Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

A powerful but frequently overlooked role of thermodynamics in environmental microbiology: inspirations from anammox

Thermodynamics has long been applied in predicting undiscovered microorganisms or analyzing energy flows in microbial metabolism, as well as evaluating microbial impacts on global element distributions. However, further development and refinement in this interdisciplinary field are still needed. This work endeavors to develop a whole-cycle framework integrating thermodynamics with microbiological studies, focusing on representative nitrogen-transforming microorganisms. Three crucial concepts (reaction favorability, energy balance, and reaction directionality) are discussed in relation to nitrogen-transforming reactions. Specifically, reaction favorability, which sheds lights on understanding the diversity of nitrogen-transforming microorganisms, has also provided guidance for novel bioprocess development. Energy balance, enabling the quantitative comparison of microbial energy efficiency, unravels the competitiveness of nitrogen-transforming microorganisms under substrate-limiting conditions. Reaction directionality, revealing the niche-differentiating patterns of nitrogen-transforming microorganisms, provides a foundation for predicting biogeochemical reactions under various environmental conditions. This review highlights the need for a more comprehensive integration of thermodynamics in environmental microbiology, aiming to comprehensively understand microbial impacts on the global environment from micro to macro scales.

Seasonal dynamics in leaf litter decomposing microbial communities in temperate forests: a whole-genome- sequencing-based study

Leaf litter decomposition, a crucial component of the global carbon cycle, relies on the pivotal role played by microorganisms. However, despite their ecological importance, leaf-litter-decomposing microorganism taxonomic and functional diversity needs additional study. This study explores the taxonomic composition, dynamics, and functional role of microbial communities that decompose leaf litter of forest-forming tree species in two ecologically unique regions of Europe. Twenty-nine microbial metagenomes isolated from the leaf litter of eight forest-forming species of woody plants were investigated by Illumina technology using read- and assembly-based approaches of sequences analysis. The taxonomic structure of the microbial community varies depending on the stage of litter decomposition; however, the community's core is formed by Pseudomonas, Sphingomonas, Stenotrophomonas, and Pedobacter genera of Bacteria and by Aureobasidium, Penicillium, Venturia genera of Fungi. A comparative analysis of the taxonomic structure and composition of the microbial communities revealed that in both regions, seasonal changes in structure take place; however, there is no clear pattern in its dynamics. Functional gene analysis of MAGs revealed numerous metabolic profiles associated with leaf litter degradation. This highlights the diverse metabolic capabilities of microbial communities and their implications for ecosystem processes, including the production of volatile organic compounds (VOCs) during organic matter decomposition. This study provides important advances in understanding of ecosystem processes and the carbon cycle, underscoring the need to unravel the intricacies of microbial communities within these contexts.

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome's functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute's Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data.

IMPORTANCE

Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome's metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

Advancing environmental biotechnology with microbial community modeling rooted in functional 'omics

Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. 'Conventional' bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional 'omics-based approaches can provide a 'lens' through which we can probe and measure in situ ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional 'omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.

New insights on ecological roles of waste activated sludge in nutrient-stressed co-digestion

There have been extensive applications of waste activated sludge (WAS) in anaerobic co-digestion (AcoD). Nonetheless, mechanisms through which AcoD systems maintain stability, particularly under nutrient-stressed conditions, are under-appreciated. In this study, the role of WAS in a nutrient-stressed WAS-food waste AcoD system was re-evaluated. Our findings demonstrated that WAS-based co-digestion increased methane production (by 20-60%) as WAS bolsters such systems' resilience via establishing a core niche-based microbial balance. The carbon utilization investigation suggested a microbial niche balance is attainable if two conditions are satisfied: 1) hydrolysis efficiency is greater than 50%; and 2) both the acidogenesis-to-hydrolysis and acetogenesis-to-hydrolysis efficiencies surpass 0.5. Metagenomic assembly genome (MAG) analysis indicated that the versatile metabolic characteristics strengthened the microbial niche balance, rendering the system resilient and efficient through a syntrophic mode, contributing to both acidogenesis and acetogenesis. The findings of this study provide new insights into the ecological effects of WAS on AcoD.

Seasonal dynamics of the microbial methane filter in the water column of a eutrophic coastal basin

In coastal waters, methane-oxidizing bacteria (MOB) can form a methane biofilter and mitigate methane emissions. The metabolism of these MOBs is versatile, and the resilience to changing oxygen concentrations is potentially high. It is still unclear how seasonal changes in oxygen availability and water column chemistry affect the functioning of the methane biofilter and MOB community composition. Here, we determined water column methane and oxygen depth profiles, the methanotrophic community structure, methane oxidation potential, and water-air methane fluxes of a eutrophic marine basin during summer stratification and in the mixed water in spring and autumn. In spring, the MOB diversity and relative abundance were low. Yet, MOB formed a methane biofilter with up to 9% relative abundance and vertical niche partitioning during summer stratification. The vertical distribution and potential methane oxidation of MOB did not follow the upward shift of the oxycline during summer, and water-air fluxes remained below 0.6 mmol m-2 d-1. Together, this suggests active methane removal by MOB in the anoxic water. Surprisingly, with a weaker stratification, and therefore potentially increased oxygen supply, methane oxidation rates decreased, and water-air methane fluxes increased. Thus, despite the potential resilience of the MOB community, seasonal water column dynamics significantly influence methane removal.

Constraining the oxygen requirements for modern microbial eukaryote diversity

Eukaryotes originated prior to the establishment of modern marine oxygen (O2) levels. According to the body fossil and lipid biomarker records, modern (crown) microbial eukaryote lineages began diversifying in the ocean no later than ~800 Ma. While it has long been predicted that increasing atmospheric O2 levels facilitated the early diversification of microbial eukaryotes, the O2 levels needed to permit this diversification remain unconstrained. Using time-resolved geochemical parameter and gene sequence information from a model marine oxygen minimum zone spanning a range of dissolved O2 levels and redox states, we show that microbial eukaryote taxonomic richness and phylogenetic diversity remain the same until O2 declines to around 2 to 3% of present atmospheric levels, below which these diversity metrics become significantly reduced. Our observations suggest that increasing O2 would have only directly promoted early crown-eukaryote diversity if atmospheric O2 was below 2 to 3% of modern levels when crown-eukaryotes originated and then later met or surpassed this range as crown-eukaryotes diversified. If atmospheric O2 was already consistently at or above 2 to 3% of modern levels by the time that crown-eukaryotes originated, then the subsequent diversification of modern microbial eukaryotes was not directly driven by atmospheric oxygenation.

Forecasting the dynamics of a complex microbial community using integrated meta-omics

Predicting the behaviour of complex microbial communities is challenging. However, this is essential for complex biotechnological processes such as those in biological wastewater treatment plants (BWWTPs), which require sustainable operation. Here we summarize 14 months of longitudinal meta-omics data from a BWWTP anaerobic tank into 17 temporal signals, explaining 91.1% of the temporal variance, and link those signals to ecological events within the community. We forecast the signals over the subsequent five years and use 21 extra samples collected at defined time intervals for testing and validation. Our forecasts are correct for six signals and hint on phenomena such as predation cycles. Using all the 17 forecasts and the environmental variables, we predict gene abundance and expression, with a coefficient of determination ≥0.87 for the subsequent three years. Our study demonstrates the ability to forecast the dynamics of open microbial ecosystems using interactions between community cycles and environmental parameters.

