Microbial assemblages and bioindicators as proxies for ecosystem health status: potential and limitations

Response of sediment microbial communities to the flow effect of the triangular artificial reef: A simulation-based experimental study

Artificial reefs (ARs), as an important tool for habitat restoration, play significant impacts on benthic microbial ecosystems. This study utilized 16S rRNA gene sequencing technology and computational fluid dynamics (CFD) flow simulation to investigate the effects of flow field distribution around ARs on microbial community structure. The results revealed distinct regional distribution patterns of microbial communities affected by different hydrodynamic conditions. Flow velocity and flow regime of water in sediment-water interface shaped the microbial community structure. The diversity and richness in R-HF were significantly decreased compared to other five regions (p < 0.05). At the phyla and OUT levels, most abundant taxa (1>%) showed an enrichment trend in R-HB. However, more than half of differentially abundant taxa were enriched in R-HB, which was significantly correlated with organic matter (OM). Bugbase phenotypic predictions indicated a low abundance of the anaerobic phenotype in R-HF and a high abundance of the biofilm-forming phenotype in R-HB.

Dual stressors of infection and warming can destabilize host microbiomes

Climate change is causing extreme heating events and intensifying infectious disease outbreaks. Animals harbour microbial communities, which are vital for their survival and fitness under stressful conditions. Understanding how microbiome structures change in response to infection and warming may be important for forecasting host performance under global change. Here, we evaluated alterations in the microbiomes of several wild Caenorhabditis elegans isolates spanning a range of latitudes, upon warming temperatures and infection by the parasite Leucobacter musarum. Using 16S rRNA sequencing, we found that microbiome diversity decreased, and dispersion increased over time, with the former being more prominent in uninfected adults and the latter aggravated by infection. Infection reduced dominance of specific microbial taxa, and increased microbiome dispersion, indicating destabilizing effects on host microbial communities. Exposing infected hosts to warming did not have an additive destabilizing effect on their microbiomes. Moreover, warming during pre-adult development alleviated the destabilizing effects of infection on host microbiomes. These results revealed an opposing interaction between biotic and abiotic factors on microbiome structure. Lastly, we showed that increased microbiome dispersion might be associated with decreased variability in microbial species interaction strength. Overall, these findings improve our understanding of animal microbiome dynamics amidst concurrent climate change and epidemics. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

Anthropogenic vs. natural habitats: Higher microbial biodiversity pays the trade-off of lower connectivity

Climate change and anthropogenic disturbances are known to influence soil biodiversity. The objectives of this study were to compare the community composition, species coexistence patterns, and ecological assembly processes of soil microbial communities in a paired setting featuring a natural and an anthropogenic ecosystem facing each other at identical climatic, pedological, and vegetational conditions. A transect gradient from forest to seashore allowed for sampling across different habitats within both sites. The field survey was carried out at two adjacent strips of land within the Po River delta lagoon system (Veneto, Italy) one of which is protected within a natural preserve and the other has been converted for decades into a tourist resort. The anthropogenic pressure interestingly led to an increase in the α-diversity of soil microbes but was accompanied by a reduction in β-diversity. The community assembly mechanisms of microbial communities differentiate in natural and anthropic ecosystems: for bacteria, in natural ecosystems deterministic variables and homogeneous selection play a main role (51.92%), while stochastic dispersal limitation (52.15%) is critical in anthropized ecosystems; for fungi, stochastic dispersal limitation increases from 38.1% to 66.09% passing from natural to anthropized ecosystems. We are on calcareous sandy soils and in more natural ecosystems a variation of topsoil pH favors the deterministic selection of bacterial communities, while a divergence of K availability favors stochastic selection. In more anthropized ecosystems, the deterministic variable selection is influenced by the values of SOC. Microbial networks in the natural system exhibited higher numbers of nodes and network edges, as well as higher averages of path length, weighted degree, clustering coefficient, and density than its equivalent sites in the more anthropically impacted environment. The latter on the other hand presented a stronger modularity. Although the influence of stochastic processes increases in anthropized habitats, niche-based selection also proves to impose constraints on communities. Overall, the functionality of the relationships between groups of microorganisms co-existing in communities appeared more relevant to the concept of functional biodiversity in comparison to the plain number of their different taxa. Fewer but functionally more organized lineages displayed traits underscoring a better use of the resources than higher absolute numbers of taxa when those are not equally interconnected in their habitat exploitation. However, considering that network complexity can have important implications for microbial stability and ecosystem multifunctionality, the extinction of complex ecological interactions in anthropogenic habitats may impair important ecosystem services that soils provide us.

