Consistent microbial dynamics and functional community patterns derived from first principles

Alternative states in microbial communities during artificial aeration: Proof of incubation experiment and development of recurrent neural network models

Artificial aeration, a widely used method of restoring the aquatic ecological environment by enhancing the re-oxygenation capacity, typically relies upon empirical models to predict ecological dynamics and determine the operating scheme of the aeration equipment. Restoration through artificial aeration is involved in oxic-anoxic transitions, whether these transitions occurred in the form of a regime shift, making the development of predictive models challenging. Here, we confirmed the existence of alternative states in microbial communities during artificial aeration through aeration incubation experiment for the first time and considered its existence in neural network modeling in order to improve model performance. By aeration incubation experiment, it was confirmed that the alternative states exist in microbial communities during artificial aeration by two independent approaches, potential analysis and "enterotyping" approach. Comparing neural network models with and without considering the existence of alternative states, it was found that considering the existence of alternative states in modeling could improve the performance of neural network model. Our study provides a reference for the prediction of systems containing time series data where the current state will have an impact on later states. The developed model could be used for optimizing the operating scheme of the artificial aeration.

Functional convergence in slow-growing microbial communities arises from thermodynamic constraints

The dynamics of microbial communities is complex, determined by competition for metabolic substrates and cross-feeding of byproducts. Species in the community grow by harvesting energy from chemical reactions that transform substrates to products. In many anoxic environments, these reactions are close to thermodynamic equilibrium and growth is slow. To understand the community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an interconnected metabolic network. The central element of the model is product inhibition, meaning that microbial growth may be limited not only by depletion of metabolic substrates but also by accumulation of products. We demonstrate that these additional constraints on microbial growth cause a convergence in the structure and function of the community metabolic network-independent of species composition and biochemical details-providing a possible explanation for convergence of community function despite taxonomic variation observed in many natural and industrial environments. Furthermore, we discovered that the structure of community metabolic network is governed by the thermodynamic principle of maximum free energy dissipation. Our results predict the decrease of functional convergence in faster growing communities, which we validate by analyzing experimental data from anaerobic digesters. Overall, the work demonstrates how universal thermodynamic principles may constrain community metabolism and explain observed functional convergence in microbial communities.

Modeling a propionate-oxidizing syntrophic coculture using thermodynamic principles

A coculture of Syntrophobacter fumaroxidans and Methanospirillum hungatei was modeled using four biokinetic models, which only differed by the functions used to describe the growth yields (dynamic or constant) and the hydrogen inhibition function (noncompetitive or based on thermodynamics). First, a batch experiment was used to train the model and analyze the fitted parameters. Two fitting procedures were followed by minimizing the error on different indicators. Second, a chemostat experiment was used as a test data set to assess the predictive power of the models. Overall, the four models were able to accurately fit the train data set following both fitting procedures. However, some parameters fitted with the ADM1-like model differed significantly from values reported in the literature and were dependent on the fitting procedure. When applied to the test data set it systematically resulted in positive Gibbs free energy changes values for propionate oxidation, in contradiction with the second law of thermodynamics. On the opposite, the parameters fitted with model including both a thermodynamic-based inhibition function and a dynamic computation of growth yields were more consistent with values reported in the literature and repeatable whatever the fitting procedure. The results highlight the potential of implementing thermodynamic-based functions in biokinetic models.

Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth

Microorganisms function as open systems that exchange matter and energy with their surrounding environment. Even though mass (carbon and nutrients) and energy exchanges are tightly linked, there is a lack of integrated approaches that combine these fluxes and explore how they jointly impact microbial growth. Such links are essential to predicting how the growth rate of microorganisms varies, especially when the stoichiometry of carbon- (C) and nitrogen (N)-uptake is not balanced. Here, we present a theoretical framework to quantify the microbial growth rate for conditions of C-, N-, and energy-(co-) limitations. We use this framework to show how the C:N ratio and the degree of reduction of the organic matter (OM), which is also the electron donor, availability of electron acceptors (EAs), and the different sources of N together control the microbial growth rate under C, nutrient, and energy-limited conditions. We show that the growth rate peaks at intermediate values of the degree of reduction of OM under oxic and C-limited conditions, but not under N-limited conditions. Under oxic conditions and with N-poor OM, the growth rate is higher when the inorganic N (N_Inorg)-source is ammonium compared to nitrate due to the additional energetic cost involved in nitrate reduction. Under anoxic conditions, when nitrate is both EA and N_Inorg-source, the growth rates of denitrifiers and microbes performing the dissimilatory nitrate reduction to ammonia (DNRA) are determined by both OM degree of reduction and nitrate-availability. Consistent with the data, DNRA is predicted to foster growth under extreme nitrate-limitation and with a reduced OM, whereas denitrifiers are favored as nitrate becomes more available and in the presence of oxidized OM. Furthermore, the growth rate is reduced when catabolism is coupled to low energy yielding EAs (e.g., sulfate) because of the low carbon use efficiency (CUE). However, the low CUE also decreases the nutrient demand for growth, thereby reducing N-limitation. We conclude that bioenergetics provides a useful conceptual framework for explaining growth rates under different metabolisms and multiple resource-limitations.

