In the Arctic, phytoplankton blooms are recurring phenomena occurring during the spring-summer seasons and influenced by the strong polar seasonality. Bloom dynamics are affected by nutrient availability, especially nitrogen, which is the main limiting nutrient in the Arctic. This study aimed to investigate the changes in an Arctic microbial community using omics approaches during a phytoplankton bloom focusing on the nitrogen cycle. Using metagenomic and metatranscriptomic samples from the Dease Strait (Canada) from March to July (2014), we reconstructed 176 metagenome-assembled genomes. Bacteria dominated the microbial community, although archaea reached up to 25% of metagenomic abundance in early spring, when Nitrososphaeria archaea actively expressed genes associated with ammonia oxidation to nitrite (amt, amo, nirK). The resulting nitrite was presumably further oxidized to nitrate by a Nitrospinota bacterium that highly expressed a nitrite oxidoreductase gene (nxr). Since May, the constant increase in chlorophyll a indicated the occurrence of a phytoplankton bloom, promoting the successive proliferation of different groups of chemoorganotrophic bacteria (Bacteroidota, Alphaproteobacteria, Gammaproteobacteria). These bacteria showed different strategies to obtain nitrogen, whether it be from organic or inorganic sources, according to the expression patterns of genes encoding transporters for nitrogen compounds. In contrast, during summer, the chemolithotrophic organisms thriving during winter reduced their relative abundance and the expression of their catabolic genes. Based on our functional analysis, we see a transition from a community where nitrogen-based chemolitotrophy plays a relevant role to a chemoorganotrophic community based on the carbohydrates released during the phytoplankton bloom, where different groups seem to specialize in different nitrogen sources.IMPORTANCEThe Arctic is one of the environments most affected by anthropogenic climate change. It is expected that the rise in temperature and change in ice cover will impact the marine microbial communities and the associated biogeochemical cycles. In this regard, nitrogen is the main nutrient limiting Arctic phytoplankton blooms. In this study, we combine genetic and expression data to study the nitrogen cycle at the community level over a time series covering from March to July. Our results indicate the importance of different taxa (from archaea to bacteria) and processes (from chemolithoautotrophy to incorporation of different nitrogen sources) in the cycling of nitrogen during this period. This study provides a baseline for future research that should include additional methodologies like biogeochemical analysis to fully understand the changes occurring on these communities due to global change.

[This corrects the article DOI: 10.3389/fmicb.2024.1481702.].

It has been suggested that temperature and pH are strong explanatory causes of protein evolutionary constraint. However, a general understanding of how changes in temperature and pH may universally impact protein evolution is lacking. Moreover, while pH is locally regulated in the different microbial compartments (e.g. periplasm, cytoplasm), temperature is not. A priori , these variables should act at different rates over evolutionary timescales with respect to genome evolution, since there are genes encoding for proteins allocated in different compartments. To test whether this is true, we can use a proxy for the number of evolutionary events (e.g. mutations, duplications, etc.). One simple yet unregarded way is to calculate the diversity of unique sequences assigned to a biochemical function (KEGG) across species. Given similar diversity of species in particular environments, the expectation for any KEGG is to be represented by a similar number of sequences. We can give answer to the question comparing this metric between metastable communities with differential values of temperature and pH. In this work, we examine the sequence diversity among 17 metagenomes from El Tatio geothermal field (Chile), spanning temperatures of 45-62°C, and pH values of 7.2-9.3. We found that, when controlling for abundance, higher temperature (>56°C) increases, while basic pH (>8.1) decreases the expected by-function diversity. The inclusion of these variables improved the prediction of sequence diversity from metagenomic abundance. Between-compartment diversity was more affected by pH than by temperature. Protein length distributions and individual, well-known taxa from hydrothermal springs showed compatible trends. Overall, our results suggest that sequence diversity is universally governed by the signature buffering of the stable compartments to some of the outside-cell factors, besides the taxonomic trends or the particularities of any function. Altogether, we provide evidence against the temperature-induced gene loss hypotheses, and in favor of the confined evolution hypothesis. ### Competing Interest Statement The authors have declared no competing interest.

