Tracking gut microbiome and bloodstream infection in critically ill adults

Abstract

Background: The gut microbiome is believed to contribute to bloodstream infection (BSI) via translocation of dominant gut bacteria in vulnerable patient populations. However, conclusively linking gut and blood organisms requires stringent approaches to establish strain-level identity.

Methods: We enrolled a convenience cohort of critically ill patients and investigated 86 bloodstream infection episodes that occurred in 57 patients. Shotgun metagenomic sequencing was used to define constituents of their gut microbiomes, and whole genome sequencing and assembly was done on 23 unique bloodstream isolates that were available from 21 patients. Whole genome sequences were downloaded from public databases and used to establish sequence-identity distribution and define thresholds for unrelated genomes of BSI species. Gut microbiome reads were then aligned to whole genome sequences of the cognate bloodstream isolate and unrelated database isolates to assess identity.

Results: Gut microbiome constituents matching the bloodstream infection species were present in half of BSI episodes, and represented >30% relative abundance of gut sequences in 10% of episodes. Among the 23 unique bloodstream organisms that were available for whole genome sequencing, 14 were present in gut at the species level. Sequence alignment applying defined thresholds for identity revealed that 6 met criteria for identical strains in blood and gut, but 8 did not. Sequence identity between BSI isolates and gut microbiome reads was more likely when the species was present at higher relative abundance in gut.

Conclusion: In assessing potential gut source for BSI, stringent sequence-based approaches are essential to determine if organisms responsible for BSI are identical to those in gut: of 14 evaluable patients in which the same species was present in both sites, they were identical in 6/14, but were non-identical in 8/14 and thus inconsistent with gut source. This report demonstrates application of sequencing as a key tool to investigate infection tracking within patients.

Fig 1. Schematic of gut microbiome and BSI whole genome sequence (WGS) analysis.

BSI isolates (A) and stool (B) were subjected to shotgun metagenomic deep sequencing. Stool samples were used for microbiome analysis of composition, diversity and dominance (C). BSI isolates were used for assembly of whole genomes (D). Stool and BSI WGS together were used to test identity between organisms in the stool and blood culture isolates by mapping stool reads to the cognate BSI WGS (E). Single nucleotide variants per mega base pair (SNV/Mbp) was calculated to represent the degree of mismatch, and WGS of BSI isolates of same species from the cohort (if available) and reference genomes downloaded from databases were included as control comparisons (hypothetical example shown in F).

Fig 2. Gut microbiota of critically ill patients are dysbiotic but do not differ among those with or without bloodstream infection.

Stool samples all critically ill subjects enrolled in the study were compared with healthy individuals reported from the Human Microbiome Project (HMP). (A) Bar graph representing phylum level composition of critically ill and healthy subjects clustered by Bray-Curtis dissimilarity. Some critically ill subjects’ gut microbiota resembles the gut microbiome composition of healthy individuals, while others are dysbiotic and dominated by Proteobacteria, Firmicutes, or fungi (Ascomycota). (B) Alpha diversity using the Shannon index. Critically ill subjects are colored based on whether they did (BSI+; green) or did not (BSI-; red) have a bloodstream infection. Healthy and critically ill are significantly different (p = 0.003; Wilcoxon rank-sum with Benjamini-Hochberg adjustment for false discovery) but there is no difference between critically ill/BSI + and critically ill/BSI- groups. (C) Dominance by the Berger-Parker dominance index. Healthy and critically ill are significantly different (p = 0.008; Wilcoxon rank-sum with Benjamini-Hochberg adjustment for false discovery), but there is no difference between critically ill/BSI+ and critically ill/BSI- groups. (D) Principal coordinate analysis of stool microbiota based on Bray-Curtis dissimilarity. Critically ill subjects diverge significantly from healthy individuals (PERMANOVA with Benjamini-Hochberg adjustment for false discovery; p = 0.008) but critically ill/BSI+ and critically ill/BSI- do not differ from each other from each other. Panel A includes all stool samples, while panels B-D include just the first collected sample for each subject.

Fig 3. Mapping stool reads against BSI whole genome sequences (WGS).

Each available BSI isolate was subjected to whole genome shotgun sequencing and then assembly into WGS (mean 97.49% coverage; mean 78.81X depth). Stool short reads were then aligned with multiple genomes (indicated on the left of the graph), which included the cognate BSI WGS (“subject”), the WGS of other subjects’ BSI organisms of the same species if any (“cohort”), and unrelated species-matched WGS downloaded from Genbank (“database”). Sequence similarity was calculated based on mismatches defined as single nucleotide variant per megabase pair (SNV/Mbp), reflected along the X axis for each stool sample compared with multiple genomes. (A) Results for six BSI episodes where stool and BSI WGS showed sequence-based strain identity. The SNV/Mbp is indicated along the X axis for each alignment; for clarity, the value is shown for those less than 1000 SNV/Mbp followed by the proportion genome aligned. The red arrow indicates the cognate BSI WGS matched to the stool sample. Each bar is colored to indicate the proportion of genome aligned with stool short reads (because of the short bar, the proportion of genome aligned is shown as a fraction next to the SNVs for the low-SNV matched sample. (B) Representative example of a stool/BSI WGS that did not match. Stool alignment with the cognate Achromobacter BSI WGS showed SNV/Mbp that was no lower than when compared with unrelated WGS of Achromobacter (which was identified only at the genus level by the clinical microbiology lab) downloaded from GenBank.