Natural bacterial communities serve as quantitative geochemical biosensors

Reproducible responses of geochemical and microbial successional patterns in the subsurface to carbon source amendment

Carbon amendments designed to remediate environmental contamination lead to substantial perturbations when injected into the subsurface. For the remediation of uranium contamination, carbon amendments promote reducing conditions to allow microorganisms to reduce uranium to an insoluble, less mobile state. However, the reproducibility of these amendments and underlying microbial community assembly mechanisms have rarely been investigated in the field. In this study, two injections of emulsified vegetable oil were performed in 2009 and 2017 to immobilize uranium in the groundwater at Oak Ridge, TN, USA. Our objectives were to determine whether and how the injections resulted in similar abiotic and biotic responses and their underlying community assembly mechanisms. Both injections caused similar geochemical and microbial succession. Uranium, nitrate, and sulfate concentrations in the groundwater dropped following the injection, and specific microbial taxa responded at roughly the same time points in both injections, including Geobacter, Desulfovibrio, and members of the phylum Comamonadaceae, all of which are well established in uranium, nitrate, and sulfate reduction. Both injections induced a transition from relatively stochastic to more deterministic assembly of microbial taxonomic and phylogenetic community structures based on 16S rRNA gene analysis. We conclude that geochemical and microbial successions after biostimulation are reproducible, likely owing to the selection of similar phylogenetic groups in response to EVO injection.

Genomic and environmental controls on Castellaniella biogeography in an anthropogenically disturbed subsurface

Castellaniella species have been isolated from a variety of mixed-waste environments including the nitrate and multiple metal-contaminated subsurface at the Oak Ridge Reservation (ORR). Previous studies examining microbial community composition and nitrate removal at ORR during biostimulation efforts reported increased abundances of members of the Castellaniella genus concurrent with increased denitrification rates. Thus, we asked how genomic and abiotic factors control the Castellaniella biogeography at the site to understand how these factors may influence nitrate transformation in an anthropogenically impacted setting. We report the isolation and characterization of several Castellaniella strains from the ORR subsurface. Five of these isolates match at 100% identity (at the 16S rRNA gene V4 region) to two Castellaniella amplicon sequence variants (ASVs), ASV1 and ASV2, that have persisted in the ORR subsurface for at least 2 decades. However, ASV2 has consistently higher relative abundance in samples taken from the site and was also the dominant blooming denitrifier population during a prior biostimulation effort. We found that the ASV2 representative strain has greater resistance to mixed metal stress than the ASV1 representative strains. We attribute this resistance, in part, to the large number of unique heavy metal resistance genes identified on a genomic island in the ASV2 representative genome. Additionally, we suggest that the relatively lower fitness of ASV1 may be connected to the loss of the nitrous oxide reductase (nos) operon (and associated nitrous oxide reductase activity) due to the insertion at this genomic locus of a mobile genetic element carrying copper resistance genes. This study demonstrates the value of integrating genomic, environmental, and phenotypic data to characterize the biogeography of key microorganisms in contaminated sites.

Contribution of Microorganisms with the Clade II Nitrous Oxide Reductase to Suppression of Surface Emissions of Nitrous Oxide

The sources and sinks of nitrous oxide, as control emissions to the atmosphere, are generally poorly constrained for most environmental systems. Initial depth-resolved analysis of nitrous oxide flux from observation wells and the proximal surface within a nitrate contaminated aquifer system revealed high subsurface production but little escape from the surface. To better understand the environmental controls of production and emission at this site, we used a combination of isotopic, geochemical, and molecular analyses to show that chemodenitrification and bacterial denitrification are major sources of nitrous oxide in this subsurface, where low DO, low pH, and high nitrate are correlated with significant nitrous oxide production. Depth-resolved metagenomes showed that consumption of nitrous oxide near the surface was correlated with an enrichment of Clade II nitrous oxide reducers, consistent with a growing appreciation of their importance in controlling release of nitrous oxide to the atmosphere. Our work also provides evidence for the reduction of nitrous oxide at a pH of 4, well below the generally accepted limit of pH 5.

Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology

Due to the complex nature of microbiome data, the field of microbial ecology has many current and potential uses for machine learning (ML) modeling. With the increased use of predictive ML models across many disciplines, including microbial ecology, there is extensive published information on the specific ML algorithms available and how those algorithms have been applied. Thus, our goal is not to summarize the breadth of ML models available or compare their performances. Rather, our goal is to provide more concrete and actionable information to guide microbial ecologists in how to select, run, and interpret ML algorithms to predict the taxa or genes associated with particular sample categories or environmental gradients of interest. Such microbial data often have unique characteristics that require careful consideration of how to apply ML models and how to interpret the associated results. This review is intended for practicing microbial ecologists who may be unfamiliar with some of the intricacies of ML models. We provide examples and discuss common opportunities and pitfalls specific to applying ML models to the types of data sets most frequently collected by microbial ecologists.

Environmental stress mediates groundwater microbial community assembly

Community assembly describes how different ecological processes shape microbial community composition and structure. How environmental factors impact community assembly remains elusive. Here we sampled microbial communities and >200 biogeochemical variables in groundwater at the Oak Ridge Field Research Center, a former nuclear waste disposal site, and developed a theoretical framework to conceptualize the relationships between community assembly processes and environmental stresses. We found that stochastic assembly processes were critical (>60% on average) in shaping community structure, but their relative importance decreased as stress increased. Dispersal limitation and 'drift' related to random birth and death had negative correlations with stresses, whereas the selection processes leading to dissimilar communities increased with stresses, primarily related to pH, cobalt and molybdenum. Assembly mechanisms also varied greatly among different phylogenetic groups. Our findings highlight the importance of microbial dispersal limitation and environmental heterogeneity in ecosystem restoration and management.

Persistence of location-specific microbial signatures on boats during voyages

Objects collect microorganisms from their surroundings and develop a microbial "fingerprint" that may be useful for determining an object's past location (provenance). It may be possible to use ubiquitous microorganisms for forensics or as environmental sensors. Here, we use microbial communities in the Chesapeake Bay region to demonstrate the use of natural microorganisms as biological sensors to determine the past location of boats. The microbiomes of two boats and of the open water were sampled as these vessels traveled from the Port of Baltimore to the Port of Norfolk, and back to Baltimore. 16S rRNA sequencing was performed to identify microorganisms. Differential abundance and machine learning analyses were utilized to identify microbial signatures and predicted probabilities which were used to determine the vessel's previous location. The work presented here provides a better understanding of how microbes in aquatic systems can be leveraged as utility for object biosensors.

An Assessment of Human Opportunistic Pathogenic Bacteria on Daily Necessities in Nanjing City during Plum Rain Season

The plum rain season is a special climatic phenomenon in east Asia, which is characterized by persistent rainfall, a high temperature, and humidity, providing suitable environmental conditions for certain pathogenic bacteria, thus increasing the incidence of respiratory, gastrointestinal, and urinary diseases. However, studies on human opportunistic pathogenic bacteria communities during the plum rain season are still limited. In this study, the characteristics of human opportunistic pathogenic bacterial communities on daily necessities during the non-plum and plum rain seasons were investigated using high-throughput sequencing technology. The results revealed that the relative abundance of human opportunistic pathogenic bacteria was higher in the plum rain season (cotton cloth: 2.469%, electric bicycles: 0.724%, rice: 3.737%, and washbasins: 5.005%) than in the non-plum rain season (cotton cloth: 1.425%, electric bicycles: 0.601%, rice: 2.426%, and washbasins: 4.801%). Both temperature and relative humidity affected human opportunistic pathogenic bacterial communities. Stochastic processes dominated the assembly process of human opportunistic pathogenic bacterial communities, and undominated processes prevailed. The stability of the co-occurrence network was higher in the non-plum rain season than that in the plum rain season. In addition, the proportion of deterministic processes showed the same trend as the complexity of the co-occurrence network.

