Disentangling the growth curve of microbial culture

Many researchers have studied the population dynamics of microbe of microbes as a typical example of population dynamics. The Monod equation, which mainly focuses on the growth and stationary phases, is used when plotting a growth curve. However, the growth potential in the late stage of culture has been overlooked. Previous studies considered the direct degradation of products to the limiting substrate. In this study, we considered microbial growth during the stationary phase, which enables us to describe the dynamics precisely. The microbes were divided into two populations: one grew by consuming the limiting substrate and the other degraded the products by metabolism. According to the numerical analysis of our model, microbes may choose one of two strategies: one consumes substrates and expands quickly, and the other grows slowly while cleaning up the environment in which they thrive. Furthermore, we found three types of microbial growth depending on their ability to detect metabolite accumulation. Using experimentally measured data, this model can estimate the dynamics of cell density, the substrates, and the metabolites used. The model's disentangling of growth curves offers novel interpretive possibilities for culture system dynamics.

Dynamic Associations of Milk Components With the Infant Gut Microbiome and Fecal Metabolites in a Mother-Infant Model by Microbiome, NMR Metabolomic, and Time-Series Clustering Analyses

Background: The gut microbiome and fecal metabolites of breastfed infants changes during lactation, and are influenced by breast milk components. This study aimed to investigate dynamic associations of milk components with the infant gut microbiome and fecal metabolites throughout the lactation period in a mother–infant model.

Methods: One month after delivery, breast milk and subsequent infant feces were collected in a pair for 5 months from a mother and an exclusively breastfed infant. Composition of the fecal microbiome was determined with 16S rRNA sequencing. Low-molecular-weight metabolites, including human milk oligosaccharides (HMOs), and antibacterial proteins were measured in feces and milk using ¹H NMR metabolomics and enzyme-linked immunosorbent assays. The association of milk bioactive components with the infant gut microbiome and fecal metabolites was determined with Python clustering and correlation analyses.

Results: The HMOs in milk did not fluctuate throughout the lactation period. However, they began to disappear in infant feces at the beginning of month 4. Notably, at this time-point, a bifidobacterium species switching (from B. breve to B. longum subsp. infantis) occurred, accompanied by fluctuations in several metabolites including acetate and butyrate in infant feces.

Conclusions: Milk bioactive components, such as HMOs, might play different roles in the exclusively breastfed infants depending on the lactation period.

Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks

Network-based assessments are important for disentangling complex microbial and microbial–host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka–Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.

Genomic characterization of closely related species in the Rumoiensis clade infers ecogenomic signatures to non-marine environments

Members of the family Vibrionaceae are generally found in marine and brackish environments, playing important roles in nutrient cycling. The Rumoiensis clade is an unconventional group in the genus Vibrio, currently comprising six species from different origins including two species isolated from non-marine environments. In this study, we performed comparative genome analysis of all six species in the clade using their complete genome sequences. We found that two non-marine species, Vibrio casei and Vibrio gangliei, lacked the genes responsible for algal polysaccharide degradation, while a number of glycoside hydrolase genes were enriched in these two species. Expansion of insertion sequences was observed in V. casei and Vibrio rumoiensis, which suggests ongoing genomic changes associated with niche adaptations. The genes responsible for the metabolism of glucosylglycerate, a compound known to play a role as compatible solutes under nitrogen limitation, were conserved across the clade. These characteristics, along with genes encoding species-specific functions, may reflect the habit expansion which has led to the current distribution of Rumoiensis clade species. Genome analysis of all species in a single clade give us valuable insights into the genomic background of the Rumoiensis clade species and emphasize the genomic diversity and versatility of Vibrionaceae.