Metagenomic perspectives on antibiotic resistance genes in tap water: The environmental characteristic, potential mobility and health threat.

As an emerging environmental contaminant, antibiotic resistance genes (ARGs) in tap water have attracted great attention. Although studies have provided ARG profiles in tap water, research on their abundance levels, composition characteristics, and potential threat is still insufficient. Here, 9 household tap water samples were collected from the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. Additionally, 75 sets of environmental sample data (9 types) were downloaded from the public database. Metagenomics was then performed to explore the differences in the abundance and composition of ARGs. 221 ARG subtypes consisting of 17 types were detected in tap water. Although the ARG abundance in tap water was not significantly different from that found in drinking water plants and reservoirs, their composition varied. In tap water samples, the three most abundant classes of resistance genes were multidrug, fosfomycin and MLS (macrolide-lincosamide-streptogramin) ARGs, and their corresponding subtypes ompR, fosX and macB were also the most abundant ARG subtypes. Regarding the potential mobility, vanS had the highest abundance on plasmids and viruses, but the absence of key genes rendered resistance to vancomycin ineffective. Generally, the majority of ARGs present in tap water were those that have not been assessed and are currently not listed as high-threat level ARG families based on the World Health Organization Guideline. Although the current potential threat to human health posed by ARGs in tap water is limited, with persistent transfer and accumulation, especially in pathogens, the potential danger to human health posed by ARGs should not be ignored.

Characterization of CRESS-DNA viruses in human vaginal secretions: An exploratory metagenomic investigation.

The Phylum Cressdnaviricota consists of a large number of circular Rep-encoding single-stranded (CRESS)-DNA viruses. Recently, metagenomic analyzes revealed their ubiquitous distribution in a diverse range of eukaryotes. Data relating to CRESS-DNA viruses in humans remains scarce. Our study investigated the presence and genetic diversity of CRESS-DNA viruses in human vaginal secretions. Vaginal swabs were collected from 28 women between 29 and 43 years old attending a fertility clinic in New York City. An exploratory metagenomic analysis was performed and detection of CRESS-DNA viruses was confirmed through analysis of near full-length sequences of the viral isolates. A phylogenetic tree was based on the REP open reading frame sequences of the CRESS-DNA virus genome. Eleven nearly complete CRESS-DNA viral genomes were identified in 16 (57.1%) women. There were no associations between the presence of these viruses and any demographic or clinical parameters. Phylogenetic analysis indicated that one of the sequences belonged to the genus Gemycircularvirus within the Genomoviridae family, while ten sequences represented previously unclassified species of CRESS-DNA viruses. Novel species of CRESS-DNA viruses are present in the vaginal tract of adult women. Although they be transient commensal agents, the potential clinical implications for their presence at this site cannot be dismissed.

Perturbations in gut microbiota composition in schizophrenia.

Schizophrenia is a severe, complex and long-term psychiatric disorder with unclear etiology. Gut microbes influence the central nervous system via the gut-brain axis. Consequently, investigations of the relationship between gut microbes and schizophrenia are warranted. This study involved 29 patients with schizophrenia and 30 age-matched normal controls. After 16S rRNA gene sequencing and whole-genome shotgun metagenomic sequencing, we analyzed microbial diversity, composition, and function. According to 16S rRNA and metagenomic gene sequencing results, patients with schizophrenia had higher abundances of Clostridium and Megasphaera. Functional analysis showed that sphingolipid, phosphonates and phosphinates, as well as glutamine metabolism were associated with the occurrence and development of schizophrenia. Our data suggest that the gut microbiota exerts an effect on patients with schizophrenia, providing valuable insights into the potential regulation of in the context of this disorder.

Metatranscriptomics-guided genome-scale metabolic reconstruction reveals the carbon flux and trophic interaction in methanogenic communities.

