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Previous studies have deciphered the genomic basis of host‐symbiont metabolic complementarity in vestimentiferans, bathymodioline mussels, vesicomyid clams and Alviniconcha snails, yet little is known about the chemosynthetic symbiosis in Thyasiridae—a family of Bivalvia regarded as an excellent model in chemosymbiosis research due to their wide distribution in both deep‐sea and shallow‐water habitats. We report the first circular thyasirid symbiont genome, named Candidatus Ruthturnera sp. Tsphm01, with a size of 1.53 Mb, 1521 coding genes and 100% completeness. Compared to its free‐living relatives, Ca. Ruthturnera sp. Tsphm01 genome is reduced, lacking components for chemotaxis, citric acid cycle and de novo biosynthesis of small molecules (e.g. amino acids and cofactors), indicating it is likely an obligate intracellular symbiont. Nevertheless, the symbiont retains complete genomic components of sulphur oxidation and assimilation of inorganic carbon, and these systems were highly and actively expressed. Moreover, the symbiont appears well‐adapted to anoxic environment, including capable of anaerobic respiration (i.e. reductions of DMSO and nitrate) and possession of a low oxygen‐adapted type of cytochrome c oxidase. Analysis of the host transcriptome revealed its metabolic complementarity to the incomplete metabolic pathways of the symbiont and the acquisition of nutrients from the symbiont via phagocytosis and exosome. By providing the first complete genome of reduced size in a thyasirid symbiont, this study enhances our understanding of the diversity of symbiosis that has enabled bivalves to thrive in chemosynthetic habitats. The resources will be widely used in phylogenetic, geographic and evolutionary studies of chemosynthetic bacteria and bivalves. [ABSTRACT FROM AUTHOR]

Ciliates are a large group of ubiquitous and highly diverse single‐celled eukaryotes that play an essential role in the functioning of microbial food webs. However, their genomic diversity is far from clear due to the need to develop cultivation methods for most species, so most research is based on wild organisms that almost invariably contain contaminants. Here we establish an integrated Genome Decontamination Pipeline (iGDP) that combines homology search, telomere reads‐assisted and clustering approaches to filter contaminated ciliate genome assemblies from wild specimens. We benchmarked the performance of iGDP using genomic data from a contaminated ciliate culture and the results showed that iGDP could recall 91.9% of the target sequences with 96.9% precision. We also used a synthetic dataset to offer guidelines for the application of iGDP in the removal of various groups of contaminants. Compared with several popular metagenome binning tools, iGDP could show better performance. To further validate the effectiveness of iGDP on real‐world data, we applied it to decontaminate genome assemblies of three wild ciliate specimens and obtained their genomes with high quality comparable to that of previously well‐studied model ciliate genomes. It is anticipated that the newly generated genomes and the established iGDP method will be valuable community resources for detailed studies on ciliate biodiversity, phylogeny, ecology and evolution. The pipeline (https://github.com/GWang2022/iGDP) can be implemented automatically to reduce manual filtering and classification and may be further developed to apply to other microeukaryotes. [ABSTRACT FROM AUTHOR]

Summary: Thermoplasmata is a widely distributed and ecologically important archaeal class in the phylum Euryarchaeota. Because few cultures and genomes are available, uncharacterized Thermoplasmata metabolisms remain unexplored. In this study, we obtained four medium‐ to high‐quality archaeal metagenome‐assembled genomes (MAGs) from the filamentous fragments of black‐odorous aquatic sediments (Foshan, Guangdong, China). Based on their 16S rRNA gene and ribosomal protein phylogenies, the four MAGs belong to the previously unnamed Thermoplasmata UBA10834 clade. We propose that this clade (five reference genomes from the Genome Taxonomy Database (GTDB) and four MAGs from this study) be considered a new order, Candidatus Gimiplasmatales. Metabolic pathway reconstructions indicated that the Ca. Gimiplasmatales MAGs can biosynthesize isoprenoids and nucleotides de novo. Additionally, some taxa have genes for formaldehyde and acetate assimilation, and the Wood–Ljungdahl CO2‐fixation pathway, indicating a mixotrophic lifestyle. Sulfur reduction, hydrogen metabolism, and arsenic detoxification pathways were predicted, indicating sulfur‐, hydrogen‐, and arsenic‐transformation potentials. Comparative genomics indicated that the H4F Wood–Ljungdahl pathway of both Ca. Gimiplasmatales and Methanomassiliicoccales was likely obtained by the interdomain lateral gene transfer from the Firmicutes. Collectively, this study elucidates the taxonomic and potential metabolic diversity of the new order Ca. Gimiplasmatales and the evolution of this subgroup and its sister lineage Methanomassiliicoccales. [ABSTRACT FROM AUTHOR]

Periodontitis is an inflammatory disease involving complex interactions between oral microorganisms and the host immune response. Understanding the structure of the microbiota community associated with periodontitis is essential for improving classifications and diagnoses of various types of periodontal diseases and will facilitate clinical decision-making. In this study, we used a 16S rRNA metagenomics approach to investigate and compare the compositions of the microbiota communities from 76 subgingival plagues samples, including 26 from healthy individuals and 50 from patients with periodontitis. Furthermore, we propose a novel feature selection algorithm for selecting features with more information from many variables with a combination of these features and machine learning methods were used to construct prediction models for predicting the health status of patients with periodontal disease. We identified a total of 12 phyla, 124 genera, and 355 species and observed differences between health- and periodontitis-associated bacterial communities at all phylogenetic levels. We discovered that the genera Porphyromonas, Treponema, Tannerella, Filifactor, and Aggregatibacter were more abundant in patients with periodontal disease, whereas Streptococcus, Haemophilus, Capnocytophaga, Gemella, Campylobacter, and Granulicatella were found at higher levels in healthy controls. Using our feature selection algorithm, random forests performed better in terms of predictive power than other methods and consumed the least amount of computational time. [ABSTRACT FROM AUTHOR]