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.

A case study on the distribution of the environmental resistome in Korean shrimp farms.

Hundreds of tons of antibiotics are widely used in aquaculture to prevent microbial infections and promote fish growth. However, the overuse of antibiotics and chemical products can lead to the selection and spreading of antibiotic-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs), which are of great concern considering the threat to public health worldwide. Here, in-depth metagenome sequencing was performed to explore the environmental resistome and ARB distribution across farming stages in shrimp farms and examine anthropogenic effects in nearby coastal waters. A genome-centric analysis using a metagenome binning approach allowed us to accurately investigate the distribution of pathogens and ARG hosts in shrimp farms. The diversity of resistomes was higher in shrimp farms than in coastal waters, and the distribution of resistomes was dependent on the farming stage. In particular, the tetracycline resistance gene was found mainly at the early post-larval stage regardless of the farm. The metagenome-assembled genomes of Vibrio spp. were dominant at this stage and harbored tet34, which is known to confer resistance to oxytetracycline. In addition, opportunistic pathogens such as Francisella, Mycoplasma, Photobacterium, and Vibrio were found in abundance in shrimp farms, which had multiple virulence factors. This study highlights the increased resistance diversity and environmental selection of pathogens in shrimp farms. The use of environmental pollutants on farms may cause an increase in resistome diversity/abundance and the transmission of pathogens to the surrounding environment, which may pose future risks to public health and aquatic organisms.

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.

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches.

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.

iMGEins: detecting novel mobile genetic elements inserted in individual genomes.

BACKGROUND: Recent advances in sequencing technology have allowed us to investigate personal genomes to find structural variations, which have been studied extensively to identify their association with the physiology of diseases such as cancer. In particular, mobile genetic elements (MGEs) are one of the major constituents of the human genomes, and cause genome instability by insertion, mutation, and rearrangement. RESULT: We have developed a new program, iMGEins, to identify such novel MGEs by using sequencing reads of individual genomes, and to explore the breakpoints with the supporting reads and MGEs detected. iMGEins is the first MGE detection program that integrates three algorithmic components: discordant read-pair mapping, split-read mapping, and insertion sequence assembly. Our evaluation results showed its outstanding performance in detecting novel MGEs from simulated genomes, as well as real personal genomes. In detail, the average recall and precision rates of iMGEins are 96.67 and 100%, respectively, which are the highest among the programs compared. In the testing with real human genomes of the NA12878 sample, iMGEins shows the highest accuracy in detecting MGEs within 20 bp proximity of the breakpoints annotated. CONCLUSION: In order to study the dynamics of MGEs in individual genomes, iMGEins was developed to accurately detect breakpoints and report inserted MGEs. Compared with other programs, iMGEins has valuable features of identifying novel MGEs and assembling the MGEs inserted.

Diverse Distribution of Resistomes in the Human and Environmental Microbiomes.

The routine therapeutic use of antibiotics has caused resistance genes to be disseminated across microbial populations. In particular, bacterial strains having antibiotic resistance genes are frequently observed in the human microbiome. Moreover, multidrug-resistant pathogens are now widely spread, threatening public health. Such genes are transferred and spread among bacteria even in different environments. Advances in high throughput sequencing technology and computational algorithms have accelerated investigation into antibiotic resistance genes of bacteria. Such studies have revealed that the antibiotic resistance genes are located close to the mobility-associated genes, which promotes their dissemination. An increasing level of information on genomic sequences of resistome should expedite research on drug-resistance in our body and environment, thereby contributing to the development of public health policy. In this review, the high prevalence of antibiotic resistance genes and their exchange in the human and environmental microbiome is discussed with respect to the genomic contents. The relationships among diverse resistomes, related bacterial species, and the antibiotics are reviewed. In addition, recent advances in bioinformatics approaches to investigate such relationships are discussed.

MGEScan: a Galaxy-based system for identifying retrotransposons in genomes.

