Environmental fluctuations explain the universal decay of species-abundance correlations with phylogenetic distance

Microbial-mediated conversion of soil organic carbon co-regulates the evolution of antibiotic resistance

The influence of organic carbon on the proliferation of antibiotic resistance genes (ARGs) in the soil has been widely documented. However, it is unclear how soil organic carbon (SOC) interacts with the evolution of antibiotic resistance in bacteria. Here, we examined the variations in ARGs abundance during SOC mineralization and explored the microbiological mechanisms and key metabolic pathways involved in their coevolution. The results showed that the SOC mineralization rate was closely correlated with ARGs abundance (p < 0.05). High organic carbon (OC) mineralization was conducive to the occurrence of multidrug resistance genes. For example, multidrug_transporter and mexB increased 2.26 and 7.83 times from the initial level. The competitor (stress) evolutionary strategy model revealed that higher OC inputs drive environmental microorganisms to evolve from stress tolerant to high resistance and strong adaptation. Meta-genomic and transcriptomic analyses revealed that the conversion process of pyruvate to acetyl-CoA to acetate was the critical metabolic pathway for the co-regulation of antibiotic resistance. Gene deletion validation trials have demonstrated that the key functional genes (ackA and pta) involved in this process can modulate the development of vancomycin and multidrug resistance. This outcome provides a preliminary framework for microbial mechanisms that target the co-regulation of microbial OC conversion and the evolution of antibiotic resistance.

Unveiling the overlooked microbial niches thriving on building exteriors

Rapid urbanization in the Asia-Pacific region is expected to place two-thirds of its population in concrete-dominated urban landscapes by 2050. While diverse architectural facades define the unique appearance of these urban systems. There remains a significant gap in our understanding of the composition, assembly, and ecological potential of microbial communities on building exteriors. Here, we examined bacterial and protistan communities on building surfaces along an urbanization gradient (urban, suburban and rural regions), investigating their spatial patterns and the driving factors behind their presence. A total of 55 bacterial and protist phyla were identified. The bacterial community was predominantly composed of Proteobacteria (33.7% to 67.5%). The protistan community exhibited a prevalence of Opisthokonta and Archaeplastida (17.5% to 82.1% and 1.8% to 61.2%, respectively). The composition and functionality of bacterial communities exhibited spatial patterns correlated with urbanization. In urban buildings, factors such as facade type, light exposure, and building height had comparatively less impact on bacterial composition compared to suburban and rural areas. The highest bacterial diversity and lowest Weighted Average Community Identity (WACI) were observed on suburban buildings, followed by rural buildings. In contrast, protists did not show spatial distribution characteristics related to facade type, light exposure, building height and urbanization level. The distinct spatial patterns of protists were primarily shaped by community diffusion and the bottom-up regulation exerted by bacterial communities. Together, our findings suggest that building exteriors serve as attachment points for local microbial metacommunities, offering unique habitats where bacteria and protists exhibit independent adaptive strategies closely tied to the overall ecological potential of the community.

Sparse species interactions reproduce abundance correlation patterns in microbial communities

During the last decades, macroecology has identified broad-scale patterns of abundances and diversity of microbial communities and put forward some potential explanations for them. However, these advances are not paralleled by a full understanding of the dynamical processes behind them. In particular, abundance fluctuations of different species are found to be correlated, both across time and across communities in metagenomic samples. Reproducing such correlations through appropriate population models remains an open challenge. The present paper tackles this problem and points to sparse species interactions as a necessary mechanism to account for them. Specifically, we discuss several possibilities to include interactions in population models and recognize Lotka-Volterra constants as a successful ansatz. For this, we design a Bayesian inference algorithm to extract sets of interaction constants able to reproduce empirical probability distributions of pairwise correlations for diverse biomes. Importantly, the inferred models still reproduce well-known single-species macroecological patterns concerning abundance fluctuations across both species and communities. Endorsed by the agreement with the empirically observed phenomenology, our analyses provide insights into the properties of the networks of microbial interactions, revealing that sparsity is a crucial feature.

Investigating macroecological patterns in coarse-grained microbial communities using the stochastic logistic model of growth

The structure and diversity of microbial communities are intrinsically hierarchical due to the shared evolutionary history of their constituents. This history is typically captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. Connecting community diversity to the joint ecological dynamics of the abundances of these groups is a central problem of community ecology. However, how microbial diversity depends on the scale of observation at which groups are defined has never been systematically examined. Here, we used a macroecological approach to quantitatively characterize the structure and diversity of microbial communities among disparate environments across taxonomic and phylogenetic scales. We found that measures of biodiversity at a given scale can be consistently predicted using a minimal model of ecology, the Stochastic Logistic Model of growth (SLM). This result suggests that the SLM is a more appropriate null-model for microbial biodiversity than alternatives such as the Unified Neutral Theory of Biodiversity. Extending these within-scale results, we examined the relationship between measures of biodiversity calculated at different scales (e.g. genus vs. family), an empirical pattern previously evaluated in the context of the Diversity Begets Diversity (DBD) hypothesis (Madi et al., 2020). We found that the relationship between richness estimates at different scales can be quantitatively predicted assuming independence among community members, demonstrating that the DBD can be sufficiently explained using the SLM as a null model of ecology. Contrastingly, only by including correlations between the abundances of community members (e.g. as the consequence of interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of organization and establish a sharp demarcation between recently proposed macroecological patterns that are not and are affected by ecological interactions.