Search

We investigated the efficiency of target-enriched long-read sequencing (TELSeq) for detecting antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) within complex matrices. We aimed to overcome limitations associated with traditional antimicrobial resistance (AMR) detection methods, including short-read shotgun metagenomics, which can lack sensitivity, specificity, and the ability to provide detailed genomic context. By combining biotinylated probe-based enrichment with long-read sequencing, we facilitated the amplification and sequencing of ARGs, eliminating the need for bioinformatic reconstruction. Our experimental design included replicates of human fecal microbiota transplant material, bovine feces, pristine prairie soil, and a mock human gut microbial community, allowing us to examine variables including genomic DNA input and probe set composition. Our findings demonstrated that TELSeq markedly improves the detection rates of ARGs and MGEs compared to traditional sequencing methods, underlining its potential for accurate AMR monitoring. A key insight from our research is the importance of incorporating mobilome profiles to better predict the transferability of ARGs within microbial communities, prompting a recommendation for the use of combined ARG-MGE probe sets for future studies. We also reveal limitations for ARG detection from low-input workflows, and describe the next steps for ongoing protocol refinement to minimize technical variability and expand utility in clinical and public health settings. This effort is part of our broader commitment to advancing methodologies that address the global challenge of AMR., (© 2024 Slizovskiy et al.; Published by Cold Spring Harbor Laboratory Press.)

The high specificity of the human skin microbiome is expected to provide a new marker for personal identification. Metagenomic sequencing of clustered regularly interspaced short palindromic repeats (CRISPRs), which we call metaCRISPR typing, was shown to achieve personal identification accurately. However, the intra-individual variability observed in previous studies, which may be due to poor DNA yields from skin samples, has resulted in non-reproducible results. Furthermore, whether metaCRISPR typing can assist in the forensic human DNA analysis of low-biomass samples, from which the information obtained is insufficient, is unknown. In the present study, we sequenced serially diluted control streptococcal CRISPRs cloned into plasmids to determine the minimum copy number required to obtain reproducible results from metaCRISPR typing. We found that at least 10 2 copies of CRISPRs are necessary to obtain reproducible results. We then analyzed the skin swab samples using both metaCRISPR typing and human DNA typing. When the DNA extracted from the skin swabs was diluted, no information was obtained from six out of eight samples by human DNA typing. On the other hand, beta diversity indices of spacer sequences compared with reference samples were below 0.8 for three out of six samples, for which no information was obtained from human DNA analysis, indicating that the spacers observed in these samples were similar to those in the references. These results indicate that metaCRISPR typing may contribute to the identification of individuals from whom the samples were obtained, even in cases where human DNA yields are insufficient to perform human DNA analysis.IMPORTANCEPrevious studies have developed new personal identification methods utilizing personal differences in the skin microbiome. However, intra-individual diversity of skin microbiome may preclude the application of microbiome-based personal identification. Moreover, no study has compared microbiome-based personal identification and practical human DNA analysis. Here, we revealed that the results of metaCRISPR typing, a previously developed microbiome-based personal identification method, are stable if the copy number of the marker gene is sufficient. We then analyzed the skin swab samples using both metaCRISPR typing and human DNA analysis. Our results indicate that metaCRISPR typing may provide additional information for personal identification using low-biomass samples that cannot be used for conventional human DNA analysis., Competing Interests: The authors declare no conflict of interest.

Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Obtaining these signatures would significantly enhance our understanding of the underlying pathophysiological processes of this condition and foster the development of improved therapeutic strategies, potentially personalized to individual patients. Here, we sequenced newly collected samples from 24 patients with periodontitis, and we collected available oral microbiome data from 24 samples in patients with periodontitis and from 214 samples in healthy individuals ( n = 262). Data were harmonized, and we performed a pooled analysis of individual patient data. By metagenomic sequencing of the plaque microbiome, we found microbial signatures for periodontitis and defined a periodontitis-related complex, composed by the most discriminative bacteria. A simple two-factor decision tree, based on Tannerella forsythia and Fretibacterium fastidiosum , was associated with periodontitis with high accuracy (area under the curve: 0.94). Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis., Importance: Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Here, using ultra-deep metagenomic sequencing and machine learning tools, we defined a simple two-factor decision tree, based on Tannerella forsythia and Fretibacterium fastidiosum , that was highly associated with periodontitis. Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis., Competing Interests: The authors declare no conflict of interest.

Alfalfa ( Medicago sativa L.) is one of the most extensively cultivated forage crops globally, and its nutritional quality critically influences the productivity of dairy cows. Silage fermentation is recognized as a crucial technique for the preservation of fresh forage, ensuring the retention of its vital nutrients. However, the detailed microbial components and their functions in silage fermentation are not fully understood. This study integrated large-scale microbial culturing with high-throughput sequencing to thoroughly examine the microbial community structure in alfalfa silage and explored the potential pathways of nutritional degradation via metagenomic analysis. The findings revealed an enriched microbial diversity in silage, indicated by the identification of amplicon sequence variants. Significantly, the large-scale culturing approach recovered a considerable number of unique microbes undetectable by high-throughput sequencing. Predominant genera, such as Lactiplantibacillus , Leuconostoc , Lentilactobacillus , Weissella , and Liquorilactobacillus , were identified based on their abundance and prevalence. Additionally, genes associated with Enterobacteriaceae were discovered, which might be involved in pathways leading to the production of ammonia-N and butyric acid. Overall, this study offers a comprehensive insight into the microbial ecology of silage fermentation and provides valuable information for leveraging microbial consortia to enhance fermentation quality., Importance: Silage fermentation is a microbial-driven anaerobic process that efficiently converts various substrates into nutrients readily absorbable and metabolizable by ruminant animals. This study, integrating culturomics and metagenomics, has successfully identified core microorganisms involved in silage fermentation, including those at low abundance. This discovery is crucial for the targeted cultivation of specific microorganisms to optimize fermentation processes. Furthermore, our research has uncovered signature microorganisms that play pivotal roles in nutrient metabolism, significantly advancing our understanding of the intricate relationships between microbial communities and nutrient degradation during silage fermentation., Competing Interests: The authors declare no conflict of interest.