Mixed waste contamination selects for a mobile genetic element population enriched in multiple heavy metal resistance genes

Mobile genetic elements (MGEs) like plasmids, viruses, and transposable elements can provide fitness benefits to their hosts for survival in the presence of environmental stressors. Heavy metal resistance genes (HMRGs) are frequently observed on MGEs, suggesting that MGEs may be an important driver of adaptive evolution in environments contaminated with heavy metals. Here, we report the meta-mobilome of the heavy metal-contaminated regions of the Oak Ridge Reservation subsurface. This meta-mobilome was compared with one derived from samples collected from unimpacted regions of the Oak Ridge Reservation subsurface. We assembled 1615 unique circularized DNA elements that we propose to be MGEs. The circular elements from the highly contaminated subsurface were enriched in HMRG clusters relative to those from the nearby unimpacted regions. Additionally, we found that these HMRGs were associated with Gamma and Betaproteobacteria hosts in the contaminated subsurface and potentially facilitate the persistence and dominance of these taxa in this region. Finally, the HMRGs were associated with conjugative elements, suggesting their potential for future lateral transfer. We demonstrate how our understanding of MGE ecology, evolution, and function can be enhanced through the genomic context provided by completed MGE assemblies.

Mixed nitrate and metal contamination influences operational speciation of toxic and essential elements

Environmental contamination constrains microbial communities impacting diversity and total metabolic activity. The former S-3 Ponds contamination site at Oak Ridge Reservation (ORR), TN, has elevated concentrations of nitric acid and multiple metals from decades of processing nuclear material. To determine the nature of the metal contamination in the sediment, a three-step sequential chemical extraction (BCR) was performed on sediment segments from a core located upgradient (EB271, non-contaminated) and one downgradient (EB106, contaminated) of the S-3 Ponds. The resulting exchangeable, reducing, and oxidizing fractions were analyzed for 18 different elements. Comparison of the two cores revealed changes in operational speciation for several elements caused by the contamination. Those present from the S-3 Ponds, including Al, U, Co, Cu, Ni, and Cd, were not only elevated in concentration in the EB106 core but were also operationally more available with increased mobility in the acidic environment. Other elements, including Mg, Ca, P, V, As, and Mo, were less operationally available in EB106 having decreased concentrations in the exchangeable fraction. The bioavailability of essential macro nutrients Mg, Ca, and P from the two types of sediment was determined using three metal-tolerant bacteria previously isolated from ORR. Mg and Ca were available from both sediments for all three strains; however, P was not bioavailable from either sediment for any strain. The decreased operational speciation of P in contaminated ORR sediment may increase the dependence of the microbial community on other pools of P or select for microorganisms with increased P scavenging capabilities. Hence, the microbial community at the former S-3 Ponds contamination site may be constrained not only by increased toxic metal concentrations but also by the availability of essential elements, including P.

DNA sequencing, microbial indicators, and the discovery of buried kimberlites

Population growth and technological advancements are placing growing demand on mineral resources. New and innovative exploration technologies that improve detection of deeply buried mineralization and host rocks are required to meet these demands. Here we used diamondiferous kimberlite ore bodies as a test case and show that DNA amplicon sequencing of soil microbial communities resolves anomalies in microbial community composition and structure that reflect the surface expression of kimberlites buried under 10 s of meters of overburden. Indicator species derived from laboratory amendment experiments were employed in an exploration survey in which the species distributions effectively delineated the surface expression of buried kimberlites. Additional indicator species derived directly from field observations improved the blind discovery of kimberlites buried beneath similar overburden types. Application of DNA sequence-based analyses of soil microbial communities to mineral deposit exploration provides a powerful illustration of how genomics technologies can be leveraged in the discovery of critical new resources.

Low-temperature thermally enhanced bioremediation of polycyclic aromatic hydrocarbon-contaminated soil: Effects on fate, toxicity and bacterial communities

Remediation of polycyclic aromatic hydrocarbon (PAH)-contaminated soil using thermal desorption technology typically requires very high temperatures, necessitating coupled microbial treatment for energy and cost reduction. This study investigated the fate and toxicity of PAHs as well as the responses of microbial communities following thermal treatment within a low temperature range. The optimal temperature for PAH mineralization was 20-28 °C, within the growth range of most mesophilic microorganisms. By contrast, 50 °C treatment almost completely inhibited PAH mineralization but resulted in the greatest detoxification effect particularly for cardiotoxicity and nephrotoxicity. A potential increase in toxicity was observed at 28 °C. Co-metabolism and non-extractable residue formation may play an interdependent role in thermally enhanced bioremediation. Moreover, alterations in bacterial communities were strongly associated with PAH mineralization and zebrafish toxicity, revealing that soil microorganisms play a direct role in PAH mineralization and served as ecological receptors reflecting changes in toxicity. Network analysis revealed that Firmicutes formed specific ecological communities at high temperature, whereas Acidobacteria and Proteobacteria act as primary PAH degraders at moderate temperature. These findings will enable better integration of strategies for thermal and microbial treatments in soil remediation.

Differential response of the soil nutrients, soil bacterial community structure and metabolic functions to different risk areas in Lead-Zine tailings

Rapid growth in the mining industry has brought about a large formation of tailings, which result in serious destruction of the ecological environment and severe soil pollution problems. This study assesses soil nutrients, soil bacterial community and soil microbes' metabolic function in heavily polluted areas (W1), moderately polluted areas (W2), lightly polluted areas (W3) and clean areas (CK) using 16S Illumina sequencing. The results of this study showed that compared with CK, a severe loss of soil nutrients and richness of OTUs (Chao1 and ACE indices) were observed with the aggravated pollution of tailings. The Chao1 and ACE indices in the W1 group decreased significantly by 15.53 and 16.03%, respectively, (p < 0.01). Besides, the relative abundance of Actinobacteria and Proteobacteria was high whereas and relative abundance of Chloroflexi in the polluted areas. Among them, W1 groups increased significantly the relative abundance of Actinobacteria and decreased significantly the relative abundance of Chloroflexi, these can be used as indicator phyla for changes in soil community structures under polluted stress. Tax4 Fun analysis showed that W1 groups affected the soil bacterial community and altered the primary types of biological metabolism in polluted areas. Tailings have adverse impacts on soil bacterial community and metabolic functions, and the deterioration in soil quality is dependent on the levels of tailings pollution. Cumulatively, this study provides valuable information on the bacterial community structure and metabolic functions in the tailing polluted soil.

Nodulation number tempers the relative importance of stochastic processes in the assembly of soybean root-associated communities

Identifying the ecological forces that structure root-associated microbial communities is an essential step toward more sustainable agriculture. Legumes are widely utilized as model plants to study selective forces and their functioning in plant-microbial interactions owing to their ability to establish mutualism with rhizobia. Root nodules act as symbiotic organs to optimize the cost-benefit balance in this mutualistic relationship by modulating the number of nodules. However, it is not known whether the number of nodules is related to the structure of root-associated bacterial communities. Here, the root-associated bacterial communities of soybean grown in native soil by means of soybean cultivars with super- or normal nodulation were investigated across four developmental stages. We compared ecological processes between communities and found decreased relative importance of neutral processes for super-nodulating soybean, although the overall structures resembled those of normal-nodulating soybean. We identified the generalist core bacterial populations in each root-associated compartment, that are shared across root-associated niches, and persist through developmental stages. Within core bacterial species, the relative abundances of bacterial species in the rhizosphere microbiome were linked to host-plant functional traits and can be used to predict these traits from microbes using machine learning algorithms. These findings broaden the comprehensive understanding of the ecological forces and associations of microbiotas in various root-associated compartments and provide novel insights to integrate beneficial plant microbiomes into agricultural production to enhance plant performance.

