Katharina Buczek
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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