The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. These methods could be used to identify ingredients that deviate from their typical microbial composition, which could indicate food fraud or safety issues. The objective of this study was to assess the feasibility of using shotgun sequencing data as input into anomaly detection algorithms using fluid milk as a model system. Contrastive principal component analysis (PCA), cluster-based methods, and explainable artificial intelligence (AI) were evaluated for the detection of two anomalous sample classes using longitudinal metagenomic profiling of fluid milk compared to baseline (BL) samples collected under comparable circumstances. Traditional methods (alpha and beta diversity, clustering-based contrastive PCA, multidimensional scaling, and dendrograms) failed to differentiate anomalous sample classes; however, explainable AI was able to classify anomalous vs baseline samples and indicate microbial drivers in association with antibiotic use. We validated the potential for explainable AI to classify different milk sources using larger publicly available fluid milk 16S rDNA sequencing data sets and demonstrated that explainable AI is able to differentiate between milk storage methods, processing stages, and seasons. Our results indicate that the application of artificial intelligence continues to hold promise in the realm of microbiome data analysis and could present further opportunities for downstream analytic automation to aid in food safety and quality., Importance: We evaluated the feasibility of using untargeted metagenomic sequencing of raw milk for detecting anomalous food ingredient content with artificial intelligence methods in a study specifically designed to test this hypothesis. We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of processing. The approach could potentially be applied in the food industry for safety and quality control., Competing Interests: The authors declare no conflict of interest.

Background: Rapid diagnosis of pulmonary tuberculosis (PTB) is urgently needed. We aimed to improve diagnosis rates by combining tuberculosis-interferon (IFN)-γ release assays (TB-IGRA) with metagenomic next-generation sequencing (mNGS) for PTB diagnosis., Methods: A retrospective study of 29 PTB and 32 non-TB patients from our hospital was conducted between October 2022 and June 2023. Samples were processed for TB-IGRA and mNGS tests according to the manufacturer's protocol., Results: The levels of IFN-γ release in PTB patients were significantly higher than those in non-TB patients (604.15 ± 112.18 pg/mL, and 1.04 ± 0.38 pg/mL, respectively; p < 0.0001). Regarding presenting symptoms or signs, cough and thoracalgia were less common in PTB patients than in non-TB patients (p = 0.001 and p = 0.024, respectively). Total protein and albumin levels in the sera of PTB patients were significantly elevated compared to non-TB patients (p = 0.039 and p = 0.004, respectively). The area under the ROC curve (AUC) for TB-IGRA in PTB diagnosis was 0.939. With an optimal IFN-γ cut-off value of 14.3 pg/mL (Youden's index 0.831), sensitivity was 86.2% and specificity was 96.9%. ROC curve analysis for mNGS and TB-IGRA combined with mNGS showed AUCs of 0.879 and 1, respectively. The AUC of TB-IGRA combined with mNGS was higher than that of TB-IGRA and mNGS alone., Conclusions: TB-IGRA combined with mNGS may be an effective method for diagnosing tuberculosis, and can be used in the clinical diagnosis of PTB., Competing Interests: Declarations Ethics approval and consent to participate The study was approved by the ethical research committee of the First Affiliated Hospital of Anhui Medical University (Approval No. PJ-2023-13-35) and was conducted in accordance with the Declaration of Helsinki. As the study was based on a retrospective review of anonymous medical records and did not involve patient interaction, informed consents of the patients were waived by the ethical research committee of the First Affiliated Hospital of Anhui Medical University. Consent for publication Not applicable. Clinical trial number Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Konjac seeds of Amorphophallus muelleri are produced through a unique form of apomixis in triploid parthenogenesis, and typically require a longer maturation period (approximately 8 months). To date, the relevant functions of endophytic microbial taxa during A. muelleri seed development and maturation remain largely unexplored. In this study, we analyzed the functional adaptability and temporal dynamics of endophytic microbial communities during three stages of A. muelleri seed maturation. Through metagenomic sequencing, we determined that the functions of the endophytic microbiome in A. muelleri seeds were driven by the seed maturation status, and the functions of the microbial communities in the seed coats and seeds differed significantly. The species annotation results show that Proteobacteria, Actinobacteria, Ascomycota, and Basidiomycota were the dominant bacterial and fungal communities in A. muelleri seeds at different maturation stages. The KEGG and COG functional gene annotation results revealed that the seed samples during the three maturation stages had higher KO functional diversity than the seed coat samples, and the COG functional diversity of the green and red seed samples was also significantly higher than that of the seed coat samples. At different maturation stages, microbial functional genes involved in energy production and conversion as well as carbon fixation were enriched in the A. muelleri seed coats, while microbial functional genes involved in signal transduction mechanisms, amino acid transport and metabolism, carbohydrate metabolism, and lipid metabolism were more highly expressed in the seeds. Moreover, in the middle to late stages of seed maturation, the microbial functional genes involved in the biosynthesis of resistant compounds such as phenols, flavonoids, and alkaloids were significantly enriched to enhance the resistance and environmental adaptation of A. muelleri seeds. The results verified that the functions of the endophytic microbial communities change dynamically during A. muelleri seed maturation to adapt to the current needs of the host plant, which has significant implications for the exploration and utilization of functional microbial resources in A. muelleri seeds., Competing Interests: Declarations Competing interests The authors declare no competing interests. Consent for publication Not applicable., (© 2024. The Author(s).)

