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[Preprint]. 2024 Aug 5:rs.3.rs-4688526.
doi: 10.21203/rs.3.rs-4688526/v1.

Evaluating urine volume and host depletion methods to enable genome-resolved metagenomics of the urobiome

Zachary J Lewis  1 , Angela Scott  1 , Christopher Madden  1 , Dean Vik  2 , Ahmed A Zayed  3 , Garrett J Smith  2 , Sheryl S Justice  4 , Adam Rudinsky  5 , Jessica Hokamp  6 , Vanessa L Hale  1
Affiliations

Evaluating urine volume and host depletion methods to enable genome-resolved metagenomics of the urobiome

Zachary J Lewis et al. Res Sq. .

Abstract

Background: The gut microbiome has emerged as a clear player in health and disease, in part by mediating host response to environment and lifestyle. The urobiome (microbiota of the urinary tract) likely functions similarly. However, efforts to characterize the urobiome and assess its functional potential have been limited due to technical challenges including low microbial biomass and high host cell shedding in urine. Here, to begin addressing these challenges, we evaluate urine sample volume (100 ml - 5 mL), and host DNA depletion methods and their effects on urobiome profiles in healthy dogs, which are a robust large animal model for the human urobiome. We collected urine from seven dogs and fractionated samples into aliquots. One set of samples was spiked with host (canine) cells to model a biologically relevant host cell burden in urine. Samples then underwent DNA extraction followed by 16S rRNA gene and shotgun metagenomic sequencing. We then assembled metagenome assembled genomes (MAGs) and compared microbial composition and diversity across groups. We tested six methods of DNA extraction: QIAamp BiOstic Bacteremia (no host depletion), QIAamp DNA Microbiome, Molzym MolYsis, NEBNext Microbiome DNA Enrichment, Zymo HostZERO, and Propidium Monoazide.

Results: In relation to urine sample volume, 3 3.0 mL resulted in the most consistent urobiome profiling. In relation to host depletion, individual (dog) but not extraction method drove overall differences in microbial composition. DNA Microbiome yielded the greatest microbial diversity in 16S rRNA sequencing data and shotgun metagenomic sequencing data, and maximized MAG recovery while effectively depleting host DNA in host-spiked urine samples. As proof-of-principle, we then mined MAGs for core metabolic functions and environmental chemical metabolism. We identified long chain alkane utilization in two of the urine MAGs. Long chain alkanes are common pollutants that result from industrial combustion processes and end up in urine.

Conclusions: This is the first study, to our knowledge, to demonstrate environmental chemical degradation potential in urine microbes through genome-resolved metagenomics. These findings provide guidelines for studying the urobiome in relation to sample volume and host depletion, and lay the foundation for future evaluation of urobiome function in relation to health and disease.

Keywords: DNA extraction; Microbiome; Urine; Urobiome; canine; genome-resolved metagenomics; host depletion; low biomass.

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Conflict of interest statement

