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1. A genomic catalog of Earth’s microbiomes

   https://doi.org/10.1038/s41587-020-0718-6  

   Nayfach, Stephen ; Roux, Simon ; Seshadri, Rekha ; Udwary, Daniel ; Varghese, Neha ; Schulz, Frederik ; Wu, Dongying ; Paez-Espino, David ; Chen, I-Min ; Huntemann, Marcel ; et al ( November 2020 , Nature Biotechnology)

   null (Ed.)

   Abstract The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes.
2. Random transposon mutagenesis identifies genes essential for transformation in Methanococcus maripaludis

   https://doi.org/10.1007/s00438-023-01994-7  

   Fonseca, Dallas R. ; Loppnow, Madison B. ; Day, Leslie A. ; Kelsey, Elisa L. ; Abdul Halim, Mohd Farid ; Costa, Kyle C. ( February 2023 , Molecular Genetics and Genomics)

   Natural transformation, the process whereby a cell acquires DNA directly from the environment, is an important driver of evolution in microbial populations, yet the mechanism of DNA uptake is only characterized in bacteria. To expand our understanding of natural transformation in archaea, we undertook a genetic approach to identify a catalog of genes necessary for transformation inMethanococcus maripaludis. Using an optimized method to generate random transposon mutants, we screened 6144 mutant strains for defects in natural transformation and identified 25 transformation-associated candidate genes. Among these are genes encoding components of the type IV-like pilus, transcription/translation associated genes, genes encoding putative membrane bound transport proteins, and genes of unknown function. Interestingly, similar genes were identified regardless of whether replicating or integrating plasmids were provided as a substrate for transformation. Using allelic replacement mutagenesis, we confirmed that several genes identified in these screens are essential for transformation. Finally, we identified a homolog of a membrane bound substrate transporter inMethanoculleus thermophilusand verified its importance for transformation using allelic replacement mutagenesis, suggesting a conserved mechanism for DNA transfer in multiple archaea. These data represent an initial characterization of the genes important for transformation which will inform efforts to understand gene flow in naturalmore »populations. Additionally, knowledge of the genes necessary for natural transformation may assist in identifying signatures of transformation machinery in archaeal genomes and aid the establishment of new model genetic systems for studying archaea.

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3. Nomenclature over 5 years in TaxonWorks: Approach, implementation, limitations and outcomes

   https://doi.org/10.3897/biss.5.75441  

   Yoder, Matthew ; Dmitriev, Dmitry ( September 2021 , Biodiversity Information Science and Standards)

   We are now over four decades into digitally managing the names of Earth's species. As the number of federating (i.e., software that brings together previously disparate projects under a common infrastructure, for example TaxonWorks) and aggregating (e.g., International Plant Name Index, Catalog of Life (CoL)) efforts increase, there remains an unmet need for both the migration forward of old data, and for the production of new, precise and comprehensive nomenclatural catalogs. Given this context, we provide an overview of how TaxonWorks seeks to contribute to this effort, and where it might evolve in the future. In TaxonWorks, when we talk about governed names and relationships, we mean it in the sense of existing international codes of nomenclature (e.g., the International Code of Zoological Nomenclature (ICZN)). More technically, nomenclature is defined as a set of objective assertions that describe the relationships between the names given to biological taxa and the rules that determine how those names are governed. It is critical to note that this is not the same thing as the relationship between a name and a biological entity, but rather nomenclature in TaxonWorks represents the details of the (governed) relationships between names. Rather than thinking of nomenclature as changingmore »(a verb commonly used to express frustration with biological nomenclature), it is useful to think of nomenclature as a set of data points, which grows over time. For example, when synonymy happens, we do not erase the past, but rather record a new context for the name(s) in question. The biological concept changes, but the nomenclature (names) simply keeps adding up. Behind the scenes, nomenclature in TaxonWorks is represented by a set of nodes and edges, i.e., a mathematical graph, or network (e.g., Fig. 1). Most names (i.e., nodes in the network) are what TaxonWorks calls "protonyms," monomial epithets that are used to construct, for example, bionomial names (not to be confused with "protonym" sensu the ICZN). Protonyms are linked to other protonyms via relationships defined in NOMEN, an ontology that encodes governed rules of nomenclature. Within the system, all data, nodes and edges, can be cited, i.e., linked to a source and therefore anchored in time and tied to authorship, and annotated with a variety of annotation types (e.g., notes, confidence levels, tags). The actual building of the graphs is greatly simplified by multiple user-interfaces that allow scientists to review (e.g. Fig. 2), create, filter, and add to (again, not "change") the nomenclatural history. As in any complex knowledge-representation model, there are outlying scenarios, or edge cases that emerge, making certain human tasks more complex than others. TaxonWorks is no exception, it has limitations in terms of what and how some things can be represented. While many complex representations are hidden by simplified user-interfaces, some, for example, the handling of the ICZN's Family-group name, batch-loading of invalid relationships, and comparative syncing against external resources need more work to simplify the processes presently required to meet catalogers' needs. The depth at which TaxonWorks can capture nomenclature is only really valuable if it can be used by others. This is facilitated by the application programming interface (API) serving its data (https://api.taxonworks.org), serving text files, and by exports to standards like the emerging Catalog of Life Data Package. With reference to real-world problems, we illustrate different ways in which the API can be used, for example, as integrated into spreadsheets, through the use of command line scripts, and serve in the generation of public-facing websites. Behind all this effort are an increasing number of people recording help videos, developing documentation, and troubleshooting software and technical issues. Major contributions have come from developers at many skill levels, from high school to senior software engineers, illustrating that TaxonWorks leads in enabling both technical and domain-based contributions. The health and growth of this community is a key factor in TaxonWork's potential long-term impact in the effort to unify the names of Earth's species.« less
4. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