Anammox Coupled with Photocatalyst for Enhanced Nitrogen Removal and the Activated Aerobic Respiration of Anammox Bacteria Based on cbb3-Type Cytochrome c Oxidase

This study introduced photogenerated electrons into the anammox system by coupling them to a g-C3N4 nanoparticle photocatalyst. A high nitrogen removal efficiency (94.25%) was achieved, exceeding the biochemical limit of 89% imposed by anammox stoichiometry. Photogenerated electrons boosted anammox metabolic activity by empowering key enzymes (NIR, HZS, and WLP-related proteins) and triggered rapid algal enrichment by enhancing the algal Calvin cycle, thus developing multiple anammox-algae synergistic nitrogen removal processes. Remarkably, the homologous expression of cbb3-type cytochrome c oxidase (CcO) in anammox bacteria was discovered and reported in this study for the first time. This conferred aerobic respiration capability to anammox bacteria and rendered them the principal oxygen consumer under 7.9-19.8 mg/L dissolved oxygen, originating from algal photosynthesis. Additionally, photogenerated electrons selectively targeted the cb1 complex and cbb3-type CcO as activation sites while mobilizing the RegA/B regulatory system to activate the expression of cbb3-type CcO. Furthermore, cbb3-type CcO blocked oxidative stress in anammox by depleting intracellular oxygen, a substrate for reactive oxygen species synthesis. This optimized the environmental sensitivity of anammox bacteria and maintained their high metabolic activity. This study expands our understanding of the physiological aptitudes of anammox bacteria and provides valuable insights into applying solar energy for enhanced wastewater treatment.

Gene-Based Modeling of Methane Oxidation in Coastal Sediments: Constraints on the Efficiency of the Microbial Methane Filter

Methane is a powerful greenhouse gas that is produced in large quantities in marine sediments. Microbially mediated oxidation of methane in sediments, when in balance with methane production, prevents the release of methane to the overlying water. Here, we present a gene-based reactive transport model that includes both microbial and geochemical dynamics and use it to investigate whether the rate of growth of methane oxidizers in sediments impacts the efficiency of the microbial methane filter. We focus on iron- and methane-rich coastal sediments and, with the model, show that at our site, up to 10% of all methane removed is oxidized by iron and manganese oxides, with the remainder accounted for by oxygen and sulfate. We demonstrate that the slow growth rate of anaerobic methane-oxidizing microbes limits their ability to respond to transient perturbations, resulting in periodic benthic release of methane. Eutrophication and deoxygenation decrease the efficiency of the microbial methane filter further, thereby enhancing the role of coastal environments as a source of methane to the atmosphere.

Stratified microbial communities in Australia's only anchialine cave are taxonomically novel and drive chemotrophic energy production via coupled nitrogen-sulphur cycling

BACKGROUND

Anchialine environments, in which oceanic water mixes with freshwater in coastal aquifers, are characterised by stratified water columns with complex physicochemical profiles. These environments, also known as subterranean estuaries, support an abundance of endemic macro and microorganisms. There is now growing interest in characterising the metabolisms of anchialine microbial communities, which is essential for understanding how complex ecosystems are supported in extreme environments, and assessing their vulnerability to environmental change. However, the diversity of metabolic strategies that are utilised in anchialine ecosystems remains poorly understood.

RESULTS

Here, we employ shotgun metagenomics to elucidate the key microorganisms and their dominant metabolisms along a physicochemical profile in Bundera Sinkhole, the only known continental subterranean estuary in the Southern Hemisphere. Genome-resolved metagenomics suggests that the communities are largely represented by novel taxonomic lineages, with 75% of metagenome-assembled genomes assigned to entirely new or uncharacterised families. These diverse and novel taxa displayed depth-dependent metabolisms, reflecting distinct phases along dissolved oxygen and salinity gradients. In particular, the communities appear to drive nutrient feedback loops involving nitrification, nitrate ammonification, and sulphate cycling. Genomic analysis of the most highly abundant members in this system suggests that an important source of chemotrophic energy is generated via the metabolic coupling of nitrogen and sulphur cycling.

CONCLUSION

These findings substantially contribute to our understanding of the novel and specialised microbial communities in anchialine ecosystems, and highlight key chemosynthetic pathways that appear to be important in these energy-limited environments. Such knowledge is essential for the conservation of anchialine ecosystems, and sheds light on adaptive processes in extreme environments. Video Abstract.

Ocean-wide comparisons of mesopelagic planktonic community structures

For decades, marine plankton have been investigated for their capacity to modulate biogeochemical cycles and provide fishery resources. Between the sunlit (epipelagic) layer and the deep dark waters, lies a vast and heterogeneous part of the ocean: the mesopelagic zone. How plankton composition is shaped by environment has been well-explored in the epipelagic but much less in the mesopelagic ocean. Here, we conducted comparative analyses of trans-kingdom community assemblages thriving in the mesopelagic oxygen minimum zone (OMZ), mesopelagic oxic, and their epipelagic counterparts. We identified nine distinct types of intermediate water masses that correlate with variation in mesopelagic community composition. Furthermore, oxygen, NO3- and particle flux together appeared as the main drivers governing these communities. Novel taxonomic signatures emerged from OMZ while a global co-occurrence network analysis showed that about 70% of the abundance of mesopelagic plankton groups is organized into three community modules. One module gathers prokaryotes, pico-eukaryotes and Nucleo-Cytoplasmic Large DNA Viruses (NCLDV) from oxic regions, and the two other modules are enriched in OMZ prokaryotes and OMZ pico-eukaryotes, respectively. We hypothesize that OMZ conditions led to a diversification of ecological niches, and thus communities, due to selective pressure from limited resources. Our study further clarifies the interplay between environmental factors in the mesopelagic oxic and OMZ, and the compositional features of communities.

A compendium of bacterial and archaeal single-cell amplified genomes from oxygen deficient marine waters

Oxygen-deficient marine waters referred to as oxygen minimum zones (OMZs) or anoxic marine zones (AMZs) are common oceanographic features. They host both cosmopolitan and endemic microorganisms adapted to low oxygen conditions. Microbial metabolic interactions within OMZs and AMZs drive coupled biogeochemical cycles resulting in nitrogen loss and climate active trace gas production and consumption. Global warming is causing oxygen-deficient waters to expand and intensify. Therefore, studies focused on microbial communities inhabiting oxygen-deficient regions are necessary to both monitor and model the impacts of climate change on marine ecosystem functions and services. Here we present a compendium of 5,129 single-cell amplified genomes (SAGs) from marine environments encompassing representative OMZ and AMZ geochemical profiles. Of these, 3,570 SAGs have been sequenced to different levels of completion, providing a strain-resolved perspective on the genomic content and potential metabolic interactions within OMZ and AMZ microbiomes. Hierarchical clustering confirmed that samples from similar oxygen concentrations and geographic regions also had analogous taxonomic compositions, providing a coherent framework for comparative community analysis.