Comparing quantile regression spline analyses and supervised machine learning for environmental quality assessment at coastal marine aquaculture installations

Organic enrichment associated with marine finfish aquaculture is a local stressor of marine coastal ecosystems. To maintain ecosystem services, the implementation of biomonitoring programs focusing on benthic diversity is required. Traditionally, impact-indices are determined by extracting and identifying benthic macroinvertebrates from samples. However, this is a time-consuming and expensive method with low upscaling potential. A more rapid, inexpensive, and robust method to infer the environmental quality of marine environments is eDNA metabarcoding of bacterial communities. To infer the environmental quality of coastal habitats from metabarcoding data, two taxonomy-free approaches have been successfully applied for different geographical regions and monitoring goals, namely quantile regression splines (QRS) and supervised machine learning (SML). However, their comparative performance remains untested for monitoring the impact of organic enrichment introduced by aquaculture on marine coastal environments. We compared the performance of QRS and SML using bacterial metabarcoding data to infer the environmental quality of 230 aquaculture samples collected from seven farms in Norway and seven farms in Scotland along an organic enrichment gradient. As a measure of environmental quality, we used the Infaunal Quality Index (IQI) calculated from benthic macrofauna data (reference index). The QRS analysis plotted the abundance of amplicon sequence variants (ASVs) as a function to the IQI from which the ASVs with a defined abundance peak were assigned to eco-groups and a molecular IQI was subsequently calculated. In contrast, the SML approach built a random forest model to directly predict the macrofauna-based IQI. Our results show that both QRS and SML perform well in inferring the environmental quality with 89% and 90% accuracy, respectively. For both geographic regions, there was high correspondence between the reference IQI and both the inferred molecular IQIs (p < 0.001), with the SML model showing a higher coefficient of determination compared to QRS. Among the 20 most important ASVs identified by the SML approach, 15 were congruent with the good quality spline ASV indicators identified via QRS for both Norwegian and Scottish salmon farms. More research on the response of the ASVs to organic enrichment and the co-influence of other environmental parameters is necessary to eventually select the most powerful stressor-specific indicators. Even though both approaches are promising to infer environmental quality based on metabarcoding data, SML showed to be more powerful in handling the natural variability. For the improvement of the SML model, addition of new samples is still required, as background noise introduced by high spatio-temporal variability can be reduced. Overall, we recommend the development of a powerful SML approach that will be onwards applied for monitoring the impact of aquaculture on marine ecosystems based on eDNA metabarcoding data.

A social niche breadth score reveals niche range strategies of generalists and specialists

Generalists can survive in many environments, whereas specialists are restricted to a single environment. Although a classical concept in ecology, niche breadth has remained challenging to quantify for microorganisms because it depends on an objective definition of the environment. Here, by defining the environment of a microorganism as the community it resides in, we integrated information from over 22,000 environmental sequencing samples to derive a quantitative measure of the niche, which we call social niche breadth. At the level of genera, we explored niche range strategies throughout the prokaryotic tree of life. We found that social generalists include opportunists that stochastically dominate local communities, whereas social specialists are stable but low in abundance. Social generalists have a more diverse and open pan-genome than social specialists, but we found no global correlation between social niche breadth and genome size. Instead, we observed two distinct evolutionary strategies, whereby specialists have relatively small genomes in habitats with low local diversity, but relatively large genomes in habitats with high local diversity. Together, our analysis shines data-driven light on microbial niche range strategies.

Effects of different heavy metal pollution levels on microbial community structure and risk assessment in Zn-Pb mining soils

Heavy metal contamination in soils seriously threatens human health and aggravates the global pollution burden. In this study, we investigated the risk of heavy metal contamination in soils at a Zn-Pb mineral processing plant in Longnan, China, and the effects of different heavy metal contamination levels on diverse microbial communities. Statistical analysis showed that, except for Ni, the average content of all detected metals (Zn, Pb, As, Cu, Cd, Hg) in the soil was higher than the background value of soil in the study area, which was most seriously contaminated with Pb and As. Comparison of functional divisions showed that heavy metal soil contamination was most serious in the raw material stacking area and the production area. Interpolation analysis showed that areas closer to the wastewater discharge area had higher contents of each heavy metal and were more seriously polluted. From the point of pollution index, the risk of heavy metal soil pollution in the study area was very high (RI = 2845.24, i.e., > 600), with Cd and Hg being the most serious pollutants compared with other heavy metals. Microbial community abundance, diversity, and structure differed at different levels of heavy metal contamination. The community diversity of bacteria decreased with increasing heavy metal concentrations, while no significant change in fungi was observed. Evidence from variation redundancy analysis (RDA) and the Spearman correlation analysis showed that the leading factors affecting microbial community composition were Cu, Cd, Hg, and pH. Actinobacteria and Gemmatimonadetes at the uncontaminated level (CL) were significantly and negatively correlated with the concentrations of Cu, Zn, Cd, and Pb. Proteobacteria and Chloroflexi at the severely contaminated level (SL) were significantly correlated with pH and Hg. However, heavy metal contamination had less effect on most of the dominant fungi. In conclusion, microbial communities such as Proteobacteria, Actinobacteria, Chloroflexi, and Ascomycota showed greater tolerance to heavy metals. These results could be used as important references for the remediation of heavy metal-contaminated soils.

Mixed heavy metal stress induces global iron starvation response

Multiple heavy metal contamination is an increasingly common global problem. Heavy metals have the potential to disrupt microbially mediated biogeochemical cycling. However, systems-level studies on the effects of combinations of heavy metals on bacteria are lacking. For this study, we focused on the Oak Ridge Reservation (ORR; Oak Ridge, TN, USA) subsurface which is contaminated with several heavy metals and high concentrations of nitrate. Using a native Bacillus cereus isolate that represents a dominant species at this site, we assessed the combined impact of eight metal contaminants, all at site-relevant concentrations, on cell processes through an integrated multi-omics approach that included discovery proteomics, targeted metabolomics, and targeted gene-expression profiling. The combination of eight metals impacted cell physiology in a manner that could not have been predicted from summing phenotypic responses to the individual metals. Exposure to the metal mixture elicited a global iron starvation response not observed during individual metal exposures. This disruption of iron homeostasis resulted in decreased activity of the iron-cofactor-containing nitrate and nitrite reductases, both of which are important in biological nitrate removal at the site. We propose that the combinatorial effects of simultaneous exposure to multiple heavy metals is an underappreciated yet significant form of cell stress in the environment with the potential to disrupt global nutrient cycles and to impede bioremediation efforts at mixed waste sites. Our work underscores the need to shift from single- to multi-metal studies for assessing and predicting the impacts of complex contaminants on microbial systems.

