FEMS microbiology ecology最新文献

Many R packages provide statistical approaches for elucidating the diversity of soil microbes, yet they still struggle to visualize microbial traits on a geographical map. This creates challenges in interpreting microbial biogeography on a regional scale, especially when the spatial scale is large or the distribution of sampling sites is uneven. Here, we developed a lightweight, flexible, and user-friendly R package called microgeo. This package integrates many functions involved in reading, manipulating, and visualizing geographical boundary data; downloading spatial datasets; and calculating microbial traits and rendering them onto a geographical map using grid-based visualization, spatial interpolation, or machine learning. Using this R package, users can visualize any trait calculated by microgeo or other tools on a map and can analyze microbiome data in conjunction with metadata derived from a geographical map. In contrast to other R packages that statistically analyze microbiome data, microgeo provides more-intuitive approaches in illustrating the biogeography of soil microbes on a large geographical scale, serving as an important supplement to statistically driven comparisons and facilitating the biogeographic analysis of publicly accessible microbiome data at a large spatial scale in a more convenient and efficient manner. The microgeo R package can be installed from the Gitee (https://gitee.com/bioape/microgeo) and GitHub (https://github.com/ChaonanLi/microgeo) repositories. Detailed tutorials for the microgeo R package are available at https://chaonanli.github.io/microgeo.

Mucin is a glycoprotein secreted throughout the mammalian gastrointestinal tract that can support endogenous microorganisms in the absence of complex polysaccharides. While several mucin-degrading bacteria have been identified, the interindividual differences in microbial communities capable of metabolizing this complex polymer are not well described. To determine whether community assembly on mucin is deterministic across individuals or whether taxonomically distinct but functionally similar mucin-degrading communities are selected across fecal inocula, we used a 10-day in vitro sequential batch culture fermentation from three human donors with mucin as the sole carbon source. For each donor, 16S rRNA gene amplicon sequencing was used to characterize microbial community succession, and the short-chain fatty acid profile was determined from the final community. All three communities reached a steady-state by day 7 in which the community composition stabilized. Taxonomic comparisons amongst communities revealed that one of the final communities had Desulfovibrio, another had Akkermansia, and all three shared other members, such as Bacteroides. Metabolic output differences were most notable for one of the donor's communities, with significantly less production of acetate and propionate than the other two communities. These findings demonstrate the feasibility of developing stable mucin-degrading communities with shared and unique taxa. Furthermore, the mechanisms and efficiencies of mucin degradation across individuals are important for understanding how this community-level process impacts human health.

Microbiology laboratories are pivotal hubs for exploring the potential of microorganisms and addressing global challenges. Particularly, Environmental Microbiology facilities hold substantial influence in advancing knowledge and capabilities crucial for achieving the United Nations Sustainable Development Goals. This raises the imperative of integrating sustainable practices to mitigate the environmental impact of research activities and foster a culture of responsibility. Such an approach not only aligns with global sustainability objectives but also catalyses innovative, eco-conscious methodologies in scientific research aimed at tackling pressing environmental issues. Concerns regarding the environmental footprint of laboratory practices have stimulated innovative improvements within the scientific community, ranging from resource-efficient initiatives to the management of essential commodities like water and energy. This perspective discusses specific areas where microbiology laboratories can enhance their sustainability efforts, drawing on reports and case studies of pioneering groups. Additionally, it explores potential collaborators to support these endeavours and emphasises the pivotal role of early career researchers in driving this transition. By initiating discussions and sparking curiosity within the environmental microbial community, this commentary seeks to propel the microbial ecology field toward a greener future, starting from within the laboratory environment.

Host-associated microbial communities are shaped by host migratory movements. These movements can have contrasting impacts on microbiota, and understanding such patterns can provide insight into the ecological processes that contribute to community diversity. Furthermore, long-distance movements to new environments are anticipated to occur with increasing frequency due to host distribution shifts resulting from climate change. Understanding how hosts transport their microbiota with them could be of importance when examining biological invasions. Although microbial community shifts are well-documented, the underlying mechanisms that lead to the restructuring of these communities remain relatively unexplored. Using literature and ecological simulations, we develop a framework to elucidate the major factors that lead to community change. We group host movements into two types-regular (repeated/cyclical migratory movements, as found in many birds and mammals) and irregular (stochastic/infrequent movements that do not occur on a cyclical basis, as found in many insects and plants). Ecological simulations and prior research suggest that movement type and frequency, alongside environmental exposure (e.g. internal/external microbiota) are key considerations for understanding movement-associated community changes. From our framework, we derive a series of testable hypotheses, and suggest means to test them, to facilitate future research into host movement and microbial community dynamics.