Ecological guilds quantify the incidence and extent of resource transformation functions, irrespective of the species involved. Therefore, tackling the microbial nitrogen guilds is key to our understanding of the oceanic nitrogen cycle, but quantitative estimates of guild contribution across varying depths and under specific environmental conditions have yet to be accomplished. In this study, we examine the main picoplankton guilds participating in nitrogen cycling within the low and mid-latitude ocean ecosystems, from the surface down to 4000 m, using data obtained from 75 samples belonging to 11 stations in the Malaspina dataset. In particular, we used a quantitative approach to investigate the stability of nitrogen acquisition and nitrogen-redox guilds separately. Our results showed that nitrogen acquisition guilds are more stable and redundant than nitrogen-redox guilds across depths and site specific conditions. For example, differential conditions such as nitrogen depletion and oxygen availability affected the two groups of guilds in different ways. These findings have implications for the understanding of global nitrogen fluxes and the biosphere's functional diversification.

The ecological role of microorganisms is of utmost importance due to their multiple interactions with the environment. However, assessing the contribution of individual taxonomic groups has proven difficult despite the availability of high throughput data, hindering our understanding of such complex systems. Here, we propose a quantitative definition of guild that is readily applicable to metagenomic data. Our framework focuses on the functional character of protein sequences, as well as their diversifying nature. First, we discriminate functional sequences from the whole sequence space corresponding to a gene annotation to then quantify their contribution to the guild composition across environments. In addition, we identify and distinguish functional implementations, which are sequence spaces that have different ways of carrying out the function. In contrast, we found that orthology delineation did not consistently align with ecologically (or functionally) distinct implementations of the function. We demonstrate the value of our approach with two case studies: the ammonia oxidation and polyamine uptake guilds from the Malaspina circumnavigation cruise, revealing novel ecological dynamics of the latter in marine ecosystems. Thus, the quantification of guilds helps us to assess the functional role of different taxonomic groups with profound implications on the study of microbial communities.

Microbial nitrogen guilds play crucial roles in the oceanic nitrogen cycle, but their composition and importance across specific depths and conditions remain unclear. In this study, we examine the guilds participating in nitrogen transformations within the low and mid-latitude ocean ecosystems, from the surface down to 4000 m, using data obtained from 75 samples belonging to 11 stations in the Malaspina (MP) dataset. In particular, we used a quantitative approach to investigate the stability of nitrogen acquisition and nitrogen-redox guilds separately. Our results showed that nitrogen acquisition guilds are more stable and redundant than nitrogen-redox guilds across depths and conditions related to specific water columns. However, differential conditions such as nitrogen depletion and oxygen availability affected the two groups of guilds in different ways. These findings have significant implications for the understanding of global nutrient fluxes and the biosphere’s functional diversification.### Competing Interest StatementThe authors have declared no competing interest.

The structure of microbial communities arises from a multitude of factors, including the interactions of microorganisms with each other and with the environment. In this work, we sought to disentangle those drivers by performing a cross-study, cross-biome meta-analysis of microbial occurrence data in more than 5000 samples, applying a novel network clustering algorithm aimed to capture conditional taxa co-occurrences. We then examined the phylogenetic and functional composition of the resulting clusters, and searched for global patterns of assembly both at the community level and in the presence/absence of individual metabolic pathways.Our analysis highlighted the prevalence of functional redundancy in microbial communities, particularly between taxa that co-occur in more than one environment, pointing to a relationship between functional redundancy and environmental adaptation. In spite of this, certain pathways were observed in fewer taxa than expected by chance, suggesting the presence of auxotrophy, and presumably cooperation among community members. This hypothetical cooperation may play a role in genome reduction, since we observed a negative relationship between the size of bacterial genomes and the size of the community they belong to.Overall, our results suggest the microbial community assembly is driven by universal principles that operate consistently across different biomes and taxonomic groups. A cross-biome meta-analysis of microbial occurrence data in more than 5000 samples suggest that microbial community assembly is driven by universal principles that operate consistently across different biomes and taxonomic groups.

A metagenomic time series from Arctic seawater was obtained from Dease Strait, to analyse the changes in bacterioplankton caused by the summer phytoplankton bloom. Bacterial clades specialized in the metabolism of polysaccharides, such as Bacteroidetes, became dominant along the bloom. These specialized taxa quickly displaced the microbial clades that dominate nutrient-poor waters during early spring, such as Archaea, Alpha-and Gammaproteobacteria. At the functional level, phyla Bacteroidetes, Planctomycetes, and Verrucomicrobia showed higher contents of polysaccharide-degradation functions. The Bacteroidetes community shifted toward species with higher polysaccharide-degrading capabilities, targeting algal polysaccharides in summer. Regarding transporters, Bacteroidetes dominated SusC-TonB transporters and had an exclusive family of glycoside-binding proteins (SusD). These proteins were used to identify polysaccharide-utilization loci that clustered transporters and polysaccharide-active enzymes, showing a higher level of specialization toward polysaccharide use. Altogether, these genomic features point to the genetic adaptations that promote the dominance of Bacteroidetes during phytoplankton blooms.