Significant tipping points of sediment microeukaryotes forewarn increasing antibiotic pollution

Antibiotic pollution imposes urgent threats to public health and microbial-mediated ecological processes. Existing studies have primarily focused on bacterial responses to antibiotic pollution, but they ignored the microeukaryotic counterpart, though microeukaryotes are functionally important (e.g., predators and saprophytes) in microbial ecology. Herein, we explored how the assembly of sediment microeukaryotes was affected by increasing antibiotic pollution at the inlet (control) and across the outlet sites along a shrimp wastewater discharge channel. The structures of sediment microeukaryotic community were substantially altered by the increasing nutrient and antibiotic pollutions, which were primarily controlled by the direct effects of phosphate and ammonium (-0.645 and 0.507, respectively). In addition, tetracyclines exerted a large effect (0.209), including direct effect (0.326) and indirect effect (-0.117), on the microeukaryotic assembly. On the contrary, the fungal subcommunity was relatively resistant to antibiotic pollution. Segmented analysis depicted nonlinear responses of microeukaryotic genera to the antibiotic pollution gradient, as supported by the significant tipping points. We screened 30 antibiotic concentration-discriminatory taxa of microeukaryotes, which can quantitatively and accurately predict (98.7% accuracy) the in-situ antibiotic concentration. Sediment microeukaryotic (except fungal) community is sensitive to antibiotic pollution, and the identified bioindicators could be used for antibiotic pollution diagnosis.

Temporal Succession of Bacterial Community Structure, Co-occurrence Patterns, and Community Assembly Process in Epiphytic Biofilms of Submerged Plants in a Plateau Lake

In shallow macrophytic lakes, epiphytic biofilms are formed on the surface of submerged plant stems and leaves because of algae and bacterial accumulation. Epiphytic biofilms significantly impact the health of the host vegetation and the biogeochemical cycling of lake elements. However, community diversity, species interactions, and community assembly mechanisms in epiphytic bacterial communities (EBCs) of plants during different growth periods are not well understood. We investigated the successional dynamics, co-occurrence patterns, and community assembly processes of epiphytic biofilm bacterial communities of submerged plants, Najas marina and Potamogeton lucens, from July to November 2020. The results showed a significant seasonal variation in EBC diversity and richness. Community diversity and richness increased from July to November, and the temperature was the most important driving factor for predicting seasonal changes in EBC community structure. Co-occurrence network analysis revealed that the average degree and graph density of the network increased from July to November, indicating that the complexity of the EBC network increased. The bacterial community co-occurrence network was limited by temperature, pH, and transparency. The phylogeny-based null model analysis showed that deterministic processes dominated the microbial community assembly in different periods, increasing their contribution. In addition, we found that as the dominance of deterministic processes increased, the microbial co-occurrence links increased, and the potential interrelationships between species became stronger. Thus, the findings provide insights into the seasonal variability of EBC assemblage and co-occurrence patterns in lacustrine ecosystems.

Ecophysiological and genomic analyses of a representative isolate of highly abundant Bacillus cereus strains in contaminated subsurface sediments

Bacillus cereus strain CPT56D-587-MTF (CPTF) was isolated from the highly contaminated Oak Ridge Reservation (ORR) subsurface. This site is contaminated with high levels of nitric acid and multiple heavy metals. Amplicon sequencing of the 16S rRNA genes (V4 region) in sediment from this area revealed an amplicon sequence variant (ASV) with 100% identity to the CPTF 16S rRNA sequence. Notably, this CPTF-matching ASV had the highest relative abundance in this community survey, with a median relative abundance of 3.77% and comprised 20%-40% of reads in some samples. Pangenomic analysis revealed that strain CPTF has expanded genomic content compared to other B. cereus species-largely due to plasmid acquisition and expansion of transposable elements. This suggests that these features are important for rapid adaptation to native environmental stressors. We connected genotype to phenotype in the context of the unique geochemistry of the site. These analyses revealed that certain genes (e.g. nitrate reductase, heavy metal efflux pumps) that allow this strain to successfully occupy the geochemically heterogenous microniches of its native site are characteristic of the B. cereus species while others such as acid tolerance are mobile genetic element associated and are generally unique to strain CPTF.

Ecological memory of prior nutrient exposure in the human gut microbiome

Many ecosystems have been shown to retain a memory of past conditions, which in turn affects how they respond to future stimuli. In microbial ecosystems, community disturbance has been associated with lasting impacts on microbiome structure. However, whether microbial communities alter their response to repeated stimulus remains incompletely understood. Using the human gut microbiome as a model, we show that bacterial communities retain an "ecological memory" of past carbohydrate exposures. Memory of the prebiotic inulin was encoded within a day of supplementation among a cohort of human study participants. Using in vitro gut microbial models, we demonstrated that the strength of ecological memory scales with nutrient dose and persists for days. We found evidence that memory is seeded by transcriptional changes among primary degraders of inulin within hours of nutrient exposure, and that subsequent changes in the activity and abundance of these taxa are sufficient to enhance overall community nutrient metabolism. We also observed that ecological memory of one carbohydrate species impacts microbiome response to other carbohydrates, and that an individual's habitual exposure to dietary fiber was associated with their gut microbiome's efficiency at digesting inulin. Together, these findings suggest that the human gut microbiome's metabolic potential reflects dietary exposures over preceding days and changes within hours of exposure to a novel nutrient. The dynamics of this ecological memory also highlight the potential for intra-individual microbiome variation to affect the design and interpretation of interventions involving the gut microbiome.

Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activity

Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales. Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed (n = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry (n = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development. IMPORTANCE The environmental implications of unconventional oil and gas extraction are only recently starting to be systematically recorded. Our research shows the utility of microbial communities paired with geochemical markers to build strong predictive random forest models of unconventional oil and gas activity and the identification of key biomarkers. Microbial communities, their transcribed genes, and key biomarkers can be used as sentinels of environmental changes. Slight changes in microbial function and composition can be detected before chemical markers of contamination. Potential contamination, specifically from biocides, is especially concerning due to its potential to promote antibiotic resistance in the environment. Additionally, as microbial communities facilitate the bulk of nutrient cycling in the environment, small changes may have long-term repercussions. Supervised random forest models can be used to identify changes in those communities, greatly enhance our understanding of what such impacts entail, and inform environmental management decisions.

CORAL: A framework for rigorous self-validated data modeling and integrative, reproducible data analysis

BACKGROUND

Many organizations face challenges in managing and analyzing data, especially when relevant datasets arise from multiple sources and methods. Analyzing heterogeneous datasets and additional derived data requires rigorous tracking of their interrelationships and provenance. This task has long been a Grand Challenge of data science and has more recently been formalized in the FAIR principles: that all data objects be Findable, Accessible, Interoperable, and Reusable, both for machines and for people. Adherence to these principles is necessary for proper stewardship of information, for testing regulatory compliance, for measuring the efficiency of processes, and for facilitating reuse of data-analytical frameworks.

FINDINGS

We present the Contextual Ontology-based Repository Analysis Library (CORAL), a platform that greatly facilitates adherence to all 4 of the FAIR principles, including the especially difficult challenge of making heterogeneous datasets Interoperable and Reusable across all parts of a large, long-lasting organization. To achieve this, CORAL's data model requires that data generators extensively document the context for all data, and our tools maintain that context throughout the entire analysis pipeline. CORAL also features a web interface for data generators to upload and explore data, as well as a Jupyter notebook interface for data analysts, both backed by a common API.

CONCLUSIONS

CORAL enables organizations to build FAIR data types on the fly as they are needed, avoiding the expense of bespoke data modeling. CORAL provides a uniquely powerful platform to enable integrative cross-dataset analyses, generating deeper insights than are possible using traditional analysis tools.