Background: Microbes produce diverse bioactive natural products with applications in fields such as medicine and agriculture. In their genomes, these natural products are encoded by physically clustered genes known as biosynthetic gene clusters (BGCs). Genome and metagenome sequencing advances have enabled high-throughput identification of BGCs as a promising avenue for natural product discovery. BGC mining from (meta)genomes using in silico tools has allowed access to a vast diversity of potentially novel natural products. However, a fundamental limitation has been the ability to assemble complete BGCs, especially from complex metagenomes. With their fragmented assemblies, short-read technologies struggle to recover complete BGCs, such as the long and repetitive nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS). Recent advances in long-read sequencing, such as the High Fidelity (HiFi) technology from PacBio, have reduced this limitation and can help retrieve both accurate and complete BGCs from metagenomes, warranting improvement in the existing BGC identification approach for better utilization of HiFi data., Results: Here, we present HiFiBGC, a command-line-based workflow to identify BGCs in PacBio HiFi metagenomes. HiFiBGC leverages an ensemble of assemblies from three HiFi-tailored metagenome assemblers and the reads not represented in these assemblies. Based on our analyses of four HiFi metagenomic datasets from four different environments, we show that HiFiBGC identifies, on average, 78% more BGCs than the top-performing single-assembler-based method. This increase is due to HiFiBGC's ensemble assembly approach, which improves recovery by 25%, as well as from the inclusion of mostly fragmented BGCs identified in the unmapped reads., Conclusions: HiFiBGC is a computational workflow for identifying BGCs in long-read HiFi metagenomes, implemented majorly using Python programming language and workflow manager Snakemake. HiFiBGC is available on GitHub at https://github.com/ay-amityadav/HiFiBGC under the MIT license. The code related to the figures and analyses presented in the manuscript is available at https://github.com/ay-amityadav/HiFiBGC&#95;analyses ., Competing Interests: Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Objective: The influence of different faecal collection methods on metagenomic analyses remains under discussion, and there is no general agreement on which collection method is preferable for gut microbiome research. We compared faecal samples collected in tubes without preservatives with those containing 10 mL of 96% ethanol for gut microbiome research when the timeframe from defecation to freezing at - 80 °C was up to 24 h. We aimed to compare the collection methods on faeces from participants with inflammatory and non-inflammatory gastrointestinal disorders and healthy controls to investigate the most suitable method when considering data yield, human fraction of sequencing reads, and ease of use. We also examined the faecal sample homogeneity., Results: Faeces collected in tubes without preservatives resulted in more sequencing reads compared to faeces collected in tubes with 96% ethanol and were also easier to handle. The human fraction of total reads in faeces collected in ethanol from participants with inflammatory bowel disease was higher than all other samples. DNA extraction and sequencing from two different locations in the same faecal sample gave similar results and showed sample homogeneity., Competing Interests: Declarations Ethics approval and consent to participate The study was performed in accordance with the Revised Declaration of Helsinki. The study was registered at www.clinicaltrials.gov as NCT04808271 and NCT04842149. All participants provided written informed consent to participate after verbal and written information was given. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Trachinotus blochii is a high-value tropical mariculture species. The present study evaluated the gut microbial impact of therapeutic exposure (80 mg/day/kg biomass for 10 days) to oxytetracycline, the most common aquaculture antibiotic in T. blochii. The cultivable counts, α-diversity measures of taxonomic and functional metagenomics, microbial dysbiosis (MD) index, and microbial taxon abundances showed the resilience of gut microbiota at 16-26 days of treatment. A significant reduction in bacterial abundance, diversity measures, Firmicutes and Actinobacteria and an increase in γ-Proteobacteria was recorded on the 6th and 11th day of treatment. The increased metagenomic stress signatures, decreased beneficial bacterial abundances, decreased abundance of microbial pathways on energy metabolism, and MD index indicated short-term transient stress during the initial days of therapeutic withdrawal, warranting health management measures. Therapeutic exposure reduced the abundance of fish pathogens, including Vibrio spp., kanamycin and ampicillin-resistant bacteria. Strikingly, oxytetracycline treatment did not increase tetracycline-resistant bacterial counts and the predicted abundance of tetracycline resistance encoding genes in the gut, illustrating that therapeutic application would not pose a risk in the context of antimicrobial resistance in short term. Altogether, the present study provides a foundation for oxytetracycline treatment to develop suitable risk minimization tactics in sustainable aquaculture., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The gut microbiota is crucial for human health, functioning as a complex adaptive system akin to a vital organ. To identify core health-relevant gut microbes, we followed the systems biology tenet that stable relationships signify core components. By analyzing metagenomic datasets from a high-fiber dietary intervention in type 2 diabetes and 26 case-control studies across 15 diseases, we identified a set of stably correlated genome pairs within co-abundance networks perturbed by dietary interventions and diseases. These genomes formed a "two competing guilds" (TCGs) model, with one guild specialized in fiber fermentation and butyrate production and the other characterized by virulence and antibiotic resistance. Our random forest models successfully distinguished cases from controls across multiple diseases and predicted immunotherapy outcomes through the use of these genomes. Our guild-based approach, which is genome specific, database independent, and interaction focused, identifies a core microbiome signature that serves as a holistic health indicator and a potential common target for health enhancement., Competing Interests: Declaration of interests L.Z. is a co-founder of Notitia Biotechnologies Company. Patent WO2023212563A1 is related to this work. Patent app. no: 63/595,189 is related to this work., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

During the coronavirus disease 2019 (COVID-19) pandemic, the exploration of microecology has been essential for elucidating the intricacies of infection mechanisms and the recovery of afflicted individuals. To decipher the interplay of microorganisms between the intestinal and respiratory tracts, we collected sputum and throat swabs and feces from COVID-19 patients and explored the mutual migration among intestinal and respiratory microorganisms. Using next-generation sequencing (NGS) technology, we investigated intestinal and respiratory microorganism intermigration in two patients with severe COVID-19 during their hospitalization. Notably, we observed an expedited recovery of microecological equilibrium in one patient harboring Mycobacterium avium. Comparative analyses between 32 healthy controls and 110 COVID-19 patients with different disease severities revealed alterations in predominant microorganisms inhabiting the respiratory and intestinal tracts of COVID-19 patients. Among the alterations, intestinal Bacteroides vulgatus (BV) was identified as a noteworthy microorganism that exhibited marked enrichment in patients with severe COVID-19. BV, when highly abundant, may inhibit the transitional growth of Escherichia coli/Enterococcus, indirectly prevent the overgrowth of salivary streptococci, and maintain lung/intestinal microecology stability. In summary, this study elucidates the bidirectional microbial intermigration between the intestinal and respiratory tracts in COVID-19 patients. These findings are expected to provide new ideas for the treatment and management of COVID-19, underscoring the essential role of microecology in infectious diseases. Nevertheless, a systematic study of the roles of BV in recovery from infection is required to gain a deeper understanding of the mechanisms of microbial migration., Competing Interests: Competing interests The authors declare no competing interests. Ethics statement This study conformed to the provisions of the Helsinki Declaration of 1975 (as revised in 2008) concerning human rights and was approved by the ethics committee at the First Affiliated Hospital of Guangzhou Medical University, China (ethical approval number: 2020-36). All patients provided written informed consent and volunteered to participate in the scientific research., (© 2024. The Author(s).)

