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.publishedVersio

Surprising prokaryotic and eukaryotic diversity, community structure and biogeography of Ethiopian soda lakes

Soda lakes are intriguing ecosystems harboring extremely productive microbial communities in spite of their extreme environmental conditions. This makes them valuable model systems for studying the connection between community structure and abiotic parameters such as pH and salinity. For the first time, we apply high-throughput sequencing to accurately estimate phylogenetic richness and composition in five soda lakes, located in the Ethiopian Rift Valley. The lakes were selected for their contrasting pH, salinities and stratification and several depths or spatial positions were covered in each lake. DNA was extracted and analyzed from all lakes at various depths and RNA extracted from two of the lakes, analyzed using both amplicon- and shotgun sequencing. We reveal a surprisingly high biodiversity in all of the studied lakes, similar to that of freshwater lakes. Interestingly, diversity appeared uncorrelated or positively correlated to pH and salinity, with the most “extreme” lakes showing the highest richness. Together, pH, dissolved oxygen, sodium- and potassium concentration explained approximately 30% of the compositional variation between samples. A diversity of prokaryotic and eukaryotic taxa could be identified, including several putatively involved in carbon-, sulfur- or nitrogen cycling. Key processes like methane oxidation, ammonia oxidation and ‘nitrifier denitrification’ were also confirmed by mRNA transcript analyses

Correlating microbial community profiles with geochemical data in highly stratified sediments from the Arctic Mid-Ocean Ridge

Microbial communities and their associated metabolic activity in marine sediments have a profound impact on global biogeochemical cycles. Their composition and structure are attributed to geochemical and physical factors, but finding direct correlations has remained a challenge. Here we show a significant statistical relationship between variation in geochemical composition and prokaryotic community structure within deep-sea sediments. We obtained comprehensive geochemical data from two gravity cores near the hydrothermal vent field Loki’s Castle at the Arctic Mid-Ocean Ridge, in the Norwegian- Greenland Sea. Geochemical properties in the rift valley sediments exhibited strong centimeter-scale stratigraphic variability. Microbial populations were profiled by pyrosequencing from 15 sediment horizons (59,364 16S rRNA gene tags), quantitatively assessed by qPCR, and phylogenetically analyzed. Although the same taxa were generally present in all samples, their relative abundances varied substantially among horizons and fluctuated between Bacteria- and Archaea-dominated communities. By independently summarizing covariance structures of the relative abundance data and geochemical data, using principal components analysis, we found a significant correlation between changes in geochemical composition and changes in community structure. Differences in organic carbon and mineralogy shaped the relative abundance of microbial taxa. We used correlations to build hypotheses about energy metabolisms, particularly of the Deep Sea Archaeal Group, specific Deltaproteobacteria, and sediment lineages of potentially anaerobic Marine Group I Archaea. We demonstrate that total prokaryotic community structure can be directly correlated to geochemistry within these sediments, thus enhancing our understanding of biogeochemical cycling and our ability to predict metabolisms of uncultured microbes in deep-sea sediments

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 (Martinez-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

Marine invasive alien species in Europe: 9 years after the IAS Regulation

Biological invasions, resulting from human activities, exert substantial impacts on ecosystems worldwide. This review focuses on marine invasive alien species (IAS) in Europe, examining the current state, proposing strategies to address the problem, and offering recommendations for enhanced management. Effective management of biological invasions relies on accessible, accurate data to inform decision-making. Information systems such as the European Alien Species Information Network (EASIN), Aquatic Non-Indigenous and Cryptogenic Species (AquaNIS), and World Register of Introduced Marine Species (WriMS) provide comprehensive databases on IAS, but their sustainability requires long-term maintenance, continuous updates, and support. Most countries lack specific monitoring programs for marine IAS, and standardization and improvement of monitoring methods are needed. Port monitoring plays a vital role in the early detection of new arrivals, and recent advancements in molecular techniques show promise for effective IAS monitoring. Risk screening tools are commonly employed to rank taxa based on their invasiveness potential in European regions, but variations in protocols can yield inconsistent results. European impact assessments highlight resource competition, novel habitat creation, and predation as primary mechanisms for negative impacts on biodiversity, while the creation of novel habitats represents a key mechanism for positive impacts. Preventing IAS introductions is critical, and measures such as ballast water treatment systems are implemented to reduce the likelihood of marine introductions. However, understanding introduction pathways remains uncertain for many IAS. Eradication and control efforts for marine IAS have limited success, emphasizing the need for enhanced biosecurity measures. Climate change, especially ocean warming, can intensify IAS impacts on native species and ecosystems. In climate change hotspots, some tropical aliens may, however, compensate for the loss of thermally sensitive natives with similar traits. Therefore, it is imperative to consider the interactions between climate change and IAS in developing effective management and conservation strategies. Enhancing IAS management in Europe entails i) securing adequate funding, ii) expanding the list of IAS of Union Concern to adequately cover marine invasions, iii) learning from countries with successful biosecurity practices, iv) sustaining information systems, v) improving monitoring and early warning systems with innovative technologies, vi) enhancing prediction models, vii) conducting integrated impact assessments and mapping cumulative IAS impacts, and vii) considering the potential benefits of IAS in ecosystem functioning and services