Protecting the invisible: Establishing guideline values for copper toxicity to marine microbiomes

Understanding the rapid responses of marine microbiomes to environmental disturbances is paramount for supporting early assessments of harm to high-value ecosystems, such as coral reefs. Yet, management guidelines aimed at protecting aquatic life from environmental pollution remain exclusively defined for organisms at higher trophic levels. In this study, 16S rRNA gene amplicon sequencing was applied in conjunction with propidium monoazide for cell-viability assessment as a sensitive tool to determine taxon- and community-level changes in a seawater microbial community under copper (Cu) exposure. Bayesian model averaging was used to establish concentration-response relationships to evaluate the effects of copper on microbial composition, diversity, and richness for the purpose of estimating microbiome Hazard Concentration (mHCx) values. Predicted mHC5 values at which a 5 % change in microbial composition, diversity, and richness occurred were 1.05, 0.72, and 0.38 μg Cu L-1, respectively. Threshold indicator taxa analysis was applied across the copper concentrations to identify taxon-specific change points for decreasing taxa. These change points were then used to generate a Prokaryotic Sensitivity Distribution (PSD), from which mHCxdec values were derived for copper, suitable for the protection of 99, 95, 90, and 80 % of the marine microbiome. The mHC5dec guideline value of 0.61 μg Cu L-1, protective of 95 % of the marine microbial community, was lower than the equivalent Australian water quality guideline value based on eukaryotic organisms at higher trophic levels. This suggests that marine microbial communities might be more vulnerable, highlighting potential insufficiencies in their protection against copper pollution. The mHCx values proposed here provide approaches to quantitatively assess the effects of contaminants on microbial communities towards the inclusion of prokaryotes in future water quality guidelines.

Assessing the effects of aquaculture on tidal flat ecological status using multi-metrics interaction-based index of biotic integrity (Mt-IBI)

Given tidal flat special environmental conditions and the degree of pollution caused by human activities, there is an urgent need to quantitatively assess their ecological status. Bioindication has become an indispensable part of environmental quality monitoring on account of its sensitivity to environmental disturbance. Thus, this study used bio-indicators to establish a multi-metrics-based index of biotic integrity (Mt-IBI) to evaluate the ecological status of the tidal flats with/without aquaculture through metagenomic sequencing. Four core indexes that were significantly correlated to other indexes with redundancy (p < 0.05), including Escherichia, beta-lactam antibiotic resistance genes, cellulase and xyloglucanases and the keystone species with 21° in the network, were selected after the screening processes. By implementing Mt-IBI in the tidal flats, the ecological health of the sampling sites was categorized into three levels, with Mt-IBI values of 2.01-2.63 (severe level), 2.81-2.93 (moderate level) and 3.23-4.18 (mild level), respectively. Through SEM analysis, water chemical oxygen demand and antibiotics were determined to be the primary controlling factors of the ecological status of tidal flat regions influenced by aquaculture, followed by salinity and total nitrogen. It is worth noting that the alteration of microbial communities impacted ecological status through the mediation of antibiotics. It is hoped that the results of our study will provide a theoretical basis for coastal environment restoration and that the use of Mt-IBI to assess ecosystem status in different aquatic environments will be further popularized in the future.

Improving the assessment of ecosystem and wildlife health: microbiome as an early indicator

Human activities are causing dramatic declines in ecosystem health, compromising the functioning of the life-support system, economic activity, and animal and human health. In this context, monitoring the health of ecosystems and wildlife populations is crucial for determining ecological dynamics and assessing management interventions. A growing body of evidence indicates that microbiome provides a meaningful early indicator of ecosystem and wildlife health. Microbiome is ubiquitous and both environmental and host-associated microbiomes rapidly reflect anthropogenic disturbances. However, we still need to overcome current limitations such as nucleic acid degradation, sequencing depth, and the establishment of baseline data to maximize the potential of microbiome studies.

Microbial indicators along a metallic contamination gradient in tropical coastal sediments

The structure and diversity of microbial community inhabiting coastal sediments reflect the exposition to contaminants. Aiming to assess the changes in the microbiota from Sepetiba Bay (SB, Brazil) sediments, correlations between the 16S rRNA gene data (V4-V5 region), metal contamination factors (CF), and the ecological risk classification provided by the Quality Ratio (QR) index were considered. The results show that microbial diversity differs significantly between the less (SB external sector) and the most (SB internal sector) polluted sectors. Also, differences in the microbial community structure regarding the ecological risk classifications validated the QR index as a reliable tool to report the SB chronic contamination. Microbial indicator genera resistant to metals (Desulfatiglans, SEEP-SRB1, Spirochaeta 2, among others) presented mainly anaerobic metabolisms. These genera are related to the sulfate reducing and methanogenic metabolisms probably participating in the natural attenuation processes but also associated with greenhouse gas emissions. In contrast, microbial indicator genera sensitive to metals (Rubripirellula, Blastopirellula, Aquibacter, among others) presented mainly aerobic metabolisms. It is suggested that future works should investigate the metabolic functions to evaluate the influence of metallic contaminants on microbial community inhabiting SB sediment.

