Increasing marine trophic web knowledge through DNA analyses of fish stomach content: a step towards an ecosystem-based approach to fisheries research.

Multispecies and ecosystem models, which are key for the implementation of ecosystem-based approaches to fisheries management, require extensive data on the trophic interactions between marine organisms, including changes over time. DNA metabarcoding, by allowing the simultaneous taxonomic identification of the community present in hundreds of samples, could be used for speeding up large-scale stomach content data collection. Yet, for DNA metabarcoding to be routinely implemented, technical challenges should be addressed, such as the potentially complicated sampling logistics, the detection of a high proportion of predator DNA, and the inability to provide reliable abundance estimations. Here, we present a DNA metabarcoding assay developed to examine the diet of five commercially important fish, which can be feasibly incorporated into routinary samplings. The method is devised to speed up the analysis process by avoiding the stomach dissection and content extraction steps, while preventing the amplification of predator DNA by using blocking primers. Tested in mock samples and in real stomach samples, the method has proven effective and shows great effectiveness discerning diet variations due to predator ecology or prey availability. Additionally, by applying our protocol to mackerel stomachs previously analyzed by visual inspection, we showcase how DNA metabarcoding could complement visually based data by detecting overlooked prey by the visual approach. We finally discuss how DNA metabarcoding-based data can contribute to trophic data collection. Our work reinforces the potential of DNA metabarcoding for the study and monitoring of fish trophic interactions and provides a basis for its incorporation into routine monitoring programs, which will be critical for the implementation of ecosystem-based approaches to fisheries management.

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.

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.

Shared and contrasting associations in the dynamic nano- and picoplankton communities of two close but contrasting sites from the Bay of Biscay.

Pico- and nanoplankton are key players in the marine ecosystems due to their implication in the biogeochemical cycles, nutrient recycling and the pelagic food webs. However, the specific dynamics and niches of most bacterial, archaeal and eukaryotic plankton remain unknown, as well as the interactions between them. Better characterization of these is critical for understanding and predicting ecosystem functioning under anthropogenic pressures. We used environmental DNA metabarcoding across a 6-year time series to explore the structure and seasonality of pico- and nanoplankton communities in two sites of the Bay of Biscay, one coastal and one offshore, and construct association networks to reveal potential keystone and connector taxa. Temporal trends in alpha diversity were similar between the two sites, and concurrent communities more similar than within the same site at different times. However, we found differences between the network topologies of the two sites, with both shared and site-specific keystones and connectors. For example, Micromonas, with lower abundance in the offshore site is a keystone here, indicating a stronger effect of associations such as resource competition. This study provides an example of how time series and association network analysis can reveal how similar communities may function differently despite being geographically close.

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.

Cropping systems impact changes in soil fungal, but not prokaryote, alpha-diversity and community composition stability over a growing season in a long-term field trial.

Crop harvest followed by a fallow period can act as a disturbance on soil microbial communities. Cropping systems intended to improve alpha-diversity of communities may also confer increased compositional stability during succeeding growing seasons. Over a single growing season in a long-term (18 year) agricultural field experiment incorporating conventional (CON), conservation (CA), organic (ORG) and integrated (INT) cropping systems, temporal changes in prokaryote, fungal and arbuscular mycorrhizal fungi (AMF) communities were investigated overwinter, during crop growth and at harvest. While certain prokaryote phyla were influenced by cropping system (e.g. Acidobacteria), the community as a whole was primarily driven by temporal changes over the growing season as distinct overwinter and crop-associated communities, with the same trend observed regardless of cropping system. Species-rich prokaryote communities were most stable over the growing season. Cropping system exerted a greater effect on fungal communities, with alpha-diversity highest and temporal changes most stable under CA. CON was particularly detrimental for alpha-diversity in AMF communities, with AMF alpha-diversity and stability improved under all other cropping systems. Practices that promoted alpha-diversity tended to also increase the similarity and temporal stability of soil fungal (and AMF) communities during a growing season, while prokaryote communities were largely insensitive to management.

