ViBE: a hierarchical BERT model to identify eukaryotic viruses using metagenome sequencing data.

Viruses are ubiquitous in humans and various environments and continually mutate themselves. Identifying viruses in an environment without cultivation is challenging; however, promoting the screening of novel viruses and expanding the knowledge of viral space is essential. Homology-based methods that identify viruses using known viral genomes rely on sequence alignments, making it difficult to capture remote homologs of the known viruses. To accurately capture viral signals from metagenomic samples, models are needed to understand the patterns encoded in the viral genomes. In this study, we developed a hierarchical BERT model named ViBE to detect eukaryotic viruses from metagenome sequencing data and classify them at the order level. We pre-trained ViBE using read-like sequences generated from the virus reference genomes and derived three fine-tuned models that classify paired-end reads to orders for eukaryotic deoxyribonucleic acid viruses and eukaryotic ribonucleic acid viruses. ViBE achieved higher recall than state-of-the-art alignment-based methods while maintaining comparable precision. ViBE outperformed state-of-the-art alignment-free methods for all test cases. The performance of ViBE was also verified using real sequencing datasets, including the vaginal virome.

MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning.

BACKGROUND: Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets. RESULTS: We introduce MegaR, an R Shiny package and web application, to build an unbiased machine learning model effortlessly with interactive visual analysis. The MegaR employs taxonomic profiles from either whole metagenome sequencing or 16S rRNA sequencing data to develop machine learning models and classify the samples into two or more categories. It provides various options for model fine tuning throughout the analysis pipeline such as data processing, multiple machine learning techniques, model validation, and unknown sample prediction that can be used to achieve the highest prediction accuracy possible for any given dataset while still maintaining a user-friendly experience. CONCLUSIONS: Metagenomic sample classification and phenotype prediction is important particularly when it applies to a diagnostic method for identifying and predicting microbe-related human diseases. MegaR provides various interactive visualizations for user to build an accurate machine-learning model without difficulty. Unknown sample prediction with a properly trained model using MegaR will enhance researchers to identify the sample property in a fast turnaround time.

Association of Microbial Dysbiosis with Gallbladder Diseases Identified by Bile Microbiome Profiling.

BACKGROUND: Cholecystitis is an important risk factor for gallbladder cancer, but the bile microbiome and its association with gallbladder disease has not been investigated fully. We aimed to analyze the bile microbiome in normal conditions, chronic cholecystitis, and gallbladder cancer, and to identify candidate bacteria that play an important role in gallbladder carcinogenesis. METHODS: We performed metagenome sequencing on bile samples of 10 healthy individuals, 10 patients with chronic cholecystitis, and 5 patients with gallbladder cancer, and compared the clinical, radiological, and pathological characteristics of the participants. RESULTS: No significant bacterial signal was identified in the normal bile. The predominant dysbiotic bacteria in both chronic cholecystitis and gallbladder cancer were those belonging to the Enterobacteriaceae family. Klebsiella increased significantly in the order of normal, chronic cholecystitis, and gallbladder cancer. Patients with chronic cholecystitis and dysbiotic microbiome patterns had larger gallstones and showed marked epithelial atypia, which are considered as precancerous conditions. CONCLUSION: We investigated the bile microbiome in normal, chronic cholecystitis, and gallbladder cancer. We suggest possible roles of Enterobacteriaceae, including Klebsiella, in gallbladder carcinogenesis. Our findings reveal a possible link between a dysbiotic bile microbiome and the development of chronic calculous cholecystitis and gallbladder cancer.

Antibiotic Sensitivity and Nasal Microbiome in Patients with Acute Bacterial Rhinosinusitis.

OBJECTIVES: Acute rhinosinusitis (ARS) is a common upper respiratory tract infection that is mostly of viral origin. However, little is known about the nasal microbiome profile at presentation and the changes caused by antibiotics in acute bacterial rhinosinusitis (ABRS). METHODS: This was a prospective single-center study. Overall, 43 ARS patients were screened and were assessed with the symptom questionnaires, nasal endoscopy, and Water's view. Five healthy subjects were recruited as controls. Middle meatal mucus samples were obtained using a cotton swab (for bacterial culture and antimicrobial susceptibility testing) and the suction technique (for 16S rRNA sequencing). After 1 week of antibiotic use (amoxicillin with clavulanic acid), we enrolled 13 patients with ABRS with positive isolates and middle meatal samples for 16S rRNA sequencing were obtained again. RESULTS: Overall, we demonstrated a significantly lower abundance of the Lactobacillaceae family in ABRS patients than in healthy controls. Resistant ABRS had different characteristics of middle meatal microbiomes when compared to sensitive ABRS as follows: (1) lower proportion of lactic acid bacteria, (2) increased pathogens such as Rhodococcus sp., Massila sp., Acinetobacter sp., and H. influenza, and (3) increased beta diversity. However, no remarkable changes were observed in the middle meatal microbiome after antibiotic use. CONCLUSION: We showed the roles of Lactobacillaceae in ABRS, and Acinetobacter and Massilia in case of amoxicillin resistance. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:1081-1088, 2024.

Skin microbe-dependent TSLP-ILC2 priming axis in early life is co-opted in allergic inflammation.