BACKGROUND: Despite rapid advances in genomic-resolved metagenomics and remarkable explosion of metagenome-assembled genomes (MAGs), the function of uncultivated anaerobic lineages and their interactions in carbon mineralization remain largely uncertain, which has profound implications in biotechnology and biogeochemistry. RESULTS: In this study, we combined long-read sequencing and metatranscriptomics-guided metabolic reconstruction to provide a genome-wide perspective of carbon mineralization flow from polymers to methane in an anaerobic bioreactor. Our results showed that incorporating long reads resulted in a substantial improvement in the quality of metagenomic assemblies, enabling the effective recovery of 132 high-quality genomes meeting stringent criteria of minimum information about a metagenome-assembled genome (MIMAG). In addition, hybrid assembly obtained 51% more prokaryotic genes in comparison to the short-read-only assembly. Metatranscriptomics-guided metabolic reconstruction unveiled the remarkable metabolic flexibility of several novel Bacteroidales-affiliated bacteria and populations from Mesotoga sp. in scavenging amino acids and sugars. In addition to recovering two circular genomes of previously known but fragmented syntrophic bacteria, two newly identified bacteria within Syntrophales were found to be highly engaged in fatty acid oxidation through syntrophic relationships with dominant methanogens Methanoregulaceae bin.74 and Methanothrix sp. bin.206. The activity of bin.206 preferring acetate as substrate exceeded that of bin.74 with increasing loading, reinforcing the substrate determinantal role. CONCLUSION: Overall, our study uncovered some key active anaerobic lineages and their metabolic functions in this complex anaerobic ecosystem, offering a framework for understanding carbon transformations in anaerobic digestion. These findings advance the understanding of metabolic activities and trophic interactions between anaerobic guilds, providing foundational insights into carbon flux within both engineered and natural ecosystems. Video Abstract.

Fecal microbiota transplantation alters gut phage communities in a clinical trial for obesity.

BACKGROUND: Fecal microbiota transplantation (FMT) is a therapeutic intervention used to treat diseases associated with the gut microbiome. In the human gut microbiome, phages have been implicated in influencing human health, with successful engraftment of donor phages correlated with FMT treatment efficacy. The impact that gastrointestinal phages exert on human health has primarily been connected to their ability to modulate the bacterial communities in the gut. Nonetheless, how FMT affects recipients' phage populations, and in turn, how this influences the gut environment, is not yet fully understood. In this study, we investigated the effects of FMT on the phageome composition of participants within the Gut Bugs Trial (GBT), a double-blind, randomized, placebo-controlled trial that investigated the efficacy of FMT in treating obesity and comorbidities in adolescents. Stool samples collected from donors at the time of treatment and recipients at four time points (i.e., baseline and 6 weeks, 12 weeks, and 26 weeks post-intervention), underwent shotgun metagenomic sequencing. Phage sequences were identified and characterized in silico to examine evidence of phage engraftment and to assess the extent of FMT-induced alterations in the recipients' phageome composition. RESULTS: Donor phages engrafted stably in recipients following FMT, composing a significant proportion of their phageome for the entire course of the study (33.8 ± 1.2% in females and 33.9 ± 3.7% in males). Phage engraftment varied between donors and donor engraftment efficacy was positively correlated with their phageome alpha diversity. FMT caused a shift in recipients' phageome toward the donors' composition and increased phageome alpha diversity and variability over time. CONCLUSIONS: FMT significantly altered recipients' phage and, overall, microbial populations. The increase in microbial diversity and variability is consistent with a shift in microbial population dynamics. This proposes that phages play a critical role in modulating the gut environment and suggests novel approaches to understanding the efficacy of FMT in altering the recipient's microbiome. TRIAL REGISTRATION: The Gut Bugs Trial was registered with the Australian New Zealand Clinical Trials Registry (ACTR N12615001351505). Trial protocol: the trial protocol is available at https://bmjopen.bmj.com/content/9/4/e026174 . Video Abstract.

Metagenomics reveals unique gut mycobiome biomarkers in psoriasis.