Microbial assemblages under sea ice of Dease Strait, Canadian Arctic, were sequenced for metagenomes and metatranscriptomes of the small size fraction (0.2 to 3 µm). The community from early March was typical for this season, with Alpha- and Gammaproteobacteria as the dominant taxa, followed by Thaumarchaeota and Bacteroidetes. Towards summer, Bacteroidetes and particularly the genus Polaribacter, became increasingly dominant, followed by the Gammaproteobacteria. Analysis of genes responsible for microbial acquisition of iron showed an abundance of ABC transporters for divalent cations and ferrous iron. The most abundant transporters, however, were the outer membrane TonB dependent transporters of iron-siderophore complexes. The abundance of iron acquisition genes suggested this element was essential for the microbial assemblage. Interestingly, Gammaproteobacteria were responsible for most of the siderophore synthesis genes. On the contrary, Bacteroidetes did not synthesize siderophores but accounted for most of the transporters, suggesting a role as cheaters in the competition for siderophores as public goods. Likely, this cheating ability of the Bacteroidetes contributed to their dominance in summer.

We present here a complete system for metagenomic analysis that allows performing the sequencing and analysis of a medium-size metagenome in less than one day. This unprecedented development was possible due to the conjunction of state-of-the art experimental and computational advances: a portable laboratory suitable for DNA extraction and sequencing with nanopore technology; the powerful metagenomic analysis pipeline SqueezeMeta, capable to provide a complete analysis in a few hours and using scarce computational resources; and tools for the automatic inspection of the results via a graphical user interface, that can be coupled to a web server to allow remote visualization of data (SQMtools and SQMxplore). We have tested the feasibility of our approach in the sequencing of the microbiota associated to volcanic rocks in La Palma, Canary Islands. Also, we did a two-day sampling campaign of marine waters in which the results obtained the first day guided the experimental design of the second day. This system offers the possibility of prospecting any habitat in a quick way, obtaining both taxonomic and functional information about the target microbiome.

Among the isoflavones and isoflavone-derived metabolites, equol, which in the human gut is synthesised from daidzein by minority bacterial populations, shows the strongest estrogenic and antioxidant activity. The beneficial effects on human health of isoflavone consumption might be partially or indeed totally attributable to this equol. Although some of the bacterial strains involved in its formation have been identified, the interplay between the composition and functionality of the gut microbiota and equol producer phenotype has hardly been studied. In this study, after shotgun metagenomic sequencing, different pipelines for the taxonomic and functional annotation of sequencing data were used in the search for similarities and differences in the faecal metagenome of equol-producing (n=3) and non-producing (n=2) women, with special focus on equol-producing taxa and their equol-associated genes. The taxonomic profiles of the samples differed significantly depending on the analytical method followed, although the microbial diversity detected by each tool was very similar at the phylum, genus and species levels. Equol-producing taxa were detected in both equol producers and non-producers, but no correlation between the abundance of equol-producing taxa and the equol producing/non-producing phenotype was found. Indeed, functional metagenomic analysis was unable to identify the genes involved in equol production, even in samples from equol producers. By aligning equol operons with the collected metagenomics data, a small number of reads mapping to equol-associated sequences were recognised in samples from both equol producers and equol non-producers, but only two reads mapping onto equol reductase-encoding genes in a sample from an equol producer. In conclusion, the taxonomic analysis of metagenomic data might not be suitable for detecting and quantifying equol-producing microbes in human faeces. Functional analysis of the data might provide an alternative. However, to detect the genetic makeup of the minority gut populations, more extensive sequencing than that achieved in the present study might be required.