   https://doi.org/10.1186/s40168-021-01213-8  

   Zhou, Zhichao ; Tran, Patricia Q. ; Breister, Adam M. ; Liu, Yang ; Kieft, Kristopher ; Cowley, Elise S. ; Karaoz, Ulas ; Anantharaman, Karthik ( February 2022 , Microbiome)

   Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.

   We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, ormore »single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.

   METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 athttps://github.com/AnantharamanLab/METABOLIC.

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5. A Genus Definition for Bacteria and Archaea Based on a Standard Genome Relatedness Index

   https://doi.org/10.1128/mBio.02475-19  

   Barco, R. A. ; Garrity, G. M. ; Scott, J. J. ; Amend, J. P. ; Nealson, K. H. ; Emerson, D. ; Giovannoni, Stephen J. ( February 2020 , mBio)

   ABSTRACT Genus assignment is fundamental in the characterization of microbes, yet there is currently no unambiguous way to demarcate genera solely using standard genomic relatedness indices. Here, we propose an approach to demarcate genera that relies on the combined use of the average nucleotide identity, genome alignment fraction, and the distinction between type- and non-type species. More than 3,500 genomes representing type strains of species from >850 genera of either bacterial or archaeal lineages were tested. Over 140 genera were analyzed in detail within the taxonomic context of order/family. Significant genomic differences between members of a genus and type species of other genera in the same order/family were conserved in 94% of the cases. Nearly 90% (92% if polyphyletic genera are excluded) of the type strains were classified in agreement with current taxonomy. The 448 type strains that need reclassification directly impact 33% of the genera analyzed in detail. The results provide a first line of evidence that the combination of genomic indices provides added resolution to effectively demarcate genera within the taxonomic framework that is currently based on the 16S rRNA gene. We also identify the emergence of natural breakpoints at the genome level that can further help inmore »the circumscription of taxa, increasing the proportion of directly impacted genera to at least 43% and pointing at inaccuracies on the use of the 16S rRNA gene as a taxonomic marker, despite its precision. Altogether, these results suggest that genomic coherence is an emergent property of genera in Bacteria and Archaea . IMPORTANCE In recent decades, the taxonomy of Bacteria and Archaea , and therefore genus designation, has been largely based on the use of a single ribosomal gene, the 16S rRNA gene, as a taxonomic marker. We propose an approach to delineate genera that excludes the direct use of the 16S rRNA gene and focuses on a standard genome relatedness index, the average nucleotide identity. Our findings are of importance to the microbiology community because the emergent properties of Bacteria and Archaea that are identified in this study will help assign genera with higher taxonomic resolution.« less