A microbial mandala for environmental monitoring: Predicting multiple impacts on estuarine prokaryote communities of the Bay of Biscay

Revolutionizing aquatic eco-environmental monitoring: Utilizing the RPA-Cas-FQ detection platform for zooplankton

The integration of recombinase polymerase amplification (RPA) with CRISPR/Cas technology has revolutionized molecular diagnostics and pathogen detection due to its unparalleled sensitivity and trans-cleavage ability. However, its potential in the ecological and environmental monitoring scenarios for aquatic ecosystems remains largely unexplored, particularly in accurate qualitative/quantitative detection, and its actual performance in handling complex real environmental samples. Using zooplankton as a model, we have successfully optimized the RPA-CRISPR/Cas12a fluorescence detection platform (RPA-Cas-FQ), providing several crucial "technical tips". Our findings indicate the sensitivity of CRISPR/Cas12a alone is 5 × 109 copies/reaction, which can be dramatically increased to 5 copies/reaction when combined with RPA. The optimized RPA-Cas-FQ enables reliable qualitative and semi-quantitative detection within 50 min, and exhibits a good linear relationship between fluorescence intensity and DNA concentration (R2 = 0.956-0.974***). Additionally, we developed a rapid and straightforward identification procedure for single zooplankton by incorporating heat-lysis and DNA-barcode techniques. We evaluated the platform's effectiveness using real environmental DNA (eDNA) samples from the Three Gorges Reservoir, confirming its practicality. The eDNA-RPA-Cas-FQ demonstrated strong consistency (Kappa = 0.43***) with eDNA-Metabarcoding in detecting species presence/absence in the reservoir. Furthermore, the two semi-quantitative eDNA technologies showed a strong positive correlation (R2 = 0.58-0.87***). This platform also has the potential to monitor environmental pollutants by selecting appropriate indicator species. The novel insights and methodologies presented in this study represent a significant advancement in meeting the complex needs of aquatic ecosystem protection and monitoring.

Harnessing the intragenomic variability of rRNA operons to improve differentiation of Vibrio species

Although the 16S rRNA gene is frequently used as a phylogenetic marker in analysis of environmental DNA, this marker often fails to distinguish closely related species, including those in the genus Vibrio. Here, we investigate whether inclusion and analysis of 23S rRNA sequence can help overcome the intrinsic weaknesses of 16S rRNA analyses for the differentiation of Vibrio species. We construct a maximum likelihood 16S rRNA gene tree to assess the use of this gene to identify clades of Vibrio species. Within the 16S rRNA tree, we identify the putative informative bases responsible for polyphyly, and demonstrate the association of these positions with tree topology. We demonstrate that concatenation of 16S and 23S rRNA genes increases the number of informative nucleotide positions, thereby overcoming ambiguities in 16S rRNA-based phylogenetic reconstructions. Finally, we experimentally demonstrate that this approach considerably improves the differentiation and identification of Vibrio species in environmental samples.

Revisiting the mercury cycle in marine sediments: A potential multifaceted role for Desulfobacterota

Marine sediments impacted by urban and industrial pollutants are typically exposed to reducing conditions and represent major reservoirs of toxic mercury species. Mercury methylation mediated by anaerobic microorganisms is favored under such conditions, yet little is known about potential microbial mechanisms for mercury detoxification. We used culture-independent (metagenomics, metabarcoding) and culture-dependent approaches in anoxic marine sediments to identify microbial indicators of mercury pollution and analyze the distribution of genes involved in mercury reduction (merA) and demethylation (merB). While none of the isolates featured merB genes, 52 isolates, predominantly affiliated with Gammaproteobacteria, were merA positive. In contrast, merA genes detected in metagenomes were assigned to different phyla, including Desulfobacterota, Actinomycetota, Gemmatimonadota, Nitrospirota, and Pseudomonadota. This indicates a widespread capacity for mercury reduction in anoxic sediment microbiomes. Notably, merA genes were predominately identified in Desulfobacterota, a phylum previously associated only with mercury methylation. Marker genes involved in the latter process (hgcAB) were also mainly assigned to Desulfobacterota, implying a potential central and multifaceted role of this phylum in the mercury cycle. Network analysis revealed that Desulfobacterota were associated with anaerobic fermenters, methanogens and sulfur-oxidizers, indicating potential interactions between key players of the carbon, sulfur and mercury cycling in anoxic marine sediments.