Tools for rapid identification of novel and/or emerging viruses are urgently needed for clinical diagnosis of unexplained infections and pandemic preparedness. Here we developed and clinically validated a largely automated metagenomic next-generation sequencing (mNGS) assay for agnostic detection of respiratory viral pathogens from upper respiratory swab and bronchoalveolar lavage samples in <24 h. The mNGS assay achieved mean limits of detection of 543 copies/mL, viral load quantification with 100% linearity, and 93.6% sensitivity, 93.8% specificity, and 93.7% accuracy compared to gold-standard clinical multiplex RT-PCR testing. Performance increased to 97.9% overall predictive agreement after discrepancy testing and clinical adjudication, which was superior to that of RT-PCR (95.0% agreement). To enable discovery of novel, sequence-divergent human viruses with pandemic potential, de novo assembly and translated nucleotide algorithms were incorporated into the automated SURPI+ computational pipeline used by the mNGS assay for pathogen detection. Using in silico analysis, we showed that after removal of all human viral sequences from the reference database, 70 (100%) of 70 representative human viral pathogens could still be identified based on homology to related animal or plant viruses. Our assay, which was granted breakthrough device designation from the US Food and Drug Administration (FDA) in August of 2023, demonstrates the feasibility of routine mNGS testing in clinical and public health laboratories, thus facilitating a robust and rapid response to the next viral pandemic., Competing Interests: Competing interests C.Y.C. is a founder of Delve Bio and on the scientific advisory board for Delve Bio, Flightpath Biosciences, Biomeme, Mammoth Biosciences, BiomeSense and Poppy Health. He is also an inventor on US patent 11380421, “Pathogen detection using next generation sequencing”, under which algorithms for taxonomic classification, filtering, and pathogen detection are used by SURPI+ software. C.Y.C. receives research support from Delve Bio and Abbott Laboratories, Inc. The other authors declare no competing interests., (© 2024. The Author(s).)

The link between the oral microbiome and neurodevelopmental disorders remains a compelling hypothesis, still requiring confirmation in large-scale datasets. Leveraging over 7000 whole-genome sequenced salivary samples from 2025 US families with children diagnosed with autism spectrum disorders (ASD), our cross-sectional study shows that the oral microbiome composition can discriminate ASD subjects from neurotypical siblings (NTs, AUC = 0.66), with 108 differentiating species (q < 0.005). The relative abundance of these species is highly correlated with cognitive impairment as measured by Full-Scale Intelligence Quotient (IQ). ASD children with IQ < 70 also exhibit lower microbiome strain sharing with parents (p < 10 -6 ) with respect to NTs. A two-pronged functional enrichment analysis suggests the contribution of enzymes from the serotonin, GABA, and dopamine degradation pathways to the distinct microbial community compositions observed between ASD and NT samples. Although measures of restrictive eating diet and proxies of oral hygiene show relatively minor effects on the microbiome composition, the observed associations with ASD and IQ may still represent unaccounted-for underlying differences in lifestyle among groups. While causal relationships could not be established, our study provides substantial support to the investigation of oral microbiome biomarkers in ASD., Competing Interests: Competing interests The authors do not have any competing interests to declare., (© 2024. The Author(s).)

Background: Sequencing-based genetic testing is widely used in biomedical research, including pathogenic microorganism detection with metagenomic next-generation sequencing (mNGS). The application of sequencing results to clinical diagnosis and treatment relies on various interpretation knowledge bases. Currently, the existing knowledge bases are primarily built through manual knowledge extraction. This method requires professionals to read extensive literature and extract relevant knowledge from it, which is time-consuming and costly. Furthermore, manual extraction unavoidably introduces subjective biases. In this study, we aimed to automatically extract knowledge for interpreting mNGS results., Method: We propose a novel approach to automatically extract pathogenic microorganism knowledge based on the question-answer (QA) model. First, we construct a MicrobeDB dataset since there is no available pathogenic microorganism QA dataset for training the model. The created dataset contains 3,161 samples from 618 published papers covering 224 pathogenic microorganisms. Then, we fine-tune the selected baseline model based on MicrobeDB. Finally, we utilize ChatGPT to enhance the diversity of training data, and employ data expansion to increase training data volume., Results: Our method achieves an Exact Match (EM) and F1 score of 88.39% and 93.18%, respectively, on the MicrobeDB test set. We also conduct ablation studies on the proposed data augmentation method. In addition, we perform comparative experiments with the ChatPDF tool based on the ChatGPT API to demonstrate the effectiveness of the proposed method., Conclusions: Our method is effective and valuable for extracting pathogenic microorganism knowledge., (© 2024. The Author(s).)

