Genomewide bisulfite sequencing reveals the origin and time-dependent fragmentation of urinary cfDNA

Abstract

Urinary cell-free (cf) DNA holds great potential as a completely noninvasive form of liquid biopsy. Knowledge of the composition of cfDNA by tissue of origin is useful for guiding its clinical uses. We conducted a global survey of urinary cfDNA composition using genomewide bisulfite sequencing. While previous studies focused on detecting cfDNA from a single source at a time, genomewide tissue specific methylation signatures allow us to simultaneously deduce the proportional contribution from each contributing tissue. The proportional contributions derived from methylation deconvolution are highly correlated with those calculated using allograft-derived donor-specific genetic markers in the urine of hematopoetic stem cell and renal transplant recipients. We found a large variation of proportional contributions from different tissues. We then assessed if cfDNA undergoes time-dependent fragmentation in urine by conducting in vitro incubation experiments. In vitro incubation at 37°C showed that urinary cfDNA concentration decreased under first order kinetics with a half-life of 2.6 to 5.1h. This is reflected in parallel by a decrease in the proportion of long fragments and increase in amplitude of 10bp periodicity seen in the cfDNA size profile. This global survey of urinary cfDNA has deepened our understanding of the composition, degradation and variation of cfDNA in the urinary tract and has laid a foundation for the use of genomewide urinary cfDNA sequencing as a molecular diagnostics tool.

The emerging role of cell-free DNA as a molecular marker for cancer management

“…In one study, a method for deconvoluting genome-wide bisulfite sequencing data was developed to produce ‘tissue methylation maps’ of cfDNA for pregnant women, cancer patients, transplantation patients, and healthy subjects, revealing percentage contributions by different tissues [135]. The clinical potential of this method has been substantiated by other studies [[182], [183], [184]], and has provided some insight regarding the composition, degradation and variation of cfDNA in urine [185]. However, some major drawbacks of bisulfite sequencing is the high level (84–96%) of degradation of input DNA during the bisulfite conversion process [186], high costs, and the recovery of limited information due to the low abundance of CpGs in the genome.…”

A cell-free DNA metagenomic sequencing assay that integrates the damage response to infection

“…We used unsupervised 22 hierarchical clustering of the cfDNA cell and tissue type composition for all samples in which BK 23 virus (RGE > 10 3 ) or a potential bacterial uropathogen was detected (RGE > 0.09, the lowest 24 corresponding relative genomic abundance observed in the comparison to clinical metrics 25 described above). This analysis, shown in figure 4d, summarizes the major layers of information 26 that are made accessible with the cfDNA assay reported here, and shows that the cfDNA tissue 27 and cell type composition is associated with the presence or absence of viral or bacterial 28 uropathogens. 29 30 31…”

“…compared CpG methylation profiles of each tissue group in a one-versus-one approach and found 25 91,275 DMRs, with an average length of 453 base pairs (bp). Principal component analysis (PCA) 26 of the methylation density measured across all DMRs revealed global tissue-specific clustering, 27 with three heterogeneous clusters representing blood, gut and a diverse group of other solid organ 28 tissues ( Fig.1d, Supplemental Fig. 1).…”

“…This observation is in line 24 with previous reports that leukocytes create a degradative environment for DNA 38,39 . The fragment 25 size profile of cfDNA may offer an additional metric by which patients with different infectious 26 pathologies can be stratified. 27 28…”

“…Reads were aligned to a C-to-T converted hg19 reference using bwa-meth 28 24 (v0.2.0) and to the standard hg19 genome using bwa 49 (v.0.7.13-r1126) to remove converted and 25 unconverted host reads. Reads were then BLASTed 52 to a list of C-to-T converted microbial 26 reference genomes, and a relative abundance of each organism was determined using 27 GRAMMy 40 . The fraction of reads of microbial origin was defined as the ratio of reads mapping to 28 a microbial reference to the total number of paired-end reads for that sample.…”