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Study reveals molecular mechanism behind Multiple Sclerosis

A new study has revealed that the loss of immune regulation seen in Multiple Sclerosis is triggered by an increase in PRDM1-S, a protein involved in immune function, triggering a dynamic interaction of multiple genetic and environmental factors, including high salt uptake.

More than two decades ago, a research team in the lab of David Hafler, a Yale researcher who at the time was at Harvard, discovered a type of T cell in humans that suppresses the immune system; they later found that these so-called regulatory T cells, when defective, are an underlying cause of autoimmune disease, specifically multiple sclerosis (MS).

For many years, however, the mechanism behind this dysfunction has remained unclear.

In a new Yale-led study, a team of researchers finds that this loss of immune regulation is triggered by an increase in PRDM1-S, a protein involved in immune function, triggering a dynamic interaction of multiple genetic and environmental factors, including high salt uptake.

The findings also reveal a new target for a universal treatment for human autoimmune disease.

The research was led by Tomokazu Sumida, an assistant professor at Yale School of Medicine (YSM), and Hafler, the William S. and Lois Stiles Edgerly Professor of Neurology and professor of immunobiology at Yale.

“These experiments reveal a key underlying mechanism for the loss of immune regulation in MS and likely other autoimmune diseases,” said Hafler, who is also chair of Yale’s Department of Neurology. “They also add mechanistic insight into how Treg [regulatory T cells] dysfunction occurs in human autoimmune diseases.”

Autoimmune diseases, among the most common disorders of young adults, are known to be affected by genetic and environmental factors, including vitamin D deficiency and fatty acids. In an earlier study, Sumida and Hafler found that high levels of salt also contribute to the development of multiple sclerosis, an autoimmune disease of the central nervous system.

Specifically, they observed that high salt induces inflammation in a type of immune cell known as CD4 T cells, while also causing a loss of regulatory T cell function. This, they found, is mediated by a salt-sensitive kinase, or enzyme critical for cell signaling, known as SGK-1.

For the new study, researchers used RNA sequencing to compare gene expression in patients with MS with expression in healthy individuals.

In patients with MS, the researchers identified upregulation, or increased expression, of a gene called PRDM1-S (primate-specific transcription factor), also known as BLIMP-1, which is involved in regulating immune function.

Surprisingly, PRDM1-S induced increased expression of the salt-sensitive SGK-1 enzyme, leading to disruption of regulatory T cells, the researchers found. Moreover, they found similar overexpression of PRDM1-S in other autoimmune diseases, suggesting that it may be a common feature of regulatory T cell dysfunction.

“Based on these insights, we are now developing drugs that can target and decrease expression of PRDM1-S in regulatory T cells,” Sumida said.

“And we have initiated collaborations with other Yale researchers using novel computational methods to increase the function of regulatory T cells to develop new approaches that will work across human autoimmune diseases.”

The study was done with Bradley Bernstein and Manolis Kellis, longtime collaborators of Hafler from the Broad Institute of MIT and Harvard, and several other research institutions. Other authors from the Yale lab include neurologist Matthew R. Lincoln, and post-graduate research assistants Alice Yi, Helen Stillwell, and Greta Leissa.

The study was led by Yale University and published in the journal Science Translational Medicine.

Researchers investigate fear of falling among people with MS

A two-year $96,812 F31 grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health has been awarded to study fear of falling in those with multiple sclerosis (MS).

The grant was awarded to Taylor Takla, a Ph.D. candidate in the translational neuroscience programme in Wayne State University’s School of Medicine.

The grant, “Investigating Fear of Falling in Multiple Sclerosis: An Interplay of Neural, Motor, Cognitive, and Psychological Factors,” aims to address a major public health concern in persons with MS that results in increased falls, decreased physical activity and loss of independence.

Takla stated: “Fear of falling is a major issue for people with MS, leading to serious negative health and quality of life outcomes.

“It’s both a risk factor and a consequence of experiencing a fall.

“This creates a vicious cycle where individuals become less confident in their balance, reduce their participation in activities, get physically weaker and experience subsequent increased fall risk and greater fear of falling.

“This cycle results in social isolation, psychological distress and reduced overall well-being.”

The long-term goal of the study is to break that cycle, ultimately reducing falls and increasing physical activity in the MS community.

Takla aims to examine the neural, motor, cognitive and psychological factors underlying fear of falling to achieve this goal.

She hopes that by understanding these factors, she and other researchers will see which contribute to fear of falling the most and — by targeting such factors through rehabilitation strategies — improve outcomes and quality of life for patients.

“We want to take a comprehensive approach,” said Takla.

“We study the neural components with an MRI by taking images of the brain and looking at brain activity.

“We are looking at three specific brain areas, the cerebellum, the hippocampus and amygdala, which are involved in motor, cognitive and psychological functioning.

“We also are conducting a battery of motor, cognitive and psychological tests to look at behavioural components like balance and walking abilities, executive functioning, and anxiety.

“Through the combination of advanced MRI techniques and a comprehensive evaluation of behavioral functioning, we hope to gain a more complete understanding of fear of falling and its downstream consequences than by using any single measure alone.”

“F31 grants from the National Institutes of Health are awarded to promising predoctoral students to conduct research training with the help of outstanding research mentors,” said Ezemenari M. Obasi, Ph.D., vice president for research & innovation at Wayne State University.

“Under the guidance of her mentor, Nora Fritz, Ph.D., associate professor of physical therapy in the Eugene Applebaum College of Pharmacy and Health Sciences, Taylor will have the opportunity to conduct research that has the potential to help many within our community and beyond.”

Machine learning models can predict MS progression – study

Machine learning models can reliably inform clinicians about the disability progression of multiple sclerosis (MS), a recent study led by Edward De Brouwer of KU Leuven, Belgium, has found.

MS is a chronic progressive autoimmune disease that leads to severe disability over time through a complex pattern of progression, recovery and relapse.

Its global prevalence has increased by more than 30 per cent over the last decade.

However, there are few tools that can predict the progression of MS to help clinicians and patients make life planning and treatment decision-making.

In the new study, De Brouwer and colleagues used data on 15,240 adults with at least three years of MS history who were being treated at 146 MS centers in 40 countries.

Data on two years of each patient’s disease progression was used to train state-of-the-artmachine learning models to predict the probability of disease progression over the subsequent months and years.

The models were trained and validated using strict clinical guidelines, promoting applicability of the models in clinical practice.

The study found that a history of disability progression was more predictive for futuredisability progression than treatment or relapse history.

The authors conclude that the models developed in the study have the potential to greatly enhance planning for individuals with MS and could be evaluated in a clinical impact study.

De Brouwer added: “Using the clinical history of more than 15,000 people with multiple sclerosis, we trained a machine learning model capable of reliably predicting the probability of disability progression in the next two years.

“The model only uses routinely collected clinical variables, which makes it widely applicable.

“Our rigorous benchmarking and external validation support the vast potential of machine learning models for helping patients planning their lives and clinicians optimising treatment strategies.”