Regularized S-Map Reveals Varying Bacterial Interactions

Recruiting high-efficiency denitrifying consortia using Pseudomonas aeruginosa

Synthesizing the microbial community with a high denitrifying capacity is the key for achieving efficient removal of nitrogen species in wastewater treatment plants. Here, we integrated the evolutionary top-down enrichment and bottom-up bioaugmentation to construct a high-efficiency Pseudomonas-recruited denitrifying consortium (PRDC). A PRDC with a high specific denitrification rate of 109.49 ± 10.58 mg N/(g MLVSS·h) was enriched after 181 days of microbiota construction with pre-inoculation of Pseudomonas strain onto carriers. The 16S rRNA gene sequencing analysis suggested that the pre-inoculated Pseudomonas was quickly washed out and replaced by dominant denitrifying genera, such as Halomonas and Thauera, under different hydraulic retention times (HRTs). The pre-inoculated Pseudomonas can facilitate PRDC by providing public goods, but compromising its nutrient requirements. The dominant community assembly processes switched from homogeneous selection to ecological drift and dispersal limitation under shortened HRT. Furthermore, a shortened HRT facilitated the colonization of new immigrants and intensified their competition with the pre-existing dominant denitrifiers. The PRDC carriers achieved a 1.65-fold enhancement in sludge denitrification and reduced the corresponding chemical oxygen demand consumption at a carrier filling ratio of 30%. Overall, our study developed a novel technique using Pseudomonas aeruginosa as a trigger to enrich high-efficiency denitrifying consortia for wastewater treatment.

Longitudinal dynamics and cross-domain interactions of eukaryotic populations in wastewater treatment plants

Activated sludge is a large reservoir of novel microorganisms and microbial genetic diversity. While much attention has been given to the profile and functions of prokaryotes, the eukaryotic diversity remains largely unexplored. In this study, we analysed longitudinal activated sludge samples spanning 13 years from the largest secondary wastewater treatment plants in Hong Kong, unveiling a wealth of eukaryotic taxa and 681 856 non-redundant protein-coding genes, the majority (416 044) of which appeared novel. Ciliophora was the most dominant phylum with a significant increase after a transient intervention (bleaching event). Our metagenomic analysis reveals close linkage and covariation of eukaryotes, prokaryotes, and prokaryotic viruses (phages), indicating common responses to environmental changes such as transient intervention and intermittent fluctuations. Furthermore, high-resolution cross-domain relationships were interpreted by S-map, demonstrating a predatory role of Arthropoda, Ascomycota, Mucoromycota, and Rotifera. This high-resolution profile of microbial dynamics expands our knowledge on yet-to-be-cultured populations and their cross-domain interactions and highlights the ecological importance of eukaryotes in the activated sludge ecosystem.

Exploring interspecific interaction variability in microbiota: A review

Interspecific interactions are an important component and a strong selective force in microbial communities. Over the past few decades, there has been a growing awareness of the variability in microbial interactions, and various studies are already unraveling the inner working dynamics in microbial communities. This has prompted scientists to develop novel techniques for characterizing the varying interspecific interactions among microbes. Here, we review the precise definitions of pairwise and high-order interactions, summarize the key concepts related to interaction variability, and discuss the strengths and weaknesses of emerging characterization techniques. Specifically, we found that most methods can accurately predict or provide direct information about microbial pairwise interactions. However, some of these methods inevitably mask the underlying high-order interactions in the microbial community. Making reasonable assumptions and choosing a characterization method to explore varying microbial interactions should allow us to better understand and engineer dynamic microbial systems.

Reconstruction of a Soil Microbial Network Induced by Stress Temperature

The microbial community is viewed as a network of diverse microorganisms connected by various interspecific interactions. While the stress gradient hypothesis (SGH) predicts that positive interactions are favored in more stressful environments, the prediction has been less explored in complex microbial communities due to the challenges of identifying interactions. Here, by applying a nonlinear time series analysis to the amplicon-based diversity time series data of the soil microbiota cultured under less stressful (30°C) or more stressful (37°C) temperature conditions, we show how the microbial network responds to temperature stress. While the genera that persisted only under the less stressful condition showed fewer positive effects, the genera that appeared only under the more stressful condition received more positive effects, in agreement with SGH. However, temperature difference also induced reconstruction of the community network, leading to an increased proportion of negative interactions at the whole-community level. The anti-SGH pattern can be explained by the stronger competition caused by increased metabolic rate and population densities. IMPORTANCE By combining amplicon-based diversity survey with recently developed nonlinear analytical tools, we successfully determined the interaction networks of more than 150 natural soil microbial genera under less or more temperature stress and explored the applicability of the stress gradient hypothesis to soil microbiota, shedding new light on the well-known hypothesis.

State-Space-Based Framework for Predicting Microbial Interaction Variability in Wastewater Treatment Plants

Substantial attempts have been made to control microbial communities for environmental integrity, biosystem performance, and human health. However, it is difficult to manipulate microbial communities in practice due to the varying and nonlinear nature of interspecific interaction networks. Here, we develop a manifold-based framework to investigate the patterns of microbial interaction variability in wastewater treatment plants using manifold geometric properties and design a simple control strategy to manipulate the microbes in nonlinear communities. We validate our framework using the readily available and nonsequential microbiome profiles of wastewater treatment plants. Our results show that some microbes in the activated sludge and anammox communities display deterministic rival or cooperative relationships and constitute a stable subnetwork within the whole nonlinear community network. We further use a simulation to demonstrate that these microbes can be used to drive a microbe in a target direction regardless of the community dynamics. Overall, our framework can provide a time-efficient solution to select effective control inputs for reliable manipulation in varying microbial networks, opening up new possibilities across a range of biological fields, including wastewater treatment plants.