Microbiome characterization of alpine water springs for human consumption reveals site- and usage-specific microbial signatures

The microbiome of water springs is gaining increasing interest, especially in water intended for human consumption. However, the knowledge about large-scale patterns in water springs microbiome is still incomplete. The presence of bacteria in water sources used for human consumption is a major concern for health authorities; nonetheless, the standard microbiological quality checks are focused only on pathogenic species and total microbial load. Using 16S rRNA high throughput sequencing, we characterized the microbiome from 38 water springs in Trentino (Northern Italy) for 2 consecutive years in order to gain precious insights on the microbiome composition of these unexplored yet hardly exploited environments. The microbiological studies were integrated with standard measurements of physico-chemical parameters performed by the Provincial Office for Environmental Monitoring in order to highlight some of the dynamics influencing the microbial communities of these waters. We found that alpha diversity showed consistent patterns of variation overtime, and showed a strong positive correlation with the water nitrate concentration and negatively with fixed residue, electrical conductivity, and calcium concentration. Surprisingly, alpha diversity did not show any significant correlation with neither pH nor temperature. We found that despite their remarkable stability, different water springs display different coefficients of variation in alpha diversity, and that springs used for similar purposes showed similar microbiomes. Furthermore, the springs could be grouped according to the number of shared species into three major groups: low, mid, and high number of shared taxa, and those three groups of springs were consistent with the spring usage. Species belonging to the phyla Planctomycetes and Verrucomicrobia were prevalent and at relatively high abundance in springs classified as low number of shared species, whereas the phylum Lentisphaerae and the Candidate Phyla radiation were prevalent at higher abundance in the mineral and potable springs. The present study constitutes an example for standard water spring monitoring integrated with microbial community composition on a regional scale, and provides information which could be useful in the design and application of future water management policies in Trentino.

Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.

Predicting the abundance of metal resistance genes in subtropical estuaries using amplicon sequencing and machine learning

Heavy metals are a group of anthropogenic contaminants in estuary ecosystems. Bacteria in estuaries counteract the highly concentrated metal toxicity through metal resistance genes (MRGs). Presently, metagenomic technology is popularly used to study MRGs. However, an easier and less expensive method of acquiring MRG information is needed to deepen our understanding of the fate of MRGs. Thus, this study explores the feasibility of using a machine learning approach-namely, random forests (RF)-to predict MRG abundance based on the 16S rRNA amplicon sequenced datasets from subtropical estuaries in China. Our results showed that the total MRG abundance could be predicted by RF models using bacterial composition at different taxonomic levels. Among them, the relative abundance of bacterial phyla had the highest predicted accuracy (71.7 %). In addition, the RF models constructed by bacterial phyla predicted the abundance of six MRG types and nine MRG subtypes with substantial accuracy (R² > 0.600). Five bacterial phyla (Firmicutes, Bacteroidetes, Patescibacteria, Armatimonadetes, and Nitrospirae) substantially determined the variations in MRG abundance. Our findings prove that RF models can predict MRG abundance in South China estuaries during the wet season by using the bacterial composition obtained by 16S rRNA amplicon sequencing.

Predicting the humification degree of multiple organic solid waste during composting using a designated bacterial community

Microbes are the drivers for disposing of organic solid waste (OSW) during aerobic fermentation. Notwithstanding, the significance of microbes is underestimated in numerous studies on aerobic fermentation product assessments. Here, we investigated the humification degree (HD), and the humic acid content was assessed in terms of the bacterial community. The bacterial communities were useful indicators for making predictions and even correctly determined the categories of OSWs with 94% accuracy. The bacterial codes can also provide a better prediction of HD. Our results demonstrate that the bacteria code is a reliable biological method to assess HD effectively. Bacterial codes can be used as ecological and biological indicators to evaluate the quality of aerobic fermentation of different materials.

Development of a Markerless Deletion Mutagenesis System in Nitrate-Reducing Bacterium Rhodanobacter denitrificans

Rhodanobacter has been found as the dominant genus in aquifers contaminated with high concentrations of nitrate and uranium in Oak Ridge, TN, USA. The in situ stimulation of denitrification has been proposed as a potential method to remediate nitrate and uranium contamination. Among the Rhodanobacter species, Rhodanobacter denitrificans strains have been reported to be capable of denitrification and contain abundant metal resistance genes. However, due to the lack of a mutagenesis system in these strains, our understanding of the mechanisms underlying low-pH resistance and the ability to dominate in the contaminated environment remains limited. Here, we developed an in-frame markerless deletion system in two R. denitrificans strains. First, we optimized the growth conditions, tested antibiotic resistance, and determined appropriate transformation parameters in 10 Rhodanobacter strains. We then deleted the upp gene, which encodes uracil phosphoribosyltransferase, in R. denitrificans strains FW104-R3 and FW104-R5. The resulting strains were designated R3_Δupp and R5_Δupp and used as host strains for mutagenesis with 5-fluorouracil (5-FU) resistance as the counterselection marker to generate markerless deletion mutants. To test the developed protocol, the narG gene encoding nitrate reductase was knocked out in the R3_Δupp and R5_Δupp host strains. As expected, the narG mutants could not grow in anoxic medium with nitrate as the electron acceptor. Overall, these results show that the in-frame markerless deletion system is effective in two R. denitrificans strains, which will allow for future functional genomic studies in these strains furthering our understanding of the metabolic and resistance mechanisms present in Rhodanobacter species. IMPORTANCE Rhodanobacter denitrificans is capable of denitrification and is also resistant to toxic heavy metals and low pH. Accordingly, the presence of Rhodanobacter species at a particular environmental site is considered an indicator of nitrate and uranium contamination. These characteristics suggest its future potential application in bioremediation of nitrate or concurrent nitrate and uranium contamination in groundwater ecosystems. Due to the lack of genetic tools in this organism, the mechanisms of low-pH and heavy metal resistance in R. denitrificans strains remain elusive, which impedes its use in bioremediation strategies. Here, we developed a genome editing method in two R. denitrificans strains. This work marks a crucial step in developing Rhodanobacter as a model for studying the diverse mechanisms of low-pH and heavy metal resistance associated with denitrification.

Sediment prokaryotic assembly, methane cycling, and ammonia oxidation potentials in response to increasing antibiotic pollution at shrimp aquafarm

Antibiotic pollution poses serious threats to public health and ecological processes. However, systematic research regarding the interactive effects of increasing nutrient and antibiotic pollutions on the prokaryotic community, particularly taxa that contribute to greenhouse gas emissions, is lacking. By exploring the complex interactions that occur between interkingdom bacteria and archaea, biotic and abiotic factors, the responses of sediment prokaryotic assembly were determined along a significant antibiotic pollution gradient. Bacterial and archaeal communities were primarily governed by sediment antibiotic pollution, ammonia, phosphate, and redox potential, which further affected enzyme activities. The two communities nonlinearly responded to increasing antibiotic pollution, with significant tipping points of 3.906 and 0.979 mg/kg antibiotics, respectively. The combined antibiotic concentration-discriminatory taxa of bacteria and archaea accurately (98.0% accuracy) diagnosed in situ antibiotic concentrations. Co-abundance analysis revealed that the methanogens, methanotrophs, sulfate-reducing bacteria, and novel players synergistically contributed to methane cycling. Antibiotic pollution caused the dominant role of ammonia-oxidizing archaea in ammonia oxidation at these alkaline sediments. Collectively, the significant tipping points and bio-indicators afford indexes for regime shift and quantitative diagnosis of antibiotic pollution, respectively. Antibiotic pollution could expedite methane cycling and mitigate nitrous oxide yield, which are previously unrecognized ecological effects. These findings provide new insights into the interactive biological and ecological consequences of increasing nutrient and antibiotic pollutions.