New tools and recommendations for a better management of harmful algal blooms under the European Marine Strategy Framework Directive

23 pages, 2 figures, supplementary material https://www.frontiersin.org/articles/10.3389/.2023.1298800/full#supplementary-materialMarine harmful algal blooms (HABs), caused by various aquatic microalgae, pose significant risks to ecosystems, some socio-economic activities and human health. Traditionally managed as a public health issue through reactive control measures such as beach closures, seafood trade bans or closure of mollusc production areas, the multifaceted linkages of HABs with environmental and socio-economic factors require more comprehensive ecosystem-based management approach tools to support policies. This study promotes a coordinated understanding and implementation of HAB assessment and management under the Marine Strategy Framework Directive (MSFD), targeting the achievement of Good Environmental Status (GES) in European marine waters. We introduce two novel tools: GES4HABs (GES for HABs) decision tree, and MAMBO (environMental mAtrix for the Management of BlOoms), a decision support matrix. These tools aim to streamline HABs reporting and prioritize resource allocation and management interventions. The GES4HABs decision tree defines a sequence of decision steps to identify HAB management strategies according to their state (evaluated against predefined baselines) and causes (anthropic or natural). MAMBO is proposed to address different HABs and their interaction with human and environmental pressures. The matrix utilizes two axes: natural trophic status and level of human influence, capturing major aspects such as nutrient supply. While acknowledging the limitations of this simplified framework, MAMBO categorizes marine regions into quadrants of varying management viability. Regions with high human influence and eutrophic conditions are identified as most suitable for effective management intervention, whereas regions with minimal or mixed human influence are deemed less amenable to active management. In addition, we explore and describe various indicators, monitoring methods and initiatives that may be relevant to support assessments of HAB status and associated pressures and impacts in the MSFD reporting. Finally, we provide some recommendations to promote the consideration of HABs in ecosystem-based management strategies, intensify efforts for harmonizing and defining best practices of analysis, monitoring and assessment methodologies, and foster international and cross-sectoral coordination to optimize resources, efforts and rolesThis manuscript is a result of the joint activity of two projects funded by the European Union, under the Horizon Europe program: GES4SEAS (Achieving Good Environmental Status for maintaining ecosystem services, by assessing integrated impacts of cumulative pressures; grant agreement no. 101059877; www.ges4seas.eu) and ACTNOW (Advancing understanding of cumulative impacts on European marine biodiversity, ecosystem functions and services for human wellbeing, grant agreement No. 101060072). JF was financially supported by the Slovenian Research and Innovation Agency (research core funding no. P1-0237). This work by EG, NS, AR, and JC acknowledges the ‘Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S) funded by AEI 10.13039/501100011033 to the Institut de Ciencies del Mar, CSICPeer reviewe

Analysis of sequencing data in environmental genomics. Exploring the diversity of the microbial biosphere

