Chemotaxis shapes the microscale organization of the ocean's microbiome.

The capacity of planktonic marine microorganisms to actively seek out and exploit microscale chemical hotspots has been widely theorized to affect ocean-basin scale biogeochemistry1-3, but has never been examined comprehensively in situ among natural microbial communities. Here, using a field-based microfluidic platform to quantify the behavioural responses of marine bacteria and archaea, we observed significant levels of chemotaxis towards microscale hotspots of phytoplankton-derived dissolved organic matter (DOM) at a coastal field site across multiple deployments, spanning several months. Microscale metagenomics revealed that a wide diversity of marine prokaryotes, spanning 27 bacterial and 2 archaeal phyla, displayed chemotaxis towards microscale patches of DOM derived from ten globally distributed phytoplankton species. The distinct DOM composition of each phytoplankton species attracted phylogenetically and functionally discrete populations of bacteria and archaea, with 54% of chemotactic prokaryotes displaying highly specific responses to the DOM derived from only one or two phytoplankton species. Prokaryotes exhibiting chemotaxis towards phytoplankton-derived compounds were significantly enriched in the capacity to transport and metabolize specific phytoplankton-derived chemicals, and displayed enrichment in functions conducive to symbiotic relationships, including genes involved in the production of siderophores, B vitamins and growth-promoting hormones. Our findings demonstrate that the swimming behaviour of natural prokaryotic assemblages is governed by specific chemical cues, which dictate important biogeochemical transformation processes and the establishment of ecological interactions that structure the base of the marine food web.

CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning.

Advances in sequencing technologies and bioinformatics tools have dramatically increased the recovery rate of microbial genomes from metagenomic data. Assessing the quality of metagenome-assembled genomes (MAGs) is a critical step before downstream analysis. Here, we present CheckM2, an improved method of predicting genome quality of MAGs using machine learning. Using synthetic and experimental data, we demonstrate that CheckM2 outperforms existing tools in both accuracy and computational speed. In addition, CheckM2's database can be rapidly updated with new high-quality reference genomes, including taxa represented only by a single genome. We also show that CheckM2 accurately predicts genome quality for MAGs from novel lineages, even for those with reduced genome size (for example, Patescibacteria and the DPANN superphylum). CheckM2 provides accurate genome quality predictions across bacterial and archaeal lineages, giving increased confidence when inferring biological conclusions from MAGs.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy.

The Genome Taxonomy Database (GTDB; https://gtdb.ecogenomic.org) provides a phylogenetically consistent and rank normalized genome-based taxonomy for prokaryotic genomes sourced from the NCBI Assembly database. GTDB R06-RS202 spans 254 090 bacterial and 4316 archaeal genomes, a 270% increase since the introduction of the GTDB in November, 2017. These genomes are organized into 45 555 bacterial and 2339 archaeal species clusters which is a 200% increase since the integration of species clusters into the GTDB in June, 2019. Here, we explore prokaryotic diversity from the perspective of the GTDB and highlight the importance of metagenome-assembled genomes in expanding available genomic representation. We also discuss improvements to the GTDB website which allow tracking of taxonomic changes, easy assessment of genome assembly quality, and identification of genomes assembled from type material or used as species representatives. Methodological updates and policy changes made since the inception of the GTDB are then described along with the procedure used to update species clusters in the GTDB. We conclude with a discussion on the use of average nucleotide identities as a pragmatic approach for delineating prokaryotic species.

GTDB-Tk v2: memory friendly classification with the genome taxonomy database.

SUMMARY: The Genome Taxonomy Database (GTDB) and associated taxonomic classification toolkit (GTDB-Tk) have been widely adopted by the microbiology community. However, the growing size of the GTDB bacterial reference tree has resulted in GTDB-Tk requiring substantial amounts of memory (∼320 GB) which limits its adoption and ease of use. Here, we present an update to GTDB-Tk that uses a divide-and-conquer approach where user genomes are initially placed into a bacterial reference tree with family-level representatives followed by placement into an appropriate class-level subtree comprising species representatives. This substantially reduces the memory requirements of GTDB-Tk while having minimal impact on classification. AVAILABILITY AND IMPLEMENTATION: GTDB-Tk is implemented in Python and licenced under the GNU General Public Licence v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evidence for non-methanogenic metabolisms in globally distributed archaeal clades basal to the Methanomassiliicoccales.

