Lyme disease is a tick-borne illness that is not well understood and is often undiagnosed or misdiagnosed. While most patients are diagnosed and treated with antibiotics at the earliest stages of Lyme disease, about 20% of the patients develop long-term complications, including arthritis, neurologic symptoms, and/or heart problems.

The study, published November 15 in the journal Cell Reports Medicine, is the first to use transcriptomics as a blood test to measure RNA levels in patients with long-term Lyme disease, according to the authors.

"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 to develop better diagnostics for this debilitating disease," Avi Ma'ayan, PhD, senior author and director of the Mount Sinai Center for Bioinformatics at Icahn Mount Sinai, said in a statement.

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

In addition, they analyzed the differentially expressed genes in the study along with genes that are differentially expressed due to other infections from other published studies and identified a subset of genes that were highly expressed, which had not been previously established for this Lyme-associated inflammatory response. Using machine learning, the researchers further reduced the group of genes to establish an mRNA biomarker set capable of distinguishing healthy patients from those with acute or post-treatment Lyme disease.

The authors contend that a gene panel that measures the expression of the genes identified in the study could be developed as a diagnostic to test for Lyme. Next, they 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.

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