TICI: a taxon-independent community index for eDNA-based ecological health assessment

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n = 16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R2 = 0.82), and TICI variation between sample replicates was minimal (CV = 0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection

Environmental DNA sequencing is the gold standard to reveal microbial community structures. In most applications, a one-fragment PCR approach is applied to amplify a taxonomic marker gene, usually a hypervariable region of the 16S rRNA gene. We used a new reverse complement (RC)-PCR-based assay that amplifies seven out of the nine hypervariable regions of the 16S rRNA gene, to interrogate bacterial communities in sediment samples collected from different coastal marine sites with an impact gradient. In parallel, we employed a traditional one-fragment analysis of the hypervariable V3-V4 region to investigate whether the RC-PCR reveals more of the 'unseen' diversity obtained by the one-fragment approach. As a benchmark for the full deck of diversity, we subjected the samples to PCR-free metagenomic sequencing. None of the two PCR-based approaches recorded the full taxonomic repertoire obtained from the metagenomics datasets. However, the RC-PCR approach detected 2.8 times more bacterial genera compared to the near-saturation sequenced V3-V4 samples. RC-PCR is an ideal compromise between the standard one-fragment approach and metagenomics sequencing and may guide future environmental sequencing studies, in which bacterial diversity is a central subject.

Supervised machine learning improves general applicability of eDNA metabarcoding for reservoir health monitoring

Effective and standardized monitoring methodologies are vital for successful reservoir restoration and management. Environmental DNA (eDNA) metabarcoding sequencing offers a promising alternative for biomonitoring and can overcome many limitations of traditional morphological bioassessment. Recent attempts have even shown that supervised machine learning (SML) can directly infer biotic indices (BI) from eDNA metabarcoding data, bypassing the cumbersome calculation process of BI regardless of the taxonomic assignment of eDNA sequences. However, questions surrounding the general applicability of this taxonomy-free approach to monitoring reservoir health remain unclear, including model stability, feature selection, algorithm choice, and multi-season biomonitoring. Here, we firstly developed a novel biological integrity index (Me-IBI) that integrates multitrophic interactions and environmental information, based on taxonomy-assigned eDNA metabarcoding data. The Me-IBI can better distinguish the actual health status of the Three Gorges Reservoir (TGR) than physicochemical assessments and have a clear response to human activity. Then, taking this reliable Me-IBI as a supervised label, we compared the impact of selecting different numbers of features and SML algorithms on the stability and predictive performance of the model for predicting ecological conditions in multiple seasons using taxonomy-free eDNA metabarcoding data. We discovered that even with a small number of features, different SML algorithms can establish a stable model and obtain excellent predictive performance. Finally, we proposed a four-step strategy for standardized routine biomonitoring using SML tools. Our study firstly explores the general applicability problem of the taxonomy-free eDNA-SML approach and establishes a solid foundation for the large-scale and standardized biomonitoring application.

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.

Vertical organization of microbial communities in Salineta hypersaline wetland, Spain

Microbial communities inhabiting hypersaline wetlands, well adapted to the environmental fluctuations due to flooding and desiccation events, play a key role in the biogeochemical cycles, ensuring ecosystem service. To better understand the ecosystem functioning, we studied soil microbial communities of Salineta wetland (NE Spain) in dry and wet seasons in three different landscape stations representing situations characteristic of ephemeral saline lakes: S1 soil usually submerged, S2 soil intermittently flooded, and S3 soil with halophytes. Microbial community composition was determined according to different redox layers by 16S rRNA gene barcoding. We observed reversed redox gradient, negative at the surface and positive in depth, which was identified by PERMANOVA as the main factor explaining microbial distribution. The Pseudomonadota, Gemmatimonadota, Bacteroidota, Desulfobacterota, and Halobacteriota phyla were dominant in all stations. Linear discriminant analysis effect size (LEfSe) revealed that the upper soil surface layer was characterized by the predominance of operational taxonomic units (OTUs) affiliated to strictly or facultative anaerobic halophilic bacteria and archaea while the subsurface soil layer was dominated by an OTU affiliated to Roseibaca, an aerobic alkali-tolerant bacterium. In addition, the potential functional capabilities, inferred by PICRUSt2 analysis, involved in carbon, nitrogen, and sulfur cycles were similar in all samples, irrespective of the redox stratification, suggesting functional redundancy. Our findings show microbial community changes according to water flooding conditions, which represent useful information for biomonitoring and management of these wetlands whose extreme aridity and salinity conditions are exposed to irreversible changes due to human activities.

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.

Microbial diversity alteration reveals biomarkers of contamination in soil-river-lake continuum

