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-term 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 findings, published November 15 in the journal Cell Reports Medicine [DOI: 10.1016/j.xcrm.2022.100816], may 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.

NEW YORK, Nov. 15, 2022 /PRNewswire-PRWeb/ -- Genes to Potentially Diagnose Long-Term Lyme Disease Identified

Findings could lead to new diagnostics and treatments for the hard-to-detect condition

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-term 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 findings, published November 15 in the journal Cell Reports Medicine [DOI: 10.1016/j.xcrm.2022.100816], may 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 illness that is not well understood. Approximately 30,000 diagnosed cases are reported to the CDC each year, but the estimated real number is closer to 476,000 cases, carrying an annual healthcare cost of about $1 billion in the United States. While most patients are diagnosed and treated with antibiotics at the earliest stages of Lyme disease, about 20 percent of the patients develop long-term complications, which could include arthritis, neurologic 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 to develop better diagnostics for this debilitating disease. There still remains a critical unmet need, as this disease so often goes undiagnosed or misdiagnosed," said Avi Ma'ayan, PhD, Professor, Pharmacological Sciences, and Director of the Mount Sinai Center for Bioinformatics at Icahn Mount Sinai, and senior author of the paper. "Not enough is understood about the molecular mechanisms of long-term Lyme disease."

As part of the study, RNA sequencing was conducted 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, the investigators 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, by analyzing the differentially expressed genes in this study along with genes that are differentially expressed due to other infections from other published studies, the researchers identified a subset of genes that were highly expressed, which have not been previously established for this Lyme-associated inflammatory response.

Using a type of artificial intelligence called 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. A gene panel that measures the expression of the genes the investigators identified 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, 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," said Dr. Ma'ayan.

Next, the investigators 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.

The paper is titled "Gene set predictor for post-treatment Lyme disease." Additional co-authors are Daniel J.B. Clarke, MS (Icahn Mount Sinai, New York), and Alison W. Rebman, MPH, Jinshui Fan, MD, PhD, Mark J. Soloski, PhD, and John N. Aucott, MD, all from Johns Hopkins University of Medicine in Baltimore.

The project was partially supported by funds from the Cohen Lyme & Tickborne Disease Initiative and National Institutes of Health grant P30AR070254.     
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About the Icahn School of Medicine at Mount Sinai
The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the eight- member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to a large and diverse patient population.

Ranked 14th nationwide in National Institutes of Health (NIH) funding and among the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges, Icahn Mount Sinai has a talented, productive, and successful faculty. More than 3,000 full-time scientists, educators, and clinicians work within and across 34 academic departments and 35 multidisciplinary institutes, a structure that facilitates tremendous collaboration and synergy. Our emphasis on translational research and therapeutics is evident in such diverse areas as genomics/big data, virology, neuroscience, cardiology, geriatrics, as well as gastrointestinal and liver diseases.

Icahn Mount Sinai offers highly competitive MD, PhD, and Master's degree programs, with current enrollment of approximately 1,300 students. It has the largest graduate medical education program in the country, with more than 2,000 clinical residents and fellows training throughout the Health System. In addition, more than 550 postdoctoral research fellows are in training within the Health System.

A culture of innovation and discovery permeates every Icahn Mount Sinai program. Mount Sinai's technology transfer office, one of the largest in the country, partners with faculty and trainees to pursue optimal commercialization of intellectual property to ensure that Mount Sinai discoveries and innovations translate into healthcare products and services that benefit the public.

Icahn Mount Sinai's commitment to breakthrough science and clinical care is enhanced by academic affiliations that supplement and complement the School's programs.

Through the Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai. Additionally, MSIP develops research partnerships with industry leaders such as Merck & Co., AstraZeneca, Novo Nordisk, and others.

The Icahn School of Medicine at Mount Sinai is located in New York City on the border between the Upper East Side and East Harlem, and classroom teaching takes place on a campus facing Central Park. Icahn Mount Sinai's location offers many opportunities to interact with and care for diverse communities. Learning extends well beyond the borders of our physical campus, to the eight hospitals of the Mount Sinai Health System, our academic affiliates, and globally.

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