The global genetic diversity of planktonic foraminifera reveals the structure of cryptic speciation in plankton.

The nature and extent of diversity in the plankton has fascinated scientists for over a century. Initially, the discovery of many new species in the remarkably uniform and unstructured pelagic environment appeared to challenge the concept of ecological niches. Later, it became obvious that only a fraction of plankton diversity had been formally described, because plankton assemblages are dominated by understudied eukaryotic lineages with small size that lack clearly distinguishable morphological features. The high diversity of the plankton has been confirmed by comprehensive metabarcoding surveys, but interpretation of the underlying molecular taxonomies is hindered by insufficient integration of genetic diversity with morphological taxonomy and ecological observations. Here we use planktonic foraminifera as a study model and reveal the full extent of their genetic diversity and investigate geographical and ecological patterns in their distribution. To this end, we assembled a global data set of ~7600 ribosomal DNA sequences obtained from morphologically characterised individual foraminifera, established a robust molecular taxonomic framework for the observed diversity, and used it to query a global metabarcoding data set covering ~1700 samples with ~2.48 billion reads. This allowed us to extract and assign 1 million reads, enabling characterisation of the structure of the genetic diversity of the group across ~1100 oceanic stations worldwide. Our sampling revealed the existence of, at most, 94 distinct molecular operational taxonomic units (MOTUs) at a level of divergence indicative of biological species. The genetic diversity only doubles the number of formally described species identified by morphological features. Furthermore, we observed that the allocation of genetic diversity to morphospecies is uneven. Only 16 morphospecies disguise evolutionarily significant genetic diversity, and the proportion of morphospecies that show genetic diversity increases poleward. Finally, we observe that MOTUs have a narrower geographic distribution than morphospecies and that in some cases the MOTUs belonging to the same morphospecies (cryptic species) have different environmental preferences. Overall, our analysis reveals that even in the light of global genetic sampling, planktonic foraminifera diversity is modest and finite. However, the extent and structure of the cryptic diversity reveals that genetic diversification is decoupled from morphological diversification, hinting at different mechanisms acting at different levels of divergence.

Microbes with higher metabolic independence are enriched in human gut microbiomes under stress.

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health versus IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

Mirusviruses link herpesviruses to giant viruses.

DNA viruses have a major influence on the ecology and evolution of cellular organisms1-4, but their overall diversity and evolutionary trajectories remain elusive5. Here we carried out a phylogeny-guided genome-resolved metagenomic survey of the sunlit oceans and discovered plankton-infecting relatives of herpesviruses that form a putative new phylum dubbed Mirusviricota. The virion morphogenesis module of this large monophyletic clade is typical of viruses from the realm Duplodnaviria6, with multiple components strongly indicating a common ancestry with animal-infecting Herpesvirales. Yet, a substantial fraction of mirusvirus genes, including hallmark transcription machinery genes missing in herpesviruses, are closely related homologues of giant eukaryotic DNA viruses from another viral realm, Varidnaviria. These remarkable chimaeric attributes connecting Mirusviricota to herpesviruses and giant eukaryotic viruses are supported by more than 100 environmental mirusvirus genomes, including a near-complete contiguous genome of 432 kilobases. Moreover, mirusviruses are among the most abundant and active eukaryotic viruses characterized in the sunlit oceans, encoding a diverse array of functions used during the infection of microbial eukaryotes from pole to pole. The prevalence, functional activity, diversification and atypical chimaeric attributes of mirusviruses point to a lasting role of Mirusviricota in the ecology of marine ecosystems and in the evolution of eukaryotic DNA viruses.

Unifying the known and unknown microbial coding sequence space.

Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we present a conceptual framework, its translation into the computational workflow AGNOSTOS and a demonstration on how we can bridge the known-unknown gap in genomes and metagenomes. By analyzing 415,971,742 genes predicted from 1749 metagenomes and 28,941 bacterial and archaeal genomes, we quantify the extent of the unknown fraction, its diversity, and its relevance across multiple organisms and environments. The unknown sequence space is exceptionally diverse, phylogenetically more conserved than the known fraction and predominantly taxonomically restricted at the species level. From the 71 M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria (also known as Candidate Phyla Radiation, CPR), which provides a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.

It is estimated that scientists do not know what half of microbial genes actually do. When these genes are discovered in microorganisms grown in the lab or found in environmental samples, it is not possible to identify what their roles are. Many of these genes are excluded from further analyses for these reasons, meaning that the study of microbial genes tends to be limited to genes that have already been described. These limitations hinder research into microbiology, because information from newly discovered genes cannot be integrated to better understand how these organisms work. Experiments to understand what role these genes have in the microorganisms are labor-intensive, so new analytical strategies are needed. To do this, Vanni et al. developed a new framework to categorize genes with unknown roles, and a computational workflow to integrate them into traditional analyses. When this approach was applied to over 400 million microbial genes (both with known and unknown roles), it showed that the share of genes with unknown functions is only about 30 per cent, smaller than previously thought. The analysis also showed that these genes are very diverse, revealing a huge space for future research and potential applications. Combining their approach with experimental data, Vanni et al. were able to identify a gene with a previously unknown purpose that could be involved in antibiotic resistance. This system could be useful for other scientists studying microorganisms to get a more complete view of microbial systems. In future, it may also be used to analyze the genetics of other organisms, such as plants and animals.