Hybrid metagenomic assembly of microbial communities, leveraging both long- and short-read sequencing technologies, is becoming an increasingly accessible approach, yet its widespread application faces several challenges. High-quality references may not be available for assembly accuracy comparisons common for benchmarking, and certain aspects of hybrid assembly may benefit from dataset-dependent, empiric guidance rather than the application of a uniform approach. In this study, several simple, reference-free characteristics-particularly coding gene content and read recruitment profiles-were hypothesized to be reliable indicators of assembly quality improvement during iterative error-fixing processes. These characteristics were compared to reference-dependent genome- and gene-centric analyses common for microbial community metagenomic studies. Two laboratory-scale bioreactors were sequenced with short- and long-read platforms, and assembled with commonly used software packages. Following long read assembly, long read correction and short read polishing were iterated up to ten times to resolve errors. These iterative processes were shown to have a substantial effect on gene- and genome-centric community compositions. Simple, reference-free assembly characteristics, specifically changes in gene fragmentation and short read recruitment, were robustly correlated with advanced analyses common in published comparative studies, and therefore are suitable proxies for hybrid metagenome assembly quality to simplify the identification of the optimal number of correction and polishing iterations. As hybrid metagenomic sequencing approaches will likely remain relevant due to the low added cost of short-read sequencing for differential coverage binning or the ability to access lower abundance community members, it is imperative that users are equipped to estimate assembly quality prior to downstream analyses., Competing Interests: The authors declare there are no competing interests., (©2024 Smith et al.)

Background: Systemic lupus erythematosus (SLE) is linked to host gut dysbiosis. Here we performed faecal gut microbiome sequencing to investigate SLE-pathogenic gut microbes and their potential mechanisms., Methods: There were 134 healthy controls (HCs) and 114 SLE cases for 16 S ribosomal RNA (rRNA) sequencing and 97 HCs and 124 SLE cases for shotgun metagenomics. Faecal microbial changes and associations with clinical phenotypes were evaluated, and SLE-associated microbial genera were identified in amplicon analysis. Next, metagenomic sequencing was applied for accurate identification of microbial species and discovery of their metabolic pathways and immunogenic peptides both relevant to SLE. Finally, contribution of specific taxa to disease development was confirmed by oral gavage into lupus-prone MRL/lpr mice., Results: SLE patients had gut microbiota richness reduction and composition alteration, particularly lupus nephritis and active patients. Proteobacteria/Bacteroidetes (P/B) ratio was remarkably up-regulated, and Escherichia was identified as the dominantly expanded genus in SLE, followed by metagenomics accurately located Escherichia coli and Escherichia unclassified species. Significant associations primarily appeared among Escherichia coli, metabolic pathways of purine nucleotide salvage or peptidoglycan maturation and SLE disease activity index (SLEDAI), and between multiple epitopes from Escherichia coli and disease activity or renal involvement phenotype. Finally, gavage with faecal Escherichia revealed that it upregulated lupus-associated serum traits and aggravated glomerular lesions in MRL/lpr mice., Conclusion: We characterize a novel SLE exacerbating Escherichia outgrowth and suggest its contribution to SLE procession may be partially associated with metabolite changes and cross-reactivity of gut microbiota-associated epitopes and host autoantigens. The findings could provide a deeper insight into gut Escherichia in the procession of SLE., (© 2024. The Author(s).)

In lichen research, metagenomes are increasingly being used for evaluating symbiont composition and metabolic potential, but the overall content and limitations of these metagenomes have not been assessed. We reassembled over 400 publicly available metagenomes, generated metagenome-assembled genomes (MAGs), constructed phylogenomic trees, and mapped MAG occurrence and frequency across the data set. Ninety-seven percent of the 1,000 recovered MAGs were bacterial or the fungal symbiont that provides most cellular mass. Our mapping of recovered MAGs provides the most detailed survey to date of bacteria in lichens and shows that 4 family-level lineages from 2 phyla accounted for as many bacterial occurrences in lichens as all other 71 families from 16 phyla combined. Annotation of highly complete bacterial, fungal, and algal MAGs reveals functional profiles that suggest interdigitated vitamin prototrophies and auxotrophies, with most lichen fungi auxotrophic for biotin, most bacteria auxotrophic for thiamine and the few annotated algae with partial or complete pathways for both, suggesting a novel dimension of microbial cross-feeding in lichen symbioses. Contrary to longstanding hypotheses, we found no annotations consistent with nitrogen fixation in bacteria other than known cyanobacterial symbionts. Core lichen symbionts such as algae were recovered as MAGs in only a fraction of the lichen symbioses in which they are known to occur. However, the presence of these and other microbes could be detected at high frequency using small subunit rRNA analysis, including in many lichens in which they are not otherwise recognized to occur. The rate of MAG recovery correlates with sequencing depth, but is almost certainly influenced by biological attributes of organisms that affect the likelihood of DNA extraction, sequencing and successful assembly, including cellular abundance, ploidy and strain co-occurrence. Our results suggest that, though metagenomes are a powerful tool for surveying microbial occurrence, they are of limited use in assessing absence, and their interpretation should be guided by an awareness of the interacting effects of microbial community complexity and sequencing depth., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tagirdzhanova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

While the ruminant gut archaeome regulates the gut microbiota and hydrogen balance, it is also a major producer of the greenhouse gas methane. However, ruminant gut archaeome diversity within the gastrointestinal tract (GIT) of ruminant animals worldwide remains largely underexplored. Here, we construct a catalogue of 998 unique archaeal genomes recovered from the GITs of ruminants, utilizing 2270 metagenomic samples across 10 different ruminant species. Most of the archaeal genomes (669/998 = 67.03%) belong to Methanobacteriaceae and Methanomethylophilaceae (198/998 = 19.84%). We recover 47/279 previously undescribed archaeal genomes at the strain level with completeness of >80% and contamination of <5%. We also investigate the archaeal gut biogeography across various ruminants and demonstrate that archaeal compositional similarities vary significantly by breed and gut location. The catalogue contains 42,691 protein clusters, and the clustering and methanogenic pathway analysis reveal strain- and host-specific dependencies among ruminant animals. We also find that archaea potentially carry antibiotic and metal resistance genes, mobile genetic elements, virulence factors, quorum sensors, and complex archaeal viromes. Overall, this catalogue is a substantial repository for ruminant archaeal recourses, providing potential for advancing our understanding of archaeal ecology and discovering strategies to regulate methane production in ruminants., (© 2024. The Author(s).)