Recent discoveries of mcr and mcr-like genes in genomes from diverse archaeal lineages suggest that methane metabolism is an ancient pathway with a complicated evolutionary history. One conventional view is that methanogenesis is an ancestral metabolism of the class Thermoplasmata. Through comparative genomic analysis of 12 Thermoplasmata metagenome-assembled genomes (MAGs) basal to the Methanomassiliicoccales, we show that these microorganisms do not encode the genes required for methanogenesis. Further analysis of 770 Ca. Thermoplasmatota genomes/MAGs found no evidence of mcrA homologues outside of the Methanomassiliicoccales. Together, these results suggest that methanogenesis was laterally acquired by an ancestor of the Methanomassiliicoccales. The 12 analysed MAGs include representatives from four orders basal to the Methanomassiliicoccales, including a high-quality MAG that likely represents a new order, Ca. Lunaplasma lacustris ord. nov. sp. nov. These MAGs are predicted to use diverse energy conservation pathways, including heterotrophy, sulfur and hydrogen metabolism, denitrification, and fermentation. Two lineages are widespread among anoxic, sedimentary environments, whereas Ca. Lunaplasma lacustris has thus far only been detected in alpine caves and subarctic lake sediments. These findings advance our understanding of the metabolic potential, ecology, and global distribution of the Thermoplasmata and provide insight into the evolutionary history of methanogenesis within the Ca. Thermoplasmatota.

AnnoTree: visualization and exploration of a functionally annotated microbial tree of life.

Bacterial genomics has revolutionized our understanding of the microbial tree of life; however, mapping and visualizing the distribution of functional traits across bacteria remains a challenge. Here, we introduce AnnoTree-an interactive, functionally annotated bacterial tree of life that integrates taxonomic, phylogenetic and functional annotation data from over 27 000 bacterial and 1500 archaeal genomes. AnnoTree enables visualization of millions of precomputed genome annotations across the bacterial and archaeal phylogenies, thereby allowing users to explore gene distributions as well as patterns of gene gain and loss in prokaryotes. Using AnnoTree, we examined the phylogenomic distributions of 28 311 gene/protein families, and measured their phylogenetic conservation, patchiness, and lineage-specificity within bacteria. Our analyses revealed widespread phylogenetic patchiness among bacterial gene families, reflecting the dynamic evolution of prokaryotic genomes. Genes involved in phage infection/defense, mobile elements, and antibiotic resistance dominated the list of most patchy traits, as well as numerous intriguing metabolic enzymes that appear to have undergone frequent horizontal transfer. We anticipate that AnnoTree will be a valuable resource for exploring prokaryotic gene histories, and will act as a catalyst for biological and evolutionary hypothesis generation. AnnoTree is freely available at http://annotree.uwaterloo.ca.

GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database.

SUMMARY: The GTDB Toolkit (GTDB-Tk) provides objective taxonomic assignments for bacterial and archaeal genomes based on the Genome Taxonomy Database (GTDB). GTDB-Tk is computationally efficient and able to classify thousands of draft genomes in parallel. Here we demonstrate the accuracy of the GTDB-Tk taxonomic assignments by evaluating its performance on a phylogenetically diverse set of 10,156 bacterial and archaeal metagenome-assembled genomes. AVAILABILITY: GTDB-Tk is implemented in Python and licensed under the GNU General Public License v3.0. Source code and documentation are available at: https://github.com/ecogenomics/gtdbtk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities.