Fungi are increasingly recognized to play diverse roles within honey bee hives, acting as pathogens, mutualists, and commensals. Pollen products, essential for hive nutrition, host significant fungal communities with potential protective and nutritional benefits. In this study, we profile the fungal communities and antifungal properties of three pollen products from healthy and stressed hives: fresh pollen collected by forager bees from local plants; stored pollen packed into the comb inside the hive; and bee bread, which is stored pollen following anaerobic fermentation used for bee and larval nutrition. Using amplicon sequencing, we found significant differences in fungal community composition, with hive health and sample type accounting for 8.8% and 19.3% of variation in beta diversity, respectively. Pollen and bee bread extracts had species-specific antimicrobial activity and inhibited the fungal hive pathogens Ascosphaera apis, Aspergillus flavus, and Aspergillus fumigatus, and the bacterial hive pathogen Paenibacillus larvae. Activity was positively correlated with phenolic and antioxidant content and was diminished in stressed hives. The plant source of pollen determined by amplicon sequencing differed in stressed hives, suggesting altered foraging behaviour. These findings illustrate the complex interplay between honey bees, fungal communities, and hive products, which should be considered in hive management and conservation.

Aerobic anoxygenic phototrophic (AAP) bacteria are an important component of freshwater bacterioplankton. They can support their heterotrophic metabolism with energy from light, enhancing their growth efficiency. Based on results from cultures, it was hypothesized that photoheterotrophy provides an advantage under carbon limitation and facilitates access to recalcitrant or low-energy carbon sources. However, verification of these hypotheses for natural AAP communities has been lacking. Here, we conducted whole community manipulation experiments and compared the growth of AAP bacteria under carbon limited and with recalcitrant or low-energy carbon sources under dark and light (near-infrared light, λ > 800 nm) conditions to elucidate how they profit from photoheterotrophy. We found that AAP bacteria induce photoheterotrophic metabolism under carbon limitation, but they overcompete heterotrophic bacteria when carbon is available. This effect seems to be driven by physiological responses rather than changes at the community level. Interestingly, recalcitrant (lignin) or low-energy (acetate) carbon sources inhibited the growth of AAP bacteria, especially in light. This unexpected observation may have ecosystem-level consequences as lake browning continues. In general, our findings contribute to the understanding of the dynamics of AAP bacteria in pelagic environments.

Microbial communities are vital to our lives, yet their ecological functioning and dynamics remain poorly understood. This understanding is crucial for assessing threats to these systems and leveraging their biotechnological applications. Given that temporal dynamics are linked to community functioning, this study investigated the drivers of community succession in the wine yeast community. We experimentally generated population dynamics data and used it to create an interpretable model with a gradient boosted regression tree approach. The model was trained on temporal data of viable species populations in various combinations, including pairs, triplets, and quadruplets, and was evaluated for predictive accuracy and input feature importance. Key findings revealed that the inoculation dosage of non-Saccharomyces species significantly influences their performance in mixed cultures, while Saccharomyces cerevisiae consistently dominates regardless of initial abundance. Additionally, we observed multispecies interactions where the dynamics of Wickerhamomyces anomalus were influenced by Torulaspora delbrueckii in pairwise cultures, but this interaction was altered by the inclusion of S. cerevisiae. This study provides insights into yeast community succession and offers valuable machine learning-based analysis techniques applicable to other microbial communities, opening new avenues for harnessing microbial communities.

The gut microbiota of vertebrates is acquired from the environment and other individuals, including parents and unrelated conspecifics. In the laboratory mouse, a key animal model, inter-individual interactions are severely limited and its gut microbiota is abnormal. Surprisingly, our understanding of how inter-individual transmission impacts house mouse gut microbiota is solely derived from laboratory experiments. We investigated the effects of inter-individual transmission on gut microbiota in two subspecies of house mice (Mus musculus musculus and M. m. domesticus) raised in a semi-natural environment without social or mating restrictions. We assessed the correlation between microbiota composition (16S rRNA profiles), social contact intensity (microtransponder-based social networks), and mouse relatedness (microsatellite-based pedigrees). Inter-individual transmission had a greater impact on the lower gut (colon and cecum) than on the small intestine (ileum). In the lower gut, relatedness and social contact independently influenced microbiota similarity. Despite female-biased parental care, both parents exerted a similar influence on their offspring's microbiota, diminishing with the offspring's age in adulthood. Inter-individual transmission was more pronounced in M. m. domesticus, a subspecies, with a social and reproductive network divided into more closed modules. This suggests that the transmission magnitude depends on the social and genetic structure of the studied population.

Periodontal diseases are among the most common bacterial-related pathologies affecting the oral cavity of dogs. Nevertheless, the canine oral ecosystem and its correlations with oral disease development are still far from being fully characterized. In this study, the species-level taxonomic composition of saliva and dental plaque microbiota of 30 healthy dogs was investigated through a shallow shotgun metagenomics approach. The obtained data allowed not only to define the most abundant and prevalent bacterial species of the oral microbiota in healthy dogs, including members of the genera Corynebacterium and Porphyromonas, but also to identify the presence of distinct compositional motifs in the two oral microniches as well as taxonomical differences between dental plaques collected from anterior and posterior teeth. Subsequently, the salivary and dental plaque microbiota of 18 dogs affected by chronic gingival inflammation and 18 dogs with periodontitis were compared to those obtained from the healthy dogs. This analysis allowed the identification of bacterial and metabolic biomarkers correlated with a specific clinical status, including members of the genera Porphyromonas and Fusobacterium as microbial biomarkers of a healthy and diseased oral status, respectively, and genes predicted to encode for metabolites with anti-inflammatory properties as metabolic biomarkers of a healthy status.