of this approach?
The random expression of KIR and HLA class I molecules is crucial for helping NK cells defend against infections and autoimmune diseases like multiple sclerosis (MS).
The main goal of my work is to use advanced statistical models to identify specific KIR and HLA class I alleles linked to MS. These models also consider how environmental factors—like sex, age, and Epstein-Barr virus (EBV) infection—interact with genetic factors. By combining these elements, we hope to better understand how genes and the environment work together to influence MS.
A major advantage of this approach is the use of large-scale biobanks, which store vast collections of samples from MS patients and healthy individuals. Using these resources, along with advanced imputation methods, we can predict KIR and HLA alleles without needing expensive and time-consuming direct sequencing. This is especially helpful because sequencing large numbers of samples can be costly and impractical. By avoiding these challenges, we can focus on uncovering meaningful insights into MS.
What are the current challenges and future directions in understanding the factors influencing MS, specifically concerning the KIR and HLA associations?
This study faces several challenges. First, accurately predicting KIR and HLA alleles requires reliable imputation methods. For HLA class I, there are well-established tools like SNP2HLA, the Michigan Imputation Server, and HIBAG. However, predicting KIR alleles is more difficult because there are fewer tools available, and KIR and HLA genes are on different chromosomes. This makes it impossible to directly use HLA-based models for KIR. The Norman Lab has developed a tool to predict KIR alleles, but it currently only works for the KIR3DL1 gene. My first goal is to improve this tool so it can predict alleles for all KIR genes. After that, I will use statistical models to identify KIR and HLA alleles linked to MS.
Looking ahead, a promising next step would be to conduct lab experiments to study how these KIR and HLA alleles affect gene expression in MS patients. This could reveal the biological mechanisms behind these genetic links. The goal of this research is to help discover new treatments for MS, offer hope to patients, and improve their quality of life. By tackling these challenges and using innovative methods, this work has the potential to deepen our understanding of MS and pave the way for better therapies.
What were some of the most surprising findings or challenges you have encountered in your research on the KIR and HLA associations in MS?
Our research has uncovered exciting insights into the complex relationship between KIR and HLA in MS. Unlike earlier studies that used all SNPs in the KIR region for imputation models, we found that focusing on specific SNPs significantly improves accuracy. This breakthrough has made our KIR imputation model more precise, offering a better way to study genetic links in MS. Likewise, the effects of KIR-HLA interactions in MS are highly context-dependent. While some combinations are linked to autoimmune diseases, their impact on MS varies across populations. For example, a protective KIR-HLA pair in one group may have no effect or even increase risk in another. This variability shows how genetic and environmental differences shape disease outcomes and highlights the challenges of applying findings to all groups.
In a recent study on KIR3DL1, we found a strong interaction between specific KIR3DL1 alleles and HLA-Bw4 alleles in MS. This suggests a potential link between these genetic variants and MS risk or progression. However, to confirm these results, further studies in diverse populations are needed. Replicating these findings will strengthen their validity and deepen our understanding of how KIR-HLA interactions influence MS.
How do you envision the future of MS treatment evolving as we gain a deeper understanding of genetic and cellular contributors like KIR and HLA?
Currently, there is no cure for multiple sclerosis. Existing treatments, called disease-modifying therapies (DMTs), focus on managing flare-ups, easing symptoms, and slowing disease progression. Interestingly, some DMTs seem to boost the number and function of NK cells, suggesting these cells play a key role in MS. The roles of KIR genes in MS—whether protective or harmful—are still being studied. However, research suggests that KIRs and their HLA ligands could be key to improving MS treatment and patients’ quality of life. For example, in addition to what I mentioned earlier, the activating KIR2DS1 gene may have a protective effect, especially when paired with its ligand HLA-C2, highlighting the potential of NK cells to regulate the immune system. By studying how different KIR genes interact with HLA class I molecules, we can better understand how NK cells influence MS. This knowledge could lead to more effective treatments and improved outcomes for patients.
What is the importance and potential impact of your research?
This research is necessary because it explores how KIR genes and their HLA partners affect MS, focusing on their role in controlling the immune system and disease progression. By studying these interactions and their impact on NK cells, we hope to better understand MS and how it develops over time. This could lead to personalized treatments tailored to individual genetic profiles, better disease management, and improved quality of life for patients. This work could transform how MS is treated, offering new hope to those living with the condition.
