Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation.

BACKGROUND: The understanding of how microbiomes assemble, function, and evolve requires metagenomic tools that can resolve microbiota compositions at the strain level. However, the identification and tracking of microbial strains in fecal metagenomes is challenging and available tools variably classify subspecies lineages, which affects their applicability to infer microbial persistence and transfer. RESULTS: We introduce SameStr, a bioinformatic tool that identifies shared strains in metagenomes by determining single-nucleotide variants (SNV) in species-specific marker genes, which are compared based on a maximum variant profile similarity. We validated SameStr on mock strain populations, available human fecal metagenomes from healthy individuals and newly generated data from recurrent Clostridioides difficile infection (rCDI) patients treated with fecal microbiota transplantation (FMT). SameStr demonstrated enhanced sensitivity to detect shared dominant and subdominant strains in related samples (where strain persistence or transfer would be expected) when compared to other tools, while being robust against false-positive shared strain calls between unrelated samples (where neither strain persistence nor transfer would be expected). We applied SameStr to identify strains that are stably maintained in fecal microbiomes of healthy adults over time (strain persistence) and that successfully engraft in rCDI patients after FMT (strain engraftment). Taxonomy-dependent strain persistence and engraftment frequencies were positively correlated, indicating that a specific core microbiota of intestinal species is adapted to be competitive both in healthy microbiomes and during post-FMT microbiome assembly. We explored other use cases for strain-level microbiota profiling, as a metagenomics quality control measure and to identify individuals based on the persisting core gut microbiota. CONCLUSION: SameStr provides for a robust identification of shared strains in metagenomic sequence data with sufficient specificity and sensitivity to examine strain persistence, transfer, and engraftment in human fecal microbiomes. Our findings identify a persisting healthy adult core gut microbiota, which should be further studied to shed light on microbiota contributions to chronic diseases. Video abstract.

Bacterial microbiota diversity and composition in red and white wines correlate with plant-derived DNA contributions and botrytis infection.

Wine is a globally produced, marketed and consumed alcoholic beverage, which is valued for its aromatic and qualitative complexity and variation. These properties are partially attributable to the bacterial involvement in the fermentation process. However, the organizational principles and dynamic changes of the bacterial wine microbiota remain poorly understood, especially in the context of red and white wine variations and environmental stress factors. Here, we determined relative and absolute bacterial microbiota compositions from six distinct cultivars during the first week of fermentation by quantitative and qualitative 16S rRNA gene amplification and amplicon sequencing. All wines harboured complex and variable bacterial communities, with Tatumella as the most abundant genus across all batches, but red wines were characterized by higher bacterial diversity and increased relative and absolute abundance of lactic and acetic acid bacteria (LAB/AAB) and bacterial taxa of predicted environmental origin. Microbial diversity was positively correlated with plant-derived DNA concentrations in the wine and Botrytis cinerea infection before harvest. Our findings suggest that exogenous factors, such as procedural differences between red and white wine production and environmental stress on grape integrity, can increase bacterial diversity and specific bacterial taxa in wine, with potential consequences for wine quality and aroma.

High intake of orange juice and cola differently affects metabolic risk in healthy subjects.

BACKGROUND: Higher consumption of sugar-containing beverages has been associated with an elevated risk of type 2 diabetes and gout. Whether this equally applies to cola with an unhealthy image and orange juice (OJ) having a healthy image remains unknown. METHODS: In order to investigate whether OJ and cola differently affect metabolic risk 26 healthy adults (24.7 ± 3.2 y; BMI 23.2 ± 3.3 kg/m2) participated in a 2 × 2-wk intervention and consumed either OJ or caffeine-free cola (20% Ereq as sugar from beverages) in-between 3 meals/d at ad libitum energy intake. Glycemic control, uric acid metabolism and gut microbiota were assessed as outcome parameters. RESULTS: Fecal microbiota, body weight, basal and OGTT-derived insulin sensitivity remained unchanged in both intervention periods. Levels of uric acid were normal at baseline and did not change with 2-wk cola consumption (-0.03 ± 0.67 mg/dL; p > 0.05), whereas they decreased with OJ intervention (-0.43 ± 0.56 mg/dL; p < 0.01) due to increased uric acid excretion (+130.2 ± 130.0 mg/d; p < 0.001). Compared to OJ, consumption of cola led to a higher daylong glycemia (ΔiAUC: 36.9 ± 83.2; p < 0.05), an increase in glucose variability (ΔMAGE-Index: 0.29 ± 0.44; p < 0.05), and a lower 24 h-insulin secretion (ΔC-peptide excretion: -31.76 ± 38.61 µg/d; p < 0.001), which may be explained by a decrease in serum potassium levels (-0.11 ± 0.24 mmol/L; p < 0.05). CONCLUSION: Despite its sugar content, regular consumption of large amounts of OJ do not increase the risk of gout but may even contribute to lower uric acid levels. The etiology of impaired insulin secretion with cola consumption needs to be further investigated.