Hydraulic characteristics in channel confluence affect the nitrogen dynamics through altering interactions among multi-trophic microbiota

Identifying the distribution of multi-trophic microbiota under the complicated hydrodynamic characteristics of channel confluences and evaluating the microbial contributions to biogeochemical processes are vital for river regulation and ecological function protection. However, relevant studies mainly focus on bacterial community distribution in confluence, neglecting the essential role of multi-trophic microbiota in the aquatic ecosystems and biogeochemical processes. To address this knowledge gap, this study investigated the distribution of multi-trophic microbiota and the underlying assembly process under the hydraulic characteristics in the confluence and described the direct and indirect effects of multi-trophic microbiota on the nitrogen dynamics. Results revealed that, in a river confluence, eukaryotic communities were governed by deterministic processes (52.4%) and bacterial communities were determined by stochastic processes (74.3%). The response of higher trophic levels to environmental factors was intensively higher than that of lower trophic microbiota, resulting in higher trophic microbiota were significantly different between regions with varied environmental conditions (P < 0.05). Flow velocity was the driving force controlling the assembly and composition of multi-trophic microbiota and interactions among multi-trophic levels, and further made a significant difference to nitrogen dynamics. In regions with lower flow velocity, interactions among multi-trophic levels were more complex. There were intense nitrate and nitrite reduction and anammox reactions via direct impacts of protozoan and metazoan and the top-down control (protozoan and metazoan prey on heterotrophic bacteria) among multi-trophic microbiota. Results and findings reveal the ecological effect on river nitrogen removal in a river confluence under complex hydraulic conditions and provide useful information for river management.

Network analysis of 16S rRNA sequences suggests microbial keystone taxa contribute to marine N2O cycling

The mechanisms by which large-scale microbial community function emerges from complex ecological interactions between individual taxa and functional groups remain obscure. We leveraged network analyses of 16S rRNA amplicon sequences obtained over a seven-month timeseries in seasonally anoxic Saanich Inlet (Vancouver Island, Canada) to investigate relationships between microbial community structure and water column N2O cycling. Taxa separately broadly into three discrete subnetworks with contrasting environmental distributions. Oxycline subnetworks were structured around keystone aerobic heterotrophs that correlated with nitrification rates and N2O supersaturations, linking N2O production and accumulation to taxa involved in organic matter remineralization. Keystone taxa implicated in anaerobic carbon, nitrogen, and sulfur cycling in anoxic environments clustered together in a low-oxygen subnetwork that correlated positively with nitrification N2O yields and N2O production from denitrification. Close coupling between N2O producers and consumers in the anoxic basin is indicated by strong correlations between the low-oxygen subnetwork, PICRUSt2-predicted nitrous oxide reductase (nosZ) gene abundances, and N2O undersaturation. This study implicates keystone taxa affiliated with common ODZ groups as a potential control on water column N2O cycling and provides a theoretical basis for further investigations into marine microbial interaction networks.

Transport-limited reactions in microbial systems

Predicting microbial metabolic rates and emergent biogeochemical fluxes remains challenging due to the many unknown population dynamical, physiological and reaction-kinetic parameters and uncertainties in species composition. Here, we show that the need for these parameters can be eliminated when population dynamics and reaction kinetics operate at much shorter time scales than physical mixing processes. Such scenarios are widespread in poorly mixed water columns and sediments. In this 'fast-reaction-transport' (FRT) limit, all that is required for predictions are chemical boundary conditions, the physical mixing processes and reaction stoichiometries, while no knowledge of species composition, physiology or population/reaction kinetic parameters is needed. Using time-series data spanning years 2001-2014 and depths 180-900 m across the permanently anoxic Cariaco Basin, we demonstrate that the FRT approach can accurately predict the dynamics of major electron donors and acceptors (Pearson r ≥ 0.9 in all cases). Hence, many microbial processes in this system are largely transport limited and thus predictable regardless of species composition, population dynamics and kinetics. Our approach enables predictions for many systems in which microbial community dynamics and kinetics are unknown. Our findings also reveal a mechanism for the frequently observed decoupling between function and taxonomy in microbial systems.

Influences of carbon sources on N2O production during denitrification in freshwaters: Activity, isotopes and functional microbes

Denitrification is one of the major sources of N₂O in freshwaters. Diverse forms of organic compounds act as the electron donors for microbial denitrification. However, the influences of carbon sources on N₂O production, N₂O reduction, isotope fractionation and functional microbes during denitrification were largely unknown. In this study, five forms of carbon sources (i.e. acetate, citrate, glucose, cellobiose and leucine) were used to enrich denitrifiers in freshwater sediments. N₂O conversion in the enrichments was investigated by a combination of inhibition technique, natural stable isotope method and metagenomics. Acetylene was effective in inhibiting N₂O reduction without influencing the isotopic characteristics during N₂O production. Glucose led to the least N₂O production and reduction, in accordance with the lowest abundance of both NO and N₂O reductases in this enrichment. δ¹⁸O and site preference value (SP, =δ¹⁵N^α-δ¹⁵N^β) of N₂O were sensitive to discriminate the five carbon sources, except when comparing acetate and leucine. Isotopic values of N₂O were not significantly different in these two enrichments due to the similarity of NO reductases - Pseudomonas-type cNorB. Specifically, the enrichment with cellobiose produced N₂O with the lowest δ¹⁸O values (39.4‰±1.1‰), due to Alicycliphilus with both cNorB and qNorB. The enrichment with glucose led to the highest SP values (8.9‰±8.6‰), caused by Thiobacillus-type cNorB. Our results demonstrated the link between carbon sources, N₂O production and reduction, isotopic signatures, microbial populations and enzymes during denitrification in freshwaters.

Integrating Genome-Resolved Metagenomics with Trait-Based Process Modeling to Determine Biokinetics of Distinct Nitrifying Communities within Activated Sludge

Conventional bioprocess models for wastewater treatment are based on aggregated bulk biomass concentrations and do not incorporate microbial physiological diversity. Such a broad aggregation of microbial functional groups can fail to predict ecosystem dynamics when high levels of physiological diversity exist within trophic guilds. For instance, functional diversity among nitrite-oxidizing bacteria (NOB) can obfuscate engineering strategies for their out-selection in activated sludge (AS), which is desirable to promote energy-efficient nitrogen removal. Here, we hypothesized that different NOB populations within AS can have different physiological traits that drive process performance, which we tested by estimating biokinetic growth parameters using a combination of highly replicated respirometry, genome-resolved metagenomics, and process modeling. A lab-scale AS reactor subjected to a selective pressure for over 90 days experienced resilience of NOB activity. We recovered three coexisting Nitrospira population genomes belonging to two sublineages, which exhibited distinct growth strategies and underwent a compositional shift following the selective pressure. A trait-based process model calibrated at the NOB genus level better predicted nitrite accumulation than a conventional process model calibrated at the NOB guild level. This work demonstrates that trait-based modeling can be leveraged to improve our prediction, control, and design of functionally diverse microbiomes driving key environmental biotechnologies.

