Keeping mammalian gastrointestinal (GI) tract communities in balance is crucial for host health maintenance. However, our understanding of microbial communities in the GI tract is still very limited. In this study, samples taken from the GI tracts of C57BL/6 mice were subjected to 16S rRNA gene sequence-based analysis to examine the characteristic bacterial communities along the mouse GI tract, including those present in the stomach, duodenum, jejunum, ileum, cecum, colon and feces. Further analyses of the 283,234 valid sequences obtained from pyrosequencing revealed that the gastric, duodenal, large intestinal and fecal samples had higher phylogenetic diversity than the jejunum and ileum samples did. The microbial communities found in the small intestine and stomach were different from those seen in the large intestine and fecal samples. A greater proportion of Lactobacillaceae were found in the stomach and small intestine, while a larger proportion of anaerobes such as Bacteroidaceae, Prevotellaceae, Rikenellaceae, Lachnospiraceae, and Ruminococcaceae were found in the large intestine and feces. In addition, inter-mouse variations of microbiota were observed between the large intestinal and fecal samples, which were much smaller than those between the gastric and small intestinal samples. As far as we can ascertain, ours is the first study to systematically characterize bacterial communities from the GI tracts of C57BL/6 mice.

Periodontitis is a kind of infectious disease initiated by colonization of subgingival periodontal pathogens, which cause destruction of tooth-supporting tissues, and is a predominant threat to oral health as the most common cause of loss of teeth. The aim of this pilot study was to characterize the subgingival bacterial biodiversity of periodontal pockets with different probing depths in patients with different forms of periodontitis. Twenty-one subgingival plaque samples were collected from three patients with chronic periodontitis (ChP), three patients with aggressive periodontitis (AgP) and three periodontally healthy subjects (PH). Each patient with periodontitis was sampled at three sites, at different probing depths (PDs, one each at 4 mm, 5–6 mm, and ≥ 7 mm). Using 16S rRNA gene high-throughput sequencing and bioinformatic analysis, we found that subgingival communities in health and periodontitis samples largely differed. Meanwhile, Acholeplasma, Fretibacterium, Porphyromonas, Peptococcus, Treponema_2, Defluviitaleaceae_UCG_011, Filifactor, and Mycoplasma increased with the deepening of the pockets in ChP, whilst only Corynebacterium was negatively associated with PD. In AgP, Corynebacterium and Klebsiella were positively associated with PD, while Serratia, Pseudoramibacter, Defluviitaleaceae_UCG_011, and Desulfobulbus were negatively associated with PD. And among these two groups, Corynebacterium shifted differently. Moreover, in subgingival plaque, the unweighted UniFrac distances between samples from pockets with different PD in the same patients were significantly lower than those from pockets within the same PD category from different patients. This study demonstrated the shift of the subgingival microbiome in individual teeth sites during disease development. Within the limitation of the relative small sample size, this pilot study shed new light on the dynamic relationship between the extent of periodontal destruction and the subgingival microbiome.

Depression is a chronic, recurrent mental disease, which could make the patients commit suicide in severe cases. Considering that gut microbiome dysbiosis could cause depressive symptoms in animals through the MGB axis, the modification of gut microbiota is expected to be a potential therapy for depression, but the daily administration of probiotics is invalid or transient.

Microorganisms can be found in most extreme environments, and they have to adapt to a wide range of environmental stresses. The two-component systems (TCSs) found in bacteria detect environmental stimuli and regulate physiological pathways for survival.

Recent characterization of the obligate episymbiont Saccharibacteria (TM7) belonging to the candidate phyla radiation (CPR) has expanded the extent of microbial diversity. However, the episymbiotic lifestyle of TM7 is still underexploited due to the deficiency of cultivated representatives. Here, we describe gene-targeted TM7 cultivation guided by repurposing epicPCR (emulsion, paired isolation, and concatenation PCR) to capture in situ TM7‒host associations. Using this method, we obtained a novel Saccharibacteria isolate TM7i and its host Leucobacter aridicollis J1 from Cicadae Periostracum, the castoff shell of cicada. Genomic analyses and microscopic characterizations revealed that TM7i could bind to J1 through twitching-like motility mediated by type IV pili (T4P). We further showed that the inhibition of T4P extrusion suppressed the motility and host adherence of TM7i, resulting in its reduced growth. However, the inactivation of T4P had little effect on the growth of TM7i that had already adhered to J1, suggesting the essential role of T4P in host recognition by TM7i. By capturing CPR‒host association and elaborating the T4P-dependent episymbiotic association mechanism, our studies shed light on the distinct yet widespread lifestyle of CPR bacteria.

Microbial communities execute metabolic pathways to drive global nutrient cycles. Within a community, functionally specialized strains can perform different yet complementary steps within a linear pathway, a phenomenon termed metabolic division of labor (MDOL). However, little is known about how such metabolic behaviors shape microbial communities. Here, we derive a theoretical framework to define the assembly of a community that degrades an organic compound through MDOL. The framework indicates that to ensure community stability, the strains performing the initial steps should hold a growth advantage (m) over the "private benefit" (n) of the strain performing the last step. The steady-state frequency of the last strain is then determined by the quotient of n and m. Our experiments show that the framework accurately predicts the assembly of our synthetic consortia that degrade naphthalene through MDOL. Our results provide insights for designing and managing stable microbial systems for metabolic pathway optimization.