Relationships between community composition, productivity and invasion resistance in semi-natural bacterial microcosms

Climate warming, environmental degradation and pollution as drivers of antibiotic resistance

Antibiotic resistance is a major challenge to public health, but human-caused environmental changes have not been widely recognized as its drivers. Here, we provide a comprehensive overview of the relationships between environmental degradation and antibiotic resistance, demonstrating that the former can potentially fuel the latter with significant public health outcomes. We describe that (i) global warming favors horizontal gene transfer, bacterial infections, the spread of drug-resistant pathogens due to water scarcity, and the release of resistance genes with wastewater; (ii) pesticide and metal pollution act as co-selectors of antibiotic resistance mechanisms; (iii) microplastics create conditions promoting and spreading antibiotic resistance and resistant bacteria; (iv) changes in land use, deforestation, and environmental pollution reduce microbial diversity, a natural barrier to antibiotic resistance spread. We argue that management of antibiotic resistance must integrate environmental goals, including mitigation of further increases in the Earth's surface temperature, better qualitative and quantitative protection of water resources, strengthening of sewage infrastructure and improving wastewater treatment, counteracting the microbial diversity loss, reduction of pesticide and metal emissions, and plastic use, and improving waste recycling. These actions should be accompanied by restricting antibiotic use only to clinically justified situations, developing novel treatments, and promoting prophylaxis. It is pivotal for health authorities and the medical community to adopt the protection of environmental quality as a part of public health measures, also in the context of antibiotic resistance management.

Niche availability and competitive loss by facilitation control proliferation of bacterial strains intended for soil microbiome interventions

Microbiome engineering - the targeted manipulation of microbial communities - is considered a promising strategy to restore ecosystems, but experimental support and mechanistic understanding are required. Here, we show that bacterial inoculants for soil microbiome engineering may fail to establish because they inadvertently facilitate growth of native resident microbiomes. By generating soil microcosms in presence or absence of standardized soil resident communities, we show how different nutrient availabilities limit outgrowth of focal bacterial inoculants (three Pseudomonads), and how this might be improved by adding an artificial, inoculant-selective nutrient niche. Through random paired interaction assays in agarose micro-beads, we demonstrate that, in addition to direct competition, inoculants lose competitiveness by facilitating growth of resident soil bacteria. Metatranscriptomics experiments with toluene as selective nutrient niche for the inoculant Pseudomonas veronii indicate that this facilitation is due to loss and uptake of excreted metabolites by resident taxa. Generation of selective nutrient niches for inoculants may help to favor their proliferation for the duration of their intended action while limiting their competitive loss.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

PGPR-Soil Microbial Communities' Interactions and Their Influence on Wheat Growth Promotion and Resistance Induction against Mycosphaerella graminicola

The efficiency of plant-growth-promoting rhizobacteria (PGPR) may not be consistently maintained under field conditions due to the influence of soil microbial communities. The present study aims to investigate their impact on three PGPR-based biofertilizers in wheat. We used the PGPR Paenibacillus sp. strain B2 (PB2), PB2 in co-inoculation with Arthrobacter agilis 4042 (Mix 2), or with Arthrobacter sp. SSM-004 and Microbacterium sp. SSM-001 (Mix 3). Inoculation of PB2, Mix 2, and Mix 3 into non-sterile field soil had a positive effect on root and aboveground dry biomass, depending on the wheat cultivar. The efficiency of the PGPR was further confirmed by the protection they provided against Mycosphaerella graminicola, the causal agent of Septoria leaf blotch disease. PB2 exhibited protection of ≥37.8%, while Mix 2 showed ≥47.9% protection in the four cultivars tested. These results suggest that the interactions between PGPR and native soil microbial communities are crucial for promoting wheat growth and protection. Additionally, high-throughput sequencing of microbial communities conducted 7 days after PGPR inoculations revealed no negative effects of PB2, Mix 2, and Mix 3 on the soil microbial community structure. Interestingly, the presence of Arthrobacter spp. appeared to mitigate the potential negative effect of PB2 on bacterial community and foster root colonization by other beneficial bacterial strains.

Statistically learning the functional landscape of microbial communities

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes-analogues to fitness landscapes-that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

Localized coevolution between microbial predator and prey alters community-wide gene expression and ecosystem function

Closely interacting microbial species pairs (e.g., predator and prey) can become coadapted via reciprocal natural selection. A fundamental challenge in evolutionary ecology is to untangle how coevolution in small species groups affects and is affected by biotic interactions in diverse communities. We conducted an experiment with a synthetic 30-species bacterial community where we experimentally manipulated the coevolutionary history of a ciliate predator and one bacterial prey species from the community. Altering the coevolutionary history of the focal prey species had little effect on community structure or carrying capacity in the presence or absence of the coevolved predator. However, community metabolic potential (represented by per-cell ATP concentration) was significantly higher in the presence of both the coevolved focal predator and prey. This ecosystem-level response was mirrored by community-wide transcriptional shifts that resulted in the differential regulation of nutrient acquisition and surface colonization pathways across multiple bacterial species. Our findings show that the disruption of localized coevolution between species pairs can reverberate through community-wide transcriptional networks even while community composition remains largely unchanged. We propose that these altered expression patterns may signal forthcoming evolutionary and ecological change.

Microbial invasion of a toxic medium is facilitated by a resident community but inhibited as the community co-evolves

Predicting whether microbial invaders will colonize an environment is critical for managing natural and engineered ecosystems, and controlling infectious disease. Invaders often face competition by resident microbes. But how invasions play out in communities dominated by facilitative interactions is less clear. We previously showed that growth medium toxicity can promote facilitation between four bacterial species, as species that cannot grow alone rely on others to survive. Following the same logic, here we allowed other bacterial species to invade the four-species community and found that invaders could more easily colonize a toxic medium when the community was present. In a more benign environment instead, invasive species that could survive alone colonized more successfully when the residents were absent. Next, we asked whether early colonists could exclude future ones through a priority effect, by inoculating the invaders into the resident community only after its members had co-evolved for 44 weeks. Compared to the ancestral community, the co-evolved resident community was more competitive toward invaders and less affected by them. Our experiments show how communities may assemble by facilitating one another in harsh, sterile environments, but that arriving after community members have co-evolved can limit invasion success.

MicrobioSee: A Web-Based Visualization Toolkit for Multi-Omics of Microbiology

With the upgrade and development of the high-throughput sequencing technology, multi-omics data can be obtained at a low cost. However, mapping tools that existed for microbial multi-omics data analysis cannot satisfy the needs of data description and result in high learning costs, complex dependencies, and high fees for researchers in experimental biology fields. Therefore, developing a toolkit for multi-omics data is essential for microbiologists to save effort. In this work, we developed MicrobioSee, a real-time interactive visualization tool based on web technologies, which could visualize microbial multi-omics data. It includes 17 modules surrounding the major omics data of microorganisms such as the transcriptome, metagenome, and proteome. With MicrobioSee, methods for plotting are simplified in multi-omics studies, such as visualization of diversity, ROC, and enrichment pathways for DEGs. Subsequently, three case studies were chosen to represent the functional application of MicrobioSee. Overall, we provided a concise toolkit along with user-friendly, time-saving, cross-platform, and source-opening for researchers, especially microbiologists without coding experience. MicrobioSee is freely available at https://microbiosee.gxu.edu.cn.