Functional repertoire convergence of distantly related eukaryotic plankton lineages abundant in the sunlit ocean.

Marine planktonic eukaryotes play critical roles in global biogeochemical cycles and climate. However, their poor representation in culture collections limits our understanding of the evolutionary history and genomic underpinnings of planktonic ecosystems. Here, we used 280 billion Tara Oceans metagenomic reads from polar, temperate, and tropical sunlit oceans to reconstruct and manually curate more than 700 abundant and widespread eukaryotic environmental genomes ranging from 10 Mbp to 1.3 Gbp. This genomic resource covers a wide range of poorly characterized eukaryotic lineages that complement long-standing contributions from culture collections while better representing plankton in the upper layer of the oceans. We performed the first, to our knowledge, comprehensive genome-wide functional classification of abundant unicellular eukaryotic plankton, revealing four major groups connecting distantly related lineages. Neither trophic modes of plankton nor its vertical evolutionary history could completely explain the functional repertoire convergence of major eukaryotic lineages that coexisted within oceanic currents for millions of years.

Accurate Annotation of Microbial Metagenomic Genes and Identification of Core Sets.

In the past decade, metagenomics studies of microbial communities have added billions of sequences to the databases. This extensive amount of data and information has the potential to widen our understanding of the functioning of microbial communities and their roles in the environment. A fundamental step in this process is the functional and taxonomic profiling of the metagenomes, through an accurate gene annotation. This gene-level information can then be placed in the genomic context of metagenome-assembled genomes. Then, on a broader level, we can place this combined data into the context of a pangenome and start characterizing core and accessory gene sets. In this chapter, we provide a workflow to create an annotated gene catalog and to identify core sets of genes in the context of a pangenome. The first section will focus on the methods to provide metagenomic genes with accurate annotations. The second part will describe how to combine the gene catalog information with metagenome-assembled genomes and how to use both to build and investigate a pangenome.

Molecular recognition of the beta-glucans laminarin and pustulan by a SusD-like glycan-binding protein of a marine Bacteroidetes.

Marine bacteria catabolize carbohydrate polymers of algae, which synthesize these structurally diverse molecules in ocean surface waters. Although algal glycans are an abundant carbon and energy source in the ocean, the molecular details that enable specific recognition between algal glycans and bacterial degraders remain largely unknown. Here we characterized a surface protein, GMSusD from the planktonic Bacteroidetes-Gramella sp. MAR_2010_102 that thrives during algal blooms. Our biochemical and structural analyses show that GMSusD binds glucose polysaccharides such as branched laminarin and linear pustulan. The 1.8 Å crystal structure of GMSusD indicates that three tryptophan residues form the putative glycan-binding site. Mutagenesis studies confirmed that these residues are crucial for laminarin recognition. We queried metagenomes of global surface water datasets for the occurrence of SusD-like proteins and found sequences with the three structurally conserved residues in different locations in the ocean. The molecular selectivity of GMSusD underscores that specific interactions are required for laminarin recognition. In conclusion, our findings provide insight into the molecular details of ß-glucan binding by GMSusD and our bioinformatic analysis reveals that this molecular interaction may contribute to glucan cycling in the surface ocean.

Correction: Pyrosequencing Unveils Cystic Fibrosis Lung Microbiome Differences Associated with a Severe Lung Function Decline.

[This corrects the article DOI: 10.1371/journal.pone.0156807.].

Pyrosequencing Unveils Cystic Fibrosis Lung Microbiome Differences Associated with a Severe Lung Function Decline.

Chronic airway infection is a hallmark feature of cystic fibrosis (CF) disease. In the present study, sputum samples from CF patients were collected and characterized by 16S rRNA gene-targeted approach, to assess how lung microbiota composition changes following a severe decline in lung function. In particular, we compared the airway microbiota of two groups of patients with CF, i.e. patients with a substantial decline in their lung function (SD) and patients with a stable lung function (S). The two groups showed a different bacterial composition, with SD patients reporting a more heterogeneous community than the S ones. Pseudomonas was the dominant genus in both S and SD patients followed by Staphylococcus and Prevotella. Other than the classical CF pathogens and the most commonly identified non-classical genera in CF, we found the presence of the unusual anaerobic genus Sneathia. Moreover, the oligotyping analysis revealed the presence of other minor genera described in CF, highlighting the polymicrobial nature of CF infection. Finally, the analysis of correlation and anti-correlation networks showed the presence of antagonism and ecological independence between members of Pseudomonas genus and the rest of CF airways microbiota, with S patients showing a more interconnected community in S patients than in SD ones. This population structure suggests a higher resilience of S microbiota with respect to SD, which in turn may hinder the potential adverse impact of aggressive pathogens (e.g. Pseudomonas). In conclusion, our findings shed a new light on CF airway microbiota ecology, improving current knowledge about its composition and polymicrobial interactions in patients with CF.