Using circulating microbial cell-free DNA to identify persistent Treponema pallidum infection in serofast syphilis patients

Abstract

Summary: The question of whether serofast status of syphilis patients indicates an ongoing low-grade Treponema pallidum (T. pallidum) infection remains unanswered. To address this, we developed a machine learning model to identify T. pallidum in cell-free DNA (cfDNA) using next-generation sequencing (NGS). Our findings showed that a TP_rate cut-off of 0.033 demonstrated superior diagnostic performance for syphilis, with a specificity of 92.3% and a sensitivity of 71.4% (AUROC = 0.92). This diagnosis model predicted that 20 out of 92 serofast patients had a persistent low-level infection. Based on these predictions, re-treatment was administered to these patients and its efficacy was evaluated. The results showed a statistically significant decrease in RPR titers in the prediction-positive group compared to the prediction-negative group after re-treatment (p < 0.05). These findings provide evidence for the existence of T. pallidum under serofast status and support the use of intensive treatment for serofast patients at higher risk in clinical practice.

Keywords

• Microbiology