Biogeographic Pattern and Network of Rhizosphere Fungal and Bacterial Communities in Panicum miliaceum Fields: Roles of Abundant and Rare Taxa

Unraveling how microbial interactions and assembly process regulate the rhizosphere abundant and rare taxa is crucial for determining how species diversity affects rhizosphere microbiological functions. We assessed the rare and abundant taxa of rhizosphere fungal and bacterial communities in proso millet agroecosystems to explore their biogeographic patterns and co-occurrence patterns based on a regional scale. The taxonomic composition was significantly distinct between the fungal and bacterial abundant and rare taxa. Additionally, the rare taxa of bacteria and fungi exhibited higher diversity and stronger phylogenetic clustering than those of the abundant ones. The phylogenetic turnover rate of abundant taxa of bacteria was smaller than that of rare ones, whereas that of fungi had the opposite trend. Environmental variables, particularly mean annual temperature (MAT) and soil pH, were the crucial factors of community structure in the rare and abundant taxa. Furthermore, a deterministic process was relatively more important in governing the assembly of abundant and rare taxa. Our network analysis suggested that rare taxa of fungi and bacteria were located at the core of maintaining ecosystem functions. Interestingly, MAT and pH were also the important drivers controlling the main modules of abundant and rare taxa. Altogether, these observations revealed that rare and abundant taxa of fungal and bacterial communities showed obvious differences in biogeographic distribution, which were based on the dynamic interactions between assembly processes and co-occurrence networks.

Microbial community metabolic alterations and resistance to metals and antibiotics driven by chronic exposition to multiple pollutants in a highly impacted tropical coastal bay

Microbial communities from Sepetiba Bay (SB, Rio de Janeiro, Brazil), characterized by 16S rRNA gene (V4-V5 region) sequencing analysis, were found to be correlated with the metallic contamination factor and the Quality Ratio (QR) index. Consistently, the predicted function of microbial communities, obtained with Tax4Fun2, showed that the functional patterns in SB internal sector under the highest anthropogenic pressure were different from that observed in the external sector with the lowest contamination level. Signal transduction, cellular community, membrane transport, and energy metabolism were among the KEGG pathways favored by metallic contamination in the SB internal sector, while lipid metabolism, transcription, and translation were among the pathways favored in the SB external sector. Noteworthy, the relative proportions of KEGG pathways and genes associated with metallic homeostasis showed significant differences according to the SB sectors, consistently with the ecological risk classification (QR index) of sediments. The functional prediction approach is an economically viable alternative and presents an overview of the main pathways/genes favored in the SB microbiota exposed to long-term pollution. In contrast, the microgAMBI, ecological status index based on bacterial community composition, was not consistent with the metallic contamination of SB, suggesting that this index requires improvements to be applied in tropical areas. Our study also revealed a strong correlation between metal resistance genes (MRG) and antibiotic resistance genes (ARG), indicating that MRG and ARG are co-selected by the metallic contamination prevailing in SB.

Development of Microbial Indicators in Ecological Systems

Indicators can monitor ecological environment changes and help maintain ecological balance. Bioindicators are divided into animal, plant, and microbial indicators, of which animal and plant indicators have previously been the most researched, but microbial indicators have drawn attention recently owing to their high sensitivity to the environment and their potential for use in monitoring environmental changes. To date, reviews of studies of animals and plants as indicator species have frequently been conducted, but reviews of research on microorganisms as indicator species have been rare. In this review, we summarize and analyze studies using microorganisms as indicator species in a variety of ecosystems, such as forests, deserts, aquatic and plateau ecosystems, and artificial ecosystems, which are contained in wetlands, farmlands, and mining ecosystems. This review provides useful information for the further use of microorganisms as indicators to reflect the changes in different environmental ecosystems.

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.

Predictable Changes in Eelgrass Microbiomes with Increasing Wasting Disease Prevalence across 23° Latitude in the Northeastern Pacific

Predicting outcomes of marine disease outbreaks presents a challenge in the face of both global and local stressors. Host-associated microbiomes may play important roles in disease dynamics but remain understudied in marine ecosystems. Host–pathogen–microbiome interactions can vary across host ranges, gradients of disease, and temperature; studying these relationships may aid our ability to forecast disease dynamics. Eelgrass, Zostera marina, is impacted by outbreaks of wasting disease caused by the opportunistic pathogen Labyrinthula zosterae. We investigated how Z. marina phyllosphere microbial communities vary with rising wasting disease lesion prevalence and severity relative to plant and meadow characteristics like shoot density, longest leaf length, and temperature across 23° latitude in the Northeastern Pacific. We detected effects of geography (11%) and smaller, but distinct, effects of temperature (30-day max sea surface temperature, 4%) and disease (lesion prevalence, 3%) on microbiome composition. Declines in alpha diversity on asymptomatic tissue occurred with rising wasting disease prevalence within meadows. However, no change in microbiome variability (dispersion) was detected between asymptomatic and symptomatic tissues. Further, we identified members of Cellvibrionaceae, Colwelliaceae, and Granulosicoccaceae on asymptomatic tissue that are predictive of wasting disease prevalence across the geographic range (3,100 kilometers). Functional roles of Colwelliaceae and Granulosicoccaceae are not known. Cellvibrionaceae, degraders of plant cellulose, were also enriched in lesions and adjacent green tissue relative to nonlesioned leaves. Cellvibrionaceae may play important roles in disease progression by degrading host tissues or overwhelming plant immune responses. Thus, inclusion of microbiomes in wasting disease studies may improve our ability to understand variable rates of infection, disease progression, and plant survival.