PURPOSE: In present, the diagnosis of psoriasis is mainly based on the patient's typical clinical manifestations, dermoscopy and skin biopsy, and unlike other immune diseases, psoriasis lacks specific indicators in the blood. Therefore, we are required to search novel biomarkers for the diagnosis of psoriasis. METHODS: In this study, we analyzed the composition and the differences of intestinal fungal communities composition between psoriasis patients and healthy individuals in order to find the intestinal fungal communities associated with the diagnosis of psoriasis. We built a machine learning model and identified potential microbial markers for the diagnosis of psoriasis. RESULTS: The results of AUROC (area under ROC) showed that Aspergillus puulaauensis (AUROC = 0.779), Kazachstania africana (AUROC = 0.750) and Torulaspora delbrueckii (AUROC = 0.745) had high predictive ability (AUROC > 0.7) for predicting psoriasis, While Fusarium keratoplasticum (AUROC = 0.670) was relatively lower (AUROC < 0.7). CONCLUSION: The strategy based on the prediction of intestinal fungal communities provides a new idea for the diagnosis of psoriasis and is expected to become an auxiliary diagnostic method for psoriasis.

A case report and literature review: Mycobacterium leprae infection diagnosed by metagenomic next-generation sequencing of cerebrospinal fluid.

BACKGROUND: Leprosy is a chronic infectious disease caused by Mycobacterium leprae (M. leprae) that is responsible for deformities and irreversible peripheral nerve damage and has a broad spectrum of clinical and serological manifestations. Leprosy primarily affects the peripheral nerves and rarely presents with central nervous system involvement. Diagnosing leprosy can still be difficult in some cases, especially when the infection involves uncommon clinical manifestations and extracutaneous sites. Delayed diagnosis and treatment of leprosy may lead to irreversible damage and death. CASE PRESENTATION: We report a case of a 30-year-old female presenting with "repeated high fever with symptoms of headache for 14 days". On the day of admission, physical signs of lost eyebrows and scattered red induration patches all over her body were observed. The patient's diagnosis was based on the clinical characteristics using a combination of metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) and slit-skin smear. After confirming Listeria meningitis and multibacillary leprosy with erythema nodosum leprosum (ENL), a type 2 reaction, she was treated with ampicillin sodium, dapsone, rifampicin, clofazimine, methylprednisolone, and thalidomide. At the 1-year follow-up, the frequency and severity of headaches have significantly decreased and a good clinical response with improved skin lesions was found. CONCLUSION: This case highlights the importance of considering leprosy, which is a rare and underrecognized disease, in the differential diagnosis of skin rashes with rheumatic manifestations, even in areas where the disease is not endemic, and physicians should be alerted about the possibility of central nervous system infections. In addition, mNGS can be used as a complementary diagnostic tool to traditional diagnostic methods to enhance the diagnostic accuracy of leprosy.

Low microbial diversity, yeast prevalence, and nematode-trapping fungal presence in fungal colonization and leaf microbiome of Serjania erecta.

Medicinal plant microbiomes undergo selection due to secondary metabolite presence. Resident endophytic/epiphytic microorganisms directly influence plant's bioactive compound synthesis. Hypothesizing low microbial diversity in Serjania erecta leaves, we assessed leaf colonization by epiphytic and endophytic fungi. Given its traditional medicinal importance, we estimated diversity in the endophytic fungal microbiome. Analyses included scanning electron microscopy (SEM), isolation of cultivable species, and metagenomics. Epiphytic fungi interacted with S. erecta leaf tissues, horizontally transmitted via stomata/trichome bases, expressing traits for nematode trapping. Cultivable endophytic fungi, known for phytopathogenic habits, didn't induce dysbiosis symptoms. This study confirms low leaf microbiome diversity in S. erecta, with a tendency towards more fungal species, likely due to antibacterial secondary metabolite selection. The classification of Halicephalobus sp. sequence corroborated the presence of nematode eggs on the epidermal surface of S. erecta by SEM. In addition, we confirmed the presence of methanogenic archaea and a considerable number of methanotrophs of the genus Methylobacterium. The metagenomic study of endophytic fungi highlighted plant growth-promoting yeasts, mainly Malassezia, Leucosporidium, Meyerozyma, and Hannaella. Studying endophytic fungi and S. erecta microbiomes can elucidate their impact on beneficial bioactive compound production, on the other hand, it is possible that the bioactive compounds produced by this plant can recruit specific microorganisms, impacting the biological system.