Qualitative and Quantitative DNA- and RNA-Based Analysis of the Bacterial Stomach Microbiota in Humans, Mice, and Gerbils.

Clinical interventions in the stomach have been linked to fecal microbiota alterations, suggesting a function of the stomach in gastrointestinal (GI) homeostasis. We sought to determine the taxonomic bacterial biogeography of the upper GI tract, including different sites within the human stomach (cardia, corpus, and antrum), adjacent upstream (esophagus) and downstream (duodenum) locations, and luminal contents (aspirate), as well as whole-stomach samples from mice and gerbils. Qualitative and quantitative DNA- and RNA-based taxonomic microbiota analyses were combined to study the relationship of relative and absolute bacterial abundances and transcriptionally active bacterial microbiota components in the stomach of humans and mice. Stomach microbiota compositions resembled those of esophagus and duodenum. However, along the descending GI tract, the relative abundances of specific oropharyngeal commensals decreased (Streptococcus) or increased (Rothia mucilaginosa, Porphyromonas, and Lachnospiraceae). Furthermore, the compositional similarity (weighted UniFrac) between stomach aspirates and esophageal biopsy samples increased with gastric Streptococcus relative abundance. In both human aspirate and mouse stomach samples, Firmicutes were more abundant among transcriptionally active bacteria than Bacteroidetes. The relative abundance of Firmicutes in the stomach was negatively correlated and that of Bacteroidetes was positively correlated with absolute bacterial abundance, suggesting a disproportionate increase of Bacteroidetes over Firmicutes at higher bacterial densities. Human, mouse, and gerbil stomach samples showed similarities at higher taxonomic levels but differences at lower taxonomic levels. Our findings suggest selective enrichment and depletion of specific bacterial taxa in the stomach and Firmicutes being transcriptionally more active than Bacteroidetes that increase in relative abundance with total bacterial load. IMPORTANCE Clinical stomach interventions, such as acid inhibition or bypass surgery, have been linked to fecal microbiota alterations. We demonstrate that the stomach microbiota largely overlaps those of adjacent gastrointestinal locations and identify gradual decreases and increases in the relative abundances of specific bacteria within the stomach, suggesting selective enrichment and depletion. Moreover, similarities between stomach and esophagus samples are proportional to the concentrations of Streptococcus (Firmicutes) in the stomach. The relative abundance of Firmicutes in the stomach, compared to that of Bacteroidetes, is increased in RNA relative to DNA, indicating higher transcriptional activity. Moreover, increased absolute bacterial loads are associated with decreased relative abundance of Firmicutes and higher relative abundance of Bacteroidetes. Our findings characterize the stomach microbiota as influenced by Bacteroidetes influx against a background of transcriptionally more active Firmicutes. Human, mouse, and gerbil stomach microbiotas differ at lower taxonomic levels, which might affect the utility of these model organisms.

Inherited genetic susceptibility to monoclonal gammopathy of unknown significance.

Monoclonal gammopathy of undetermined significance (MGUS) is present in ∼2% of individuals age >50 years. The increased risk of multiple myeloma (MM) in relatives of individuals with MGUS is consistent with MGUS being a marker of inherited genetic susceptibility to MM. Common single-nucleotide polymorphisms (SNPs) at 2p23.3 (rs6746082), 3p22.1 (rs1052501), 3q26.2 (rs10936599), 6p21.33 (rs2285803), 7p15.3 (rs4487645), 17p11.2 (rs4273077), and 22q13.1 (rs877529) have recently been shown to influence MM risk. To examine the impact of these 7 SNPs on MGUS, we analyzed two case-control series totaling 492 cases and 7306 controls. Each SNP independently influenced MGUS risk with statistically significant associations (P < .02) for rs1052501, rs2285803, rs4487645, and rs4273077. SNP associations were independent, with risk increasing with a larger number of risk alleles carried (per allele odds ratio, 1.18; P < 10(-7)). Collectively these data are consistent with a polygenic model of disease susceptibility to MGUS.

Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk.

To identify variants for multiple myeloma risk, we conducted a genome-wide association study with validation in additional series totaling 4,692 individuals with multiple myeloma (cases) and 10,990 controls. We identified four risk loci at 3q26.2 (rs10936599, P = 8.70 × 10(-14)), 6p21.33 (rs2285803, PSORS1C2, P = 9.67 × 10(-11)), 17p11.2 (rs4273077, TNFRSF13B, P = 7.67 × 10(-9)) and 22q13.1 (rs877529, CBX7, P = 7.63 × 10(-16)). These data provide further evidence for genetic susceptibility to this B-cell hematological malignancy, as well as insight into the biological basis of predisposition.