The class Deltaproteobacteria comprises an ecologically and metabolically diverse group of bacteria best known for dissimilatory sulphate reduction and predatory behaviour. Although this lineage is the fourth described class of the phylum Proteobacteria, it rarely affiliates with other proteobacterial classes and is frequently not recovered as a monophyletic unit in phylogenetic analyses. Indeed, one branch of the class Deltaproteobacteria encompassing Bdellovibrio-like predators was recently reclassified into a separate proteobacterial class, the Oligoflexia. Here we systematically explore the phylogeny of taxa currently assigned to these classes using 120 conserved single-copy marker genes as well as rRNA genes. The overwhelming majority of markers reject the inclusion of the classes Deltaproteobacteria and Oligoflexia in the phylum Proteobacteria. Instead, the great majority of currently recognized members of the class Deltaproteobacteria are better classified into four novel phylum-level lineages. We propose the names Desulfobacterota phyl. nov. and Myxococcota phyl. nov. for two of these phyla, based on the oldest validly published names in each lineage, and retain the placeholder name SAR324 for the third phylum pending formal description of type material. Members of the class Oligoflexia represent a separate phylum for which we propose the name Bdellovibrionota phyl. nov. based on priority in the literature and general recognition of the genus Bdellovibrio. Desulfobacterota phyl. nov. includes the taxa previously classified in the phylum Thermodesulfobacteria, and these reclassifications imply that the ability of sulphate reduction was vertically inherited in the Thermodesulfobacteria rather than laterally acquired as previously inferred. Our analysis also indicates the independent acquisition of predatory behaviour in the phyla Myxococcota and Bdellovibrionota, which is consistent with their distinct modes of action. This work represents a stable reclassification of one of the most taxonomically challenging areas of the bacterial tree and provides a robust framework for future ecological and systematic studies.

CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes.

Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of "marker" genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.

First International Public Health Film Competition 2016-reflections on the development and use of competition judging criteria.

Background: Film competitions can be a helpful method to understand issues of quality in health films. In this paper, we describe the development and use of explicit quality criteria to identify the 'best' films for the first ever international public health film competition. Methods: A film selection committee encompassing a range of stakeholders was compiled. The committee drew up 10 explicit quality criteria to judge films drawing upon other film festival's selection criteria. These criteria were then applied to a broad range of health-related films entered into a film competition to select the 'best' film to screen. Results: Eighty-four films from 20 different countries were submitted to the public health film competition. The originality of the subject covered by the film, the public health importance of the issue and story-telling approach in the film were found to be the most discriminatory criteria to select films. Conclusion: Selection of health films for festivals can be undertaken using explicit quality criteria. There are a number of advantages to such an approach; however, explicit selection involves a large commitment of resources from film festival organizers and there is further research required to test the validity of the quality criteria applied to health-related films.

Comparative Genomics of Candidate Phylum TM6 Suggests That Parasitism Is Widespread and Ancestral in This Lineage.

Candidate phylum TM6 is a major bacterial lineage recognized through culture-independent rRNA surveys to be low abundance members in a wide range of habitats; however, they are poorly characterized due to a lack of pure culture representatives. Two recent genomic studies of TM6 bacteria revealed small genomes and limited gene repertoire, consistent with known or inferred dependence on eukaryotic hosts for their metabolic needs. Here, we obtained additional near-complete genomes of TM6 populations from agricultural soil and upflow anaerobic sludge blanket reactor metagenomes which, together with the two publicly available TM6 genomes, represent seven distinct family level lineages in the TM6 phylum. Genome-based phylogenetic analysis confirms that TM6 is an independent phylum level lineage in the bacterial domain, possibly affiliated with the Patescibacteria superphylum. All seven genomes are small (1.0-1.5 Mb) and lack complete biosynthetic pathways for various essential cellular building blocks including amino acids, lipids, and nucleotides. These and other features identified in the TM6 genomes such as a degenerated cell envelope, ATP/ADP translocases for parasitizing host ATP pools, and protein motifs to facilitate eukaryotic host interactions indicate that parasitism is widespread in this phylum. Phylogenetic analysis of ATP/ADP translocase genes suggests that the ancestral TM6 lineage was also parasitic. We propose the name Dependentiae (phyl. nov.) to reflect dependence of TM6 bacteria on host organisms.