Approaches to Unmask Functioning of the Uncultured Microbial Majority From Extreme Habitats on the Seafloor

Researchers have recognized the potential of enzymes and metabolic pathways hidden among the unseen majority of Earth's microorganisms for decades now. Most of the microbes expected to colonize the seafloor and its subsurface are currently uncultured. Thus, their ability and contribution to element cycling remain enigmatic. Given that the seafloor covers ∼70% of our planet, this amounts to an uncalled potential of unrecognized metabolic properties and interconnections catalyzed by this microbial dark matter. Consequently, a tremendous black box awaits discovery of novel enzymes, catalytic abilities, and metabolic properties in one of the largest habitats on Earth. This mini review summarizes the current knowledge of cultivation-dependent and -independent techniques applied to seafloor habitats to unravel the role of the microbial dark matter. It highlights the great potential that combining microbiological and biogeochemical data from in situ experiments with molecular tools has for providing a holistic understanding of bio-geo-coupling in seafloor habitats and uses hydrothermal vent systems as a case example.

Abundant and persistent sulfur-oxidizing microbial populations are responsive to hypoxia in the Chesapeake Bay

The number, size and severity of aquatic low‐oxygen dead zones are increasing worldwide. Microbial processes in low‐oxygen environments have important ecosystem‐level consequences, such as denitrification, greenhouse gas production and acidification. To identify key microbial processes occurring in low‐oxygen bottom waters of the Chesapeake Bay, we sequenced both 16S rRNA genes and shotgun metagenomic libraries to determine the identity, functional potential and spatiotemporal distribution of microbial populations in the water column. Unsupervised clustering algorithms grouped samples into three clusters using water chemistry or microbial communities, with extensive overlap of cluster composition between methods. Clusters were strongly differentiated by temperature, salinity and oxygen. Sulfur‐oxidizing microorganisms were found to be enriched in the low‐oxygen bottom water and predictive of hypoxic conditions. Metagenome‐assembled genomes demonstrate that some of these sulfur‐oxidizing populations are capable of partial denitrification and transcriptionally active in a prior study. These results suggest that microorganisms capable of oxidizing reduced sulfur compounds are a previously unidentified microbial indicator of low oxygen in the Chesapeake Bay and reveal ties between the sulfur, nitrogen and oxygen cycles that could be important to capture when predicting the ecosystem response to remediation efforts or climate change.

Reactive Transport: A Review of Basic Concepts with Emphasis on Biochemical Processes

Reactive transport (RT) couples bio-geo-chemical reactions and transport. RT is important to understand numerous scientific questions and solve some engineering problems. RT is highly multidisciplinary, which hinders the development of a body of knowledge shared by RT modelers and developers. The goal of this paper is to review the basic conceptual issues shared by all RT problems, so as to facilitate advancement along the current frontier: biochemical reactions. To this end, we review the basic equations to indicate that chemical systems are controlled by the set of equilibrium reactions, which are easy to model, but whose rate is controlled by mixing. Since mixing is not properly represented by the standard advection-dispersion equation (ADE), we conclude that this equation is poor for RT. This leads us to review alternative transport formulations, and the methods to solve RT problems using both the ADE and alternative equations. Since equilibrium is easy, difficulties arise for kinetic reactions, which is especially true for biochemistry, where numerous challenges are open (how to represent microbial communities, impact of genomics, effect of biofilms on flow and transport, etc.). We conclude with the basic eleven conceptual issues that we consider fundamental for any conceptually sound RT effort.

Biogeochemistry Goes Viral: towards a Multifaceted Approach To Study Viruses and Biogeochemical Cycling

Viruses are ubiquitous on Earth and are keystone components of environments, ecosystems, and human health. Yet, viruses remain poorly studied because most cannot be isolated in a laboratory. In the field of biogeochemistry, which aims to understand the interactions between biology, geology, and chemistry, there is progress to be made in understanding the different roles played by viruses in nutrient cycling, food webs, and elemental transformations. In this commentary, we outline current microbial ecology frameworks for understanding biogeochemical cycling in aquatic ecosystems. Next, we review some existing experimental and computational techniques that are enabling us to study the role of viruses in biogeochemical cycling, using examples from aquatic environments. Finally, we provide a conceptual model that balances limitations of computational tools when combined with biogeochemistry and ecological data. We envision meeting the grand challenge of understanding how viruses impact biogeochemical cycling by using a multifaceted approach to viral ecology.

Prospects for multi-omics in the microbial ecology of water engineering

Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions - including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.

Integrating experiments with system-level biogeochemical modeling to understand nitrogen cycling of reservoir sediments at elevated hydrostatic pressure

Impoundment of rivers to construct reservoirs for hydropower and irrigation greatly increase the hydrostatic pressure acting on river sediments with potential repercussions for ecosystem-level microbial activity and metabolism. Understanding the functioning and responses of key biogeochemical cycles such as that of nitrogen cycling to shifting hydrostatic pressure is needed to estimate and predict the systemic nutrient dynamics in deep-water reservoirs. We studied the functioning of bacterial communities involved in nitrogen transformation in bioreactors maintained under contrasting hydrostatic pressures (0.5 MPa-3.0 MPa) and complemented the experimental approach with a functional gene-informed biogeochemical model. The model predictions were broadly consistent with observations from the experiment, suggesting that the rates of N₂O production decreased while the sediment concentration of nitrite increased significantly with increasing pressure, at least when exceeding 1.0 MPa. Changes in nitrite reduction (nirS) and aerobic ammonia oxidation (amoA) genes abundances were in accordance with the observed changes in N₂O production and nitrite levels. Moreover, the model predicted that the higher pressures (P > 1.5 MPa) would intensify the inhibition of N₂ production via denitrification and result in an accumulation of ammonia in the sediment along with a decrease in dissolved oxygen. The results imply that increased hydrostatic pressure caused by dam constructions may have a strong effect on microbial nitrogen conversion, and that this may result in lower nitrogen removal.

Activity-Based Cell Sorting Reveals Resistance of Functionally Degenerate Nitrospira during a Press Disturbance in Nitrifying Activated Sludge

Managing and engineering activated sludge wastewater treatment microbiomes for low-energy nitrogen removal requires process control strategies to stop the oxidation of ammonium at nitrite. Our ability to out-select nitrite-oxidizing bacteria (NOB) from activated sludge is challenged by their metabolic and physiological diversity, warranting measurements of their in situ physiology and activity under selective growth pressures. Here, we examined the stability of nitrite oxidation in activated sludge during a press disturbance induced by treating a portion of return activated sludge with a sidestream flow containing free ammonia (FA) at 200 mg NH₃-N/liter. The nitrite accumulation ratio peaked at 42% by day 40 in the experimental bioreactor with the press disturbance, while it did not increase in the control bioreactor. A subsequent decrease in nitrite accumulation within the experimental bioreactor coincided with shifts in dominant Nitrospira 16S rRNA amplicon sequence variants (ASVs). We applied bioorthogonal noncanonical amino acid tagging (BONCAT) coupled with fluorescence-activated cell sorting (FACS) to investigate changes in the translational activity of NOB populations throughout batch exposure to FA. BONCAT-FACS confirmed that the single Nitrospira ASV washed out of the experimental bioreactor had reduced translational activity following exposure to FA, whereas the two Nitrospira ASVs that emerged after process acclimation were not impacted by FA. Thus, the coexistence of functionally degenerate and physiologically resistant Nitrospira populations provided resilience to the nitrite-oxidizing function during the press disturbance. These results highlight how BONCAT-FACS can resolve ecological niche differentiation within activated sludge and inform strategies to engineer and control microbiome function.

