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Bacterial growth often alters the environment, which in turn can impact interspecies interactions among bacteria. Here, we used an in vitro batch system containing mucin beads to emulate the dynamic host environment and to study its impact on the interactions between two abundant and prevalent human gut bacteria, the primary fermenter Bacteroides thetaiotaomicron and the butyrate producer Roseburia intestinalis. By combining machine learning and flow cytometry, we found that the number of viable B. thetaiotaomicron cells decreases with glucose consumption due to acid production, while R. intestinalis survives post-glucose depletion by entering a slow growth mode. Both species attach to mucin beads, but only viable cell counts of B. thetaiotaomicron increase significantly. The number of viable co-culture cells varies significantly over time compared to those of monocultures. A combination of targeted metabolomics and RNA-seq showed that the slow growth mode of R. intestinalis represents a diauxic shift towards acetate and lactate consumption, whereas B. thetaiotaomicron survives glucose depletion and low pH by foraging on mucin sugars. In addition, most of the mucin monosaccharides we tested inhibited the growth of R. intestinalis but not B. thetaiotaomicron. We encoded these causal relationships in a kinetic model, which reproduced the observed dynamics. In summary, we explored how R. intestinalis and B. thetaiotaomicron respond to nutrient scarcity and how this affects their dynamics. We highlight the importance of understanding bacterial metabolic strategies to effectively modulate microbial dynamics in changing conditions., (© 2023. The Author(s).)

Microbial network construction is a popular explorative data analysis technique in microbiome research. Although a large number of microbial network construction tools has been developed to date, there are several issues concerning the construction and interpretation of microbial networks that have received less attention. The purpose of this perspective is to draw attention to these underexplored challenges of microbial network construction and analysis., (© 2021. The Author(s).)

Soil ecological functions are largely determined by the activities of soil microorganisms, which, in turn, are regulated by relevant interactions between genes and their corresponding pathways. Therefore, the genetic network can theoretically elucidate the functional organization that supports complex microbial community functions, although this has not been previously attempted. We generated a genetic correlation network based on 5421 genes derived from metagenomes of forest soils, identifying 7191 positive and 123 negative correlation relationships. This network consisted of 27 clusters enriched with sets of genes within specific functions, represented with corresponding cluster hubs. The clusters revealed a hierarchical architecture, reflecting the functional organization in the soil metagenomes. Positive correlations mapped functional associations, whereas negative correlations often mapped regulatory processes. The potential functions of uncharacterized genes were predicted based on the functions of located clusters. The global genetic correlation network highlights the functional organization in soil metagenomes and provides a resource for predicting gene functions. We anticipate that the genetic correlation network may be exploited to comprehensively decipher soil microbial community functions.

The study of host-associated microbial community composition has suggested the presence of alternative community types. We discuss three mechanisms that could explain these observations. The most commonly invoked mechanism links community types to a response to environmental change; alternatively, community types were shown to emerge from interactions between members of local communities sampled from a metacommunity. Here, we emphasize multi-stability as a third mechanism, giving rise to different community types in the same environmental conditions. We illustrate with a toy model how multi-stability can generate community types and discuss the consequences of multi-stability for data interpretation.

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Nucleo-cytoplasmic large DNA viruses (NCLDVs) constitute a group of eukaryotic viruses that can have crucial ecological roles in the sea by accelerating the turnover of their unicellular hosts or by causing diseases in animals. To better characterize the diversity, abundance and biogeography of marine NCLDVs, we analyzed 17 metagenomes derived from microbial samples (0.2-1.6 μm size range) collected during the Tara Oceans Expedition. The sample set includes ecosystems under-represented in previous studies, such as the Arabian Sea oxygen minimum zone (OMZ) and Indian Ocean lagoons. By combining computationally derived relative abundance and direct prokaryote cell counts, the abundance of NCLDVs was found to be in the order of 10(4)-10(5) genomes ml(-1) for the samples from the photic zone and 10(2)-10(3) genomes ml(-1) for the OMZ. The Megaviridae and Phycodnaviridae dominated the NCLDV populations in the metagenomes, although most of the reads classified in these families showed large divergence from known viral genomes. Our taxon co-occurrence analysis revealed a potential association between viruses of the Megaviridae family and eukaryotes related to oomycetes. In support of this predicted association, we identified six cases of lateral gene transfer between Megaviridae and oomycetes. Our results suggest that marine NCLDVs probably outnumber eukaryotic organisms in the photic layer (per given water mass) and that metagenomic sequence analyses promise to shed new light on the biodiversity of marine viruses and their interactions with potential hosts.