Beyond the snapshot: identification of the timeless, enduring indicator microbiome informing soil fertility and crop production in alkaline soils

BACKGROUND

Microorganisms are known to be important drivers of biogeochemical cycling in soil and hence could act as a proxy informing on soil conditions in ecosystems. Identifying microbiomes indicative for soil fertility and crop production is important for the development of the next generation of sustainable agriculture. Earlier researches based on one-time sampling have revealed various indicator microbiomes for distinct agroecosystems and agricultural practices as well as their importance in supporting sustainable productivity. However, these microbiomes were based on a mere snapshot of a dynamic microbial community which is subject to significant changes over time. Currently true indicator microbiomes based on long-term, multi-annual monitoring are not available.

RESULTS

Here, using samples from a continuous 20-year field study encompassing seven fertilization strategies, we identified the indicator microbiomes ecophysiologically informing on soil fertility and crop production in the main agricultural production base in China. Among a total of 29,184 phylotypes in 588 samples, we retrieved a streamlined consortium including 2% of phylotypes that were ubiquitously present in alkaline soils while contributing up to half of the whole community; many of them were associated with carbon and nutrient cycling. Furthermore, these phylotypes formed two opposite microbiomes. One indicator microbiome dominated by Bacillus asahii, characterized by specific functional traits related to organic matter decomposition, was mainly observed in organic farming and closely associated with higher soil fertility and crop production. The counter microbiome, characterized by known nitrifiers (e.g., Nitrosospira multiformis) as well as plant pathogens (e.g., Bacillus anthracis) was observed in nutrient-deficit chemical fertilizations. Both microbiomes are expected to be valuable indictors in informing crop yield and soil fertility, regulated by agricultural management.

CONCLUSIONS

Our findings based on this more than 2-decade long field study demonstrate the exciting potential of employing microorganisms and maximizing their functions in future agroecosystems. Our results report a "most-wanted" or "most-unwanted" list of microbial phylotypes that are ready candidates to guide the development of sustainable agriculture in alkaline soils.

Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

Microbial communities are ubiquitous and carry an exceptionally broad metabolic capability. Upon environmental perturbation, microbes are also amongst the first natural responsive elements with perturbation-specific cues and markers. These communities are thereby uniquely positioned to inform on the status of environmental conditions. The advent of microbial omics has led to an unprecedented volume of complex microbiological data sets. Importantly, these data sets are rich in biological information with potential for predictive environmental classification and forecasting. However, the patterns in this information are often hidden amongst the inherent complexity of the data. There has been a continued rise in the development and adoption of machine learning (ML) and deep learning architectures for solving research challenges of this sort. Indeed, the interface between molecular microbial ecology and artificial intelligence (AI) appears to show considerable potential for significantly advancing environmental monitoring and management practices through their application. Here, we provide a primer for ML, highlight the notion of retaining biological sample information for supervised ML, discuss workflow considerations, and review the state of the art of the exciting, yet nascent, interdisciplinary field of ML-driven microbial ecology. Current limitations in this sphere of research are also addressed to frame a forward-looking perspective toward the realization of what we anticipate will become a pivotal toolkit for addressing environmental monitoring and management challenges in the years ahead.

Environmental DNA metabarcoding for benthic monitoring: A review of sediment sampling and DNA extraction methods

Environmental DNA (eDNA) metabarcoding (parallel sequencing of DNA/RNA for identification of whole communities within a targeted group) is revolutionizing the field of aquatic biomonitoring. To date, most metabarcoding studies aiming to assess the ecological status of aquatic ecosystems have focused on water eDNA and macroinvertebrate bulk samples. However, the eDNA metabarcoding has also been applied to soft sediment samples, mainly for assessing microbial or meiofaunal biota. Compared to classical methodologies based on manual sorting and morphological identification of benthic taxa, eDNA metabarcoding offers potentially important advantages for assessing the environmental quality of sediments. The methods and protocols utilized for sediment eDNA metabarcoding can vary considerably among studies, and standardization efforts are needed to improve their robustness, comparability and use within regulatory frameworks. Here, we review the available information on eDNA metabarcoding applied to sediment samples, with a focus on sampling, preservation, and DNA extraction steps. We discuss challenges specific to sediment eDNA analysis, including the variety of different sources and states of eDNA and its persistence in the sediment. This paper aims to identify good-practice strategies and facilitate method harmonization for routine use of sediment eDNA in future benthic monitoring.

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.

The influence of the Doce River mouth on the microbiome of nearby coastal areas three years after the Fundão Dam failure, Brazil

The failure of the Fundão Dam, considered the world's largest mining disaster, released more than 55 million m³ of ore tailings into the environment. The sediment plume formed by water and tailings spread along approximately 663 km of water bodies of the Doce River basin. It reached the Atlantic Ocean sixteen days after the dam failure. However, the effects of the dam failure in the marine coastal areas years after the disaster are still unknown. This study aims to evaluate water and sediment microbial communities of nearby coastal areas three years after the Fundão Dam failure, using 16S rRNA gene amplicon sequencing. A total of 441 samples from 25 locations were collected during two different seasons (dry and rainy). The results showed that the Doce River mouth seems to divide the microbial communities from the southern and northern stations into two groups. The plume of sediments from the Doce River seems to be impacting the marine microbiome even at the furthest sampling stations. Bacterial (Anaerolineaceae, Thermodesulfovibrionia and Rhodopirellula) and Archaeal (Bathyarchaeia and Woesearchaeia) taxa, found in high abundance in the sediment of the Doce River mouth, have been previously described in high abundance in heavy metal contaminated sediments, including the Doce River itself and in mine tailing sediments. Cyanobium, found in great abundance in the water samples from the Doce River mouth, was also reported as the most abundant in the water of the Doce River after the Fundão Dam failure. Overall, the farther from the Doce River mouth the sample was, the lower the relative abundances of these taxa were. These results provide strong evidence that the sediment plume released by the Fundão Dam failure is probably impacting the marine microbiome of nearby coastal areas, even three years after the dam failure.

Sustained Ability of a Natural Microbial Community to Remove Nitrate from Groundwater

Microbial-mediated nitrate removal from groundwater is widely recognized as the predominant mechanism for nitrate attenuation in contaminated aquifers and is largely dependent on the presence of a carbon-bearing electron donor. The repeated exposure of a natural microbial community to an electron donor can result in the sustained ability of the community to remove nitrate; this phenomenon has been clearly demonstrated at the laboratory scale. However, in situ demonstrations of this ability are lacking. For this study, ethanol (electron donor) was repeatedly injected into a groundwater well (treatment) for six consecutive weeks to establish the sustained ability of a microbial community to remove nitrate. A second well (control) located upgradient was not injected with ethanol during this time. The treatment well demonstrated strong evidence of sustained ability as evident by ethanol, nitrate, and subsequent sulfate removal up to 21, 64, and 68%, respectively, as compared to the conservative tracer (bromide) upon consecutive exposures. Both wells were then monitored for six additional weeks under natural (no injection) conditions. During the final week, ethanol was injected into both treatment and control wells. The treatment well demonstrated sustained ability as evident by ethanol and nitrate removal up to 20 and 21%, respectively, as compared to bromide, whereas the control did not show strong evidence of nitrate removal (5% removal). Surprisingly, the treatment well did not indicate a sustained and selective enrichment of a microbial community. These results suggested that the predominant mechanism(s) of sustained ability likely exist at the enzymatic- and/or genetic-levels. The results of this study demonstrated the in situ ability of a microbial community to remove nitrate can be sustained in the prolonged absence of an electron donor.

