BSocial: Deciphering Social Behaviors within Mixed Microbial Populations

The social microbiome: gut microbiome diversity and abundance are negatively associated with sociality in a wild mammal

The gut microbiome has a well-documented relationship with host fitness. Greater microbial diversity and abundance of specific microbes have been associated with improved fitness outcomes. Intestinal microbes also may be associated with patterns of social behaviour. However, these associations have been largely studied in captive animal models; we know less about microbiome composition as a potential driver of individual social behaviour and position in the wild. We used linear mixed models to quantify the relationship between fecal microbial composition, diversity and social network traits in a wild population of yellow-bellied marmots (Marmota flaviventer). We focused our analyses on microbes previously linked to sociability and neurobehavioural alterations in captive rodents, primates and humans. Using 5 years of data, we found microbial diversity (Shannon-Wiener and Faith's phylogenetic diversity) has a modest yet statistically significant negative relationship with the number of social interactions an individual engaged in. We also found a negative relationship between Streptococcus spp. relative abundance and two social network measures (clustering coefficient and embeddedness) that quantify an individual's position relative to others in their social group. These findings highlight a potentially consequential relationship between microbial composition and social behaviour in a wild social mammal.

Interaction of tetrahydrofuran and methyl tert-butyl ether in waste gas treatment by a biotrickling filter bioaugmented with Piscinibacter caeni MQ-18 and Pseudomonas oleovorans DT4

Bioaugmented biotrickling filter (BTF) seeded with Piscinibacter caeni MQ-18, Pseudomonas oleovorans DT4, and activated sludge was established to investigate the treatment performance and biodegradation kinetics of the gaseous mixtures of tetrahydrofuran (THF) and methyl tert-butyl ether (MTBE). Experimental results showed an enhanced startup performance with a startup period of 9 d in bioaugmented BTF (25 d in control BTF seeded with activated sludge). The interaction parameter I_2,1 of control (7.462) and bioaugmented BTF (3.267) obtained by the elimination capacity-sum kinetics with interaction parameter (EC-SKIP) model indicated that THF has a stronger inhibition of MTBE biodegradation in the control BTF than in the bioaugmented BTF. Similarly, the self-inhibition EC-SKIP model quantified the positive effects of MTBE on THF biodegradation, as well as the negative effects of THF on MTBE biodegradation and the self-inhibition of MTBE and THF. Metabolic intermediate analysis, real-time quantitative polymerase chain reaction, biofilm-biomass determination, and high-throughput sequencing revealed the possible mechanism of the enhanced treatment performance and biodegradation interactions of MTBE and THF.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.

Decoding social behaviors in a glycerol dependent bacterial consortium during Reactive Blue 28 degradation

Biodegradation of reactive azo dyes has been an arduous problem for decades. Several efficient biosystems have been proposed for dye degradation, but most of them are dependent on the availability of costly co-substrates such as peptone, yeast extract, and/or glucose. The present study describes the azo dye degradation by a bacterial consortium using glycerol as a sole co-substrate. The consortium was developed from a mixed bacterial culture obtained upon enrichment of soil sediment for Reactive Blue 28 (RB28) decolorization in the presence of glycerol (0.1%; v/v). The consortium with three bacterial species, i.e., Stenotrophomonas acidaminiphila APG1, Cellulomonas sp. APG4, and Pseudomonas stutzeri APG2, designated as "SCP," decolorized 92% of 100 ppm dye in 96 h. The intricacies of the interactions existing within the members of the consortium were resolved by a simple and unique analysis called "BSocial." Among all the members, Cellulomonas sp. APG4 exerted a net-positive impact for decolorization (%) on the consortium. The net fitness of the community increased when all the three species were present, and thus, all of them were selected for further analysis. Moreover, APG4 seemed to be central in the reductive decolorization as it possessed the highest reductase activity. The dye degradation by the consortium was demonstrated by UV-Visible spectroscopy, HPTLC, and FTIR spectroscopy of control and decolorized cell-free supernatant. The LC-ESI-MS analysis of metabolites extracted from decolorized cell-free medium led to the identification of degradation products, thus leading us to propose the plausible pathway for degradation of RB28 by bacterial consortium.

Stochasticity and non-additivity expose hidden evolutionary pathways to cooperation

Cooperation is widespread across the tree of life, with examples ranging from vertebrates to lichens to multispecies biofilms. The initial evolution of such cooperation is likely to involve interactions that produce non-additive fitness effects among small groups of individuals in local populations. However, most models for the evolution of cooperation have focused on genealogically related individuals, assume that the factors influencing individual fitness are deterministic, that populations are very large, and that the benefits of cooperation increase linearly with the number of cooperative interactions. Here we show that stochasticity and non-additive interactions can facilitate the evolution of cooperation in small local groups. We derive a generalized model for the evolution of cooperation and show that if cooperation reduces the variance in individual fitness (separate from its effect on average fitness), this can aid in the evolution of cooperation through directional stochastic effects. In addition, we show that the potential for the evolution of cooperation is influenced by non-additivity in benefits with cooperation being more likely to evolve when the marginal benefit of a cooperative act increases with the number of such acts. Our model compliments traditional cooperation models (kin selection, reciprocal cooperation, green beard effect, etc.) and applies to a broad range of cooperative interactions seen in nature.

Social microbial inocula confer functional stability in a methyl tert-butyl ether extractive membrane biofilm bioreactor

Methyl tert-butyl ether (MTBE) degradation technologies based on two-phase partitioning systems such as extractive membrane biofilm reactors (EMBFR) permit separation of biological and contaminant compartments, thus allowing optimization of the biological section. In this study, we set-up an EMBFR with three MTBE-degrading and cooperating strains (termed social biofilm: Agrobacterium sp. MS2, Paenibacillus etheri SH7^T and Rhodococcus ruber EE6). The removal efficiency of the social-biofilm EMBFR was 80%, and functional stability was observed in the reactor, i.e. more efficient than previous studies (single-strain inoculated EMBFR, <50% removal efficiency and unstable function). Metabolite tert-butyl alcohol was not observed, and the EC₅₀ values were higher than those observed in single-strain EMBFRs. Comparative analysis of the MTBE enzymatic pathway and the social-biofilm was performed, where the mechanism of cooperation observed within the social-biofilm is likely due to enzymatic redundancy. Functional outcomes were equal to previous batch tests, hence 100% scalability was obtained. Overall, higher functional and stability outcomes are obtained with the use of the social-biofilm in an MTBE-EMBFR.