Researchers identify 35 genes that are highly expressed in patients with long-term Lyme disease

Researchers identify 35 genes that are highly expressed in patients with long-term Lyme disease

Researchers at the Icahn School of Medicine at Mount Sinai in New York have identified 35 genes that are particularly highly expressed in people with long-standing Lyme disease. These genes could potentially be used as biomarkers to diagnose patients with the condition, which is otherwise difficult to diagnose and treat.

The results, published November 15 in the Journal Cell Reports Medicine [DOI: 10.1016/j.xcrm.2022.100816],can also lead to new therapeutic targets. The study is the first to use transcriptomics as a blood test to measure RNA levels in patients with long-term Lyme disease.

Lyme disease is a tick-borne disease that is not well understood. About 30,000 diagnosed cases are reported to the CDC each year, but the estimated actual number is closer to 476,000 cases, costing about $1 billion in annual health care costs in the United States. While most patients are diagnosed in the earliest stages of Lyme disease and treated with antibiotics, about 20 percent of patients develop long-term complications. These may include arthritis, neurological symptoms and/or heart problems.

We wanted to understand whether there is a specific immune response that can be detected in the blood of patients with long-term Lyme disease in order to develop better diagnostics for this debilitating disease. There is still a critical unmet need because this disease is so often undiagnosed or misdiagnosed. Not enough is known about the molecular mechanisms of long-term Lyme disease.”

Avi Ma’ayan, PhD, professor of pharmacological sciences and director of the Mount Sinai Center for Bioinformatics at Icahn Mount Sinai and senior author of the article

As part of the study, RNA sequencing was performed using blood samples from 152 patients with Lyme disease symptoms after treatment to measure their immune response. Combined with RNA sequencing data from 72 patients with acute Lyme disease and 44 uninfected controls, researchers observed differences in gene expression and found that most of the patients with Lyme disease had a distinctive inflammatory signature after treatment compared to the acute Lyme disease group.

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Additionally, by analyzing the differentially expressed genes in this study, along with genes that were differentially expressed due to other infections from other published studies, the researchers identified a subset of genes that were highly expressed and that had not previously been identified for this Lyme disease. associated inflammatory reaction.

Using a type of artificial intelligence called machine learning, the researchers further reduced the set of genes to create an mRNA biomarker set that can distinguish healthy patients from those with acute or post-treatment Lyme disease. A gene panel that measures the expression of the genes identified by the researchers could be developed as a diagnostic to test for Lyme.

“We should not underestimate the value of using omics technologies, including transcriptomics, to measure RNA levels to detect the presence of many complex diseases such as Lyme disease, provide reliable diagnosis and, as a result, potentially better management of this disease,” said Dr. Ma’ayan.

Next, the researchers plan to repeat the study using data from single-cell transcriptomics and whole blood, apply the machine learning approach to other complex diseases that are difficult to diagnose, and develop the diagnostic gene panel and test it on samples from patients.

Source:

Mount Sinai Health System

Reference:

Willis, MD, et al. (2022) Gene set predictor of post-treatment Lyme disease. Cell reports medicine. doi.org/10.1016/j.xcrm.2022.100816.

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