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.

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.

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

Routine monitoring of benthic biodiversity is critical for managing and understanding the anthropogenic impacts on marine, transitional and freshwater ecosystems. However, traditional reliance on morphological identification generally makes it cost-prohibitive to increase the scale of monitoring programmes. Metabarcoding of environmental DNA has clear potential to overcome many of the problems associated with traditional monitoring, with prokaryotes and other microorganisms showing particular promise as bioindicators. However, due to the limited knowledge regarding the ecological roles and responses of environmental microorganisms to different types of pressure, the use of de novo approaches is necessary. Here, we use two such approaches for the prediction of multiple impacts present in estuaries and coastal areas of the Bay of Biscay based on microbial communities. The first (Random Forests) is a machine learning method while the second (Threshold Indicator Taxa Analysis and quantile regression splines) is based on de novo identification of bioindicators. Our results show that both methods overlap considerably in the indicator taxa identified, but less for sequence variants. Both methods also perform well in spite of the complexity of the studied ecosystem, providing predictive models with strong correlation to reference values and fair to good agreement with ecological status groups. The ability to predict several specific types of pressure is especially appealing. The cross-validated models and biotic indices developed can be directly applied to predict the environmental status of estuaries in the same geographical region, although more work is needed to evaluate and improve them for use in new regions or habitats.

Planctomycetes as a Vital Constituent of the Microbial Communities Inhabiting Different Layers of the Meromictic Lake Sælenvannet (Norway).

Meromictic lakes are permanently stratified lakes that display steep gradients in salinity, oxygen and sulphur compounds tightly linked to bacterial community structure and diversity. Lake Sælenvannet is a meromictic lake located south of Bergen, Norway. The 26 m deep lake is connected to the open sea and permanently stratified into two layers separated by a chemocline. The upper water layer is brackish with major input from water runoff from the surroundings. The bottom layer consists of old saline water with low or no oxygen concentrations. Bacteria from phylum Planctomycetes are reported to be ubiquitous in lake environments. They are involved in the degradation of complex carbon sources in aquatic environments and are also linked to anaerobic processes such as fermentation and sulphur reduction. To study Planctomycete distribution along a chemical gradient, we sampled the water column throughout Lake Sælenvannet in 2012 and profiled the microbial community using 16S rRNA amplicon sequencing (metabarcoding) with 454 pyrosequencing. Planctomycete-related 16S rRNA gene sequences were found to be present both in the oxic and anoxic parts of the lake and showed an uneven distribution throughout the water column, with the highest relative abundance of 10% found in the saline anoxic layer at 15 m depth. In a follow-up study in 2014, samples from eight different depths were collected for enrichment and isolation of novel Planctomycetes. This study resulted in successful isolation in pure culture of 10 isolates affiliated to four different genera from the family Planctomycetaceae. One strain closely related to Blastopirellula cremea was isolated from 9 m depth, and two novel strains affiliated to the genera Stieleria and Gimesia were isolated at 7 and 9 m depths, respectively. Furthermore, seven isolates with identical 16S rRNA gene sequences were retrieved from seven different depths which varied greatly in salinity and chemical composition. These isolates likely represent a new species affiliated to Rubinisphaera. The adaptation of this novel Planctomycete to water depths spanning the entire chemical gradient could indicate a high phenotypic plasticity and/or a very efficient survival strategy. Overall, our results show the presence of a diverse group of Planctomycetes in Lake Sælenvannet, with a strong potential for novel adaptations to chemical stress factors.

Evaluation of a eukaryote phylogenetic microarray for environmental monitoring of marine sediments.