Metagenomic and metabolomic analysis showing the adverse risk-benefit trade-off of the ketogenic diet.

BACKGROUND: Ketogenic diets are increasingly popular for addressing obesity, but their impacts on the gut microbiota and metabolome remain unclear. This paper aimed to investigate how a ketogenic diet affects intestinal microorganisms and metabolites in obesity. METHODS: Male mice were provided with one of the following dietary regimens: normal chow, high-fat diet, ketogenic diet, or high-fat diet converted to ketogenic diet. Body weight and fat mass were measured weekly using high-precision electronic balances and minispec body composition analyzers. Metagenomics and non-targeted metabolomics data were used to analyze differences in intestinal contents. RESULTS: Obese mice on the ketogenic diet exhibited notable improvements in weight and body fat. However, these were accompanied by a significant decrease in intestinal microbial diversity, as well as an increase in Firmicutes abundance and a 247% increase in the Firmicutes/Bacteroidetes ratio. The ketogenic diet also altered multiple metabolic pathways in the gut, including glucose, lipid, energy, carbohydrate, amino acid, ketone body, butanoate, and methane pathways, as well as bacterial secretion and colonization pathways. These changes were associated with increased intestinal inflammation and dysbiosis in obese mice. Furthermore, the ketogenic diet enhanced the secretion of bile and the synthesis of aminoglycoside antibiotics in obese mice, which may impair the gut microbiota and be associated with intestinal inflammation and immunity. CONCLUSIONS: The study suggest that the ketogenic diet had an unfavorable risk-benefit trade-off and may compromise metabolic homeostasis in obese mice.

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets.

BACKGROUND: Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration. RESULTS: In this study, we have developed a Java-based statistical framework ( https://github.com/KociOrges/cviewer ) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case-control relationships. CONCLUSIONS: CViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn's disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential. Video Abstract.

Emerging importance of stool preservation methods in OMICS studies with special focus on cancer biology.

The intricate consortium of microorganisms in the human gut plays a crucial role in different physiological functions. The complex known-unknown elements of the gut microbiome are perplexing and the absence of standardized procedures for collecting and preserving samples has hindered continuous research in comprehending it. The technological bias produced because of lack of standard protocols has affected the reproducibility of results. The complex nature of diseases like colorectal cancer, gastric cancer, hepatocellular carcinoma and breast cancer require a thorough understanding of its etiology for an efficient and timely diagnosis. The designated protocols for collection and preservation of stool specimens have great variance, hence generate inconsistencies in OMICS studies. Due to the complications associated to the nature of sample, it is important to preserve the sample to be studied later in a laboratory or to be used in the future research purpose. Stool preservation is gaining importance due to the increased use of treatment options like fecal microbiota transplantation to cure conditions like recurrent Clostridium difficile infections and for OMICS studies including metagenomics, metabolomics and culturomics. This review provides an insight into the importance of omics studies for the identification and development of novel biomarkers for quick and noninvasive diagnosis of various diseases.

Shotgun metagenomic insights into secondary metabolite biosynthetic gene clusters reveal taxonomic and functional profiles of microbiomes in natural farmland soil.