Machine Learning Predicts the Presence of 2,4,6-Trinitrotoluene in Sediments of a Baltic Sea Munitions Dumpsite Using Microbial Community Compositions

Bacteria are ubiquitous and live in complex microbial communities. Due to differences in physiological properties and niche preferences among community members, microbial communities respond in specific ways to environmental drivers, potentially resulting in distinct microbial fingerprints for a given environmental state. As proof of the principle, our goal was to assess the opportunities and limitations of machine learning to detect microbial fingerprints indicating the presence of the munition compound 2,4,6-trinitrotoluene (TNT) in southwestern Baltic Sea sediments. Over 40 environmental variables including grain size distribution, elemental composition, and concentration of munition compounds (mostly at pmol⋅g^–1 levels) from 150 sediments collected at the near-to-shore munition dumpsite Kolberger Heide by the German city of Kiel were combined with 16S rRNA gene amplicon sequencing libraries. Prediction was achieved using Random Forests (RFs); the robustness of predictions was validated using Artificial Neural Networks (ANN). To facilitate machine learning with microbiome data we developed the R package phyloseq2ML. Using the most classification-relevant 25 bacterial genera exclusively, potentially representing a TNT-indicative fingerprint, TNT was predicted correctly with up to 81.5% balanced accuracy. False positive classifications indicated that this approach also has the potential to identify samples where the original TNT contamination was no longer detectable. The fact that TNT presence was not among the main drivers of the microbial community composition demonstrates the sensitivity of the approach. Moreover, environmental variables resulted in poorer prediction rates than using microbial fingerprints. Our results suggest that microbial communities can predict even minor influencing factors in complex environments, demonstrating the potential of this approach for the discovery of contamination events over an integrated period of time. Proven for a distinct environment future studies should assess the ability of this approach for environmental monitoring in general.

Effects of polycyclic aromatic hydrocarbon structure on PAH mineralization and toxicity to soil microorganisms after oxidative bioremediation by laccase

While bioremediation using soil microorganisms is considered an energy-efficient and eco-friendly approach to treat polycyclic aromatic hydrocarbon (PAH)-contaminated soils, a variety of polar PAH metabolites, particularly oxygenated ones, could increase the toxicity of the soil after biodegradation. In this study, a typical bio-oxidative transformation of PAH into quinones was investigated in soil amended with laccase using three PAHs with different structures (anthracene, benzo[a]anthracene, and benzo[a]pyrene) to assess the toxicity after oxidative bioremediation. The results show that during a 2-month incubation period the oxidation process promoted the formation of non-extractable residues (NERs) of PAHs, and different effects on mineralization were observed among the three PAHs. Oxidation enhanced the mineralization of the high-molecular-weight (HMW) PAHs (benzo[a]anthracene and benzo[a]pyrene) but inhibited the mineralization of the low-molecular-weight (LMW) PAH (anthracene). The inhibition of anthracene suggests increased toxicity after oxidative bioremediation, which coincided with a decrease in soil nitrification activity, bacterial diversity and PAH-ring hydroxylating dioxygenase gene copies. The analysis of PAH metabolites in soil extract indicated that oxidation by laccase was competitive with the natural transformation processes of PAHs and revealed that intermediates other than quinone metabolites increased the toxicity of soil during subsequent degradation. The different metabolic profiles of the three PAHs indicated that the toxicity of soil after PAH oxidation by laccase was strongly affected by the PAH structure. Despite the potential increase in toxicity, the results suggest that oxidative bioremediation is still an eco-friendly method for the treatment of HMW PAHs since the intermediates from HMW PAHs are more easily detoxified via NER formation than LMW PAHs.

Deciphering Microbial Metal Toxicity Responses via Random Bar Code Transposon Site Sequencing and Activity-Based Metabolomics

To uncover metal toxicity targets and defense mechanisms of the facultative anaerobe Pantoea sp. strain MT58 (MT58), we used a multiomic strategy combining two global techniques, random bar code transposon site sequencing (RB-TnSeq) and activity-based metabolomics. MT58 is a metal-tolerant Oak Ridge Reservation (ORR) environmental isolate that was enriched in the presence of metals at concentrations measured in contaminated groundwater at an ORR nuclear waste site. The effects of three chemically different metals found at elevated concentrations in the ORR contaminated environment were investigated: the cation Al³⁺, the oxyanion CrO₄^2-, and the oxycation UO₂²⁺. Both global techniques were applied using all three metals under both aerobic and anaerobic conditions to elucidate metal interactions mediated through the activity of metabolites and key genes/proteins. These revealed that Al³⁺ binds intracellular arginine, CrO₄^2- enters the cell through sulfate transporters and oxidizes intracellular reduced thiols, and membrane-bound lipopolysaccharides protect the cell from UO₂²⁺ toxicity. In addition, the Tol outer membrane system contributed to the protection of cellular integrity from the toxic effects of all three metals. Likewise, we found evidence of regulation of lipid content in membranes under metal stress. Individually, RB-TnSeq and metabolomics are powerful tools to explore the impact various stresses have on biological systems. Here, we show that together they can be used synergistically to identify the molecular actors and mechanisms of these pertubations to an organism, furthering our understanding of how living systems interact with their environment. IMPORTANCE Studying microbial interactions with their environment can lead to a deeper understanding of biological molecular mechanisms. In this study, two global techniques, RB-TnSeq and activity metabolomics, were successfully used to probe the interactions between a metal-resistant microorganism, Pantoea sp. strain MT58, and metals contaminating a site where the organism can be located. A number of novel metal-microbe interactions were uncovered, including Al³⁺ toxicity targeting arginine synthesis, which could lead to a deeper understanding of the impact Al³⁺ contamination has on microbial communities as well as its impact on higher-level organisms, including plants for whom Al³⁺ contamination is an issue. Using multiomic approaches like the one described here is a way to further our understanding of microbial interactions and their impacts on the environment overall.

Long-Term Biocide Efficacy and Its Effect on a Souring Microbial Community

Reservoir souring, which is the production of H₂S mainly by sulfate-reducing microorganisms (SRM) in oil reservoirs, has been a long-standing issue for the oil industry. While biocides have been frequently applied to control biogenic souring, the effects of biocide treatment are usually temporary, and biocides eventually fail. The reasons for biocide failure and the long-term response of the microbial community remain poorly understood. In this study, one-time biocide treatments with glutaraldehyde (GA) and an aldehyde-releasing biocide (ARB) at low (100 ppm) and high (750 ppm) doses were individually applied to a complex SRM community, followed by 1 year of monitoring of the chemical responses and the microbial community succession. The chemical results showed that souring control failed after 7 days at a dose of 100 ppm regardless of the biocide type and lasting souring control for the entire 1-year period was achieved only with ARB at 750 ppm. Microbial community analyses suggested that the high-dose biocide treatments resulted in 1 order of magnitude lower average total microbial abundance and average SRM abundance, compared to the low-dose treatments. The recurrence of souring was associated with reduction of alpha diversity and with long-term microbial community structure changes; therefore, monitoring changes in microbial community metrics may provide early warnings of the failure of a biocide-based souring control program in the field. Furthermore, spore-forming sulfate reducers (Desulfotomaculum and Desulfurispora) were enriched and became dominant in both GA-treated groups, which could cause challenges for the design of long-lasting remedial souring control strategies.