IMPORTANCE Nitrogen removal from activated sludge wastewater treatment systems is an energy-intensive process due to the large aeration requirement for nitrification. This energy footprint could be minimized with engineering control strategies that wash out nitrite-oxidizing bacteria (NOB) to limit oxygen demands. However, NOB populations can have a high degree of physiological diversity, and it is currently difficult to decipher the behavior of individual taxa during applied selective pressures. Here, we utilized a new substrate analog probing approach to measure the activity of NOB at the cellular translational level in the face of a press disturbance applied to the activated sludge process. Substrate analog probing corroborated the time series reactor sampling, showing that coexisting and functionally degenerate Nitrospira populations provided resilience to the nitrite oxidation process. Taken together, these results highlight how substrate analog approaches can illuminate in situ ecophysiologies within shared niches, and can inform strategies to improve microbiome engineering and management.

Towards omics-based predictions of planktonic functional composition from environmental data

Marine microbes play a crucial role in climate regulation, biogeochemical cycles, and trophic networks. Unprecedented amounts of data on planktonic communities were recently collected, sparking a need for innovative data-driven methodologies to quantify and predict their ecosystemic functions. We reanalyze 885 marine metagenome-assembled genomes through a network-based approach and detect 233,756 protein functional clusters, from which 15% are functionally unannotated. We investigate all clusters' distributions across the global ocean through machine learning, identifying biogeographical provinces as the best predictors of protein functional clusters' abundance. The abundances of 14,585 clusters are predictable from the environmental context, including 1347 functionally unannotated clusters. We analyze the biogeography of these 14,585 clusters, identifying the Mediterranean Sea as an outlier in terms of protein functional clusters composition. Applicable to any set of sequences, our approach constitutes a step towards quantitative predictions of functional composition from the environmental context.

Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates.

The bacterial sulfur cycle in expanding dysoxic and euxinic marine waters

Dysoxic marine waters (DMW, < 1 μM oxygen) are currently expanding in volume in the oceans, which has biogeochemical, ecological and societal consequences on a global scale. In these environments, distinct bacteria drive an active sulfur cycle, which has only recently been recognized for open-ocean DMW. This review summarizes the current knowledge on these sulfur-cycling bacteria. Critical bottlenecks and questions for future research are specifically addressed. Sulfate-reducing bacteria (SRB) are core members of DMW. However, their roles are not entirely clear, and they remain largely uncultured. We found support for their remarkable diversity and taxonomic novelty by mining metagenome-assembled genomes from the Black Sea as model ecosystem. We highlight recent insights into the metabolism of key sulfur-oxidizing SUP05 and Sulfurimonas bacteria, and discuss the probable involvement of uncultivated SAR324 and BS-GSO2 bacteria in sulfur oxidation. Uncultivated Marinimicrobia bacteria with a presumed organoheterotrophic metabolism are abundant in DMW. Like SRB, they may use specific molybdoenzymes to conserve energy from the oxidation, reduction or disproportionation of sulfur cycle intermediates such as S0 and thiosulfate, produced from the oxidation of sulfide. We expect that tailored sampling methods and a renewed focus on cultivation will yield deeper insight into sulfur-cycling bacteria in DMW.

Coupling Genomics and Hydraulic Information to Predict the Nitrogen Dynamics in a Channel Confluence

The simulation of nitrogen dynamics in urban channel confluences is essential for the evaluation and improvement of water quality. The omics-based modeling approaches that have been rapidly developed have been increasingly applied to characterize metabolisms of the microbial community and transformation of the associated materials. However, the transport of microorganisms and chemicals within and among different phases, which could be the rate-limiting step for the nitrogen dynamics, are always neglected or oversimplified in omics-based models. Therefore, this study proposes a novel simulation system coupling genomic and hydraulic information to simulate transport and transformation processes and provide predictions of nitrogen dynamics in a confluence. The proposed model was able to capture multiphase mass transport, microbial population dynamics, and nitrogen transformation and accurately predict gene abundances and nitrogen concentrations in both water and sediment; the mean relative errors were all lower than 40%. The model emphasized the importance of transport processes, which contributed more than 90% to gene abundances and chemical concentrations. Moreover, the simulation of reaction rates exhibited the specific nitrogen transformation processes in the confluence. The sulfide oxidation and the nitrate reduction and anaerobic ammonium oxidation, with the participation of the genes nap and hzo, respectively, were promoted as the main processes of nitrate and nitrite reduction.

On how the power supply shapes microbial survival

Understanding how environmental factors affect microbial survival is an important open problem in microbial ecology. Patterns of microbial community structure have been characterized across a wide range of different environmental settings, but the mechanisms generating these patterns remain poorly understood. Here, we use mathematical modelling to investigate fundamental connections between chemical power supply to a system and patterns of microbial survival. We reveal a complex set of interdependences between power supply and distributions of survival probability across microbial habitats, in a case without interspecific resource competition. We also find that different properties determining power supply, such as substrate fluxes and Gibbs energies of reactions, affect microbial survival in fundamentally different ways. Moreover, we show how simple connections between power supply and growth can give rise to complex patterns of microbial survival across physicochemical gradients, such as pH gradients. Our findings show the importance of taking energy fluxes into account in order to reveal fundamental connections between microbial survival and environmental conditions, and to obtain a better understanding of microbial population dynamics in natural environments.

Anthropogenic and Environmental Constraints on the Microbial Methane Cycle in Coastal Sediments

Large amounts of methane, a potent greenhouse gas, are produced in anoxic sediments by methanogenic archaea. Nonetheless, over 90% of the produced methane is oxidized via sulfate-dependent anaerobic oxidation of methane (S-AOM) in the sulfate-methane transition zone (SMTZ) by consortia of anaerobic methane-oxidizing archaea (ANME) and sulfate-reducing bacteria (SRB). Coastal systems account for the majority of total marine methane emissions and typically have lower sulfate concentrations, hence S-AOM is less significant. However, alternative electron acceptors such as metal oxides or nitrate could be used for AOM instead of sulfate. The availability of electron acceptors is determined by the redox zonation in the sediment, which may vary due to changes in oxygen availability and the type and rate of organic matter inputs. Additionally, eutrophication and climate change can affect the microbiome, biogeochemical zonation, and methane cycling in coastal sediments. This review summarizes the current knowledge on the processes and microorganisms involved in methane cycling in coastal sediments and the factors influencing methane emissions from these systems. In eutrophic coastal areas, organic matter inputs are a key driver of bottom water hypoxia. Global warming can reduce the solubility of oxygen in surface waters, enhancing water column stratification, increasing primary production, and favoring methanogenesis. ANME are notoriously slow growers and may not be able to effectively oxidize methane upon rapid sedimentation and shoaling of the SMTZ. In such settings, ANME-2d (Methanoperedenaceae) and ANME-2a may couple iron- and/or manganese reduction to AOM, while ANME-2d and NC10 bacteria (Methylomirabilota) could couple AOM to nitrate or nitrite reduction. Ultimately, methane may be oxidized by aerobic methanotrophs in the upper millimeters of the sediment or in the water column. The role of these processes in mitigating methane emissions from eutrophic coastal sediments, including the exact pathways and microorganisms involved, are still underexplored, and factors controlling these processes are unclear. Further studies are needed in order to understand the factors driving methane-cycling pathways and to identify the responsible microorganisms. Integration of the knowledge on microbial pathways and geochemical processes is expected to lead to more accurate predictions of methane emissions from coastal zones in the future.

