The soil microbiome represents one of the most complex microbial communities on the planet, encompassing thousands of taxa and metabolic pathways, rendering holistic analyses computationally intensive and difficult. Here, we developed an alternative approach in which the complex soil microbiome was broken into components ("functional modules"), based on metabolic capacities, for individual characterization. We hypothesized that reproducible, low-complexity communities that represent functional modules could be obtained through targeted enrichments and that, in combination, they would encompass a large extent of the soil microbiome diversity. Enrichments were performed on a starting soil inoculum with defined media based on specific carbon substrates, antibiotics, alternative electron acceptors under anaerobic conditions, or alternative growing conditions reflective of common field stresses. The resultant communities were evaluated through 16S rRNA amplicon sequencing. Less permissive modules (anaerobic conditions, complex polysaccharides, and certain stresses) resulted in more distinct community profiles with higher richness and more variability between replicates, whereas modules with simple substrates were dominated by fewer species and were more reproducible. Collectively, approximately 27% of unique taxa present in the liquid soil extract control were found across functional modules. Taxa that were underrepresented or undetected in the source soil were also enriched across the modules. Metatranscriptomic analyses were carried out on a subset of the modules to investigate differences in functional gene expression. These results demonstrate that by dissecting the soil microbiome into discrete components it is possible to obtain a more comprehensive view of the soil microbiome and its biochemical potential than would be possible using more holistic analyses.IMPORTANCE The taxonomic and functional diversity inherent to the soil microbiome complicate assessments of the metabolic potential carried out by the community members. An alternative approach is to break down the soil microbiome into reduced-complexity subsets based on metabolic capacities (functional modules) prior to sequencing and analysis. Here, we demonstrate that this approach successfully identified specific phylogenetic and biochemical traits of the soil microbiome that otherwise remained hidden from a more top-down analysis.

Background

Many human microbial infectious diseases including dental caries are polymicrobial in nature. How these complex multi-species communities evolve from a healthy to a diseased state is not well understood. Although many health- or disease-associated oral bacteria have been characterized in vitro, their physiology within the complex oral microbiome is difficult to determine with current approaches. In addition, about half of these species remain uncultivated to date with little known besides their 16S rRNA sequence. Lacking culture-based physiological analyses, the functional roles of uncultivated species will remain enigmatic despite their apparent disease correlation. To start addressing these knowledge gaps, we applied a combination of Magnetic Resonance Spectroscopy (MRS) with RNA and DNA based Stable Isotope Probing (SIP) to oral plaque communities from healthy children for in vitro temporal monitoring of metabolites and identification of metabolically active and inactive bacterial species.

Methodology/principal findings

Supragingival plaque samples from caries-free children incubated with (13)C-substrates under imposed healthy (buffered, pH 7) and diseased states (pH 5.5 and pH 4.5) produced lactate as the dominant organic acid from glucose metabolism. Rapid lactate utilization upon glucose depletion was observed under pH 7 conditions. SIP analyses revealed a number of genera containing cultured and uncultivated taxa with metabolic capabilities at pH 5.5. The diversity of active species decreased significantly at pH 4.5 and was dominated by Lactobacillus and Propionibacterium species, both of which have been previously found within carious lesions from children.

Conclusions/significance

Our approach allowed for identification of species that metabolize carbohydrates under different pH conditions and supports the importance of Lactobacilli and Propionibacterium in the development of childhood caries. Identification of species within healthy subjects that are active at low pH can lead to a better understanding of oral caries onset and generate appropriate targets for preventative measures in the early stages.

The gut microbiome plays an important role in the mammalian host and when in proper balance helps protect health and prevent disease. Host environmental stress and its influence on the gut microbiome, health, and disease is an emerging area of research. Exposures to unnatural light cycles are becoming increasingly common due to travel and shift work. However, much remains unknown about how these changes influence the microbiome and host health. This information is needed to understand and predict the relationship between the microbiome and host response to altered sleep cycles. In the present study, we exposed three cohorts of mice to different light cycle regimens for 12 consecutive weeks; including continuous light, continuous dark, and a standard light dark regimen consisting of 12 h light followed by 12 h of dark. After exposure, motor and memory behavior, and the composition of the fecal microbiome and plasma metabolome were measured. Memory potential was significantly reduced in mice exposed to continuous light, whereas rotarod performance was minimally affected. The overall composition of the microbiome was relatively constant over time. However, Bacteroidales Rikenellaceae was relatively more abundant in mice exposed to continuous dark, while Bacteroidales S24-7 was relatively more abundant in mice exposed to continuous light. The plasma metabolome after the continuous dark exposure differed from the other exposure conditions. Several plasma metabolites, including glycolic acid, tryptophan, pyruvate, and several unidentified metabolites, were correlated to continuous dark and light exposure conditions. Networking analyses showed that serotonin was positively correlated with three microbial families (Rikenellaceae, Ruminococcaceae, and Turicibacteraceae), while tryptophan was negatively correlated with abundance of Bacteroidales S24-7 based on light exposure. This study provides the foundation for future studies into the mechanisms underlying the role of the gut microbiome on the murine host during light-dark stress.

Although the gut microbiome plays important roles in host physiology, health and disease¹, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut. We used the genetically diverse Collaborative Cross mouse system² to discover that early life history impacts the microbiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes in the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.

Background

Recent evidence has linked the gut microbiome to host behavior via the gut-brain axis [1-3]; however, the underlying mechanisms remain unexplored. Here, we determined the links between host genetics, the gut microbiome and memory using the genetically defined Collaborative Cross (CC) mouse cohort, complemented with microbiome and metabolomic analyses in conventional and germ-free (GF) mice.

Results

A genome-wide association analysis (GWAS) identified 715 of 76,080 single-nucleotide polymorphisms (SNPs) that were significantly associated with short-term memory using the passive avoidance model. The identified SNPs were enriched in genes known to be involved in learning and memory functions. By 16S rRNA gene sequencing of the gut microbial community in the same CC cohort, we identified specific microorganisms that were significantly correlated with longer latencies in our retention test, including a positive correlation with Lactobacillus. Inoculation of GF mice with individual species of Lactobacillus (L. reuteri F275, L. plantarum BDGP2 or L. brevis BDGP6) resulted in significantly improved memory compared to uninoculated or E. coli DH10B inoculated controls. Untargeted metabolomics analysis revealed significantly higher levels of several metabolites, including lactate, in the stools of Lactobacillus-colonized mice, when compared to GF control mice. Moreover, we demonstrate that dietary lactate treatment alone boosted memory in conventional mice. Mechanistically, we show that both inoculation with Lactobacillus or lactate treatment significantly increased the levels of the neurotransmitter, gamma-aminobutyric acid (GABA), in the hippocampus of the mice.

Conclusion

Together, this study provides new evidence for a link between Lactobacillus and memory and our results open possible new avenues for treating memory impairment disorders using specific gut microbial inoculants and/or metabolites. Video Abstract.