Quantitative microbiome profiling links gut community variation to microbial load

Abstract

Current sequencing-based analyses of faecal microbiota quantify microbial taxa and metabolic pathways as fractions of the sample sequence library generated by each analysis. Although these relative approaches permit detection of disease-associated microbiome variation, they are limited in their ability to reveal the interplay between microbiota and host health. Comparative analyses of relative microbiome data cannot provide information about the extent or directionality of changes in taxa abundance or metabolic potential. If microbial load varies substantially between samples, relative profiling will hamper attempts to link microbiome features to quantitative data such as physiological parameters or metabolite concentrations. Saliently, relative approaches ignore the possibility that altered overall microbiota abundance itself could be a key identifier of a disease-associated ecosystem configuration. To enable genuine characterization of host-microbiota interactions, microbiome research must exchange ratios for counts. Here we build a workflow for the quantitative microbiome profiling of faecal material, through parallelization of amplicon sequencing and flow cytometric enumeration of microbial cells. We observe up to tenfold differences in the microbial loads of healthy individuals and relate this variation to enterotype differentiation. We show how microbial abundances underpin both microbiota variation between individuals and covariation with host phenotype. Quantitative profiling bypasses compositionality effects in the reconstruction of gut microbiota interaction networks and reveals that the taxonomic trade-off between Bacteroides and Prevotella is an artefact of relative microbiome analyses. Finally, we identify microbial load as a key driver of observed microbiota alterations in a cohort of patients with Crohn's disease, here associated with a low-cell-count Bacteroides enterotype (as defined through relative profiling).

Major microbiota dysbiosis in severe obesity: fate after bariatric surgery

“…We herein showed the presence of the same four enterotypes described recently25 explaining 40% of the variation in microbiome composition (R 2 =0.4; permutational multivariate analysis of variance (PERMANOVA) tests) (online supplementary figure 1), yet there was no significant association of enterotype composition and BSS at baseline (Fisher’s test p=0.97 (AGB) and p=0.57 (RYGB)). Interestingly, we observed that patients with the B2 enterotype were those with the lower MGR, whereas those with the Ruminococcaceae enterotype (although few in numbers) were indeed those with the higher MGR.…”

“…We described related microbiome functional alterations and low MGR-related metabolomics signatures. We showed in severe obesity the presence of four enterotypes recently described in 40 individuals ranging from underweight to moderate obesity 25. Herein, very few severely obese patients harbour a Ruminococcaceae enterotype (ie, those who displayed the higher MGR).…”

“…Vandeputte et al 25 reported that B2 enterotype was associated with decreased cell count, increased cell moisture and decreased gene richness. Here, we did not observe any relation between enterotypes and Bristol Stool Score linked to cell moisture, yet we observed an association between BSS and MGR as previously reported 33.…”

Spike-in Genomic DNA for Validating Performance of Metagenomics Workflows

“…We used DNA as the spike-in rather than whole cells because DNA quantitation is easier, more accurate and reproducible. Precise whole-cell count demands specialized equipment such as a flow cytometer [12], adds significant time and money to the endeavor, and is less reproducible. A whole-cell-based complex mock community of microbes was used to validate that the DNA extraction process lysed bacteria and fungi.…”

Range-expanding plant species and their belowground neighbours : digging into novel interactions

“…Molecular analyses only would have limited comparisons to relative abundances, rather than enabling quantitative analyses (Vandeputte et al 2017;Geisen et al 2018). However, we acknowledge that the community composition that we provide might not represent abundances as provided by morphological identifications (Darby, Todd & Herman 2013).…”