The circulating cell-free DNA landscape in sepsis is dominated by impaired liver clearance

Abstract:

Circulating cell-free DNA (cfDNA) is a promising molecular biomarker. However, its utility in severe infection remains poorly understood. Here, we isolated cfDNA from sepsis patients and controls, demonstrating a 41-fold increase in the amount of cfDNA in circulation during disease. We used sequencing to reconstruct cfDNA methylomes, fragmentation profiles, and nucleosome footprints across 58 samples. We observed no difference in cfDNA composition between patients and controls, challenging the idea that cfDNA increases due to higher immune cell death during sepsis. Instead, we suggest that liver dysfunction prevents efficient clearance of cfDNA during disease. This was supported by fragmentation and end-motif patterns, both of which showed evidence of cfDNA being exposed to circulating nucleases for a prolonged period, proportionally to the extent of liver dysfunction. Variation in cfDNA of megakaryocyte-erythroid progenitor origin was also a significant contributor in sepsis, increasing over time. Moreover, we showed that cfDNA retains nucleosome footprints with cell type-specific gene activity information. We developed a novel approach to study nucleosome phasing that successfully recovers tissue-specific signatures. By combining this with single-cell data, we demonstrated that sepsis patients with liver dysfunction have higher amounts of cfDNA derived from Kupffer cells and the liver parenchyma. In conclusion, we present the first high-throughput multi-modal study of cfDNA during sepsis, which will serve as a reference point for future studies on the role of this biomarker in critical illness.

Contents:

This data set contains methylation estimates (i.e. percentage of methylated reads) for 56 high-quality cfDNA samples from sepsis patients and healthy controls. Methylation is reported for 19.28 million autosomal CpG sites which passed QC filters and were considered variable. The data set is compsoed of three interlinked files:

a) Methylation matrix: Measured CpG methylation perecentages

b) CpG annotations: Genomic position (corresponding to the GRCh38 refernece genome) and coverage information for each CpG site included in the study

c) Sample metadata: Techincal and biological information for each sample included in the study (e.g. disease status, sequencing batch, etc)

Associated software and methods:

For a more detailed understanding of how these methylation estimates were generated from raw sequencing reads, please refer to the associated data analysis pipeline available in GitHub: https://github.com/jknightlab/TAPS-pipeline/