Sequencing has been widely used to study the composition of the oral microbiome present in various health conditions. The extent of the coverage of the 16S rRNA gene primers employed for this purpose has not, however, been evaluated in silico using oral-specific databases. This paper analyses these primers using two databases containing 16S rRNA sequences from bacteria and archaea found in the human mouth and describes some of the best primers for each domain. A total of 369 distinct individual primers were identified from sequencing studies of the oral microbiome and other ecosystems. These were evaluated against a database reported in the literature of 16S rRNA sequences obtained from oral bacteria, which was modified by our group, and a self-created oral archaea database. Both databases contained the genomic variants detected for each included species. Primers were evaluated at the variant and species levels, and those with a species coverage (SC) ≥75.00

Microbial communities are complex and dynamic entities, and their structure arises from the interplay of a multitude of factors, including the interactions of microorganisms with each other and with the environment. Since each extant community has a unique eco-evolutionary history, it might appear that contingency rather than general rules govern their assembly. In spite of this, there is evidence that some general assembly principles exist, at least to a certain extent. In this work, we sought to identify those principles by performing a cross-study, cross-biome meta-analysis of microbial occurrence data in more than 5,000 samples from ten different environmental groups. We adopted a novel algorithm that allows the same taxa to aggregate with different partners in different habitats, capturing the complexity of interactions inherent to natural microbial communities. We tried to decouple function from phylogeny, the environment, and genome size, in order to provide an unbiased characterization of phylogenetic and functional redundancy in environmental microbial assemblages. We then examined the phylogenetic and functional composition of the resulting inferred communities, and searched for global patterns of assembly both at the community level and in individual metabolic pathways. Our analysis of the resulting microbial assemblages highlighted that environmental communities are more functionally redundant than expected by chance. This effect is greater for communities appearing in more than one environment, suggesting a link between functional redundancy and environmental adaptation. In spite of this, certain pathways are observed in fewer taxa than expected by chance, suggesting the presence of auxotrophy, and presumably cooperation among community members, which is supported by our analysis of amino acid biosynthesis pathways. Furthermore, this hypothetical cooperation may play a role in genome reduction, since we observed a negative relationship between the size of bacterial genomes and the number of taxa of the community they belong to. Overall, our results provide a global characterization of environmental microbial communities, and offer design principles for engineering robust bacterial communities.

SUMMARY:Advances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze 16S rDNA sequencing data. As these tools need to be tested, it is important to simulate datasets that resemble samples from different environments. Here, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce different 16S rDNA datasets from reference sequences, based on pragmatic ecological parameters. It creates sequence libraries for 'in silico' microbial communities with user-controlled richness, evenness, microdiversity and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools.AVAILABILITY AND IMPLEMENTATION:The source code of M&Ms is freely available at https://github.com/ggnatalia/MMs (GPL-3.0 License).SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Background Mitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions. Results The beta -diversity analyses suggested a subtle association between methane production and overall microbiota composition (0.01 < R-2 < 0.02). Differential abundance analysis identified 36 genera and 279 KEGGs as significantly associated with methane production (P-adj < 0.05). Those genera associated with high methane production were Eukaryota from Alveolata and Fungi clades, while Bacteria were associated with low methane emissions. The genus-level association network showed 2 clusters grouping Eukaryota and Bacteria, respectively. Regarding microbial gene functions, 41 KEGGs were found to be differentially abundant between low- and high-emission animals and were mainly involved in metabolic pathways. No KEGGs included in the methane metabolism pathway (ko00680) were detected as associated with high methane emissions. The KEGG network showed 3 clusters grouping KEGGs associated with high emissions, low emissions, and not differentially abundant in either. A deeper analysis of the differentially abundant KEGGs revealed that genes related with anaerobic respiration through nitrate degradation were more abundant in low-emission animals. Conclusions Methane emissions are largely associated with the relative abundance of ciliates and fungi. The role of nitrate electron acceptors can be particularly important because this respiration mechanism directly competes with methanogenesis. Whole metagenome sequencing is necessary to jointly consider the relative abundance of Bacteria, Archaea, and Eukaryota in the statistical analyses. Nutritional and genetic strategies to reduce CH4 emissions should focus on reducing the relative abundance of Alveolata and Fungi in the rumen. This experiment has generated the largest ONT ruminal metagenomic dataset currently available.