Redox-informed models of global biogeochemical cycles

Microbial activity mediates the fluxes of greenhouse gases. However, in the global models of the marine and terrestrial biospheres used for climate change projections, typically only photosynthetic microbial activity is resolved mechanistically. To move forward, we argue that global biogeochemical models need a theoretically grounded framework with which to constrain parameterizations of diverse microbial metabolisms. Here, we explain how the key redox chemistry underlying metabolisms provides a path towards this goal. Using this first-principles approach, the presence or absence of metabolic functional types emerges dynamically from ecological interactions, expanding model applicability to unobserved environments.

“Nothing is less real than realism. It is only by selection, by elimination, by emphasis, that we get at the real meaning of things.” –Georgia O’Keefe

Marine microbial activities fuel biogeochemical cycles that impact the climate, but global models do not account for the myriad physiological processes that microbes perform. Here the authors argue for a model framework that reinterprets the ocean as physics coupled to biologically-driven redox chemistry.

Integrating Microbial Community Assembly and Fluid Kinetics to Decouple Nitrogen Dynamics in an Urban Channel Confluence

Understanding the characteristics of biogeochemical processes in urban channel confluences is essential for the evaluation and improvement of water environmental capacity. However, influences of biogeochemical processes in confluence were always overlooked or simply parametrized since the transformation processes controlled by microbial community assembly were hard to quantify. To address this knowledge gap, the present study proposed a novel mathematical modeling system, based on microbial community assembly theory and fluid kinetics, to decouple nitrogen dynamics into flow-induced transport and microorganism-induced transformation processes, and quantified their contributions to nitrogen concentrations. Results revealed that variable selection processes (including hydrodynamic conditions) contributed to significant difference in microbial communities among different hydraulic regions. Variation in microbial communities further shifted transformation processes. Rhodobacterales and Sphingomonadales, which were reported to be vital participants in denitrification process, were enriched in flow separation region, and promoted it as a hotspot for nitrogen removal. In the flow separation region, microorganism-induced transformation processes accounted for 56% of total nitrogen removal, which was significantly higher than that in other regions (12% on average; p < 0.01). Results and findings could provide useful information for the improvement of water environmental capacity.

Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?

Increasing nitrogen (N) loads present a threat to estuaries, which are among the most heavily populated and perturbed parts of the world. N removal is largely mediated by the sediment microbial process of denitrification, in direct competition to dissimilatory nitrate reduction to ammonium (DNRA), which recycles nitrate to ammonium. Molecular proxies for N pathways are increasingly measured and analyzed, a major question in microbial ecology, however, is whether these proxies can add predictive power around the fate of N. We analyzed the diversity and community composition of sediment nirS and nrfA genes in 11 temperate estuaries, covering four types of land use in Australia, and analyzed how these might be used to predict N removal. Our data suggest that sediment microbiomes play a central role in controlling the magnitude of the individual N removal rates in the 11 estuaries. Inclusion, however, of relative gene abundances of 16S, nirS, nrfA, including their ratios did not improve physicochemical measurement-based regression models to predict rates of denitrification or DNRA. Co-occurrence network analyses of nirS showed a greater modularity and a lower number of keystone OTUs in pristine sites compared to urban estuaries, suggesting a higher degree of niche partitioning in pristine estuaries. The distinctive differences between the urban and pristine network structures suggest that the nirS gene could be a likely gene candidate to understand the mechanisms by which these denitrifying communities form and respond to anthropogenic pressures.

The microbiomes of deep-sea hydrothermal vents: distributed globally, shaped locally

The discovery of chemosynthetic ecosystems at deep-sea hydrothermal vents in 1977 changed our view of biology. Chemosynthetic bacteria and archaea form the foundation of vent ecosystems by exploiting the chemical disequilibrium between reducing hydrothermal fluids and oxidizing seawater, harnessing this energy to fix inorganic carbon into biomass. Recent research has uncovered fundamental aspects of these microbial communities, including their relationships with underlying geology and hydrothermal geochemistry, interactions with animals via symbiosis and distribution both locally in various habitats within vent fields and globally across hydrothermal systems in diverse settings. Although 'black smokers' and symbioses between microorganisms and macrofauna attract much attention owing to their novelty and the insights they provide into life under extreme conditions, habitats such as regions of diffuse flow, subseafloor aquifers and hydrothermal plumes have important roles in the global cycling of elements through hydrothermal systems. Owing to sharp contrasts in physical and chemical conditions between these various habitats and their dynamic, extreme and geographically isolated nature, hydrothermal vents provide a valuable window into the environmental and ecological forces that shape microbial communities and insights into the limits, origins and evolution of microbial life.

Combining Molecular Observations and Microbial Ecosystem Modeling: A Practical Guide

Advances in technologies for molecular observation are leading to novel types of data, including gene, transcript, protein, and metabolite levels, which are fundamentally different from the types traditionally compared with microbial ecosystem models, such as biomass (e.g., chlorophyll a) and nutrient concentrations. A grand challenge is to use these data to improve predictive models and use models to explain observed patterns. This article presents a framework that aligns observations and models along the dimension of abstraction or biological organization-from raw sequences to ecosystem patterns for observations, and from sequence simulators to ecological theory for models. It then reviews 16 studies that compared model results with molecular observations. Molecular data can and are being combined with microbial ecosystem models, but to keep up with and take advantage of the full scope of observations, models need to become more mechanistically detailed and complex, which is a technical and cultural challenge for the ecological modeling community.

Stable aerobic and anaerobic coexistence in anoxic marine zones

Mechanistic description of the transition from aerobic to anaerobic metabolism is necessary for diagnostic and predictive modeling of fixed nitrogen loss in anoxic marine zones (AMZs). In a metabolic model where diverse oxygen- and nitrogen-cycling microbial metabolisms are described by underlying redox chemical reactions, we predict a transition from strictly aerobic to predominantly anaerobic regimes as the outcome of ecological interactions along an oxygen gradient, obviating the need for prescribed critical oxygen concentrations. Competing aerobic and anaerobic metabolisms can coexist in anoxic conditions whether these metabolisms represent obligate or facultative populations. In the coexistence regime, relative rates of aerobic and anaerobic activity are determined by the ratio of oxygen to electron donor supply. The model simulates key characteristics of AMZs, such as the accumulation of nitrite and the sustainability of anammox at higher oxygen concentrations than denitrification, and articulates how microbial biomass concentrations relate to associated water column transformation rates as a function of redox stoichiometry and energetics. Incorporating the metabolic model into an idealized two-dimensional ocean circulation results in a simulated AMZ, in which a secondary chlorophyll maximum emerges from oxygen-limited grazing, and where vertical mixing and dispersal in the oxycline also contribute to metabolic co-occurrence. The modeling approach is mechanistic yet computationally economical and suitable for global change applications.