Increased exploitation of resources in sensitive marine ecosystems emphasizes the importance of knowledge regarding ecological impacts. However, current bio-monitoring practices are limited in terms of target-organisms and temporal resolution. Hence, developing new technologies is vital for enhanced ecosystem understanding. In this study, we have applied a prototype version of a phylogenetic microarray to assess the eukaryote community structures of marine sediments from an area with ongoing oil and gas drilling activity. The results were compared with data from both sequencing (metabarcoding) and morphology-based monitoring to evaluate whether microarrays were capable of detecting ecosystem disturbances. A significant correlation between microarray data and chemical pollution indicators, as well as sequencing-based results, was demonstrated, and several potential indicator organisms for pollution-associated parameters were identified, among them a large fraction of microorganisms not covered by traditional morphology-based monitoring. This suggests that microarrays have a potential in future environmental monitoring.

Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data.

MOTIVATION: Technological advances in meta-transcriptomics have enabled a deeper understanding of the structure and function of microbial communities. 'Total RNA' meta-transcriptomics, sequencing of total reverse transcribed RNA, provides a unique opportunity to investigate both the structure and function of active microbial communities from all three domains of life simultaneously. A major step of this approach is the reconstruction of full-length taxonomic marker genes such as the small subunit ribosomal RNA. However, current tools for this purpose are mainly targeted towards analysis of amplicon and metagenomic data and thus lack the ability to handle the massive and complex datasets typically resulting from total RNA experiments. RESULTS: In this work, we introduce MetaRib, a new tool for reconstructing ribosomal gene sequences from total RNA meta-transcriptomic data. MetaRib is based on the popular rRNA assembly program EMIRGE, together with several improvements. We address the challenge posed by large complex datasets by integrating sub-assembly, dereplication and mapping in an iterative approach, with additional post-processing steps. We applied the method to both simulated and real-world datasets. Our results show that MetaRib can deal with larger datasets and recover more rRNA genes, which achieve around 60 times speedup and higher F1 score compared to EMIRGE in simulated datasets. In the real-world dataset, it shows similar trends but recovers more contigs compared with a previous analysis based on random sub-sampling, while enabling the comparison of individual contig abundances across samples for the first time. AVAILABILITY AND IMPLEMENTATION: The source code of MetaRib is freely available at https://github.com/yxxue/MetaRib. CONTACT: yaxin.xue@uib.no or Inge.Jonassen@uib.no. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Total RNA sequencing reveals multilevel microbial community changes and functional responses to wood ash application in agricultural and forest soil.

Recycling of wood ash from energy production may counteract soil acidification and return essential nutrients to soils. However, wood ash amendment affects soil physicochemical parameters that control composition and functional expression of the soil microbial community. Here, we applied total RNA sequencing to simultaneously assess the impact of wood ash amendment on the active soil microbial communities and the expression of functional genes from all microbial taxa. Wood ash significantly affected the taxonomic (rRNA) as well as functional (mRNA) profiles of both agricultural and forest soil. Increase in pH, electrical conductivity, dissolved organic carbon and phosphate were the most important physicochemical drivers for the observed changes. Wood ash amendment increased the relative abundance of the copiotrophic groups Chitinonophagaceae (Bacteroidetes) and Rhizobiales (Alphaproteobacteria) and resulted in higher expression of genes involved in metabolism and cell growth. Finally, total RNA sequencing allowed us to show that some groups of bacterial feeding protozoa increased concomitantly to the enhanced bacterial growth, which shows their pivotal role in the regulation of bacterial abundance in soil.

To assemble or not to resemble-A validated Comparative Metatranscriptomics Workflow (CoMW).

