Sepsis is a life-threatening condition that is difficult to diagnose quickly in critically ill patients. Current tests, such as microbial cultures, are slow and often lack sensitivity, which delays treatment and increases risks. Plasma cell-free DNA (cfDNA) has emerged as a noninvasive screening tool for multiple diseases. This project examined its potential application for diagnosing and monitoring sepsis. The researchers demonstrated that differences in cfDNA fragmentation patterns can distinguish sepsis patients from non-infectious ICU patients. They also developed strategies to estimate the relative contributions of different tissues to plasma cfDNA and infer the site of infection in sepsis patients.
The Challenge
Sepsis is a life-threatening condition caused by the body’s extreme response to an infection. It is difficult to diagnose because its early symptoms overlap with many other illnesses. There is a critical need for early and rapid identification of sepsis, as prompt treatment significantly improves the chance for survival.
Project Goals
The goal of this project was to develop computational approaches for analyzing plasma cfDNA fragmentation patterns to improve the diagnosis of sepsis in critically ill patients.
Results
The researchers analyzed cfDNA fragmentation patterns using samples from 17 sepsis patients, 120 non-infectious Intensive Care Unit (ICU) patients, 101 Emergency Department patients, and 59 healthy individuals. The researchers identified cfDNA end motifs that differ between sepsis and non-infectious ICU patients. Among these, the frequency of the GTGA motif was significantly higher in sepsis patients than in other groups. These end motif frequencies have strong potential as biomarkers for sepsis diagnosis and disease monitoring.
Cell-free DNA fragmentation patterns contain tissue-specific open chromatin information, which can provide insight into tissue injury and infection status. The research team developed strategies to estimate the relative contributions of different tissues to plasma cfDNA. T cells were identified as the primary contributors in healthy individuals, and lung-derived cfDNA signals were significantly increased in sepsis patients compared with ED patients.
Preliminary data from this project supported a NIH grant application for Dr. Zhang. Future work will extend their sequencing approach to further evaluate whether this strategy can improve discrimination between sepsis and non-infectious critical illness. They will also expand analysis to additional immune cell types to assess whether these immune cell-specific cfDNA signals can reflect host immune response.