Microbial metabolite fluxes in a model marine anoxic ecosystem

Permanently anoxic regions in the ocean are widespread and exhibit unique microbial metabolic activity exerting substantial influence on global elemental cycles and climate. Reconstructing microbial metabolic activity rates in these regions has been challenging, due to the technical difficulty of direct rate measurements. In Cariaco Basin, which is the largest permanently anoxic marine basin and an important model system for geobiology, long-term monitoring has yielded time series for the concentrations of biologically important compounds; however, the underlying metabolite fluxes remain poorly quantified. Here, we present a computational approach for reconstructing vertical fluxes and in situ net production/consumption rates from chemical concentration data, based on a 1-dimensional time-dependent diffusive transport model that includes adaptive penalization of overfitting. We use this approach to estimate spatiotemporally resolved fluxes of oxygen, nitrate, hydrogen sulfide, ammonium, methane, and phosphate within the sub-euphotic Cariaco Basin water column (depths 150-900 m, years 2001-2014) and to identify hotspots of microbial chemolithotrophic activity. Predictions of the fitted models are in excellent agreement with the data and substantially expand our knowledge of the geobiology in Cariaco Basin. In particular, we find that the diffusivity, and consequently fluxes of major reductants such as hydrogen sulfide, and methane, is about two orders of magnitude greater than previously estimated, thus resolving a long-standing apparent conundrum between electron donor fluxes and measured dark carbon assimilation rates.

Microbial niches in marine oxygen minimum zones

In the ocean's major oxygen minimum zones (OMZs), oxygen is effectively absent from sea water and life is dominated by microorganisms that use chemicals other than oxygen for respiration. Recent studies that combine advanced genomic and chemical detection methods are delineating the different metabolic niches that microorganisms can occupy in OMZs. Understanding these niches, the microorganisms that inhabit them, and their influence on marine biogeochemical cycles is crucial as OMZs expand with increasing seawater temperatures.

Circumventing kinetics in biogeochemical modeling

Microbial metabolism drives biogeochemical fluxes in virtually every ecosystem. Modeling these fluxes is challenged by the incredible diversity of microorganisms, whose kinetic parameters are largely unknown. In poorly mixed systems, such as stagnant water columns or sediments, however, long-term bulk microbial metabolism may become limited by physical transport rates of substrates across space. Here we mathematically show that under these conditions, biogeochemical fluxes are largely predictable based on the system's transport properties, chemical boundary conditions, and the stoichiometry of metabolic pathways, regardless of the precise kinetics of the resident microorganisms. We formalize these considerations into a predictive modeling framework and demonstrate its use for the Cariaco Basin subeuphotic zone, one of the largest anoxic marine basins worldwide. Using chemical concentration data solely from the upper boundary (depth 180 m) and lower boundary (depth 900 m), but without a priori knowledge of metabolite fluxes, chemical depth profiles, kinetic parameters, or microbial species composition, we predict the concentrations and vertical fluxes of biologically important substances, including oxygen, nitrate, hydrogen sulfide, and ammonium, across the entire considered depth range (180-900 m). Our predictions largely agree with concentration measurements over a period of 14 years ([Formula: see text] = 0.78-0.92) and become particularly accurate during a period where the system was near biogeochemical steady state (years 2007-2009, [Formula: see text] = 0.86-0.95). Our work enables geobiological predictions for a large class of ecosystems without knowledge of kinetic parameters or geochemical depth profiles. Conceptually, our work provides a possible explanation for the decoupling between microbial species composition and bulk metabolic function, observed in various ecosystems.

A robust, cost-effective method for DNA, RNA and protein co-extraction from soil, other complex microbiomes and pure cultures

The soil microbiome is inherently complex with high biological diversity, and spatial heterogeneity typically occurring on the submillimetre scale. To study the microbial ecology of soils, and other microbiomes, biomolecules, that is, nucleic acids and proteins, must be efficiently and reliably co-recovered from the same biological samples. Commercial kits are currently available for the co-extraction of DNA, RNA and proteins but none has been developed for soil samples. We present a new protocol drawing on existing phenol-chloroform-based methods for nucleic acids co-extraction but incorporating targeted precipitation of proteins from the phenol phase. The protocol is cost-effective and robust, and easily implemented using reagents commonly available in laboratories. The method is estimated to be eight times cheaper than using disparate commercial kits for the isolation of DNA and/or RNA, and proteins, from soil. The method is effective, providing good quality biomolecules from a diverse range of soil types, with clay contents varying from 9.5% to 35.1%, which we successfully used for downstream, high-throughput gene sequencing and metaproteomics. Additionally, we demonstrate that the protocol can also be easily implemented for biomolecule co-extraction from other complex microbiome samples, including cattle slurry and microbial communities recovered from anaerobic bioreactors, as well as from Gram-positive and Gram-negative pure cultures.

Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake

BACKGROUND

Microbial processes are intricately linked to the depletion of oxygen in in-land and coastal water bodies, with devastating economic and ecological consequences. Microorganisms deplete oxygen during biomass decomposition, degrading the habitat of many economically important aquatic animals. Microbes then turn to alternative electron acceptors, which alter nutrient cycling and generate potent greenhouse gases. As oxygen depletion is expected to worsen with altered land use and climate change, understanding how chemical and microbial dynamics impact dead zones will aid modeling efforts to guide remediation strategies. More work is needed to understand the complex interplay between microbial genes, populations, and biogeochemistry during oxygen depletion.

RESULTS

Here, we used 16S rRNA gene surveys, shotgun metagenomic sequencing, and a previously developed biogeochemical model to identify genes and microbial populations implicated in major biogeochemical transformations in a model lake ecosystem. Shotgun metagenomic sequencing was done for one time point in Aug., 2013, and 16S rRNA gene sequencing was done for a 5-month time series (Mar.-Aug., 2013) to capture the spatiotemporal dynamics of genes and microorganisms mediating the modeled processes. Metagenomic binning analysis resulted in many metagenome-assembled genomes (MAGs) that are implicated in the modeled processes through gene content similarity to cultured organism and the presence of key genes involved in these pathways. The MAGs suggested some populations are capable of methane and sulfide oxidation coupled to nitrate reduction. Using the model, we observe that modulating these processes has a substantial impact on overall lake biogeochemistry. Additionally, 16S rRNA gene sequences from the metagenomic and amplicon libraries were linked to processes through the MAGs. We compared the dynamics of microbial populations in the water column to the model predictions. Many microbial populations involved in primary carbon oxidation had dynamics similar to the model, while those associated with secondary oxidation processes deviated substantially.

