We explore the microbiota of 18 body sites in over 200 individuals using sequences amplified V1-V3 and the V3-V5 small subunit ribosomal RNA (16S) hypervariable regions as part of the NIH Common Fund ...Human Microbiome Project. The body sites with the greatest number of core OTUs, defined as OTUs shared amongst 95% or more of the individuals, were the oral sites (saliva, tongue, cheek, gums, and throat) followed by the nose, stool, and skin, while the vaginal sites had the fewest number of OTUs shared across subjects. We found that commonalities between samples based on taxonomy could sometimes belie variability at the sub-genus OTU level. This was particularly apparent in the mouth where a given genus can be present in many different oral sites, but the sub-genus OTUs show very distinct site selection, and in the vaginal sites, which are consistently dominated by the Lactobacillus genus but have distinctly different sub-genus V1-V3 OTU populations across subjects. Different body sites show approximately a ten-fold difference in estimated microbial richness, with stool samples having the highest estimated richness, followed by the mouth, throat and gums, then by the skin, nasal and vaginal sites. Richness as measured by the V1-V3 primers was consistently higher than richness measured by V3-V5. We also show that when such a large cohort is analyzed at the genus level, most subjects fit the stool "enterotype" profile, but other subjects are intermediate, blurring the distinction between the enterotypes. When analyzed at the finer-scale, OTU level, there was little or no segregation into stool enterotypes, but in the vagina distinct biotypes were apparent. Finally, we note that even OTUs present in nearly every subject, or that dominate in some samples, showed orders of magnitude variation in relative abundance emphasizing the highly variable nature across individuals.
Single-cell expression analysis on a large scaleTo understand why cells differ from each other, we need to understand which genes are transcribed at a single-cell level. Several methods measure ...messenger RNA (mRNA) expression in single cells, but most are limited to relatively low numbers of cells or genes. Fan et al. labeled each mRNA molecule in a cell with both a cellular barcode and a molecular barcode. Further analysis did not then require single-cell technologies. Instead, the labeled mRNA from all cells was pooled, amplified, and sequenced, and the gene expression profile of individual cells was reconstructed based on the barcodes. The technique successfully revealed heterogeneity across several thousand blood cells.Science, this issue 10.1126/science.1258367 We present a technically simple approach for gene expression cytometry combining next-generation sequencing with stochastic barcoding of single cells. A combinatorial library of beads bearing cell- and molecular-barcoding capture probes is used to uniquely label transcripts and reconstruct the digital gene expression profile of thousands of individual cells in a single experiment without the need for robotics or automation. We applied the technology to dissect the human hematopoietic system and to characterize heterogeneous response to in vitro stimulation. High sensitivity is demonstrated by detection of low-abundance transcripts and rare cells. Under current implementation, the technique can analyze a few thousand cells simultaneously and can readily scale to 10,000s or 100,000s of cells.
Despite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the ...functional profiles of microbial communities based on their taxonomic composition. In this study, we evaluated the performance of three commonly used metagenome prediction tools (PICRUSt, PICRUSt2, and Tax4Fun) by comparing the significance of the differential abundance of predicted functional gene profiles to those from shotgun metagenome sequencing across different environments.
We selected 7 datasets of human, non-human animal, and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between predicted gene compositions and gene relative abundance measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the abundance of genes were permuted across samples. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of metagenome prediction tools. As an alternative, we compared the performance of genes predicted with PICRUSt, PICRUSt2, and Tax4Fun to sequenced metagenome genes in inference models associated with metadata within each dataset. With this approach, we found reasonable performance for human datasets, with the metagenome prediction tools performing better for inference on genes related to "housekeeping" functions. However, their performance degraded sharply outside of human datasets when used for inference.
We conclude that the utility of PICRUSt, PICRUSt2, and Tax4Fun for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted. Video abstract.
