Description
Lyme disease (LD), caused by Borrelia burgdorferi, is the most common tick-borne infectious disease in the United States. We examined gene expression patterns in the blood of individuals with early disseminated LD at the time of diagnosis (Acute LD) and also at approximately 1 month and 6 months following antibiotic treatment. A distinct acute LD profile was observed that was sustained during early convalescence (1 month) but returned to control levels six months after treatment. Using a computer learning algorithm, we identified sets of 20 classifier genes that discriminate LD from other bacterial and viral infections. In addition, these novel LD biomarkers are highly acurate in distinvuishing patients with acute LD from healthy subjects and in discriminating between individuals with active and resolved infecitons. This computational approach offers the potential for more accurate diagnosis of early dissminated Lyme disease. It may also allow improved monitoring of treatment efficacy and disease resolution.