BACKGROUND: Metatranscriptomics has been used widely for investigation and quantification of microbial communities' activity in response to external stimuli. By assessing the genes expressed, metatranscriptomics provides an understanding of the interactions between different major functional guilds and the environment. Here, we present a de novo assembly-based Comparative Metatranscriptomics Workflow (CoMW) implemented in a modular, reproducible structure. Metatranscriptomics typically uses short sequence reads, which can either be directly aligned to external reference databases ("assembly-free approach") or first assembled into contigs before alignment ("assembly-based approach"). We also compare CoMW (assembly-based implementation) with an assembly-free alternative workflow, using simulated and real-world metatranscriptomes from Arctic and temperate terrestrial environments. We evaluate their accuracy in precision and recall using generic and specialized hierarchical protein databases. RESULTS: CoMW provided significantly fewer false-positive results, resulting in more precise identification and quantification of functional genes in metatranscriptomes. Using the comprehensive database M5nr, the assembly-based approach identified genes with only 0.6% false-positive results at thresholds ranging from inclusive to stringent compared with the assembly-free approach, which yielded up to 15% false-positive results. Using specialized databases (carbohydrate-active enzyme and nitrogen cycle), the assembly-based approach identified and quantified genes with 3-5 times fewer false-positive results. We also evaluated the impact of both approaches on real-world datasets. CONCLUSIONS: We present an open source de novo assembly-based CoMW. Our benchmarking findings support assembling short reads into contigs before alignment to a reference database because this provides higher precision and minimizes false-positive results.

The microbiota of technosols resembles that of a nearby forest soil three years after their establishment.

Technosols can be used to rehabilitate degraded land and reuse wastes. Ideally, these newly formed soils should also fulfil the main soil functions. In this study, initially, we characterized the physicochemical and microbial properties of different formulations and their ingredients (i.e., dirt from a waste recovery plant, recycled bentonite, sewage sludge). When these technosols were then used for the rehabilitation of a quarry, the evolution of such properties was monitored for three consecutive years. Physicochemical and microbial properties were compared to those of a reference soil from a nearby forest. Diversity and composition of prokaryotes and eukaryotes were determined using 16S and 18S rRNA amplicon sequencing. Three years after establishment, as much as 78.8% and 63.9% of the prokaryotic and eukaryotic orders, respectively, were shared between the technosols and the reference forest soil. Although technosols initially showed lower values of CO2 emission, compaction and functional diversity (Biolog EcoPlates™), at the end of the study these values were similar to those observed in the reference forest soil. It was concluded that the microbiota of the studied technosols resembles that of the nearby forest soil after just three years of establishment.

Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring.

Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement of the burdensome morphological identification, to screen known morphologically distinguishable bioindicator taxa. While prokaryotic and eukaryotic microbial diversity is of key importance in ecosystem functioning, its implementation in biomonitoring programs is still largely unappreciated, mainly because of difficulties in identifying microbes and limited knowledge of their ecological functions. Here, we argue that the combination of massive environmental genomics microbial data with machine learning algorithms can be extremely powerful for biomonitoring programs and pave the way to fill important gaps in our understanding of microbial ecology.

Links between data on chemical and biological quality parameters in wastewater-impacted river sediment and water samples.

In many urban catchments, the discharge of effluents from wastewater treatment plants (WWTPs), as well as untreated wastewaters (UWWs), presents a major challenge for the maintenance of river sediment and water quality. The discharge of these effluents cannot only increase the concentration of metals, nutrients and organic compounds in fluvial ecosystems, but also alter the abundance, structure and function of river bacterial communities. Here, we present data on chemical and biological quality parameters in wastewater-impacted and non-impacted river surface sediment and water samples. Overall, the concentration of nutrients (inorganic nitrogen) and some heavy metals (Zn, Ni and Cr) was positively correlated with the nirS/16S rRNA ratio, while nirK- and nosZ-denitrifier populations were negatively affected by the presence of ammonium in sediments. Bacterial community structure was significantly correlated with the (i) combined influence of nutrient and metal concentrations, (ii) the contamination level (non-impacted vs. impacted sites), (iii) type of contamination (WWTP or UWW), and (iv) location of the sampling sites. Moreover, the higher abundance of five genera of the family Rhodocyclaceae detected in wastewater-impacted sites is also likely to be an effect of effluent discharge. The data presented here complement a broader study (Martínez-Santos et al., 2018) [1] and they are particularly useful for those interested in understanding the impact of wastewater effluents on the abundance, structure and function of river bacterial communities involved in nitrogen cycling.