Time-lagged interspecies interactions prevail during biofilm development in moving bed biofilm reactor

Clarifying the essential succession dynamics of interspecies interactions during biofilm development is crucial for the regulation and application of biofilm-based processes. In this study, regular and time-series phylogenetic molecular ecological networks (pMENs) were constructed to investigate ordinary and time-lagged interspecies interactions during biofilm development in a moving bed biofilm reactor (MBBR). Positive interactions dominated both regular (89.78%) and time-series (77.04%) ecological networks, suggesting that extensive cooperative behaviors facilitated biofilm development. The pronounced directional interactions (72.52%) in the time-series network further indicated that time-lagged interspecies interactions prevailed in the biofilm development process. Specifically, the proportion of directional negative interactions was higher than that of positive interactions, implying that interspecific competition preferred to be time-lagged. The time-series network revealed that module hubs exhibited extensive time-lagged positive interactions with their neighbors, and most of them exhibited altruistic behaviors. Keystone species possessing more positive interactions were positively correlated with biofilm biomass, NO₃ ^- -N concentrations, and the removal efficiencies of NH₄ ⁺ -N and COD. However, keystone species and peripherals that were negatively targeted by their neighbors showed positive correlations with the concentrations of NO₂ ^- -N, polysaccharides, and proteins in the soluble microbial products. The data highlight that the time-series network can provide directional microbial interactions along with the biofilm development process, which would help to predict the tendency of community shifts and propose efficient strategies for the regulation of biofilm-based processes. This article is protected by copyright. All rights reserved.

Causal networks of phytoplankton diversity and biomass are modulated by environmental context

Untangling causal links and feedbacks among biodiversity, ecosystem functioning, and environmental factors is challenging due to their complex and context-dependent interactions (e.g., a nutrient-dependent relationship between diversity and biomass). Consequently, studies that only consider separable, unidirectional effects can produce divergent conclusions and equivocal ecological implications. To address this complexity, we use empirical dynamic modeling to assemble causal networks for 19 natural aquatic ecosystems (N24^◦~N58^◦) and quantified strengths of feedbacks among phytoplankton diversity, phytoplankton biomass, and environmental factors. Through a cross-system comparison, we identify macroecological patterns; in more diverse, oligotrophic ecosystems, biodiversity effects are more important than environmental effects (nutrients and temperature) as drivers of biomass. Furthermore, feedback strengths vary with productivity. In warm, productive systems, strong nitrate-mediated feedbacks usually prevail, whereas there are strong, phosphate-mediated feedbacks in cold, less productive systems. Our findings, based on recovered feedbacks, highlight the importance of a network view in future ecosystem management.

Disentangling causal interactions among biodiversity, ecosystem functioning and environmental factors is key to understanding how ecosystems respond to changing environment. This study presents a global scale analysis quantifying causal interactions and feedbacks among phytoplankton diversity, biomass and nutrients along environmental gradients of aquatic ecosystems.

Conceptual strategies for characterizing interactions in microbial communities

Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions pose several challenges owing to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.

Convergency and Stability Responses of Bacterial Communities to Salinization in Arid and Semiarid Areas: Implications for Global Climate Change in Lake Ecosystems

Climate change has given rise to salinization and nutrient enrichment in lake ecosystems of arid and semiarid areas, which have posed the bacterial communities not only into an ecotone in lake ecosystems but also into an assemblage of its own unique biomes. However, responses of bacterial communities to climate-related salinization and nutrient enrichment remain unclear. In September 2019, this study scrutinized the turnover of bacterial communities along gradients of increasing salinity and nutrient by a space-for-time substitution in Xinjiang Uyghur Autonomous Region, China. We find that salinization rather than nutrient enrichment primarily alters bacterial communities. The homogenous selection of salinization leads to convergent response of bacterial communities, which is revealed by the combination of a decreasing β-nearest taxon index (βNTI) and a pronounced negative correlation between niche breadth and salinity. Furthermore, interspecific interactions within bacterial communities significantly differed among distinct salinity levels. Specifically, mutualistic interactions showed an increase along the salinization. In contrast, topological parameters show hump-shaped curves (average degree and density) and sunken curves (modularity, density, and average path distance), the extremums of which all appear in the high-brackish environment, hinting that bacterial communities are comparatively stable at freshwater and brine environments but are unstable in moderately high-brackish lake.

Reconstructing large interaction networks from empirical time series data

Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality is usually high. However, these pose a challenge to existing methods that can quantify only small interaction networks. Here, we proposed a novel approach to reconstruct high-dimensional interaction Jacobian networks using empirical time series without specific model assumptions. This method, named "multiview distance regularised S-map," generalised the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When evaluating this method using time series generated from theoretical models involving hundreds of interacting species, estimated strengths of interaction Jacobians were in good agreement with theoretical expectations. Applying this method to a natural bacterial community helped identify important species from the interaction network and revealed mechanisms governing the dynamical stability of a bacterial community. The proposed method overcame the challenge of high dimensionality in large natural dynamical systems.