IMPORTANCE Reservoir souring is a problem for the oil and gas industry, because H₂S corrodes the steel infrastructure, downgrades oil quality, and poses substantial risks to field personnel and the environment. Biocides have been widely applied to remedy souring, but the long-term performance of biocide treatments is hard to predict or to optimize due to limited understanding of the microbial ecology affected by biocide treatment. This study investigates the long-term biocide performance and associated changes in the abundance, diversity, and structure of the souring microbial community, thus advancing the knowledge toward a deeper understanding of the microbial ecology of biocide-treated systems and contributing to the improvement of current biocide-based souring control practices. The study showcases the potential application of incorporating microbial community analyses to forecast souring, and it highlights the long-term consequences of biocide treatment in the microbial communities, with relevance to both operators and regulators.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Machine learning approach identifies water sample source based on microbial abundance

Water quality can change along a river system due to differences in adjacent land use patterns and discharge sources. These variations can induce rapid responses of the aquatic microbial community, which may be an indicator of water quality characteristics. In the current study, we used a random forest model to predict water sample sources from three different river ecosystems along a gradient of anthropogenic disturbance (i.e., less disturbed mountainous area, wastewater discharged urban area, and pesticide and fertilizer applied agricultural area) based on environmental physicochemical indices (PCIs), microbiological indices (MBIs), and their combination. Results showed that among the PCI-based models, using conventional water quality indices as inputs provided markedly better prediction of water sample source than using pharmaceutical and personal care products (PPCPs), and much better prediction than using polycyclic aromatic hydrocarbons (PAHs) and substituted PAHs (SPAHs). Among the MBI-based models, using the abundances of the top 30 bacteria combined with pathogenic antibiotic resistant bacteria (PARB) as inputs achieved the lowest median out-of-bag error rate (9.9%) and increased median kappa coefficient (0.8694), while adding fungal inputs reduced the kappa coefficient. The model based on the top 30 bacteria still showed an advantage compared with models based on PCIs or the combination of PCIs and MBIs. With improvement in sequencing technology and increase in data availability in the future, the proposed method provides an economical, rapid, and reliable way in which to identify water sample sources based on abundance data of microbial communities.

Predicting microbiomes through a deep latent space

Motivation

Microbial communities influence their environment by modifying the availability of compounds, such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improve productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features.

Results

Integrating deep learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity (>0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables, such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray–Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only 100 sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions.

Availability and implementation

Software, results and data are available at https://github.com/jorgemf/DeepLatentMicrobiome

Supplementary information

Supplementary data are available at Bioinformatics online.

Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning

Environmental DNA metabarcoding is a powerful approach for use in biomonitoring and impact assessments. Amplicon-based eDNA sequence data are characteristically highly divergent in sequencing depth (total reads per sample) as influenced inter alia by the number of samples simultaneously analyzed per sequencing run. The random forest (RF) machine learning algorithm has been successfully employed to accurately classify unknown samples into monitoring categories. To employ RF to eDNA data, and avoid sequencing-depth artifacts, sequence data across samples are normalized using rarefaction, a process that inherently loses information. The aim of this study was to inform future sampling designs in terms of the relationship between sampling depth and RF accuracy. We analyzed three published and one new bacterial amplicon datasets, using a RF, based initially on the maximal rarefied data available (minimum mean of > 30,000 reads across all datasets) to give our baseline performance. We then evaluated the RF classification success based on increasingly rarefied datasets. We found that extreme to moderate rarefaction (50-5000 sequences per sample) was sufficient to achieve prediction performance commensurate to the full data, depending on the classification task. We did not find that the number of classification classes, data balance across classes, or the total number of sequences or samples, were associated with predictive accuracy. We identified the ability of the training data to adequately characterize the classes being mapped as the most important criterion and discuss how this finding can inform future sampling design for eDNA based biomonitoring to reduce costs and computation time.

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

Sulfur Metabolites Play Key System-Level Roles in Modulating Denitrification

Nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) colonize diverse anoxic environments, including soil subsurface, groundwater, and wastewater. NRB and SRB compete for resources, and their interplay has major implications on the global cycling of nitrogen and sulfur species, with undesirable outcomes in some contexts.

Competition between nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) for resources in anoxic environments is generally thought to be governed largely by thermodynamics. It is now recognized that intermediates of nitrogen and sulfur cycling (e.g., hydrogen sulfide, nitrite, etc.) can also directly impact NRB and SRB activities in freshwater, wastewater, and sediment and therefore may play important roles in competitive interactions. Here, through comparative transcriptomic and metabolomic analyses, we have uncovered mechanisms of hydrogen sulfide- and cysteine-mediated inhibition of nitrate respiratory growth for the NRB Intrasporangium calvum C5. Specifically, the systems analysis predicted that cysteine and hydrogen sulfide inhibit growth of I. calvum C5 by disrupting distinct steps across multiple pathways, including branched-chain amino acid (BCAA) biosynthesis, utilization of specific carbon sources, and cofactor metabolism. We have validated these predictions by demonstrating that complementation with BCAAs and specific carbon sources relieves the growth inhibitory effects of cysteine and hydrogen sulfide. We discuss how these mechanistic insights give new context to the interplay and stratification of NRB and SRB in diverse environments.

IMPORTANCE Nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) colonize diverse anoxic environments, including soil subsurface, groundwater, and wastewater. NRB and SRB compete for resources, and their interplay has major implications on the global cycling of nitrogen and sulfur species, with undesirable outcomes in some contexts. For instance, the removal of reactive nitrogen species by NRB is desirable for wastewater treatment, but in agricultural soils, NRB can drive the conversion of nitrates from fertilizers into nitrous oxide, a potent greenhouse gas. Similarly, the hydrogen sulfide produced by SRB can help sequester and immobilize toxic heavy metals but is undesirable in oil wells where competition between SRB and NRB has been exploited to suppress hydrogen sulfide production. By characterizing how reduced sulfur compounds inhibit growth and activity of NRB, we have gained systems-level and mechanistic insight into the interplay of these two important groups of organisms and drivers of their stratification in diverse environments.

The microbial dimension of submarine groundwater discharge: current challenges and future directions

Despite the relevance of submarine groundwater discharge (SGD) for ocean biogeochemistry, the microbial dimension of SGD remains poorly understood. SGD can influence marine microbial communities through supplying chemical compounds and microorganisms, and in turn, microbes at the land–ocean transition zone determine the chemistry of the groundwater reaching the ocean. However, compared with inland groundwater, little is known about microbial communities in coastal aquifers. Here, we review the state of the art of the microbial dimension of SGD, with emphasis on prokaryotes, and identify current challenges and future directions. Main challenges include improving the diversity description of groundwater microbiota, characterized by ultrasmall, inactive and novel taxa, and by high ratios of sediment-attached versus free-living cells. Studies should explore microbial dynamics and their role in chemical cycles in coastal aquifers, the bidirectional dispersal of groundwater and seawater microorganisms, and marine bacterioplankton responses to SGD. This will require not only combining sequencing methods, visualization and linking taxonomy to activity but also considering the entire groundwater–marine continuum. Interactions between traditionally independent disciplines (e.g. hydrogeology, microbial ecology) are needed to frame the study of terrestrial and aquatic microorganisms beyond the limits of their presumed habitats, and to foster our understanding of SGD processes and their influence in coastal biogeochemical cycles.

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.