UNLABELLED: : MGEScan-long terminal repeat (LTR) and MGEScan-non-LTR are successfully used programs for identifying LTRs and non-LTR retrotransposons in eukaryotic genome sequences. However, these programs are not supported by easy-to-use interfaces nor well suited for data visualization in general data formats. Here, we present MGEScan, a user-friendly system that combines these two programs with a Galaxy workflow system accelerated with MPI and Python threading on compute clusters. MGEScan and Galaxy empower researchers to identify transposable elements in a graphical user interface with ready-to-use workflows. MGEScan also visualizes the custom annotation tracks for mobile genetic elements in public genome browsers. A maximum speed-up of 3.26× is attained for execution time using concurrent processing and MPI on four virtual cores. MGEScan provides four operational modes: as a command line tool, as a Galaxy Toolshed, on a Galaxy-based web server, and on a virtual cluster on the Amazon cloud. AVAILABILITY AND IMPLEMENTATION: MGEScan tutorials and source code are available at http://mgescan.readthedocs.org/ CONTACT: hatang@indiana.edu or syoh@ajou.ac.kr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deciphering the human microbiome using next-generation sequencing data and bioinformatics approaches.

The human microbiome is one of the key factors affecting the host immune system and metabolic functions that are not encoded in the human genome. Culture-independent analysis of the human microbiome using metagenomics approach allows us to investigate the compositions and functions of the human microbiome. Computational methods analyze the microbial community by using specific marker genes or by using shotgun sequencing of the entire microbial community. Taxonomy profiling is conducted by using the reference sequences or by de novo clustering of the specific region of sequences. Functional profiling, which is mainly based on the sequence similarity, is more challenging since about half of ORFs predicted in the metagenomic data could not find homology with known protein families. This review examines computational methods that are valuable for the analysis of human microbiome, and highlights the results of several large-scale human microbiome studies. It is becoming increasingly evident that dysbiosis of the gut microbiome is strongly associated with the development of immune disorder and metabolic dysfunction.

Diverse CRISPRs evolving in human microbiomes.

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) loci, together with cas (CRISPR-associated) genes, form the CRISPR/Cas adaptive immune system, a primary defense strategy that eubacteria and archaea mobilize against foreign nucleic acids, including phages and conjugative plasmids. Short spacer sequences separated by the repeats are derived from foreign DNA and direct interference to future infections. The availability of hundreds of shotgun metagenomic datasets from the Human Microbiome Project (HMP) enables us to explore the distribution and diversity of known CRISPRs in human-associated microbial communities and to discover new CRISPRs. We propose a targeted assembly strategy to reconstruct CRISPR arrays, which whole-metagenome assemblies fail to identify. For each known CRISPR type (identified from reference genomes), we use its direct repeat consensus sequence to recruit reads from each HMP dataset and then assemble the recruited reads into CRISPR loci; the unique spacer sequences can then be extracted for analysis. We also identified novel CRISPRs or new CRISPR variants in contigs from whole-metagenome assemblies and used targeted assembly to more comprehensively identify these CRISPRs across samples. We observed that the distributions of CRISPRs (including 64 known and 86 novel ones) are largely body-site specific. We provide detailed analysis of several CRISPR loci, including novel CRISPRs. For example, known streptococcal CRISPRs were identified in most oral microbiomes, totaling ∼8,000 unique spacers: samples resampled from the same individual and oral site shared the most spacers; different oral sites from the same individual shared significantly fewer, while different individuals had almost no common spacers, indicating the impact of subtle niche differences on the evolution of CRISPR defenses. We further demonstrate potential applications of CRISPRs to the tracing of rare species and the virus exposure of individuals. This work indicates the importance of effective identification and characterization of CRISPR loci to the study of the dynamic ecology of microbiomes.

The repatterning of eukaryotic genomes by random genetic drift.