Water and soil contain a multiplicity of particulate material coated with bacterial populations and communities. Microbiotic particles are any type of small particle (measuring less than 2 mm) to which bacteria and other microbes attach, resulting in medium to long-term colonization. This study reviews the interactions of ecologically distant bacterial organisms on microbiotic particles in soil and water as a method for explaining the evolution and spread of antibiotic resistance traits. These particles include bacteria-bacteria aggregates, which can merge with particles from fungi, protozoa, phytoplankton, zooplankton, and biodetritus resulting from animal and vegetal decomposition, humus, mineral particles (clay, carbonates, silicates), and anthropogenic particles (including wastewater particles and microplastics). In turn, these complex particles can interact and coalesce. Natural phenomena (waterflow, tides, tsunamis, currents, and strong winds) and anthropogenic activity (agriculture, waste-water management, mining, excavation/construction) favor the interaction and merging of microbiotic particles in soil and water, resulting in enhanced recombinant communities capable of exchanging genetic material, including antimicrobial resistance genes, particularly in antimicrobial-polluted environments. In this review, we propose that the worldwide spread of antimicrobial resistance might be related to the environmental dynamics of microbiotic particles, and we discuss possible methods for reducing this problem that threatens One Health and Planetary Health.

El Chichón volcano is one of the most active volcanoes in Mexico. Previous studies have described its poly-extreme conditions and its bacterial composition, although the functional features of the complete microbiome have not been characterized yet. By using metabarcoding analysis, metagenomics, metabolomics and enzymology techniques, the microbiome of the crater lake was characterized in this study. New information is provided on the taxonomic and functional diversity of the representative Archaea phyla, Crenarchaeota and Euryarchaeota, as well as those that are representative of Bacteria, Thermotogales and Aquificae. With culture of microbial consortia and with the genetic information collected from the natural environment sampling, metabolic interactions were identified between prokaryotes, which can withstand multiple extreme conditions. The existence of a close relationship between the biogeochemical cycles of carbon and sulfur in an active volcano has been proposed, while the relationship in the energy metabolism of thermoacidophilic bacteria and archaea in this multi-extreme environment was biochemically revealed for the first time. These findings contribute towards understanding microbial metabolism under extreme conditions, and provide potential knowledge pertaining to "microbial dark matter", which can be applied to biotechnological processes and evolutionary studies.

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.

Bacterial organisms like surfaces. Water and soil contain a multiplicity of particulated material where bacterial populations and communities might attach. Microbiotic particles refers to any type of small particles (less than 2 mm) where bacteria (and other microbes) might attach, resulting in medium- long-term colonization. In this work, the interactions of bacterial organisms with microbiotic particles of the soil and water are reviewed. These particles include bacteria-bacteria aggregates, and aggregates with particles of fungi (particularly in the rhizosphere), protozoa, phytoplankton, zooplankton, biodetritus resulting from animal and vegetal decomposition, humus, mineral particles (clay, carbonates, silicates), and anthropogenic particles (including wastewater particles or microplastics). At they turn, these particles might interact and coalesce (as in the marine snow). Natural phenomena (from river flows to tides, tsunamis, currents, or heavy winds) and anthropogenic activity (such as agriculture, waste-water management, mining, soil-mass movement) favors interaction and merging between all these soil and water particles, and consequently coalescence of their bacterial-associated populations and communities, resulting in an enhancement of mixed-recombinant communities capable of genetic exchange, including antimicrobial resistance genes, particularly in antimicrobial-polluted environments. Particles also favor compartmentalization of bacterial populations favoring diversification and acquisition of mutational resistance by random drift. In general, microbial evolution is accelerated by the aggregation of microbiotic particles. We propose that the world spread of antimicrobial resistance might relate with the environmental dynamics of microbiotic particles, and discuss possible methods to reduce this problem influencing One Health and Planetary Health.

BACKGROUND:The dramatic decrease in sequencing costs over the last decade has boosted the adoption of high-throughput sequencing applications as a standard tool for the analysis of environmental microbial communities. Nowadays even small research groups can easily obtain raw sequencing data. After that, however, non-specialists are faced with the double challenge of choosing among an ever-increasing array of analysis methodologies, and navigating the vast amounts of results returned by these approaches.RESULTS:Here we present a workflow that relies on the SqueezeMeta software for the automated processing of raw reads into annotated contigs and reconstructed genomes (bins). A set of custom scripts seamlessly integrates the output into the anvi'o analysis platform, allowing filtering and visual exploration of the results. Furthermore, we provide a software package with utility functions to expose the SqueezeMeta results to the R analysis environment.CONCLUSIONS:Altogether, our workflow allows non-expert users to go from raw sequencing reads to custom plots with only a few powerful, flexible and well-documented commands.