Spatial Metagenomic Analysis in Understanding the Microbial Diversity of Thar Desert

Simple Summary

We present a systematic investigation of the distribution of microbial communities in arid and semi-arid regions of Thar Desert Rajasthan, India. Their responses in multiple environmental stresses, including surface soil, surface water and underground water were evaluated. We further assess the biotechnological potential of native microorganisms and discover functional species with results providing a detailed understanding of the abundance of microbial communities in these regions, associated with various stress-related biogeochemical and biotechnological processes. We hope our work will facilitate the development of effective future strategies for the use of extremophiles in complex environments.

Abstract

The arid and semi-arid regions of Rajasthan are one of the most extreme biomes of India, possessing diverse microbial communities that exhibit immense biotechnological potential for industries. Herein, we sampled study sites from arid and semi-arid regions of Thar Desert, Rajasthan, India and subjected them to chemical, physical and metagenomics analysis. The microbial diversity was studied using V3–V4 amplicon sequencing of 16S rRNA gene by Illumina MiSeq. Our metagenomic analyses revealed that the sampled sites consist mainly of Proteobacteria (19–31%) followed by unclassified bacteria (5–21%), Actinobacteria (3–25%), Planctomycetes (5–13%), Chloroflexi (2–14%), Bacteroidetes (3–12%), Firmicutes (3–7%), Acidobacteria (1–4%) and Patescibacteria (1–4%). We have found Proteobacteria in abundance which is associated with a range of activities involved in biogeochemical cycles such as carbon, nitrogen, and sulphur. Our study is perhaps the first of its kind to explore soil bacteria from arid and semi-arid regions of Rajasthan, India. We believe that the new microbial candidates found can be further explored for various industrial and biotechnological applications.

Hydrothermal plumes as hotspots for deep-ocean heterotrophic microbial biomass production

Carbon budgets of hydrothermal plumes result from the balance between carbon sinks through plume chemoautotrophic processes and carbon release via microbial respiration. However, the lack of comprehensive analysis of the metabolic processes and biomass production rates hinders an accurate estimate of their contribution to the deep ocean carbon cycle. Here, we use a biogeochemical model to estimate the autotrophic and heterotrophic production rates of microbial communities in hydrothermal plumes and validate it with in situ data. We show how substrate limitation might prevent net chemolithoautotrophic production in hydrothermal plumes. Elevated prokaryotic heterotrophic production rates (up to 0.9 gCm^-2y^-1) compared to the surrounding seawater could lead to 0.05 GtCy^-1 of C-biomass produced through chemoorganotrophy within hydrothermal plumes, similar to the Particulate Organic Carbon (POC) export fluxes reported in the deep ocean. We conclude that hydrothermal plumes must be accounted for as significant deep sources of POC in ocean carbon budgets.

Variation of soil microbial carbon use efficiency (CUE) and its Influence mechanism in the context of global environmental change: a review

Soil microbial carbon utilization efficiency (CUE) is the efficiency with which microorganisms convert absorbed carbon (C) into their own biomass C, also referred to as microorganism growth efficiency. Soil microbial CUE is a critical physiological and ecological parameter in the ecosystem’s C cycle, influencing the processes of C retention, turnover, soil mineralization, and greenhouse gas emission. Understanding the variation of soil microbial CUE and its influence mechanism in the context of global environmental change is critical for a better understanding of the ecosystem’s C cycle process and its response to global changes. In this review, the definition of CUE and its measurement methods are reviewed, and the research progress of soil microbial CUE variation and influencing factors is primarily reviewed and analyzed. Soil microbial CUE is usually expressed as the ratio of microbial growth and absorption, which is divided into methods based on the microbial growth rate, microbial biomass, substrate absorption rate, and substrate concentration change, and varies from 0.2 to 0.8. Thermodynamics, ecological environmental factors, substrate nutrient quality and availability, stoichiometric balance, and microbial community composition all influence this variation. In the future, soil microbial CUE research should focus on quantitative analysis of trace metabolic components, analysis of the regulation mechanism of biological-environmental interactions, and optimization of the carbon cycle model of microorganisms’ dynamic physiological response process.

Thermodynamically favorable reactions shape the archaeal community affecting bacterial community assembly in oil reservoirs