In order for human microbiome studies to translate into actionable outcomes for health, meta-analysis of reproducible data from population-scale cohorts is needed. Achieving sufficient ...reproducibility in microbiome research has proven challenging. We report a baseline investigation of variability in taxonomic profiling for the Microbiome Quality Control (MBQC) project baseline study (MBQC-base). Blinded specimen sets from human stool, chemostats, and artificial microbial communities were sequenced by 15 laboratories and analyzed using nine bioinformatics protocols. Variability depended most on biospecimen type and origin, followed by DNA extraction, sample handling environment, and bioinformatics. Analysis of artificial community specimens revealed differences in extraction efficiency and bioinformatic classification. These results may guide researchers in experimental design choices for gut microbiome studies.
Abstract Purpose Human microbiome studies are within the realm of compositional data with the absolute abundances of microbes not recoverable from sequence data alone. In compositional data analysis ...each sample consists of proportions of various organisms with a sum constrained to a constant. This simple feature can lead traditional statistical treatments when naively applied to produce errant results and spurious correlations. Methods We review the origins of compositionality in microbiome data, the theory and usage of compositional data analysis in this setting and some recent attempts at solutions to these problems. Results Microbiome sequence datasets are typically high-dimensional, with the number of taxa much greater than the number of samples, and sparse as most taxa are only observed in a small number of samples. These features of microbiome sequence data interact with compositionality to produce additional challenges in analysis. Conclusions Despite sophisticated approaches to statistical transformation, the analysis of compositional data may remain a partially intractable problem, limiting inference. We suggest that current research needs include better generation of simulated data and further study of how the severity of compositional effects changes when sampling microbial communities of widely differing diversity.
Enterobacteria, especially Escherichia coli, are abundant in patients with inflammatory bowel disease or colorectal cancer (CRC). However, it is unclear whether cancer is promoted by ...inflammation-induced expansion of E. coli and/or changes in expression of specific microbial genes. Here we use longitudinal (2, 12 and 20 weeks) 16S rRNA sequencing of luminal microbiota from ex-germ-free mice to show that inflamed Il10(-/-) mice maintain a higher abundance of Enterobacteriaceae than healthy wild-type mice. Experiments with mono-colonized Il10(-/-) mice reveal that host inflammation is necessary for E. coli cancer-promoting activity. RNA-sequence analysis indicates significant changes in E. coli gene catalogue in Il10(-/-) mice, with changes mostly driven by adaptation to the intestinal environment. Expression of specific genes present in the tumour-promoting E. coli pks island are modulated by inflammation/CRC development. Thus, progression of inflammation in Il10(-/-) mice supports Enterobacteriaceae and alters a small subset of microbial genes important for tumour development.
Inflammation and microbiota are critical components of intestinal tumorigenesis. To dissect how the microbiota contributes to tumor distribution, we generated germ-free (GF)
and
;
mice and exposed ...them to specific-pathogen-free (SPF) or colorectal cancer-associated bacteria. We found that colon tumorigenesis significantly correlated with inflammation in SPF-housed
;
, but not in
mice. In contrast, small intestinal neoplasia development significantly correlated with age in both
;
and
mice. GF
;
mice conventionalized by an SPF microbiota had significantly more colon tumors compared with GF mice. Gnotobiotic studies revealed that while
clinical isolates with FadA and Fap2 adhesins failed to induce inflammation and tumorigenesis,
promoted tumorigenesis in the
;
model in a colibactin-dependent manner, suggesting colibactin is a driver of carcinogenesis. Our results suggest a distinct etiology of cancers in different locations of the gut, where colon cancer is primarily driven by inflammation and the microbiome, while age is a driving force for small intestine cancer.