The rapid advancements in sequencing technologies and bioinformatics have enabled metagenomic research of complex microbial systems, but reliable results depend on consistent laboratory and bioinformatics approaches. Current efforts to identify best practices often focus on optimizing specific steps, making it challenging to understand the influence of each stage on microbial population analysis and compare data across studies. This study evaluated DNA extraction, library construction methodologies, sequencing platforms, and computational approaches using a dog stool sample, two synthetic microbial community mixtures, and various sequencing data sources. Our work, the most comprehensive evaluation of metagenomic methods to date. We developed a software tool, termed minitax, which provides consistent results across the range of platforms and methodologies. Our findings showed that the Zymo Research Quick-DNA HMW MagBead Kit, Illumina DNA Prep library preparation method, and the minitax bioinformatics tool were the most effective for high-quality microbial diversity analysis. However, the effectiveness of pipelines or method combinations is sample-specific, making it difficult to identify a universally optimal approach. Therefore, employing multiple approaches is crucial for obtaining reliable outcomes in microbial systems., (© 2024. The Author(s).)

Trillions of microbes colonize the ungulate gastrointestinal tract, playing a pivotal role in enhancing host nutrient utilization by breaking down cellulose and hemicellulose present in plants. Here, through large-scale metagenomic assembly, we established a catalog of 131,416 metagenome-assembled genomes (MAGs) and 11,175 high-quality species-level genome bins (SGBs) from 17 species of ungulates in China. Our study revealed the convergent evolution of high relative abundances of carbohydrate-active enzymes (CAZymes) in the gut microbiomes of plateau-dwelling ungulates. Notably, two significant factors contribute to this phenotype: structural variations in their gut microbiome genomes, which contain more CAZymes, and the presence of novel gut microbiota species, particularly those in the genus Cryptobacteroides, which are undergoing independent rapid evolution and speciation and have higher gene densities of CAZymes. Furthermore, these enrichment CAZymes in the gut microbiomes are highly enrichment in known metabolic pathways for short-chain fatty acid (SCFA) production. Our findings not only provide a valuable genomic resource for understanding the gut microbiomes of ungulates but also offer fresh insights into the interaction between gut microbiomes and their hosts, as well as the co-adaptation of hosts and their gut microbiomes to their environments., (© 2024. The Author(s).)

Tourism in Paleolithic caves can cause an imbalance in cave microbiota and lead to cave wall alterations, such as dark zones. However, the mechanisms driving dark zone formation remain unclear. Using shotgun metagenomics in Lascaux Cave's Apse and Passage across two years, we tested metabarcoding-derived functional hypotheses regarding microbial diversity and metabolic potential in dark zones vs unmarked surfaces nearby. Taxonomic and functional metagenomic profiles were consistent across years but divergent between cave locations. Aromatic compound degradation genes were prevalent inside and outside dark zones, as expected from past biocide usage. Dark zones exhibited enhanced pigment biosynthesis potential (melanin and carotenoids) and melanin was evidenced chemically, while unmarked surfaces showed genes for antimicrobials production, suggesting that antibiosis might restrict the development of pigmented microorganisms and dark zone extension. Thus, this work revealed key functional microbial traits associated with dark zone formation, which helps understand cave alteration processes under severe anthropization., (© 2024. The Author(s).)

Background: Identifying the responsible pathogen is crucial for precision medicine in intracranial infections, and Cerebrospinal Fluid (CSF) Metagenomic Next-Generation Sequencing (mNGS) is a reliable method for this detection. However, the indiscriminate utilization of this approach may impose a financial burden on both patients and society. The study aims to investigate the optimal conditions for applying CSF mNGS in patients with suspected intracranial infections, offering valuable references for precision medicine of intracranial infections., Methods: A total of 175 hospitalized patients presenting with suspected intracranial infections were selected for retrospective analysis. Base on the detection of responsible pathogens using CSF mNGS, the patients were categorized into two groups, responsible pathogens in Group A were detected but not in Group B. The types of responsible pathogens in group A and the final diagnosis of patients in group B were analyzed. Demographic data, clinical presentation, CSF analysis, imaging results, and electroencephalography (EEG) findings were analyzed for both groups. Finally, a scoring system was established to promptly assess the appropriateness of CSF mNGS for patients with suspected intracranial infections. Each independent predictor was assigned a score of 1, and the patients were subsequently scored. We advocate sending patients' CSF for mNGS when the cumulative score is ≥ 2., Results: In Group A, the predominant responsible pathogen was the varicella-zoster virus (VZV), while Group B exhibited the highest proportion of final diagnoses related to epilepsy. The logistic regression model indicates that headache [OR = 2.982, 95% CI (1.204-7.383), p = 0.018], increased cerebrospinal fluid white cell count [OR = 4.022, 95% CI (1.331-12.156), p = 0.014], and decreased cerebrospinal fluid glucose levels [OR = 9.006, 95% CI (2.778-29.194), P < 0.001] are independent predictive factors for intracranial infection pathogens detected by CSF mNGS. Under this scoring system, the sensitivity for detecting the responsible pathogen was 57.5%, and the specificity was 87.4%., Conclusion: The likelihood of detecting the responsible pathogen through CSF mNGS in patients with suspected intracranial infections can be evaluated using the scoring system. Furthermore, it is crucial to consider the possibility of another condition, such as epilepsy, when the responsible pathogen is not detected using cerebrospinal fluid mNGS., (© 2024. The Author(s).)

