Lyme disease is often undiagnosed or misdiagnosed – here we report that there may finally be a genetic biomarker profile that can be used for diagnosis and treatment. A recent study, published in Cell Reports Medicine, identified 35 genes that are highly expressed in patients with post-treatment Lyme disease.
Feeling tick-ed off by Lyme disease
Lyme disease is a tick-borne infectious disease. It is endemic in some parts of the UK, especially in heathland and woodland areas. Current testing to diagnose the disease is based on laboratory tests, which lack sensitivity, and clinical presentation, which varies between patients. Testing is flawed and false-negative results are common.
Left untreated, the infection spreads and can cause neurological, cardiac and dermatological problems. Also, some patients do not respond to treatment and develop more symptoms. This is called post-treatment or long-term Lyme disease. The underlying mechanisms of this are a mystery. Many patients would benefit from an earlier and more accurate diagnosis.
Researchers at the Icahn School of Medicine at Mount Sinai in New York analysed and compared the gene expression profiles of patients with Lyme disease to healthy controls. Two interesting findings were reported: first, there was a distinct immune response in post-treatment Lyme disease compared to acute Lyme disease, which may provide insight into the disease’s underlying mechanism; second, 35 genes were highly expressed in patients with long-term Lyme disease.
Professor Avi Ma’ayan, senior author of the paper said, “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. Not enough is understood about the molecular mechanisms of long-term Lyme disease.”
Unravelling the mystery with transcriptomics
Researchers used a transcriptomic approach to compare gene expression between three cohorts: 152 patients with post-treatment Lyme disease, 72 patients with acute Lyme disease and 44 uninfected healthy controls.
RNA-sequencing data from the three groups were compared to identify differentially expressed genes. The genes that were relevant and important to Lyme disease were identified using a machine learning approach.
They were able to develop an mRNA biomarker set that could be used to distinguish acute Lyme disease patients, post-treatment Lyme disease patients and healthy controls (figure 1).
The study raises the prospect of a more effective approach to diagnosing Lyme disease. Using the identified genes, an mRNA-based diagnostic biomarker panel measuring gene expression could be developed and implemented in primary care.
Professor Ma’ayan said, “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.”