STAMP: statistical analysis of taxonomic and functional profiles.

UNLABELLED: STAMP is a graphical software package that provides statistical hypothesis tests and exploratory plots for analysing taxonomic and functional profiles. It supports tests for comparing pairs of samples or samples organized into two or more treatment groups. Effect sizes and confidence intervals are provided to allow critical assessment of the biological relevancy of test results. A user-friendly graphical interface permits easy exploration of statistical results and generation of publication-quality plots. AVAILABILITY AND IMPLEMENTATION: STAMP is licensed under the GNU GPL. Python source code and binaries are available from our website at: http://kiwi.cs.dal.ca/Software/STAMP.

Associations between adjuvant endocrine therapy and onset of physical and emotional concerns among breast cancer survivors.

BACKGROUND: Breast cancer survivors often receive long-term adjuvant endocrine therapy (AET) to reduce recurrence risk. Adherence to AET is suboptimal, which may be due to the experience of symptoms and/or concerns. Few studies have comprehensively assessed self-reported concerns between those who currently, previously or have never received AET. The study objective is to describe self-reported physical and emotional concerns of breast cancer survivors who are current, prior, or never-recipients of AET. METHODS: Secondary analysis was performed on a subset of survey data collected in the 2010 LIVESTRONG Survey. Breast cancer survivors (n = 1,013, mean 5.4 years post-diagnosis) reported on 14 physical and eight emotional concerns that began after diagnosis and were experienced within 6 months of participation in the survey. Bivariate analyses examined the prevalence of each concern by AET status. The relationships between AET and burden of physical or emotional concerns were modeled with logistic regression. RESULTS: More than 50% of the participants reported currently experiencing cognitive issues, fatigue, fear of recurrence, emotional distress, and identity/grief issues. Thyroid dysfunction and stigma concerns were more common among participants with prior AET (p < 0.01), while fear of recurrence, emotional distress, and concern about appearance were more common among those currently receiving AET (p < 0.01). Fatigue, sexual dysfunction, and pain were more common among prior and current AET recipients (p < 0.01). In adjusted models, receipt of AET was associated with a higher number of physical, but not emotional concerns. A higher number of concerns was associated with younger age, having children, receipt of chemotherapy, longer duration of cancer treatment, and shorter time since diagnosis (p < 0.01). CONCLUSIONS: Breast cancer survivors who received AET were at risk of developing a variety of physical and emotional concerns, many of which persisted after treatment. These findings suggest the importance of developing individualized, supportive resources for breast cancer survivors.

Rapid identification of high-confidence taxonomic assignments for metagenomic data.

Determining the taxonomic lineage of DNA sequences is an important step in metagenomic analysis. Short DNA fragments from next-generation sequencing projects and microbes that lack close relatives in reference sequenced genome databases pose significant problems to taxonomic attribution methods. Our new classification algorithm, RITA (Rapid Identification of Taxonomic Assignments), uses the agreement between composition and homology to accurately classify sequences as short as 50 nt in length by assigning them to different classification groups with varying degrees of confidence. RITA is much faster than the hybrid PhymmBL approach when comparable homology search algorithms are used, and achieves slightly better accuracy than PhymmBL on an artificial metagenome. RITA can also incorporate prior knowledge about taxonomic distributions to increase the accuracy of assignments in data sets with varying degrees of taxonomic novelty, and classified sequences with higher precision than the current best rank-flexible classifier. The accuracy on short reads can be increased by exploiting paired-end information, if available, which we demonstrate on a recently published bovine rumen data set. Finally, we develop a variant of RITA that incorporates accelerated homology search techniques, and generate predictions on a set of human gut metagenomes that were previously assigned to different 'enterotypes'. RITA is freely available in Web server and standalone versions.