The use of metagenomic next-generation sequencing technology to obtain complete viral genome sequences directly from clinical samples with low viral load remains challenging-especially in the case of respiratory viruses-due to the low copy number of viral versus host genomes. To overcome this limitation, target capture sequencing for the enrichment of specific genomes has been developed and applied for direct genome sequencing of viruses. However, as the efficiency of enrichment varies depending on the probes, the type of clinical sample, etc., validation is essential before target capture sequencing can be applied to clinical diagnostics. In this study, we evaluated the utility of target capture sequencing with a comprehensive viral probe panel for clinical respiratory specimens collected from patients diagnosed with SARS-CoV-2 or influenza type A. We focused on clinical specimens containing low copy numbers of viral genomes. Target capture sequencing yielded approximately 180- and 2,000-fold higher read counts of SARS-CoV-2 and influenza A virus, respectively, than metagenomic sequencing when the RNA extracted from specimens contained 59.3 copies/µL of SARS-CoV-2 or 625.1 copies/µL of influenza A virus. In addition, the target capture sequencing identified sequence reads in all SARS-CoV-2- or influenza type A-positive specimens with <26 RNA copies/µL, some of which also yielded >70% of the full-length genomes of SARS-CoV-2 or influenza A virus. Furthermore, the target capture sequencing using comprehensive probes identified co-infections with viruses other than SARS-CoV-2, suggesting that this approach will not only detect a wide range of viruses but also contribute to epidemiological studies.IMPORTANCETarget capture sequencing has been developed and applied for direct genome sequencing of viruses in clinical specimens to overcome the low detection sensitivity of metagenomic next-generation sequencing. In this study, we evaluated the utility of target capture sequencing with a comprehensive viral probe panel for clinical respiratory specimens collected from patients diagnosed with SARS-CoV-2 or influenza type A, focusing on clinical specimens containing low copy numbers of viral genomes. Our results showed that the target capture sequencing yielded dramatically higher read counts than metagenomic sequencing for both viruses. Furthermore, the target capture sequencing using comprehensive probes identified co-infections with other viruses, suggesting that this approach will not only detect a wide range of viruses but also contribute to epidemiological studies., Competing Interests: The authors declare no conflict of interest.

With the development of sequencing technology and analytic tools, studying within-species variations enhances the understanding of microbial biological processes. Nevertheless, most existing methods designed for strain-level analysis lack the capability to concurrently assess both strain proportions and genome-wide single nucleotide variants (SNVs) across longitudinal metagenomic samples. In this study, we introduce LongStrain, an integrated pipeline for the analysis of large-scale metagenomic data from individuals with longitudinal or repeated samples. In LongStrain, we first utilize two efficient tools, Kraken2 and Bowtie2, for the taxonomic classification and alignment of sequencing reads, respectively. Subsequently, we propose to jointly model strain proportions and shared haplotypes across samples within individuals. This approach specifically targets tracking a primary strain and a secondary strain for each subject, providing their respective proportions and SNVs as output. With extensive simulation studies of a microbial community and single species, our results demonstrate that LongStrain is superior to two genotyping methods and two deconvolution methods across a majority of scenarios. Furthermore, we illustrate the potential applications of LongStrain in the real data analysis of The Environmental Determinants of Diabetes in the Young study and a gastric intestinal metaplasia microbiome study. In summary, the proposed analytic pipeline demonstrates marked statistical efficiency over the same type of methods and has great potential in understanding the genomic variants and dynamic changes at strain level. LongStrain and its tutorial are freely available online at https://github.com/BoyanZhou/LongStrain., Importance: The advancement in DNA-sequencing technology has enabled the high-resolution identification of microorganisms in microbial communities. Since different microbial strains within species may contain extreme phenotypic variability (e.g., nutrition metabolism, antibiotic resistance, and pathogen virulence), investigating within-species variations holds great scientific promise in understanding the underlying mechanism of microbial biological processes. To fully utilize the shared genomic variants across longitudinal metagenomics samples collected in microbiome studies, we develop an integrated analytic pipeline (LongStrain) for longitudinal metagenomics data. It concurrently leverages the information on proportions of mapped reads for individual strains and genome-wide SNVs to enhance the efficiency and accuracy of strain identification. Our method helps to understand strains' dynamic changes and their association with genome-wide variants. Given the fast-growing longitudinal studies of microbial communities, LongStrain which streamlines analyses of large-scale raw sequencing data should be of great value in microbiome research communities., Competing Interests: The authors declare no conflict of interest.

Failure to consider population structure when managing harvested fishes increases the risk of stock depletion, yet empirical estimates of population structure are often lacking for important fishery species. In this study, we characterise genetic variation in single nucleotide polymorphisms (SNPs) to assess population structure for three harvested species of tropical snappers across the broad (up to 300 km wide) and extensive (~ 4000 km) continental shelf of north-western Australia. Comparisons across ~ 300 individuals per species, showed remarkably similar patterns of genetic structure among Lutjanus sebae (red emperor), L. malabaricus (saddletail snapper) and Pristipomoides multidens (goldband snapper) despite subtle differences in biological and ecological traits. Low levels of genetic subdivision were reflected in an isolation by distance relationship where genetic connectivity increased with geographic proximity. This indicates extensive but not unlimited dispersal across the north-western Australian shelf. Our findings provide evidence of connectivity between current management areas, violating the assumption of multiple independent stocks. Spatial stock assessment models may be more suitable for the management of these species however demographic connectivity rates cannot be accurately estimated from the conventional population genetic approaches applied in this study. We recommend that managers aim to maintain adequate spawning biomass across current management areas, and assess stocks at finer scales, where practical., (© 2024. Crown.)

Background: Metagenomics, the whole genome sequencing of microbial communities, has provided insight into complex ecosystems. It has facilitated the discovery of novel microorganisms, explained community interactions and found applications in various fields. Advances in high-throughput and third-generation sequencing technologies have further fuelled its popularity. Nevertheless, managing the vast data produced and addressing variable dataset quality remain ongoing challenges. Another challenge arises from the number of assembly and binning strategies used across studies. Comparing datasets and analysis tools is complex as it requires the quantitative assessment of metagenome quality. The inherent limitations of metagenomic sequencing, which often involves sequencing complex communities, mean community members are challenging to interrogate with traditional culturing methods leading to many lacking reference sequences. MIMAG standards aim to provide a method to assess metagenome quality for comparison but have not been widely adopted., Results: To address the need for simple and quick metagenome quality assignation, here we introduce the pipeline MAGqual (Metagenome-Assembled Genome qualifier) and demonstrate its effectiveness at determining metagenomic dataset quality in the context of the MIMAG standards., Conclusions: The MAGqual pipeline offers an accessible way to evaluate metagenome quality and generate metadata on a large scale. MAGqual is built in Snakemake to ensure readability and scalability, and its open-source nature promotes accessibility, community development, and ease of updates. MAGqual is built in Snakemake, R, and Python and is available under the MIT license on GitHub at https://github.com/ac1513/MAGqual . Video Abstract., (© 2024. The Author(s).)