IMPORTANCE The roles of marine microbiomes in disease remain poorly understood due, in part, to the challenging nature of sampling at appropriate spatiotemporal scales and across natural gradients of disease throughout host ranges. This is especially true for marine vascular plants like eelgrass (Zostera marina) that are vital for ecosystem function and biodiversity but are susceptible to rapid decline and die-off from pathogens like eukaryotic slime-mold Labyrinthula zosterae (wasting disease). We link bacterial members of phyllosphere tissues to the prevalence of wasting disease across the broadest geographic range to date for a marine plant microbiome-disease study (3,100 km). We identify Cellvibrionaceae, plant cell wall degraders, enriched (up to 61% relative abundance) within lesion tissue, which suggests this group may be playing important roles in disease progression. These findings suggest inclusion of microbiomes in marine disease studies will improve our ability to predict ecological outcomes of infection across variable landscapes spanning thousands of kilometers.

Potential of Meta-Omics to Provide Modern Microbial Indicators for Monitoring Soil Quality and Securing Food Production

Soils are fundamental resources for agricultural production and play an essential role in food security. They represent the keystone of the food value chain because they harbor a large fraction of biodiversity—the backbone of the regulation of ecosystem services and “soil health” maintenance. In the face of the numerous causes of soil degradation such as unsustainable soil management practices, pollution, waste disposal, or the increasing number of extreme weather events, it has become clear that (i) preserving the soil biodiversity is key to food security, and (ii) biodiversity-based solutions for environmental monitoring have to be developed. Within the soil biodiversity reservoir, microbial diversity including Archaea, Bacteria, Fungi and protists is essential for ecosystem functioning and resilience. Microbial communities are also sensitive to various environmental drivers and to management practices; as a result, they are ideal candidates for monitoring soil quality assessment. The emergence of meta-omics approaches based on recent advances in high-throughput sequencing and bioinformatics has remarkably improved our ability to characterize microbial diversity and its potential functions. This revolution has substantially filled the knowledge gap about soil microbial diversity regulation and ecology, but also provided new and robust indicators of agricultural soil quality. We reviewed how meta-omics approaches replaced traditional methods and allowed developing modern microbial indicators of the soil biological quality. Each meta-omics approach is described in its general principles, methodologies, specificities, strengths and drawbacks, and illustrated with concrete applications for soil monitoring. The development of metabarcoding approaches in the last 20 years has led to a collection of microbial indicators that are now operational and available for the farming sector. Our review shows that despite the recent huge advances, some meta-omics approaches (e.g., metatranscriptomics or meta-proteomics) still need developments to be operational for environmental bio-monitoring. As regards prospects, we outline the importance of building up repositories of soil quality indicators. These are essential for objective and robust diagnosis, to help actors and stakeholders improve soil management, with a view to or to contribute to combining the food and environmental quality of next-generation farming systems in the context of the agroecological transition.

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.

Impact of fumigants on non-target soil microorganisms: a review

Fumigants have been used for decades to control soil-borne pathogens of high-value crops, and increasing evidence indicates they can affect non-target soil microbial communities. Understanding the impacts of these products on soil microorganisms is of critical importance not only for evaluating their environmental safety, but also because soil microbial communities have a central role in soil quality and nutrient cycling, plant growth, and crop production. Thus, we conducted a systematic review and metanalysis study of fumigant impacts on non-target soil microorganisms. In general, we found that fumigation decreases the bacterial diversity and abundance of total bacteria and nitrogen-cycling genes by approximately 10-50% during the first four weeks after application compared to non-treated soils. These decreases appear transient and tend to diminish or disappear after four weeks. Increases in bacterial diversity and abundance can occur after fumigation but are less common. Fumigant application can also alter bacterial community composition during the first six weeks after treatment by significantly increasing and/or decreasing the relative abundance of bacterial taxa involved in key soil functions such as N-cycling and plant-growth promotion. Knowledge gaps and areas where future research efforts should be prioritized to improve our understanding of the impact of organic fumigants on non-target soil microorganisms are discussed.

Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture

BACKGROUND

The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities.

RESULTS

By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target products n-caproate and n-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predict n-caproate and n-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction of n-caproate and n-caprylate productivities was achieved. Four inferred bioindicators belonging to the genera Olsenella, Lactobacillus, Syntrophococcus and Clostridium IV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production.

CONCLUSIONS

Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites. Video Abstract.

Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmland

Microbial communities are early responders to wetland degradation, and instrumental players in the reversal of this degradation. However, our understanding of soil microbial community structure and function throughout wetland development remains incomplete. We conducted a survey across cranberry farms, young retired farms, old retired farms, flooded former farms, ecologically restored former farms, and natural reference wetlands with no history of cranberry farming. We investigated the relationship between the microbial community and soil characteristics that restoration intends to maximize, such as soil organic matter, cation exchange capacity and denitrification potential. Among the five treatments considered, flooded and restored sites had the highest prokaryote and microeukaryote community similarity to natural wetlands. In contrast, young retired sites had similar communities to farms, and old retired sites failed to develop wetland microbial communities or functions. Canonical analysis of principal coordinates revealed that soil variables, in particular potassium base saturation, sodium, and denitrification potential, explained 45% of the variation in prokaryote communities and 44% of the variation in microeukaryote communities, segregating soil samples into two clouds in ordination space: farm, old retired and young retired sites on one side and restored, flooded, and natural sites on the other. Heat trees revealed possible prokaryotic (Gemmatimonadetes) and microeukaryotic (Rhizaria) indicators of wetland development, along with a drop in the dominance of Nucletmycea in restored sites, a class that includes suspected mycorrhizal symbionts of the cranberry crop. Flooded sites showed the strongest evidence of wetland development, with triple the soil organic matter accumulation, double the cation exchange capacity, and seventy times the denitrification potential compared to farms. However, given that flooding does not promote any of the watershed or habitat benefits as ecological restoration, we suggest that flooding can be used to stimulate beneficial microbial communities and soil functions during the restoration waiting period, or when restoration is not an option.

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

The microbiome emits informative signals of biological organization and environmental pressure that aid ecosystem monitoring and prediction. Are the many signals reducible to a habitat-specific portfolio that characterizes ecosystem health? Does an optimally structured microbiome imply a resilient microbiome? To answer these questions, we applied our novel Eco-Evo Mandala to bacterioplankton data from four habitats within the Great Barrier Reef, to explore how patterns in community structure, function and genetics signal habitat-specific organization and departures from theoretical optimality. The Mandala revealed communities departing from optimality in habitat-specific ways, mostly along structural and functional traits related to bacterioplankton abundance and interaction distributions (reflected by ϵ and λ as power law and exponential distribution parameters), which are not linearly associated with each other. River and reef communities were similar in their relatively low abundance and interaction disorganization (low ϵ and λ) due to their protective structured habitats. On the contrary, lagoon and estuarine inshore reefs appeared the most disorganized due to the ocean temperature and biogeochemical stress. Phylogenetic distances (D) were minimally informative in characterizing bacterioplankton organization. However, dominant populations, such as Proteobacteria, Bacteroidetes, and Cyanobacteria, were largely responsible for community patterns, being generalists with a large functional gene repertoire (high D) that increases resilience. The relative balance of these populations was found to be habitat-specific and likely related to systemic environmental stress. The position on the Mandala along the three fundamental traits, as well as fluctuations in this ecological state, conveys information about the microbiome's health (and likely ecosystem health considering bacteria-based multitrophic dependencies) as divergence from the expected relative optimality. The Eco-Evo Mandala emphasizes how habitat and the microbiome's interaction network topology are first- and second-order factors for ecosystem health evaluation over taxonomic species richness. Unhealthy microbiome communities and unbalanced microbes are identified not by macroecological indicators but by mapping their impact on the collective proportion and distribution of interactions, which regulates the microbiome's ecosystem function.

The Future Is Big-and Small: Remote Sensing Enables Cross-Scale Comparisons of Microbiome Dynamics and Ecological Consequences

Coupling remote sensing with microbial omics-based approaches provides a promising new frontier for scientists to scale microbial interactions across space and time. These data-rich, interdisciplinary methods allow us to better understand interactions between microbial communities and their environments and, in turn, their impact on ecosystem structure and function. Here, we highlight current and novel examples of applying remote sensing, machine learning, spatial statistics, and omics data approaches to marine, aquatic, and terrestrial systems. We emphasize the importance of integrating biochemical and spatiotemporal environmental data to move toward a predictive framework of microbiome interactions and their ecosystem-level effects. Finally, we emphasize lessons learned from our collaborative research with recommendations to foster productive and interdisciplinary teamwork.

Gammaproteobacteria, a core taxon in the guts of soil fauna, are potential responders to environmental concentrations of soil pollutants

BACKGROUND

The ubiquitous gut microbiotas acquired from the environment contribute to host health. The gut microbiotas of soil invertebrates are gradually assembled from the microecological region of the soil ecosystem which they inhabit, but little is known about their characteristics when the hosts are under environmental stress. The rapid development of high-throughput DNA sequencing in the last decade has provided unprecedented insights and opportunities to characterize the gut microbiotas of soil invertebrates. Here, we characterized the core, transient, and rare bacterial taxa in the guts of soil invertebrates using the core index (CI) and developed a new theory of global microbial diversity of soil ecological microregions.

RESULTS

We found that the Gammaproteobacteria could respond indiscriminately to the exposure to environmental concentrations of soil pollutants and were closely associated with the physiology and function of the host. Meanwhile, machine-learning models based on metadata calculated that Gammaproteobacteria were the core bacteria with the highest colonization potential in the gut, and further identified that they were the best indicator taxon of the response to environmental concentrations of soil pollution. Gammaproteobacteria also closely correlated with the abundance of antibiotic resistance genes.