Competing Interests The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Urine sample volume influences contaminant abundance and microbial diversity (16S).
A) The abundance of contaminants (contaminating microbial sequences) decreased significantly as sample volume increased (overall p=0.026, Friedman, no pairwise comparisons were significant, Table S6). B) Microbial richness, or the number of unique amplicon sequence variants (ASVs), increased significantly with increased sample volume (p=0.015, Friedman) and 5.0mL samples had significantly greater numbers of unique ASVs compared to 0.5mL (p=0.031), 0.2mL (p=0.031), and 0.1mL samples (p=0.048), (multiple comparisons were FDR-corrected at 0.05, Table S7). C) Sequencing depth (reads) was increased at greater urine sample volumes although this difference was not significant (p=0.075, Friedman). Box and whisker plots show the median, IQR, and min/max.
Figure 2
Figure 2. Urine sample volume and microbial composition (16S).
A) Microbial composition (Bray-Curtis) of urine samples differed significantly by dog but not by sample volume (PERMANOVA: by dog p = 0.001; by sample volume p = 0.98). B) Representative Bray-Curtis plot of a single dog’s (Dog = MS) urine samples. C) High volume (≥ 3 mL) samples were significantly less variable (shorter distance to centroid) than low volume (≤ 1 mL) samples (Bray Curtis, p = 0.0017, PERMDISP).
Figure 3
Figure 3. Total and Bacterial DNA recovery differed by extraction method.
A) Total DNA concentrations (ng/ul, Qubit fluorometry) did not differ by extraction method (p=0.62, Friedman). B) Bacterial DNA concentrations (qPCR) differed significantly by extraction method (overall p=0.014, Friedman); although no pairwise comparisons were significant. C) Total DNA concentrations recovered from urine samples spiked with canine (CTAC) cells varied significantly by extraction method (p<0.0001, Friedman). Pairwise comparisons are indicated by letter above each bar. Bars with differing letters were significantly different (p<0.05, FDR 0.05). D) Bacterial DNA concentration from spiked urine samples marginally differed by extraction method (p=0.051, Friedman). Bars represent the mean with standard error. Pipettor icon in C) and D) indicates that all samples shown in these graphs were spiked with canine thyroid adenocarcinoma (CTAC) cells.
Figure 4
Figure 4. Microbial diversity and composition by extraction method (16S).
A) Microbial richness, or number of unique ASVs, and B) Microbial diversity (Shannon entropy) differed significantly by extraction method (Richness p=0.0018, Shannon p=0.0091, Friedman, multiple comparisons with FDR at 0.05, Table S8). Whiskers represent minimum, maximum, and median. *p<0.05. C) Microbial composition (Bray-Curtis) differed significantly by dog (PERMANOVA p=0.001), but not extraction method (PERMANOVA p=0.92) D) When microbial composition was weighted by phylogeny (Unweighted UniFrac), composition differed significantly by both extraction method (PERMANOVA p=0.002), and by dog (PERMANOVA p=0.001). E) Bray-Curtis, F) Jaccard, and G) Unweighted UniFrac distances from Bacteremia-extracted samples to samples of the same dog extracted via other extraction methods. E) Bray-Curtis F) Jaccard, and G) Unweighted UniFrac distances differed significantly with DNA Microbiome samples being the most similar (shortest distance) to Bacteremia-extracted samples (Friedman, Bray-Curtis p=0.034; Jaccard p=0.0342; Unweighted UniFrac p=0.0071). Pairwise comparison p-values are outlined in Table S9. *p<0.05. Bar represents median.
Figure 5
Figure 5. Extraction method impacted host and microbial read abundances (Shotgun metagenomics).
A) Total sequencing reads did not vary by extraction method (p=0.12, Friedman). B) However, microbial reads did vary significantly by extraction method (p=0.0039, Friedman), with DNA Microbiome, Molzym MolYsis, and Zymo HostZERO exhibiting a greater number of microbial reads compared to Bacteremia (all pairwise p=0.01). C) The proportion of microbial reads also varied significantly by extraction method with Molzym MolYsis and Zymo HostZERO yielding the greatest proportion of microbial reads (overall p<0.0001, all pairwise p<0.02, Friedman). D) The abundance of contaminant reads also differed significantly by extraction method (overall Friedman p=0.014) and was lowest in DNA Microbiome (DNA microbiome vs. Zymo HostZERO pairwise p=0.01). (See Table S5 for a list of contaminants).
Figure 6
Figure 6. Extraction method and microbial diversity and composition (Shotgun metagenomics).
Microbial diversity as measured by A) Observed Species (richness) and B) Shannon Entropy varied significantly by extraction method (Observed Species p=0.011, Shannon entropy, p=0.002, Friedman) with DNA Microbiome yielding significantly greater microbial diversity than other extraction methods (Observed Species DNA Microbiome vs. Bacteremia pairwise p=0.014, Shannon DNA Microbiome vs. all other methods (except Molzym MolYsis) pairwise p=0.014). Microbial species were identified via MetaPhlAn4. C) Microbial composition as measured by Jaccard or D) Bray-Curtis differed significantly by dog (Jaccard p=0.001, Bray-Curtis p=0.001, PERMANOVA), but not extraction method (Jaccard p=0.67, Bray-Curtis p=0.96, PERMANOVA). Urine samples extracted with Bacteremia contained little microbial DNA and did not produce reads that were assignable to a taxa by MetaPhlAn4. As such, Bacteremia samples were excluded from C) and D) and beta diversity testing.
Figure 7
Figure 7. Top 20 microbial genera represented in urine samples.
Relative abundances of the top 20 microbial genera identified in A) 16S rRNA sequencing of unspiked urine samples B) Shotgun metagenomic sequencing (MetaPhlAn4) of spiked urine samples, C) Metagenome-assembled-genomes (MAG) generated from spikedurine samples. The same urine samples were used for 16S and shotgun metagenomic sequencing. Across methods, Staphylococcus (pseudintermedius), Bacillus (cereus), Streptococcus (canis), and Arcanobacterium repeatedly emerge as abundant taxa. For 16S samples, ASVs were filtered to a minimum 0.5% abundance in at least 10% of samples. Host-spiked samples extracted with Bacteremia contained little microbial DNA and did not produce reads that were assignable to a taxa by MetaPhlAn4 and are thus excluded from B).
Figure 8
Figure 8. Metabolic potential of urine-associated MAGs.
A) and B) feature reconstructed alkane metabolism in urine-associated MAGs. Shown are the regulon in which the predicted alkane metabolism gene occurs, as well as the reconstructed relevant pathway. For the depiction of the regulon, only up to ten neighboring genes on each side were included, and the coloring denotes arbitrary groupings with the gene responsible for alkane activation the darkest (i.e., ssuDand ladB), and genes that weren’t directly related to the predicted alkane metabolism colored grey. The numbers below the arrows indicate the gene number on the contig. For the depiction of the reconstructed alkane metabolism pathways, colors denote the number of genes that may be involved at each reaction, noting that for simplicity beta-oxidation has been summarized in one ellipse broken into five pieces. Further description of these results in Supplementary Information 1.
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