Researchers at Mount Sinai’s Icahn School of Medicine in New York discovered 35 genes that are overexpressed in people who suffer from long-term Lyme disease. Potentially, these genes could be employed as biomarkers to identify patients who have the condition, which is now difficult to identify and treat.

The findings, which were published in the journal Cell Reports Medicine today [DOI: 10.1016/j.xcrm.2022.100816], may also lead to novel therapeutic targets. In this work, RNA levels in patients with chronic Lyme disease were measured using transcriptomics as a blood test for the first time.

Lyme disease is a poorly known sickness spread by ticks. The CDC receives reports of roughly 30,000 confirmed cases each year, but the actual number is likely closer to 476,000, with an associated yearly healthcare expenditure of about $1 billion in the United States. While the majority of individuals with Lyme disease receive antibiotic treatment and early diagnosis, about 20% of patients experience long-term consequences, which may include arthritis, neurologic symptoms, and/or cardiac issues.

In order to create improved diagnostics for this crippling illness, “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,” says senior author Avi Ma’ayan.

“There still remains a critical unmet need, as this disease so often goes undiagnosed or misdiagnosed. Not enough is understood about the molecular mechanisms of long-term Lyme disease.”

Blood samples from 152 individuals with post-treatment Lyme disease symptoms were used in the study’s RNA sequencing procedure to gauge their immunological response. The researchers noticed variations in gene expression and discovered that most of the post-treatment Lyme disease patients had a distinct inflammatory signature in comparison to the acute Lyme disease group. This was combined with RNA sequencing data from 72 patients with acute Lyme disease and 44 uninfected controls.

The researchers also discovered a subset of genes that were highly expressed that had not previously been established for this Lyme-associated inflammatory response by comparing the genes that were differentially expressed in this study with genes that are differentially expressed due to other infections from other published studies.

Using machine learning, the researchers narrowed down the group of genes to create an mRNA biomarker set capable of identifying healthy patients from those with acute or post-treatment Lyme disease. As a diagnostic for Lyme disease, a gene panel that gauges the expression of the genes the researchers found might be created.

“We should not underestimate the value of using omics technologies, including transcriptomics, to measure RNA levels to detect the presence of many complex diseases, like Lyme disease. A diagnostic for Lyme disease may not be a panacea but could represent meaningful progress toward a more reliable diagnosis and, as a result, potentially better management of this disease,” adds Dr. Ma’ayan.

Next, the researchers want to repeat the experiment using data from single-cell transcriptomics and whole blood, apply the machine learning method to other complex diseases that are hard to diagnose, and create the diagnostic gene panel and test it on samples from patients.

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