CONCLUSIONS

Our results determined that Gammaproteobacteria were an indicator taxon in the guts of the soil invertebrates that responded to environmental concentrations of soil pollutants, thus providing an effective theoretical basis for subsequent assessments of soil ecological risk. The results of the physiological and biochemical analyses of the host and the microbial-community functions, and the antibiotic resistance of Gammaproteobacteria, provide new insights for evaluating global soil ecological health. Video abstract.

Diversity of metal and antibiotic resistance genes in Enterococcus spp. from the last century reflects multiple pollution and genetic exchange among phyla from overlapping ecosystems

Arsenic (As), mercury (Hg), and copper (Cu) are among the major historical and contemporary metal pollutants linked to global anthropogenic activities. Enterococcus have been considered indicators of fecal pollution and antibiotic resistance for years, but its largely underexplored metallome precludes understanding their role as metal pollution bioindicators as well. Our goal was to determine the occurrence, diversity, and phenotypes associated with known acquired genes/operons conferring tolerance to As, Hg or Cu among Enterococcus and to identify their genetic context (381 field isolates from diverse epidemiological and genetic backgrounds; 3547 enterococcal genomes available in databases representing a time span during 1900-2019). Genes conferring tolerance to As (arsA), Hg (merA) or Cu (tcrB) were used as biomarkers of widespread metal tolerance operons. Different variants of metal tolerance (MeT) genes (13 arsA, 6 merA, 1 tcrB) were more commonly recovered from the food-chain (arsA, tcrB) or humans (merA), and were shared with 49 other bacterial taxa. Comparative genomics analysis revealed that MeT genes occurred in heterogeneous operons, at least since the 1900s, with an increasing accretion of antibiotic resistance genes since the 1960's, reflecting diverse antimicrobial pollution. Multiple MeT genes were co-located on the chromosome or conjugative plasmids flanked by elements with high potential for recombination, often along with antibiotic resistance genes. Phenotypic analysis of some isolates carrying MeT genes revealed up to 128× fold increase in the minimum inhibitory concentrations to metals. The main distribution of functional MeT genes among Enterococcus faecium and Enterococcus faecalis from different sources, time spans, and clonal lineages, and their ability to acquire diverse genes from multiple taxa bacterial communities places these species as good candidates to be used as model organisms in future projects aiming at the identification and quantification of bioindicators of metal polluted environments by anthropogenic activities.

Evaluation of Three Prokaryote Primers for Identification of Prokaryote Community Structure and Their Abode Preference in Three Distinct Wetland Ecosystems

The ultimate role of prokaryote (bacteria and archaea), the decomposer of the wetland ecosystem, depends on its community structure and its interaction with the environment. The present study has used three universal prokaryote primers to compare prokaryote community structure and diversity of three distinctly different wetlands. The study results revealed that α-diversity indices and phylogenetic differential abundance patterns did not differ significantly among primers, but they did differ significantly across wetlands. Microbial community composition revealed a distinct pattern for each primer in each wetland. Overall comparison of prokaryote communities in sediments of three wetlands revealed the highest prokaryote richness and diversity in Bhomra (freshwater wetland) followed by Malencho (brackish-water wetland) and East Kolkata wetland (EKW) (sewage-fed wetland). Indicator genus analysis identified 21, 4, and 29 unique indicator genera, having preferential abode for Bhomra, EKW, and Malencho, respectively. Prediction of potential roles of these microbes revealed a preference for sulfate-reducing microbes in Malencho and methanogens in Bhomra. The distinct phylogenetic differential abundance pattern, microbial abode preference, and their potential functional role predict ecosystem variables shaping microbial diversity. The variation in community composition of prokaryotes in response to ecosystem variables can serve as the most sensitive bioindicator of wetland ecosystem assessment and management.

Deployment of Engineered Microbes: Contributions to the Bioeconomy and Considerations for Biosecurity

Engineering at microscopic scales has an immense effect on the modern bioeconomy. Microbes contribute to such disparate markets as chemical manufacturing, fuel production, crop optimization, and pharmaceutical synthesis, to name a few. Due to new and emerging synthetic biology technologies, and the sophistication and control afforded by them, we are on the brink of deploying engineered microbes to not only enhance traditional applications but also to introduce these microbes to sectors, contexts, and formats not previously attempted. In microbially managed medicine, microbial engineering holds promise for increasing efficacy, improving tissue penetration, and sustaining treatment. In the environment, the most effective areas for deployment are in the management of crops and protection of ecosystems. However, caution is warranted before introducing engineered organisms to new environments where they may proliferate without control and could cause unforeseen effects. We summarize ideas and data that can inform identification and assessment of the risks that these tools present to ensure that realistic hazards are described and unrealistic ones do not hinder advancement. Further, because modes of containment are crucial complements to deployment, we describe the state of the art in microbial biocontainment strategies, current gaps, and how these gaps might be addressed through technological advances in synthetic engineering. Collectively, this work highlights engineered microbes as a foundational and expanding facet of the bioeconomy, projects their utility in upcoming deployments outside the laboratory, and identifies knowns and unknowns that will be necessary considerations and points of focus in this endeavor.