Microbial communities inhabiting soil-water-sediment continuum in coastal areas provide important ecosystem services. Their adaptation in response to environmental stressors, particularly mitigating the impact of pollutants discharged from human activities, has been considered for the development of microbial biomonitoring tools, but their use is still in the infancy. Here, chemical and molecular (16S rRNA gene metabarcoding) approaches were combined in order to determine the impact of pollutants on microbial assemblages inhabiting the aquatic network of a soil-water-sediment continuum around the Ichkeul Lake (Tunisia), an area highly impacted by human activities. Samples were collected within the soil-river-lake continuum at three stations in dry (summer) and wet (winter) seasons. The contaminant pressure index (PI), which integrates Polycyclic aromatic hydrocarbons (PAHs), alkanes, Organochlorine pesticides (OCPs) and metal contents, and the microbial pressure index microgAMBI, based on bacterial community structure, showed significant correlation with contamination level and differences between seasons. The comparison of prokaryotic communities further revealed specific assemblages for soil, river and lake sediments. Correlation analyses identified potential "specialist" genera for the different compartments, whose abundances were correlated with the pollutant type found. Additionally, PICRUSt analysis revealed the metabolic potential for pollutant transformation or degradation of the identified "specialist" species, providing information to estimate the recovery capacity of the ecosystem. Such findings offer the possibility to define a relevant set of microbial indicators for assessing the effects of human activities on aquatic ecosystems. Microbial indicators, including the detection of "specialist" and sensitive taxa, and their functional capacity, might be useful, in combination with integrative microbial indices, to constitute accurate biomonitoring tools for the management and restoration of complex coastal aquatic systems.

Comparing sediment preservation methods for genomic biomonitoring of coastal marine ecosystems

To avoid loss of genetic information in environmental DNA (eDNA) field samples, the preservation of nucleic acids during field sampling is a critical step. In the development of standard operating procedures (SOPs) for eDNA-based compliance monitoring, the effect of different routinely used sediment preservations on biological community structures serving as bioindicators has gone untested. We compared eDNA metabarcoding results of marine bacterial communities from sample aliquots that were treated with a nucleic acid preservation solution (treated samples) and aliquots that were frozen without further treatment (non-treated samples). Sediment samples were obtained from coastal locations subjected to different stressors (aquaculture, urbanization, industry). DNA extraction efficiency, bacterial community profiles, and measures of alpha- and beta-diversity were highly congruent between treated and non-treated samples. As both preservation methods provide the same relevant information to environmental managers and regulators, we recommend the inclusion of both methods into SOPs for biomonitoring in marine coastal environments.

Grab what you can-an evaluation of spatial replication to decrease heterogeneity in sediment eDNA metabarcoding

Environmental DNA methods such as metabarcoding have been suggested as possible alternatives or complements to the current practice of morphology-based diversity assessment for characterizing benthic communities in marine sediment. However, the source volume used in sediment eDNA studies is several magnitudes lower than that used in morphological identification. Here, we used data from a North Sea benthic sampling station to investigate to what extent metabarcoding data is affected by sampling bias and spatial heterogeneity. Using three grab parallels, we sampled five separate sediment samples from each grab. We then made five DNA extraction replicates from each sediment sample. Each extract was amplified targeting both the 18S SSU rRNA V1–V2 region for total eukaryotic composition, and the cytochrome c oxidase subunit I (COI) gene for metazoans only. In both datasets, extract replicates from the same sediment sample were significantly more similar than different samples from the same grab. Further, samples from different grabs were less similar than those from the same grab for 18S. Interestingly, this was not true for COI metabarcoding, where the differences within the same grab were similar to the differences between grabs. We also investigated how much of the total identified richness could be covered by extract replicates, individual sediment samples and all sediment samples from a single grab, as well as the variability of Shannon diversity and, for COI, macrofaunal biotic indices indicating environmental status. These results were largely consistent with the beta diversity findings, and show that total eukaryotic diversity can be well represented using 18S metabarcoding with a manageable number of biological replicates. Based on these results, we strongly recommend the combination of different parts of the surface of single grabs for eDNA extraction as well as several grab replicates, or alternatively box cores or similar. This will dilute the effects of dominating species and increase the coverage of alpha diversity. COI-based metabarcoding consistency was found to be lower compared to 18S, but COI macrofauna-based indices were more consistent than direct COI alpha diversity measures.

Global Trends of Benthic Bacterial Diversity and Community Composition Along Organic Enrichment Gradients of Salmon Farms

The analysis of benthic bacterial community structure has emerged as a powerful alternative to traditional microscopy-based taxonomic approaches to monitor aquaculture disturbance in coastal environments. However, local bacterial diversity and community composition vary with season, biogeographic region, hydrology, sediment texture, and aquafarm-specific parameters. Therefore, without an understanding of the inherent variation contained within community complexes, bacterial diversity surveys conducted at individual farms, countries, or specific seasons may not be able to infer global universal pictures of bacterial community diversity and composition at different degrees of aquaculture disturbance. We have analyzed environmental DNA (eDNA) metabarcodes (V3-V4 region of the hypervariable SSU rRNA gene) of 138 samples of different farms located in different major salmon-producing countries. For these samples, we identified universal bacterial core taxa that indicate high, moderate, and low aquaculture impact, regardless of sampling season, sampled country, seafloor substrate type, or local farming and environmental conditions. We also discuss bacterial taxon groups that are specific for individual local conditions. We then link the metabolic properties of the identified bacterial taxon groups to benthic processes, which provides a better understanding of universal benthic ecosystem function(ing) of coastal aquaculture sites. Our results may further guide the continuing development of a practical and generic bacterial eDNA-based environmental monitoring approach.

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.