Putative genome contamination has minimal impact on the GTDB taxonomy.

The Genome Taxonomy Database (GTDB) provides a species to domain classification of publicly available genomes based on average nucleotide identity (ANI) (for species) and a concatenated gene phylogeny normalized by evolutionary rates (for genus to phylum), which has been widely adopted by the scientific community. Here, we use the Genome UNClutterer (GUNC) software to identify putatively contaminated genomes in GTDB release 07-RS207. We found that GUNC reported 35,723 genomes as putatively contaminated, comprising 11.25 % of the 317,542 genomes in GTDB release 07-RS207. To assess the impact of this high level of inferred contamination on the delineation of taxa, we created 'clean' versions of the 34,846 putatively contaminated bacterial genomes by removing the most contaminated half. For each clean half, we re-calculated the ANI and concatenated gene phylogeny and found that only 77 (0.22 %) of the genomes were not consistent with their original classification. We conclude that the delineation of taxa in GTDB is robust to the putative contamination detected by GUNC.

Measuring community similarity with phylogenetic networks.

Environmental drivers of biodiversity can be identified by relating patterns of community similarity to ecological factors. Community variation has traditionally been assessed by considering changes in species composition and more recently by incorporating phylogenetic information to account for the relative similarity of taxa. Here, we describe how an important class of measures including Bray-Curtis, Canberra, and UniFrac can be extended to allow community variation to be computed on a phylogenetic network. We focus on phylogenetic split systems, networks that are produced by the widely used median network and neighbor-net methods, which can represent incongruence in the evolutionary history of a set of taxa. Calculating ß diversity over a split system provides a measure of community similarity averaged over uncertainty or conflict in the available phylogenetic signal. Our freely available software, Network Diversity, provides 11 qualitative (presence-absence, unweighted) and 14 quantitative (weighted) network-based measures of community similarity that model different aspects of community richness and evenness. We demonstrate the broad applicability of network-based diversity approaches by applying them to three distinct data sets: pneumococcal isolates from distinct geographic regions, human mitochondrial DNA data from the Indonesian island of Nias, and proteorhodopsin sequences from the Sargasso and Mediterranean Seas. Our results show that major expected patterns of variation for these data sets are recovered using network-based measures, which indicates that these patterns are robust to phylogenetic uncertainty and conflict. Nonetheless, network-based measures of community similarity can differ substantially from measures ignoring phylogenetic relationships or from tree-based measures when incongruent signals are present in the underlying data. Network-based measures provide a methodology for assessing the robustness of ß-diversity results in light of incongruent phylogenetic signal and allow ß diversity to be calculated over widely used network structures such as median networks.

Proposal of names for 329 higher rank taxa defined in the Genome Taxonomy Database under two prokaryotic codes.

The Genome Taxonomy Database (GTDB) is a taxonomic framework that defines prokaryotic taxa as monophyletic groups in concatenated protein reference trees according to systematic criteria. This has resulted in a substantial number of changes to existing classifications (https://gtdb.ecogenomic.org). In the case of union of taxa, GTDB names were applied based on the priority of publication. The division of taxa or change in rank led to the formation of new Latin names above the rank of genus that were only made publicly available via the GTDB website without associated published taxonomic descriptions. This has sometimes led to confusion in the literature and databases. A number of the provisional GTDB names were later published in other studies, while many still lack authorships. To reduce further confusion, here we propose names and descriptions for 329 GTDB-defined prokaryotic taxa, 223 of which are suitable for validation under the International Code of Nomenclature of Prokaryotes (ICNP) and 49 under the Code of Nomenclature of Prokaryotes described from Sequence Data (SeqCode). For the latter, we designated 23 genomes as type material. An additional 57 taxa that do not currently satisfy the validation criteria of either code are proposed as Candidatus.

Greengenes2 unifies microbial data in a single reference tree.

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.