Antibiotic resistance is a worldwide problem that imposes a devastating effect on developing countries and requires immediate interventions. Initially, most of the antibiotic drugs were identified by culturing soil microbes. However, this method is prone to discovering the same antibiotics repeatedly. The present study employed a shotgun metagenomics approach to investigate the taxonomic diversity, functional potential, and biosynthetic capacity of microbiomes from two natural agricultural farmlands located in Bekeka and Welmera Choke Kebelle in Ethiopia for the first time. Analysis of the small subunit rRNA revealed bacterial domain accounting for 83.33% and 87.24% in the two selected natural farmlands. Additionally, the analysis showed the dominance of Proteobacteria representing 27.27% and 28.79% followed by Actinobacteria making up 12.73% and 13.64% of the phyla composition. Furthermore, the analysis revealed the presence of unassigned bacteria in the studied samples. The metagenome functional analysis showed 176,961 and 104, 636 number of protein-coding sequences (pCDS) from the two samples found a match with 172,655 and 102, 275 numbers of InterPro entries, respectively. The Genome ontology annotation suggests the presence of 5517 and 3293 pCDS assigned to the "biosynthesis process". Numerous Kyoto Encyclopedia of Genes and Genomes modules (KEGG modules) involved in the biosynthesis of terpenoids and polyketides were identified. Furthermore, both known and novel Biosynthetic gene clusters, responsible for the production of secondary metabolites, such as polyketide synthases, non-ribosomal peptide synthetase, ribosomally synthesized and post-translationally modified peptides (Ripp), and Terpene, were discovered. Generally, from the results it can be concluded that the microbiomes in the selected sampling sites have a hidden functional potential for the biosynthesis of secondary metabolites. Overall, this study can serve as a strong preliminary step in the long journey of bringing new antibiotics to the market.

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes.

BACKGROUND: In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline. RESULTS: Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives. CONCLUSIONS: gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

16S rRNA, metagenomics and 2bRAD-M sequencing to decode human thanatomicrobiome.

Microorganisms are essential in the decomposition of corpses and play a significant role in forensic science. However, previous studies have primarily focused on animal remains, specifically the gut, skin, and burial environment. Insufficient research has been conducted on the microbiota of human cadavers, especially in cases of advanced decomposition and additional tissues, resulting in a lack of relevant reference data. In this study, the microbiota of eight cadavers at different stages of decomposition were detected using 16S rRNA, metagenomic sequencing and 2bRAD-M sequencing. Nine different sites, including oral and nasal cavities, heart, liver, spleen, lung, kidney, muscle and gut, were analysed and the efficacy of these methods was evaluated. The results showed that 16S rRNA sequencing was the most cost-effective method for the study of cadavers in the early stages of decomposition, whereas for cadaveric tissues in the late stages of decomposition, 2bRAD-M could overcome host contamination more effectively than metagenomic sequencing. This paves the way for new opportunities in data retrieval and promotes in-depth investigations into the microbiota.

MAGinator enables accurate profiling of de novo MAGs with strain-level phylogenies.

Metagenomic sequencing has provided great advantages in the characterisation of microbiomes, but currently available analysis tools lack the ability to combine subspecies-level taxonomic resolution and accurate abundance estimation with functional profiling of assembled genomes. To define the microbiome and its associations with human health, improved tools are needed to enable comprehensive understanding of the microbial composition and elucidation of the phylogenetic and functional relationships between the microbes. Here, we present MAGinator, a freely available tool, tailored for profiling of shotgun metagenomics datasets. MAGinator provides de novo identification of subspecies-level microbes and accurate abundance estimates of metagenome-assembled genomes (MAGs). MAGinator utilises the information from both gene- and contig-based methods yielding insight into both taxonomic profiles and the origin of genes and genetic content, used for inference of functional content of each sample by host organism. Additionally, MAGinator facilitates the reconstruction of phylogenetic relationships between the MAGs, providing a framework to identify clade-level differences.

Metagenomic comparison of intestinal microbiota between normal and liver fibrotic rhesus macaques (Macaca mulatta).

Liver fibrosis is an important pathological process in chronic liver disease and cirrhosis. Recent studies have found a close association between intestinal microbiota and the development of liver fibrosis. To determine whether there are differences in the intestinal microbiota between rhesus macaques with liver fibrosis (MG) and normal rhesus macaques (MN), fecal samples were collected from 8 male MG and 12 male MN. The biological composition of the intestinal microbiota was then detected using 16S rRNA gene sequencing. The results revealed statistically significant differences in ASVs and Chao1 in the alpha-diversity and the beta-diversity of intestinal microbiota between MG and MN. Both groups shared Prevotella and Lactobacillus as common dominant microbiota. However, beneficial bacteria such as Lactobacillus were significantly less abundant in MG (P = 0.02). Predictive functional analysis using PICRUSt2 gene prediction revealed that MG exhibited a higher relative abundance of functions related to substance transport and metabolic pathways. This study may provide insight into further exploration of the mechanisms by which intestinal microbiota affect liver fibrosis and its potential future use in treating liver fibrosis.