Recent observations on rates of mutation, recombination, and random genetic drift highlight the dramatic ways in which fundamental evolutionary processes vary across the divide between unicellular microbes and multicellular eukaryotes. Moreover, population-genetic theory suggests that the range of variation in these parameters is sufficient to explain the evolutionary diversification of many aspects of genome size and gene structure found among phylogenetic lineages. Most notably, large eukaryotic organisms that experience elevated magnitudes of random genetic drift are susceptible to the passive accumulation of mutationally hazardous DNA that would otherwise be eliminated by efficient selection. Substantial evidence also suggests that variation in the population-genetic environment influences patterns of protein evolution, with the emergence of certain kinds of amino-acid substitutions and protein-protein complexes only being possible in populations with relatively small effective sizes. These observations imply that the ultimate origins of many of the major genomic and proteomic disparities between prokaryotes and eukaryotes and among eukaryotic lineages have been molded as much by intrinsic variation in the genetic and cellular features of species as by external ecological forces.

Detection of PIWI and piRNAs in the mitochondria of mammalian cancer cells.

Piwi-interacting RNAs (piRNAs) are 26-31 nt small noncoding RNAs that are processed from their longer precursor transcripts by Piwi proteins. Localization of Piwi and piRNA has been reported mostly in nucleus and cytoplasm of higher eukaryotes germ-line cells, where it is believed that known piRNA sequences are located in repeat regions of nuclear genome in germ-line cells. However, localization of PIWI and piRNA in mammalian somatic cell mitochondria yet remains largely unknown. We identified 29 piRNA sequence alignments from various regions of the human mitochondrial genome. Twelve out 29 piRNA sequences matched stem-loop fragment sequences of seven distinct tRNAs. We observed their actual expression in mitochondria subcellular fractions by inspecting mitochondrial-specific small RNA-Seq datasets. Of interest, the majority of the 29 piRNAs overlapped with multiple longer transcripts (expressed sequence tags) that are unique to the human mitochondrial genome. The presence of mature piRNAs in mitochondria was detected by qRT-PCR of mitochondrial subcellular RNAs. Further validation showed detection of Piwi by colocalization using anti-Piwil1 and mitochondria organelle-specific protein antibodies.

Genome-wide characterization of endogenous retroviruses in the bat Myotis lucifugus reveals recent and diverse infections.

Bats are increasingly recognized as reservoir species for a variety of zoonotic viruses that pose severe threats to human health. While many RNA viruses have been identified in bats, little is known about bat retroviruses. Endogenous retroviruses (ERVs) represent genomic fossils of past retroviral infections and, thus, can inform us on the diversity and history of retroviruses that have infected a species lineage. Here, we took advantage of the availability of a high-quality genome assembly for the little brown bat, Myotis lucifugus, to systematically identify and analyze ERVs in this species. We mined an initial set of 362 potentially complete proviruses from the three main classes of ERVs, which were further resolved into 13 major families and 86 subfamilies by phylogenetic analysis. Consensus or representative sequences for each of the 86 subfamilies were then merged to the Repbase collection of known ERV/long terminal repeat (LTR) elements to annotate the retroviral complement of the bat genome. The results show that nearly 5% of the genome assembly is occupied by ERV-derived sequences, a quantity comparable to findings for other eutherian mammals. About one-fourth of these sequences belong to subfamilies newly identified in this study. Using two independent methods, intraelement LTR divergence and analysis of orthologous loci in two other bat species, we found that the vast majority of the potentially complete proviruses identified in M. lucifugus were integrated in the last ~25 million years. All three major ERV classes include recently integrated proviruses, suggesting that a wide diversity of retroviruses is still circulating in Myotis bats.

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.

Stitching gene fragments with a network matching algorithm improves gene assembly for metagenomics.