A step towards the validation of bacteria biotic indices using DNA metabarcoding for benthic monitoring

Environmental genomics is a promising field for monitoring biodiversity in a timely fashion. Efforts have increasingly been dedicated to the use of bacteria DNA derived data to develop biotic indices for benthic monitoring. However, a substantial debate exists about whether bacteria-derived data using DNA metabarcoding should follow, for example, a taxonomy-based or a taxonomy-free approach to marine bioassessments. Here, we showcase the value of DNA-based monitoring using the impact of fish farming as an example of anthropogenic disturbances in coastal areas and compare the performance of taxonomy-based and taxonomy-free approaches in detecting environmental alterations. We analysed samples collected near to the farm cages and along distance gradients from two aquaculture installations, and at control sites, to evaluate the effect of this activity on bacterial assemblages. Using the putative response of bacterial taxa to stress we calculated the taxonomy-based biotic index microgAMBI. The distribution of individual amplicon sequence variants (ASVs), as a function of a gradient in sediment acid volatile sulphides, was then used to derive a taxonomy-free bacterial biotic index specific for this data set using a de novo approach based on quantile regression splines. Our results show that microgAMBI revealed a organically enriched environment along the gradient. However, the de novo biotic index outperformed microgAMBI by providing a higher discriminatory power in detecting changes in abiotic factors directly related to fish production, whilst allowing the identification of new ASVs bioindicators. The de novo strategy applied here represents a robust method to define new bioindicators in regions or habitats where no previous information about the response of bacteria to environmental stressors exists.

Quantification of the covariation of lake microbiomes and environmental variables using a machine learning-based framework

It is known that microorganisms are essential for the functioning of ecosystems, but the extent to which microorganisms respond to different environmental variables in their natural habitats is not clear. In the current study, we present a methodological framework to quantify the covariation of the microbial community of a habitat and environmental variables of this habitat. It is built on theoretical considerations of systems ecology, makes use of state-of-the-art machine learning techniques and can be used to identify bioindicators. We apply the framework to a data set containing operational taxonomic units (OTUs) as well as more than twenty physicochemical and geographic variables measured in a large-scale survey of European lakes. While a large part of variation (up to 61%) in many environmental variables can be explained by microbial community composition, some variables do not show significant covariation with the microbial lake community. Moreover, we have identified OTUs that act as "multitask" bioindicators, i.e., that are indicative for multiple environmental variables, and thus could be candidates for lake water monitoring schemes. Our results represent, for the first time, a quantification of the covariation of the lake microbiome and a wide array of environmental variables for lake ecosystems. Building on the results and methodology presented here, it will be possible to identify microbial taxa and processes that are essential for functioning and stability of lake ecosystems.

Spatial factors and plant attributes influence soil fungal community distribution patterns in the lower reaches of the Heihe River Basin, Northwest China

Inland river basins include critical habitats and provide various ecosystem services in extremely arid lands. However, we know little about the distribution patterns of soil fungal communities in these river basins. We investigated the distribution patterns of soil fungal communities from the riparian oasis zone (ROZ) to the circumjacent desert zone (CDZ) at the lower reaches of the Heihe River. The results indicated that soil fungal communities were mainly dominated by the phyla Ascomycota and Basidiomycota across all samples. The dominant soil fungi taxa were significantly different between ROZ and CDZ habitats at both the phylum and genus levels. Fungal alpha diversity was mainly affected by spatial factors and plant functional traits, and Pearson correlation analysis revealed that fungal alpha diversity was more closely related to plant functional traits than soil properties. Furthermore, fungal community structure was best explained by spatial factors and plant attributes (including plant diversity and plant functional traits). Together, our findings provide new insights into the significance of spatial factors and plant attributes for predicting distributions of fungal communities in arid inland river basins, which will help us better understand the functions and services of these ecosystems.

Disentangling of the ecotoxicological signal using "omics" analyses, a lesson from the survey of the impact of cyanobacterial proliferations on fishes

Omics technologies offer unprecedented perspectives for the rational investigation of complex biological systems. Indeed, omics present the ability of offering an extensive perception of the biochemistry and physiology of the cell and of any perturbing consequences of contaminants through the joint investigation of thousands of molecular responses simultaneously; then it has recently conducted to a fervent attention by research ecotoxicologists. Beyond the presentation of latest advances, exemplified here by omics investigation of cyanobacterial deleterious effects on various fishes (at various experimental and biological scales and with various analytical tools and pipeline), the present review paper re-explores the promising perspectives and also the pitfalls of such holistic investigations of the ecotoxicological response of organisms for environmental assessment.

