New Research Identified Genes That Could Potentially Diagnose Long-Term Lyme Disease
Lyme disease is an illness most often caused by tick bites, in which humans are infected with borrelia bacteria and may suffer from symptoms such as fatigue, headache, fever, and a skin rash known as erythema migrans.
Additionally, if the disease is left untreated, the bacterial infection can become severe– spreading to the joints, nervous system, and heart.
This is particularly alarming since, according to the CDC, only thirty thousand cases of Lyme disease are reported each year.
However, the true number of cases is believed to be substantially higher, the vast majority of which go undiagnosed– with an estimated four hundred and seventy-six thousand people contracting Lyme disease yearly.
So, researchers from the Icahn School of Medicine at Mount Sinai, located in New York City, recently conducted a study on genes– which they believed could be used as biomarkers to help diagnose patients with the difficult-to-catch and hard-to-treat 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,” said Avi Ma’ayan, the study’s senior author.
The team first conducted RNA sequencing using blood samples collected from one hundred and fifty-two patients who reported symptoms of Lyme disease post-treatment in order to measure the patients’ immune responses.
Then, the researchers compared their findings against RNA sequencing results from seventy-two patients with acute Lyme disease, as well as forty-four patients who were uninfected and served as a control group.
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Through this comparison, the team observed gene expression differences and discovered that the majority of post-treatment Lyme disease patients possessed a distinct inflammatory signature. Afterward, the researchers analyzed the genes that were expressed differently and ultimately identified a subset of thirty-five genes that were substantially expressed.
And finally, using machine learning, the team was able to further reduce that group of genes down to establish an mRNA biomarker set. This set allowed them to distinguish healthy, uninfected patients from those with either post-treatment or acute Lyme disease. It could also be developed into a diagnostic test for Lyme disease– potentially helping curb the high number of unreported cases.
“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,” explained Ma’ayan.
“A diagnostic for Lyme disease may not be a panacea [solution] but could represent meaningful progress toward a more reliable diagnosis and, as a result, potentially better management of this disease.”
As for the team’s next steps, the researchers are now planning to repeat the study utilizing data from whole blood and single-cell transcriptomics. Then, the same machine learning approach will be applied to other diseases that are complex and difficult to diagnose– developing a gene diagnostic panel that would be tested on patient samples.
To read the study’s complete findings, which have since been published in Cell Reports Medicine, visit the link here.
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