MOTIVATION: One of the difficulties in metagenomic assembly is that homologous genes from evolutionarily closely related species may behave like repeats and confuse assemblers. As a result, small contigs, each representing a short gene fragment, instead of complete genes, may be reported by an assembler. This further complicates annotation of metagenomic datasets, as annotation tools (such as gene predictors or similarity search tools) typically perform poorly on configs encoding short gene fragments. RESULTS: We present a novel way of using the de Bruijn graph assembly of metagenomes to improve the assembly of genes. A network matching algorithm is proposed for matching the de Bruijn graph of contigs against reference genes, to derive 'gene paths' in the graph (sequences of contigs containing gene fragments) that have the highest similarities to known genes, allowing gene fragments contained in multiple contigs to be connected to form more complete (or intact) genes. Tests on simulated and real datasets show that our approach (called GeneStitch) is able to significantly improve the assembly of genes from metagenomic sequences, by connecting contigs with the guidance of homologous genes-information that is orthogonal to the sequencing reads. We note that the improvement of gene assembly can be observed even when only distantly related genes are available as the reference. We further propose to use 'gene graphs' to represent the assembly of reads from homologous genes and discuss potential applications of gene graphs to improving functional annotation for metagenomics. AVAILABILITY: The tools are available as open source for download at http://omics.informatics.indiana.edu/GeneStitch CONTACT: yye@indiana.edu.

Discovery and Characterization of Polymyxin-Resistance Genes pmrE and pmrF from Sediment and Seawater Microbiome.

Polymyxins are the last-line antibiotics used to treat Gram-negative pathogens. Thus, the discovery and biochemical characterization of the resistance genes against polymyxins are urgently needed for diagnosis, treatment, and novel antibiotic design. Herein, we report novel polymyxin-resistance genes identified from sediment and seawater microbiome. Despite their low sequence identity against the known pmrE and pmrF, they show in vitro activities in UDP-glucose oxidation and l-Ara4N transfer to undecaprenyl phosphate, respectively, which occur as the part of lipid A modification that leads to polymyxin resistance. The expression of pmrE and pmrF also showed substantially high MICs in the presence of vanadate ions, indicating that they constitute polymyxin resistomes. IMPORTANCE Polymyxins are one of the last-resort antibiotics. Polymyxin resistance is a severe threat to combat multidrug-resistant pathogens. Thus, up-to-date identification and understanding of the related genes are crucial. Herein, we performed structure-guided sequence and activity analysis of five putative polymyxin-resistant metagenomes. Despite relatively low sequence identity to the previously reported polymyxin-resistance genes, at least four out of five discovered genes show reactivity essential for lipid A modification and polymyxin resistance, constituting antibiotic resistomes.

Comparative analyses of the faecal resistome against ß-lactam and quinolone antibiotics in humans and livestock using metagenomic sequencing.

To assess the prevalence and abundance of antibiotic resistance genes in human and livestock gut microbiomes, 87 humans (healthy individuals and patients with Clostridioides difficile infection (CDI)) and 108 livestock (swine, cattle, and chickens) were enrolled. Gut microbiomes and fluoroquinolone-resistant Escherichia coli isolates were sequenced, and mobile genetic elements adjacent to the ß-lactamase (bla) and transferable quinolone resistance (qnr) genes were compared using metagenomic contigs. Each group of humans and livestock exhibited distinctive microbiota and resistome compositions in the gut. Concerning the resistome of bla and qnr, the prevalence rates between chickens and patients with CDI were the most similar (R2 = 0.46); blaTEM, blaOXA, blaCTX-M, and qnrS were highly prevalent in both groups. According to genomic and phylogenetic analyses, blaCTX-M and blaOXA expressed lineage specificity to either humans or livestock, while qnrS and blaTEM displayed a shared lineage between humans and livestock. A qnrS1 mobilome comprising five genes, including two recombinases, a transposase, and a plasmid gene, is commonly found in human and chicken gut microbiomes. Humans and chickens showed the most similar gut resistomes to ß-lactams and quinolones. QnrS and blaTEM displayed especially strong co-occurrence between the guts of humans and livestock.