Microbial community assembly mechanisms are pivotal for understanding the ecological functions of microorganisms in biogeochemical cycling in Earth's ecosystems, yet rarely investigated in the context of deep terrestrial ecology. Here, the microbial communities in the production waters collected from water injection wells and oil production wells across eight oil reservoirs throughout northern China were determined and analyzed by proportional distribution analysis and null model analysis. A 'core' microbiota consisting of three bacterial genera, including Arcobacter, Pseudomonas and Acinetobacter, and eight archaeal genera, including Archaeoglobus, Methanobacterium, Methanothermobacter, unclassified Methanobacteriaceae, Methanomethylovorans, Methanoculleus, Methanosaeta and Methanolinea, was found to be present in all production water samples. Canonical correlation analysis reflected that the core archaea were significantly influenced by temperature and reservoir depth, while the core bacteria were affected by the combined impact of the core archaea and environmental factors. Thermodynamic calculations indicate that bioenergetic constraints are the driving force that governs the enrichment of two core archaeal guilds, aceticlastic methanogens versus hydrogenotrophic methanogens, in low- and high-temperature oil reservoirs, respectively. Collectively, our study indicates that microbial community structures in wells of oil reservoirs are structured by the thermodynamic window of opportunity, through which the core archaeal communities are accommodated directly followed by the deterministic recruiting of core bacterial genera, and then the stochastic selection of some other microbial members from local environments. Our study enhances the understanding of the microbial assembly mechanism in deep terrestrial habitats. Meanwhile, our findings will support the development of functional microbiota used for bioremediation and bioaugmentation in microbial enhanced oil recovery.

Not Just Numbers: Mathematical Modelling and Its Contribution to Anaerobic Digestion Processes

Mathematical modelling of bioprocesses has a long and notable history, with eminent contributions from fields including microbiology, ecology, biophysics, chemistry, statistics, control theory and mathematical theory. This richness of ideas and breadth of concepts provide great motivation for inquisitive engineers and intrepid scientists to try their hand at modelling, and this collaboration of disciplines has also delivered significant milestones in the quality and application of models for both theoretical and practical interrogation of engineered biological systems. The focus of this review is the anaerobic digestion process, which, as a technology that has come in and out of fashion, remains a fundamental process for addressing the global climate emergency. Whether with conventional anaerobic digestion systems, biorefineries, or other anaerobic technologies, mathematical models are important tools that are used to design, monitor, control and optimise the process. Both highly structured, mechanistic models and data-driven approaches have been used extensively over half a decade, but recent advances in computational capacity, scientific understanding and diversity and quality of process data, presents an opportunity for the development of new modelling paradigms, augmentation of existing methods, or even incorporation of tools from other disciplines, to ensure that anaerobic digestion research can remain resilient and relevant in the face of emerging and future challenges.

Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis

Microbial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and plant nutrition, and is exploited in biotechnology. A prerequisite to predicting the dynamics and stability of community-mediated metabolic conversions is the development and calibration of appropriate mathematical models. Here, we present a generic, extendable thermodynamic model for community dynamics and calibrate a key parameter of this thermodynamic model, the minimum energy requirement associated with growth-supporting metabolic pathways, using experimental population dynamics data from synthetic communities composed of a sulfate reducer and two methanogens. Our findings show that accounting for thermodynamics is necessary in capturing the experimental population dynamics of these synthetic communities that feature relevant species using low energy growth pathways. Furthermore, they provide the first estimates for minimum energy requirements of methanogenesis (in the range of −30 kJ mol⁻¹) and elaborate on previous estimates of lactate fermentation by sulfate reducers (in the range of −30 to −17 kJ mol⁻¹ depending on the culture conditions). The open-source nature of the developed model and demonstration of its use for estimating a key thermodynamic parameter should facilitate further thermodynamic modelling of microbial communities.

Bringing Community Ecology to Bear on the Issue of Antimicrobial Resistance

Antimicrobial resistance (AMR) is a global concern, pertaining not only to human health but also to the health of industry and the environment. AMR research has traditionally focused on genetic exchange mechanisms and abiotic environmental constraints, leaving important aspects of microbial ecology unresolved. The genetic and ecological aspects of AMR, however, not only contribute separately to the problem but also are interrelated. For example, mutualistic associations among microbes such as biofilms can both serve as a barrier to antibiotic penetration and a breeding ground for horizontal exchange of antimicrobial resistance genes (ARGs). In this review, we elucidate how species interactions promote and impede the establishment, maintenance, and spread of ARGs and indicate how management initiatives might benefit from leveraging the principles and tools of community ecology to better understand and manipulate the processes underlying AMR.

Proto-dolomite formation in microbial consortia dominated by Halomonas strains

Microbes can be found in hypersaline environments forming diverse populations with complex ecological interactions. Microbes in such environments were found to be involved in the formation of minerals including dolomite, a mineral of economic importance and whose origin has been long-debated. Various reports on in vitro experiments using pure cultures provided evidence for the microbial role in dolomite formation. However, culturing experiments have been limited in scope and do not fully address the possible interactions of the naturally occurring microbial communities; consequently, the ability of microbes as a community to form dolomite has been investigated in this study. Our experiments focused on examining the microbial composition by culturing aerobic heterotrophs from the top hypersaline sediments of Al-Khiran sabkha in Kuwait, a modern dolomite-forming environment. The objectives of this study were to assess the ability of two microbial consortia to form dolomite using enrichment culture experiments, mineralogy, and metagenomics. Proto-dolomite was formed by a microbial community dominated by Halomonas strains whereby degradation of the extracellular polymeric substances (EPS) was observed and the pH changed from 7.00 to 8.58. Conversely, proto-dolomite was not observed within a microbial community dominated by Clostridiisalibacter in which EPS continuously accumulated and the pH slightly changed from 7.00 to 7.29.