CONCLUSIONS

This work demonstrates that the unique capabilities of resident microbial populations will substantially impact the concentration and speciation of chemicals in the water column, unless other microbial processes adjust to compensate for these differences. It further highlights the importance of the biological aspects of biogeochemical processes, such as fluctuations in microbial population dynamics. Integrating gene and population dynamics into biogeochemical models has the potential to improve predictions of the community response under altered scenarios to guide remediation efforts.

Profiling physicochemical and planktonic features from discretely/continuously sampled surface water

There is an increasing need for assessing aquatic ecosystems that are globally endangered. Since aquatic ecosystems are complex, integrated consideration of multiple factors utilizing omics technologies can help us better understand aquatic ecosystems. An integrated strategy linking three analytical (machine learning, factor mapping, and forecast-error-variance decomposition) approaches for extracting the features of surface water from datasets comprising ions, metabolites, and microorganisms is proposed herein. The three developed approaches can be employed for diverse datasets of sample sizes and experimentally analyzed factors. The three approaches are applied to explore the features of bay water surrounding Odaiba, Tokyo, Japan, as a case study. Firstly, the machine learning approach separated 681 surface water samples within Japan into three clusters, categorizing Odaiba water into seawater with relatively low inorganic ions, including Mg, Ba, and B. Secondly, the factor mapping approach illustrated Odaiba water samples from the summer as rich in multiple amino acids and some other metabolites and poor in inorganic ions relative to other seasons based on their seasonal dynamics. Finally, forecast-error-variance decomposition using vector autoregressive models indicated that a type of microalgae (Raphidophyceae) grows in close correlation with alanine, succinic acid, and valine on filters and with isobutyric acid and 4-hydroxybenzoic acid in filtrate, Ba, and average wind speed. Our integrated strategy can be used to examine many biological, chemical, and environmental physical factors to analyze surface water.

Short-Term Transcriptional Response of Microbial Communities to Nitrogen Fertilization in a Pine Forest Soil

Numerous studies have examined the long-term effect of experimental nitrogen (N) deposition in terrestrial ecosystems; however, N-specific mechanistic markers are difficult to disentangle from responses to other environmental changes. The strongest picture of N-responsive mechanistic markers is likely to arise from measurements over a short (hours to days) time scale immediately after inorganic N deposition. Therefore, we assessed the short-term (3-day) transcriptional response of microbial communities in two soil strata from a pine forest to a high dose of N fertilization (ca. 1 mg/g of soil material) in laboratory microcosms. We hypothesized that N fertilization would repress the expression of fungal and bacterial genes linked to N mining from plant litter. However, despite N suppression of microbial respiration, the most pronounced differences in functional gene expression were between strata rather than in response to the N addition. Overall, ∼4% of metabolic genes changed in expression with N addition, while three times as many (∼12%) were significantly different across the different soil strata in the microcosms. In particular, we found little evidence of N changing expression levels of metabolic genes associated with complex carbohydrate degradation (CAZymes) or inorganic N utilization. This suggests that direct N repression of microbial functional gene expression is not the principle mechanism for reduced soil respiration immediately after N deposition. Instead, changes in expression with N addition occurred primarily in general cell maintenance areas, for example, in ribosome-related transcripts. Transcriptional changes in functional gene abundance in response to N addition observed in longer-term field studies likely result from changes in microbial composition.IMPORTANCE Ecosystems are receiving increased nitrogen (N) from anthropogenic sources, including fertilizers and emissions from factories and automobiles. High levels of N change ecosystem functioning. For example, high inorganic N decreases the microbial decomposition of plant litter, potentially reducing nutrient recycling for plant growth. Understanding how N regulates microbial decomposition can improve the prediction of ecosystem functioning over extended time scales. We found little support for the conventional view that high N supply represses the expression of genes involved in decomposition or alters the expression of bacterial genes for inorganic N cycling. Instead, our study of pine forest soil 3 days after N addition showed changes in microbial gene expression related to cell maintenance and stress response. This highlights the challenge of establishing predictive links between microbial gene expression levels and measures of ecosystem function.

Oxygen minimum zone cryptic sulfur cycling sustained by offshore transport of key sulfur oxidizing bacteria

Members of the gammaproteobacterial clade SUP05 couple water column sulfide oxidation to nitrate reduction in sulfidic oxygen minimum zones (OMZs). Their abundance in offshore OMZ waters devoid of detectable sulfide has led to the suggestion that local sulfate reduction fuels SUP05-mediated sulfide oxidation in a so-called “cryptic sulfur cycle”. We examined the distribution and metabolic capacity of SUP05 in Peru Upwelling waters, using a combination of oceanographic, molecular, biogeochemical and single-cell techniques. A single SUP05 species, ^UThioglobus perditus, was found to be abundant and active in both sulfidic shelf and sulfide-free offshore OMZ waters. Our combined data indicated that mesoscale eddy-driven transport led to the dispersal of ^UT. perditus and elemental sulfur from the sulfidic shelf waters into the offshore OMZ region. This offshore transport of shelf waters provides an alternative explanation for the abundance and activity of sulfide-oxidizing denitrifying bacteria in sulfide-poor offshore OMZ waters.

The presence and activity of sulfide-oxidizing denitrifying bacteria in sulfide-poor offshore oxygen minimum zone waters remains unclear. Here, the authors combine oceanography, molecular, biogeochemical and single-cell techniques to examine their distribution, metabolic capacity, and origins.

Function and functional redundancy in microbial systems

Microbial communities often exhibit incredible taxonomic diversity, raising questions regarding the mechanisms enabling species coexistence and the role of this diversity in community functioning. On the one hand, many coexisting but taxonomically distinct microorganisms can encode the same energy-yielding metabolic functions, and this functional redundancy contrasts with the expectation that species should occupy distinct metabolic niches. On the other hand, the identity of taxa encoding each function can vary substantially across space or time with little effect on the function, and this taxonomic variability is frequently thought to result from ecological drift between equivalent organisms. Here, we synthesize the powerful paradigm emerging from these two patterns, connecting the roles of function, functional redundancy and taxonomy in microbial systems. We conclude that both patterns are unlikely to be the result of ecological drift, but are inevitable emergent properties of open microbial systems resulting mainly from biotic interactions and environmental and spatial processes.

Genomic features of bacterial adaptation to plants

Plants intimately associate with diverse bacteria. Plant-associated (PA) bacteria have ostensibly evolved genes enabling adaptation to the plant environment. However, the identities of such genes are mostly unknown and their functions are poorly characterized. We sequenced 484 genomes of bacterial isolates from roots of Brassicaceae, poplar, and maize. We then compared 3837 bacterial genomes to identify thousands of PA gene clusters. Genomes of PA bacteria encode more carbohydrate metabolism functions and fewer mobile elements than related non-plant associated genomes. We experimentally validated candidates from two sets of PA genes, one involved in plant colonization, the other serving in microbe-microbe competition between PA bacteria. We also identified 64 PA protein domains that potentially mimic plant domains; some are shared with PA fungi and oomycetes. This work expands the genome-based understanding of plant-microbe interactions and provides leads for efficient and sustainable agriculture through microbiome engineering.