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Cystic fibrosis (CF) is characterized by defective mucociliary clearance and chronic airway infection by a complex microbiota. Infection, persistent inflammation and periodic episodes of acute ...pulmonary exacerbation contribute to an irreversible decline in CF lung function. While the factors leading to acute exacerbations are poorly understood, antibiotic treatment can temporarily resolve pulmonary symptoms and partially restore lung function. Previous studies indicated that exacerbations may be associated with changes in microbial densities and the acquisition of new microbial species. Given the complexity of the CF microbiota, we applied massively parallel pyrosequencing to identify changes in airway microbial community structure in 23 adult CF patients during acute pulmonary exacerbation, after antibiotic treatment and during periods of stable disease. Over 350,000 sequences were generated, representing nearly 170 distinct microbial taxa. Approximately 60% of sequences obtained were from the recognized CF pathogens Pseudomonas and Burkholderia, which were detected in largely non-overlapping patient subsets. In contrast, other taxa including Prevotella, Streptococcus, Rothia and Veillonella were abundant in nearly all patient samples. Although antibiotic treatment was associated with a small decrease in species richness, there was minimal change in overall microbial community structure. Furthermore, microbial community composition was highly similar in patients during an exacerbation and when clinically stable, suggesting that exacerbations may represent intrapulmonary spread of infection rather than a change in microbial community composition. Mouthwash samples, obtained from a subset of patients, showed a nearly identical distribution of taxa as expectorated sputum, indicating that aspiration may contribute to colonization of the lower airways. Finally, we observed a strong correlation between low species richness and poor lung function. Taken together, these results indicate that the adult CF lung microbiome is largely stable through periods of exacerbation and antibiotic treatment and that short-term compositional changes in the airway microbiota do not account for CF pulmonary exacerbations.
Inflammation alters host physiology to promote cancer, as seen in colitis-associated colorectal cancer (CRC). Here, we identify the intestinal microbiota as a target of inflammation that affects the ...progression of CRC. High-throughput sequencing revealed that inflammation modifies gut microbial composition in colitis-susceptible interleukin-10—deficient (Il10⁻ / ⁻) mice. Monocolonization with the commensal Escherichia coli NC101 promoted invasive carcinoma in azoxymethane (AOM)—treated Il10⁻ / ⁻ mice. Deletion of the polyketide synthase (pks) genotoxic island from E. coli NC101 decreased tumor multiplicity and invasion in AOM/Il10⁻ / ⁻ mice, without altering intestinal inflammation. Mucosa-associated pks⁺ E. coli were found in a significantly high percentage of inflammatory bowel disease and CRC patients. This suggests that in mice, colitis can promote tumorigenesis by altering microbial composition and inducing the expansion of microorganisms with genotoxic capabilities.
Mucus-invasive bacterial biofilms are identified on the colon mucosa of approximately 50% of colorectal cancer (CRC) patients and approximately 13% of healthy subjects. Here, we test the hypothesis ...that human colon biofilms comprise microbial communities that are carcinogenic in CRC mouse models. Homogenates of human biofilm-positive colon mucosa were prepared from tumor patients (tumor and paired normal tissues from surgical resections) or biofilm-positive biopsies from healthy individuals undergoing screening colonoscopy; homogenates of biofilm-negative colon biopsies from healthy individuals undergoing screening colonoscopy served as controls. After 12 weeks, biofilm-positive, but not biofilm-negative, human colon mucosal homogenates induced colon tumor formation in 3 mouse colon tumor models (germ-free ApcMinΔ850/+;Il10-/- or ApcMinΔ850/+ and specific pathogen-free ApcMinΔ716/+ mice). Remarkably, biofilm-positive communities from healthy colonoscopy biopsies induced colon inflammation and tumors similarly to biofilm-positive tumor tissues. By 1 week, biofilm-positive human tumor homogenates, but not healthy biopsies, displayed consistent bacterial mucus invasion and biofilm formation in mouse colons. 16S rRNA gene sequencing and RNA-Seq analyses identified compositional and functional microbiota differences between mice colonized with biofilm-positive and biofilm-negative communities. These results suggest human colon mucosal biofilms, whether from tumor hosts or healthy individuals undergoing screening colonoscopy, are carcinogenic in murine models of CRC.
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