Most life on this planet is microbial and for the last two decades, environmental genomics has contributed to reveal an impressive biodiversity of this microbial life. This approach applies DNA sequencing to environmental samples, with the significant advantage of not relying on cell cultures, since only a minority of microorganisms are easily cultured in the laboratory. This thesis deals primarily with analysis of microbial diversity based on community profiling. This variant of environmental genomics targets defined marker genes to study the structure of microbial communities. The use of the small subunit ribosomal RNA as a phylogenetic marker is discussed and evaluated, with emphasis on taxonomic classification, estimation of diversity and comparison of community structure between samples. Thanks to improved sequencing technologies, community profiling is an increasingly powerful and cost-efficient technique. Like all methodologies it has limitations and sources of random- and systematic errors, many of which remain poorly understood. In relation to this, a number of recommendations and novel analysis methods are developed and provided. These are subsequently applied to study environmental communities, targeting issues like the “rare biosphere” concept, and variation of community structure across space and environmental gradients. Taxonomic classification is the process of placing environmental sequences in context of previously studied organisms. Thus, ecologically meaningful information such as putative metabolic functions can be derived. In Paper I, a set of resources for taxonomic classification is provided and evaluated. The performance of the resulting framework, CREST (Classification Resources for Environmental Sequence Tags), is shown to compare favourably to existing methods. It also provides a manually curated taxonomy and functionality for comparing composition across datasets. In Paper II, a hydrothermal vent-associated microbial mat community is studied, using a set of different environmental genomics methods. Based on this study, several important sources of bias and reproducibility of community profiling are evaluated and discussed. The results highlight the importance of applying complementary methods. They also illustrate the influence of primer choice, PCR bias and whether RNA or DNA is targeted. Random variation, or noise, is another important factor to consider in community profiling studies. Papers III and IV, examines the effect of such noise from PCR amplification and pyrosequencing. Currently, this is the most common sequencing method applied to environmental samples. The results of Paper III demonstrate that early community profiling studies using pyrosequencing have significantly overestimated the extent of biodiversity, because of noise. To compensate for such noise in amplicon sequence datasets, the program AmpliconNoise was developed. Using “mock communities”, a mix of clones with known sequences, the performance of AmpliconNoise is demonstrated and compared to alternative methods. Analyses of diversity in the microbial mat community studied in Paper II utilise AmpliconNoise. Resulting estimates are compared to previous findings, from similar environments. In addition to biodiversity per se, the underlying diversity structures of communities and the mechanisms shaping them, remain important but poorly understood issues in microbial ecology. Because of their many useful characteristics, alkaline soda lakes are used as model ecosystem to study several such issues, in Paper V. Results reveal that these extreme environments harbour surprisingly high microbial diversity. Interestingly, the most alkaline and saline lakes studied also appear to be the most diverse. Further, it is shown that pH, oxygen level, and sodium- and potassium concentrations can explain 30% of the compositional variance between the lakes studied. The existence of organisms endemic to individual lakes is also indicated. Although soda lakes are relatively uncommon environments, this study provides an example of how fundamental biogeographical questions can be targeted using a careful choice of experimental design and analysis methodology. The results call into question several established notions such as extreme environments generally being less diverse and that few prokaryotic organisms are endemic. Hopefully the findings will inspire future studies, exploring these relationships further. In summary, the work presented here illustrates the importance of evaluating and optimising the methodology used in environmental genomics, particularly for amplicon sequencing, taxonomic classification, and estimation of phylogenetic diversity. It is likely that methodological limitations have biassed and slowed down data analysis and interpretation of important ecological issues like the rare biosphere and microbial biogeography

Keystone species of coastal ecosystems from the Bay of Biscay

Estuarine and coastal ecosystems play a fundamental role in human activities. Since millions of people depend on the numerous resources they offer (i.e., fisheries, transportation, and recreational activities), these ecosystems are also exposed to a high anthropogenic pressure. In order to better monitor and regulate such pressures, it is critical to improve our understanding of the functioning of these ecosystems. This is especially important for microorganisms, considering, for instance, their important roles in biogeochemical cycles. In this study we investigate estuarine and coastal microbial communities along the coast of the Basque Country using eDNA metabarcoding of bacterioplankton and microbenthos samples along a time series, from locations that present different degrees of disturbance. Metabarcoding of the 16S and 18S rRNA gene was carried out, and the OTU and taxa tables generated were used to reconstruct ecological networks, representing potential biological interactions. These ecological networks were used to identify on one hand the species that play a major role in the maintenance of the whole community structure (keystones), and on the other, complex network modules affected by environmental impacts. Here, we define keystone taxa as those that presented the highest ratio between the degree of connectivity (number of associations they established within the network) and their relative abundance. The underlying aim of this study is to develop novel “bioindicators” based on taxa that are both sensitive to impacts and important for community structure. Results of two seawater communities, one from offshore waters and the other from the coast, showed similar bacterial composition at family level, dominated by Flavobacteraceae and Rhodobacteraceae. Nevertheless, their ecological network properties differed strongly: for a similar number of taxa that established associations, the conectance was similar in both communities, but the number of associations in the coastal community was twice as high as for the offshore one, resulting in a higher modularity. Identified keystone taxa were taxonomically different between both communities: those from the coastal community belonged to Protobacteria, Bacteroidetes, Firmicutes, and Fusobacteria phyla, while the keystones from the offshore community belonged to Proteobacteria, Actinobacteria, Chloroflexi, Thaumarchaeota, PAUC34f and Verrucomicrobia. Moreover, we identified “connector” taxa that presented the highest values of betweenness centrality. These taxa, without having a high degree of connectivity, may be important for the interaction structure because they connect modules, i.e., highly connected subnetworks within the whole network. Further, hierarchical clustering was performed to identify seasonal trends and to better understand the associations retrieved. Taxa with different seasonal preference often grouped together in the same modules, which indicates that modularity was not caused primarily by seasonality

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.publishedVersio

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