Characterizing a riverine microbiome impacted by extreme disturbance caused by a mining sludge tsunami

Studies on disturbance events in riverine systems caused by environmental disasters and their effects on microbial diversity are scarce. Here, we evaluated the impact of the collapse of an iron ore dam holding approximately 50 million cubic meters of waste on both water and sediment microbiomes by deeply sequencing the 16S rRNA gene. Samples were taken from two impacted rivers and one reference river 7, 30 and 150 days postdisturbance. The impacted community structure changed greatly over spatiotemporal scales, being less diverse and more uneven, particularly on day 7 for the do Carmo River (the closest to the dam). However, the reference community structure remained similar between sampling events. Moreover, the impacted sediments were positively correlated with metals. The taxa abundance varied greatly over spatiotemporal scales, allowing for the identification of several potential bioindicators, e.g., Comamonadaceae, Novosphingobium, Sediminibacterium and Bacteriovorax. Our results showed that the impacted communities consisted mostly of Fe(II) oxidizers and Fe(III) reducers, aromatic compound degraders and predator bacteria. Network analysis showed a highly interconnected microbiome whose interactions switched from positive to negative or vice versa between the impacted and reference communities. This work revealed potential molecular signatures associated with the rivers heavily impacted by metals that might be useful sentinels for predicting riverine health.

Using soil bacterial communities to predict physico-chemical variables and soil quality

BACKGROUND

Soil ecosystems consist of complex interactions between biological communities and physico-chemical variables, all of which contribute to the overall quality of soils. Despite this, changes in bacterial communities are ignored by most soil monitoring programs, which are crucial to ensure the sustainability of land management practices. We applied 16S rRNA gene sequencing to determine the bacterial community composition of over 3000 soil samples from 606 sites in New Zealand. Sites were classified as indigenous forests, exotic forest plantations, horticulture, or pastoral grasslands; soil physico-chemical variables related to soil quality were also collected. The composition of soil bacterial communities was then used to predict the land use and soil physico-chemical variables of each site.

RESULTS

Soil bacterial community composition was strongly linked to land use, to the extent where it could correctly determine the type of land use with 85% accuracy. Despite the inherent variation introduced by sampling across ~ 1300 km distance gradient, the bacterial communities could also be used to differentiate sites grouped by key physico-chemical properties with up to 83% accuracy. Further, individual soil variables such as soil pH, nutrient concentrations and bulk density could be predicted; the correlations between predicted and true values ranged from weak (R2 value = 0.35) to strong (R2 value = 0.79). These predictions were accurate enough to allow bacterial communities to assign the correct soil quality scores with 50-95% accuracy.

CONCLUSIONS

The inclusion of biological information when monitoring soil quality is crucial if we wish to gain a better, more accurate understanding of how land management impacts the soil ecosystem. We have shown that soil bacterial communities can provide biologically relevant insights on the impacts of land use on soil ecosystems. Furthermore, their ability to indicate changes in individual soil parameters shows that analysing bacterial DNA data can be used to screen soil quality. Video Abstract.

Quantifying stream periphyton assemblage responses to nutrient amendments with a molecular approach

Nutrient (nitrogen [N] and phosphorus [P]) pollution is a pervasive water quality issue in the USA for small streams and rivers. The effect of nutrients on the biotic condition of streams is often evaluated with biological indicators such as macroinvertebrate assemblages or periphyton assemblages, particularly diatoms. Molecular approaches facilitate the use of periphyton assemblages as bioindicators because periphyton is diverse and its composition as a whole, rather than just diatoms, soft-bodied algae, or any single group, may convey additional information about responses to nutrients. To further develop the concept that a taxonomically-broad evaluation of periphyton assemblages could be useful for developing stream bioindicators, we examined microbial assemblage composition with both 16S and 18S rRNA genes, enabling us to evaluate composition in 3 domains. We measured otherwise unknown nutrient responses of different periphyton groups in situ with experiments that used glass fiber filters to allow diffusion of amended nutrients into a stream. We deployed these experimental setups in 2 streams that differ in the extent of agricultural land-use in their catchments in the southeastern USA. Experiments consisted of controls, N amendments, P amendments, and both N and P amendments. Periphyton assemblages that grew on the filters differed significantly by stream, date or season, and nutrient treatment. Assemblage differences across treatments were more consistent among Bacteria and Archaea than among eukaryotes. Effects of nutrient amendments were more pronounced in the stream with less agricultural land use and, therefore, lower nutrient loading than in the stream with more agricultural land use and higher nutrient loading. Combined N and P amendments decreased species richness and evenness for Bacteria and Archaea by ∼36 and ∼9%, respectively, compared with controls. Indicator species analysis revealed that specific clades varied in their response to treatments. Indicators based on the responses of these indicator clades were related to nutrient treatments across sites and seasons. Analyses that included all the taxa in a domain did not resolve differences in responses to N vs P. Instead, better resolution was achieved with an analysis focused on diatoms, which responded more strongly to P than N. Overall, our results showed that in situ nutrient-diffusing substrate experiments are a useful approach for describing assemblage responses to nutrients in streams. This type of molecular approach may be useful to environmental agencies and stakeholders responsible for assessing and managing stream water quality and biotic condition.