A genome and gene catalog of the aquatic microbiomes of the Tibetan Plateau.

The Tibetan Plateau supplies water to nearly 2 billion people in Asia, but climate change poses threats to its aquatic microbial resources. Here, we construct the Tibetan Plateau Microbial Catalog by sequencing 498 metagenomes from six water ecosystems (saline lakes, freshwater lakes, rivers, hot springs, wetlands and glaciers). Our catalog expands knowledge of regional genomic diversity by presenting 32,355 metagenome-assembled genomes that de-replicated into 10,723 representative genome-based species, of which 88% were unannotated. The catalog contains nearly 300 million non-redundant gene clusters, of which 15% novel, and 73,864 biosynthetic gene clusters, of which 50% novel, thus expanding known functional diversity. Using these data, we investigate the Tibetan Plateau aquatic microbiome's biogeography along a distance of 2,500 km and >5 km in altitude. Microbial compositional similarity and the shared gene count with the Tibetan Plateau microbiome decline along with distance and altitude difference, suggesting a dispersal pattern. The Tibetan Plateau Microbial Catalog stands as a substantial repository for high-altitude aquatic microbiome resources, providing potential for discovering novel lineages and functions, and bridging knowledge gaps in microbiome biogeography.

Isolation, characterization and genomic analysis of the novel Arthrobacter sp. phage SWEP2.

A new virulent phage, SWEP2, infecting the Arthrobacter sp. 5B strain, was isolated from black soil samples in northeastern China. SWEP2 has a latent period of 80 min and a burst size of 45 PFU (evaluated at an MOI of 0.1). Genomic analysis revealed that the 43,398-bp dsDNA genome of phage SWEP2 contains 64 open reading frames (ORFs) and one tRNA gene. Phylogenetic analysis indicated a close relationship between SWEP2 and Arthrobacter phage Liebe, with 82.98% identity and a query coverage of 48%. Based on its distinct phenotypic and genetic characteristics, SWEP2 is identified as a novel Arthrobacter phage.

Microbial biogeochemical cycling reveals the sustainability of the rice-crayfish co-culture model.

Aquaculture has great potential in nourishing the global growing population, while such staggering yields are coupled with environmental pollution. Rice-crayfish co-culture models (RCFP) have been widely adopted in China due to their eco-friendliness. However, little is known about RCFP's microbiome pattern, which hinders our understanding of its sustainability. This study has conducted metagenomic analysis across aquaculture models and habitats, which revealed aquaculture model-specific biogeochemical cycling pattern (e.g., nitrogen (N), sulfur (S), and carbon (C)): RCFP is advantageous in N-assimilation, N-contamination, and S-pollutants removal, while non-RCFP features N denitrification process and higher S metabolism ability, producing several hazardous pollutants in non-RCFP (e.g., nitric oxide, nitrogen monoxide, and sulfide). Moreover, RCFP has greater capacity for carbohydrate enzyme metabolism compared with non-RCFP in environmental habitats, but not in crayfish gut. Collectively, RCFP plays an indispensable role in balancing aquaculture productivity and environmental protection, which might be applied to the blue transformation of aquaculture.

Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics.

Microbiota residing on the urban transit systems (UTSs) can be shared by travelers and have niche-specific assemblage. However, it remains unclear how the assemblages are influenced by city characteristics, rendering city-specific and microbial-aware urban planning challenging. Here, we analyzed 3,359 UTS microbial samples collected from 16 cities around the world. We found the stochastic process dominated in all UTS microbiota assemblages, with the explanation rate (R2) of the neutral community model (NCM) higher than 0.7. Moreover, city characteristics predominantly drove such assemblage, largely responsible for the variation in the stochasticity ratio (50.1%). Furthermore, by utilizing an artificial intelligence model, we quantified the ability of UTS microbes in discriminating between cities and found that the ability was also strongly affected by city characteristics, especially climate and continent. From these, we found that although the NCM R2 of the New York City UTS microbiota was 0.831, the accuracy of the microbial-based city characteristic classifier was higher than 0.9. This is the first study to demonstrate the effects of city characteristics on the UTS microbiota assemblage, paving the way for city-specific and microbial-aware applications. IMPORTANCE We analyzed the urban transit system microbiota assemblage across 16 cities. The stochastic process was dominant in the urban transit system microbiota assemblage. The urban transit system microbe's ability in discriminating between cities was quantified using transfer learning based on random forest (RF) methods. Certain urban transit system microbes were strongly affected by city characteristics.

EXPERT: transfer learning-enabled context-aware microbial community classification.

Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.

Phenotypic and genotypic characterization of the new Bacillus cereus phage SWEP1.

A new Bacillus cereus phage, SWEP1, was isolated from black soil. The host lysis activity of phage SWEP1 has a relatively short latent time (20 min) and a small burst size of 83 PFU. The genome of SWEP1 consists of 162,461 bp with 37.77% G+C content. The phage encodes 278 predicted proteins, 103 of which were assigned functionally. No tRNA genes were found. Comparative genomics analysis indicated that SWEP1 is related to Bacillus phage B4 (86.91% identity, 90% query coverage). Phenotypic and genotypic characterization suggested that SWEP1 is a new member of a new species in the genus Bequatrovirus, family Herelleviridae.

Using QC-Blind for Quality Control and Contamination Screening of Bacteria DNA Sequencing Data Without Reference Genome.

Quality control for next generation sequencing (NGS) has become increasingly important with the ever increasing importance of sequencing data for omics studies. Tools have been developed for filtering possible contaminants from species with known reference genome. Unfortunately, reference genomes for all the species involved, including the contaminants, are required for these tools to work. This precludes many real-life samples that have no information about the complete genome of the target species, and are contaminated with unknown microbial species. In this work we proposed QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. The pipeline merely requires the information about a few marker genes of the target species. The entire pipeline consists of unsupervised read assembly, contig binning, read clustering, and marker gene assignment. When evaluated on in silico, ab initio and in vivo datasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind could serve well in situations where limited information is available for both target and contamination species.