Treated and untreated wastewater effluents alter river sediment bacterial communities involved in nitrogen and sulphur cycling.

Studying the dynamics of nitrogen and sulphur cycling bacteria in river surface sediments is essential to better understand their contribution to global biogeochemical cycles. Evaporitic rocks settled at the headwater of the Deba River catchment (northern Spain) lead to high values of sulphate concentration in its waters. Besides, the discharge of effluents from untreated and treated residual (urban and industrial) wastewaters increases the concentration of metals, nutrients and organic compounds in its mid- and low-water courses. The aim of this study was to assess the impact of anthropogenic contamination from untreated and treated residual and industrial wastewaters on the structure and function of bacterial communities present in surface sediments of the Deba River catchment. The application of a quantitative functional approach (qPCR) based on denitrification genes (nir: nirS+nirK; and nosZ), together with a 16S rRNA gene metabarcoding structural analysis, revealed (i) the high relevance of the sulphur cycle at headwater surface sediments (as reflected by the abundance of members of the Syntrophobacterales order, and the Sulfuricurvum and Thiobacillus genera) and (ii) the predominance of sulphide-driven autotrophic denitrification over heterotrophic denitrification. Incomplete heterotrophic denitrification appeared to be predominant in surface sediments strongly impacted by treated and untreated effluents, as reflected by the lower values of the nosZ/nir ratio, thus favouring N2O emissions. Understanding nitrogen and sulphur cycling pathways has profound implications for the management of river ecosystems, since this knowledge can help us determine whether a specific river is acting or not as a source of greenhouse gases (i.e., N2O).

Diversity patterns and isolation of Planctomycetes associated with metalliferous deposits from hydrothermal vent fields along the Valu Fa Ridge (SW Pacific).

The microbial diversity associated with diffuse venting deep-sea hydrothermal deposits is tightly coupled to the geochemistry of the hydrothermal fluids. Previous 16S rRNA gene amplicon sequencing (metabarcoding) of marine iron-hydroxide deposits along the Arctic Mid Ocean Ridge, revealed the presence of diverse bacterial communities associated with these deposits (Storesund and Øvreås in Antonie van Leeuwenhoek 104:569-584, 2013). One of the most abundant and diverse phyla detected was the enigmatic Planctomycetes. Here we report on the comparative analyses of the diversity and distribution patterns of Planctomycetes associated with metalliferous deposits from two diffuse-flow hydrothermal vent fields (Mariner and Vai Lili) from the Valu Fa Ridge in the Southwestern Pacific. Metabarcoding of 16S rRNA genes showed that the major prokaryotic phyla were Proteobacteria (51-73% of all 16S rRNA gene reads), Epsilonbacteraeota (0.5-19%), Bacteriodetes (5-17%), Planctomycetes (0.4-11%), Candidatus Latescibacteria (0-5%) and Marine Benthic Group E (Hydrothermarchaeota) (0-5%). The two different sampling sites differed considerably in overall community composition. The abundance of Planctomycetes also varied substantially between the samples and the sites, with the majority of the sequences affiliated with uncultivated members of the classes Planctomycetacia and Phycisphaerae, and other deep branching lineages. Seven different strains affiliated with the order Planctomycetales were isolated, mostly from the Vai Lili samples, where also the highest Planctomycetales diversity was seen. Most of the isolates were affiliated with the genera Gimesia, Rhodopirellula and Blastopirellula. One isolate was only distantly related to known cultured, but uncharacterized species within the Pir4 group. This study shows that the deep-sea Planctomycetes represent a very heterogeneous group with a high phylogenetic diversity and a substantial potential for novel organism discovery in these deep ocean environments.