A metagenomic DNA sequencing assay that is robust against environmental DNA contamination

Abstract

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present contamination-free metagenomic DNA sequencing (Coffee-seq), a metagenomic sequencing assay that is robust against environmental contamination. The core idea of Coffee-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied Coffee-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of inflammatory bowel disease in blood.

Figure 1.. Coffee-seq proof-of-principle.

A) Experiment workflow. Tagging of sample-intrinsic DNA by bisulfite DNA treatment is performed directly on urine or plasma. Contaminating DNA introduced after the tagging step is identified based on lack of cytosine conversion. B) Bioinformatics workflow. C Representative example of the cytosine fraction of mapped reads in an unfiltered (top) dataset, a read-level filtered dataset (middle) and a fully filtered dataset (bottom). D) Number of reads assigned to Cutibacterium acnes (common environmental DNA contaminant) in ΦX174 DNA after conventional sequencing (green) and Coffee-seq (purple). E) Deliberate contamination assay. Detection of known contaminants before (top) and after (bottom) filtering. F) Number of reads assigned to contaminants.

Figure 2.. Coffee-Seq applied to cell-free DNA in urine and plasma.

A) Microbial abundance of 25 most abundant common contaminant genera (selected from the 68 genera) before and after Coffee-seq filtering in plasma and urine from five independent subject cohorts (Tx = transplant). Total abundance of all contaminant genera B) and C. acnes C) before and after Coffee-seq filtering (KUCP = Kidney Transplant cohort with positive urine culture, KUCN = Kidney Transplant cohort with negative urine culture, EPTx = Early Post Transplant cohort). Bray-Curtis dissimilarity index before D) and after E) filtering. Samples are organized by: sequencing batch, researcher performing the experiment, cohort, and biofluid. *** p-value < 0.001

Figure 3.. Application of Coffee-seq to plasma and urine.

A) Heatmap of abundance of species (molecules per million, MPM) identified in patients with and without UTI, before and after application of Coffee-seq filter. B) Boxplot of the relative number of microbe-derived molecules (MPM) in samples from patients with and without UTI, before and after Coffee-seq filtering. C) Dot plot of the most abundant genera in urine from male and female kidney transplant recipients. D-E) Boxplot showing Bray-Curtis similarity index (as defined in D) of the urine microbiome within individual patients and between patients before and after stent removal. F-G) Heatmaps of the abundance of species identified in plasma from COVID-19 patients with and without culture confirmed F) lung and G) blood infection, before and after application of Coffee-seq filter (red * indicates detection by sputum culture only). Red boxes indicate positive culture tests. H) Barplot of the prevalence of Epstein-Barr Virus (EBV), Torque teno virus (TV), Malaria, or Shigellosis pathogens in different patient cohorts. I) Heatmap of the abundance of species identified in matched stool and plasma cfDNA samples in patients diagnosed with Crohn’s disease or ulcerative colitis. J) Heatmap of the change in abundance of gut specific bacteria before and after treatment. (Black * in panels A, F, and G indicates agreement with urine, respiratory and blood culture, respectively).