Although early life colonization of commensal microbes contributes to long-lasting immune imprinting in host tissues, little is known regarding the pathophysiological consequences of postnatal microbial tuning of cutaneous immunity. Here, we show that postnatal exposure to specific skin commensal Staphylococcus lentus (S. lentus) promotes the extent of atopic dermatitis (AD)-like inflammation in adults through priming of group 2 innate lymphoid cells (ILC2s). Early postnatal skin is dynamically populated by discrete subset of primed ILC2s driven by microbiota-dependent induction of thymic stromal lymphopoietin (TSLP) in keratinocytes. Specifically, the indole-3-aldehyde-producing tryptophan metabolic pathway, shared across Staphylococcus species, is involved in TSLP-mediated ILC2 priming. Furthermore, we demonstrate a critical contribution of the early postnatal S. lentus-TSLP-ILC2 priming axis in facilitating AD-like inflammation that is not replicated by later microbial exposure. Thus, our findings highlight the fundamental role of time-dependent neonatal microbial-skin crosstalk in shaping the threshold of innate type 2 immunity co-opted in adulthood.

Application of computational approaches to analyze metagenomic data.

Microorganisms play a vital role in living systems in numerous ways. In the soil or ocean environment, microbes are involved in diverse processes, such as carbon and nitrogen cycle, nutrient recycling, and energy acquisition. The relation between microbial dysbiosis and disease developments has been extensively studied. In particular, microbial communities in the human gut are associated with the pathophysiology of several chronic diseases such as inflammatory bowel disease and diabetes. Therefore, analyzing the distribution of microorganisms and their associations with the environment is a key step in understanding nature. With the advent of next-generation sequencing technology, a vast amount of metagenomic data on unculturable microbes in addition to culturable microbes has been produced. To reconstruct microbial genomes, several assembly algorithms have been developed by incorporating metagenomic features, such as uneven depth. Since it is difficult to reconstruct complete microbial genomes from metagenomic reads, contig binning approaches were suggested to collect contigs that originate from the same genome. To estimate the microbial composition in the environment, various methods have been developed to classify individual reads or contigs and profile bacterial proportions. Since microbial communities affect their hosts and environments through metabolites, metabolic profiles from metagenomic or metatranscriptomic data have been estimated. Here, we provide a comprehensive review of computational methods that can be applied to investigate microbiomes using metagenomic and metatranscriptomic sequencing data. The limitations of metagenomic studies and the key approaches to overcome such problems are discussed.

Data-Driven Modeling for Species-Level Taxonomic Assignment From 16S rRNA: Application to Human Microbiomes.

With the emergence of next-generation sequencing (NGS) technology, there have been a large number of metagenomic studies that estimated the bacterial composition via 16S ribosomal RNA (16S rRNA) amplicon sequencing. In particular, subsets of the hypervariable regions in 16S rRNA, such as V1-V2 and V3-V4, are targeted using high-throughput sequencing. The sequences from different taxa are assigned to a specific taxon based on the sequence homology. Since such sequences are highly homologous or identical between species in the same genus, it is challenging to determine the exact species using 16S rRNA sequences only. Therefore, in this study, homologous species groups were defined to obtain maximum resolution related with species using 16S rRNA. For the taxonomic assignment using 16S rRNA, three major 16S rRNA databases are independently used since the lineage of certain bacteria is not consistent among these databases. On the basis of the NCBI taxonomy classification, we re-annotated inconsistent lineage information in three major 16S rRNA databases. For each species, we constructed a consensus sequence model for each hypervariable region and determined homologous species groups that consist of indistinguishable species in terms of sequence homology. Using a k-nearest neighbor method and the species consensus sequence models, the species-level taxonomy was determined. If the species determined is a member of homologous species groups, the species group is assigned instead of a specific species. Notably, the results of the evaluation on our method using simulated and mock datasets showed a high correlation with the real bacterial composition. Furthermore, in the analysis of real microbiome samples, such as salivary and gut microbiome samples, our method successfully performed species-level profiling and identified differences in the bacterial composition between different phenotypic groups.

Functional dynamics of bacterial species in the mouse gut microbiome revealed by metagenomic and metatranscriptomic analyses.

BACKGROUND: Microbial communities of the mouse gut have been extensively studied; however, their functional roles and regulation are yet to be elucidated. Metagenomic and metatranscriptomic analyses may allow us a comprehensive profiling of bacterial composition and functions of the complex gut microbiota. The present study aimed to investigate the active functions of the microbial communities in the murine cecum by analyzing both metagenomic and metatranscriptomic data on specific bacterial species within the microbial communities, in addition to the whole microbiome. RESULTS: Bacterial composition of the healthy mouse gut microbiome was profiled using the following three different approaches: 16S rRNA-based profiling based on amplicon and shotgun sequencing data, and genome-based profiling based on shotgun sequencing data. Consistently, Bacteroidetes, Firmicutes, and Deferribacteres emerged as the major phyla. Based on NCBI taxonomy, Muribaculaceae, Lachnospiraceae, and Deferribacteraceae were the predominant families identified in each phylum. The genes for carbohydrate metabolism were upregulated in Muribaculaceae, while genes for cofactors and vitamin metabolism and amino acid metabolism were upregulated in Deferribacteraceae. The genes for translation were commonly enhanced in all three families. Notably, combined analysis of metagenomic and metatranscriptomic sequencing data revealed that the functions of translation and metabolism were largely upregulated in all three families in the mouse gut environment. The ratio of the genes in the metagenome and their expression in the metatranscriptome indicated higher expression of carbohydrate metabolism in Muribaculum, Duncaniella, and Mucispirillum. CONCLUSIONS: We demonstrated a fundamental methodology for linking genomic and transcriptomic datasets to examine functional activities of specific bacterial species in a complicated microbial environment. We investigated the normal flora of the mouse gut using three different approaches and identified Muribaculaceae, Lachnospiraceae, and Deferribacteraceae as the predominant families. The functional distribution of these families was reflected in the entire microbiome. By comparing the metagenomic and metatranscriptomic data, we found that the expression rates differed for different functional categories in the mouse gut environment. Application of these methods to track microbial transcription in individuals over time, or before and after administration of a specific stimulus will significantly facilitate future development of diagnostics and treatments.