Rapid diagnosis of alveolar echinococcosis from lung puncture sample using metagenomic next-generation sequencing: a case report.

INTRODUCTION: Alveolar echinococcosis (AE), caused by the larval forms of Echinococcus multilocularis, is a zoonotic disease affecting the liver, lungs, lymph nodes, kidneys, brain, bones, thyroid, and other organs. Diagnosing AE in a non-endemic area is usually challenging. With the rapid development and increasing application of sequencing techniques in recent years, metagenomic next-generation sequencing (mNGS) has become a powerful tool for diagnosing rare infectious diseases. CASE PRESENTATION: A 45-year-old woman was admitted to the hospital for the presence of pulmonary shadows for more than 3 months. The lung computed tomography (CT) at a local hospital revealed scattered solid and quasi-circular nodules in the left upper lobe, left lower lobe, right middle lobe, and right lower lobe. The largest nodule was located in the dorsal part of the right lung, measuring 2.0 × 1.7 × 1.5 cm. Moreover, abdominal CT revealed one space-occupying lesion each in the left and right lobes. The pathological analysis of the lung biopsy specimen revealed infiltration of lymphocytes, plasma cells, and eosinophils in the alveolar wall and interstitial area. No pathogenic bacteria were observed in the sputum smear and culture tests. There were no parasite eggs in the stool. The mNGS of the lung puncture tissue revealed 6156 sequence reads matching E. multilocularis; thus, the condition was diagnosed as AE. Albendazole 400 mg was administered twice daily, and the patient was stable during follow-up. CONCLUSION: This case emphasizes the role of mNGS in diagnosing AE. As a novel, sensitive, and accurate diagnostic method, mNGS could be an attractive approach for facilitating early diagnosis and prompt treatment of infectious diseases, especially when the infection was caused by rare pathogens.

Clinical diagnostic value of targeted next­generation sequencing for infectious diseases (Review).

As sequencing technology transitions from research to clinical settings, due to technological maturity and cost reductions, metagenomic next­generation sequencing (mNGS) is increasingly used. This shift underscores the growing need for more cost­effective and universally accessible sequencing assays to improve patient care and public health. Therefore, targeted NGS (tNGS) is gaining prominence. tNGS involves enrichment of target pathogens in patient samples based on multiplex PCR amplification or probe capture with excellent sensitivity. It is increasingly used in clinical diagnostics due to its practicality and efficiency. The present review compares the principles of different enrichment methods. The high positivity rate of tNGS in the detection of pathogens was found in respiratory samples with specific instances. tNGS maintains high sensitivity (70.8­95.0%) in samples with low pathogen loads, including blood and cerebrospinal fluid. Furthermore, tNGS is effective in detecting drug­resistant strains of Mycobacterium tuberculosis, allowing identification of resistance genes and guiding clinical treatment decisions, which is difficult to achieve with mNGS. In the present review, the application of tNGS in clinical settings and its current limitations are assessed. The continued development of tNGS has the potential to refine diagnostic accuracy and treatment efficacy and improving infectious disease management. However, further research to overcome technical challenges such as workflow time and cost is required.

VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes.

Identifying viruses from metagenomes is a common step to explore the virus composition in the human gut. Here, we introduce VirRep, a hybrid language representation learning framework, for identifying viruses from human gut metagenomes. VirRep combines a context-aware encoder and an evolution-aware encoder to improve sequence representation by incorporating k-mer patterns and sequence homologies. Benchmarking on both simulated and real datasets with varying viral proportions demonstrates that VirRep outperforms state-of-the-art methods. When applied to fecal metagenomes from a colorectal cancer cohort, VirRep identifies 39 high-quality viral species associated with the disease, many of which cannot be detected by existing methods.