Objective: This study aims to evaluate the clinical management and effectiveness of metagenomic next-generation sequencing (mNGS) in patients with pyogenic spinal infections., Methods: We conducted a retrospective review of 17 patients diagnosed with pyogenic spinal infections and treated at our institution between October 2022 and February 2024. The cohort included 8 males and 9 females, with a mean age of 63.59 ± 10.18 years (range: 41-71 years). The infections comprised 9 epidural abscesses, 6 intervertebral space infections, and 2 deep abscesses. All patients underwent open surgical procedures and mNGS-based bacterial identification using intraoperative pus or tissue specimens, in addition to conventional blood bacterial cultures. Clinical outcomes were assessed using CRP, PCT, WBC inflammatory markers, and VAS scores postoperatively., Results: All 17 patients with pyogenic spinal infections underwent open surgery and mNGS bacterial detection at our institution. Among the 17 patients, mNGS yielded positive results in 14 cases (82.4%), significantly higher than the 5.9% positivity rate of conventional bacterial cultures (p < 0.001). The mNGS test time was notably shorter than conventional cultures (1.0 vs. 5.88 days, p < 0.001). Postoperative antibiotic therapy was adjusted based on mNGS findings. There were significant reductions in postoperative VAS, WBC, PCT, and CRP values compared to preoperative levels (p < 0.01)., Conclusion: Metagenomic next-generation sequencing is effective in managing pyogenic spinal infections by facilitating rapid and sensitive detection of pathogens. This technique improves the timeliness and accuracy of diagnosis, highlighting its potential for broader clinical use., (© 2024. The Author(s).)

Background: For patients with pneumonia, the rapid detection of pathogens is still a major global problem in clinical practice because traditional diagnostic techniques for infection are time-consuming and insensitive. Metagenomic next-generation sequencing (mNGS) is a novel technique that has the potential to improve pathogen diagnosis. This study aimed to investigate the microbiological diagnostic ability of mNGS compared with conventional culture and to determine the optimal time to test patients for pneumonia., Methods: A prospective study using data from June 2020 to June 2021 was performed at a tertiary teaching hospital in China. We included 56 patients from all adult patients with a clinical diagnosis of pneumonia. Blood and bronchoalveolar lavage fluid (BALF) samples were taken for simultaneous mNGS and conventional culture testing., Results: All 56 patients underwent both conventional culture and mNGS. Of these patients, 37 were diagnosed with severe pneumonia and 17 were diagnosed with non-severe pneumonia. The top three pathogenic bacteria detected by mNGS were Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Enterococcus faecium was detected more frequently in the non-severe pneumonia group (4 vs. 0, p < 0.05). The findings revealed that the detection rate of mNGS (84%) was superior to that of conventional culture methods (48%). Notably, the percentage of mNGS-positive BALF samples (46/56, 82.14%) was significantly greater than that of blood samples (27/56, 48.21%). The etiological comparison demonstrated that mNGS-positive samples, which received clinical approval, tended to be associated with a more normalized temperature, lower PCO2 levels, and a higher SOFA score than mNGS-negative samples (p = 0.022, p = 0.0.028, and p = 0.038, respectively)., Conclusions: In this study, we discovered that the etiology of lung infections frequently involves multiple pathogens. The use of mNGS in BALF is instrumental for detecting nonviral pathogens associated with lung infections. Although the rate of positive blood NGS results is significantly influenced by various clinical factors, for patients suspected of having viral, Legionella, or tsutsugamushi infections, plasma mNGS could serve as a complementary diagnostic tool., (© 2024. The Author(s).)

Background/aims: Colorectal cancer (CRC) is one of the deadliest cancers worldwide, mostly arising from adenomatous polyps. Mounting evidence has demonstrated that changes in the gut microbiome play key roles in CRC progression, while quite few studies focused on the altered microbiota architecture of advanced adenoma (AA), a crucial precancerous stage of CRC. Thus, we aimed to investigate the microbial profiles of AA patients., Materials and Methods: Fecal samples were collected from 26 AA patients and 26 age- and sex-matched normal controls (NC), and analyzed by shotgun metagenomic sequencing., Results: Gut microbial dysbiosis was observed in AA patients with lower alpha diversity. Advanced adenoma was characterized by an increased Bacillota/Bacteroidota ratio and higher Pseudomonadota levels compared to normal individuals. Linear discriminant analysis effect size (LEfSe) analysis was performed and identified 14 microbiota with significantly different abundance levels between AA and NC groups. Functional analysis revealed that tryptophan metabolism was upregulated in AA. Correspondingly, the expressions of gut microbes implicated in tryptophan metabolism also changed, including Akkermansia muciniphila, Bacteroides ovatus, Clostridium sporogenes, and Limosilactobacillus reuteri. The microbial network suggested that AA exhibited decreased correlation complexity, with Escherichia coli and Enterobacteriaceae unclassified harboring the strongest connectivity. A diagnostic model consisting of 3 microbial species was established based on random forest, yielding an area under the curve (AUC) of 0.799., Conclusion: Our study profiled the alterations of the gut microbiome in AA patients, which may enrich the knowledge of microbial signatures along with colorectal tumorigenesis and provide promising biomarkers for AA diagnosis.

The cervicovaginal microbiome (CVM) may contribute to human papillomavirus (HPV)-associated cervical carcinogenesis. We summarized the literature on the CVM in cervical carcinogenesis by searching Medline, Web of Science, and Embase for articles that sequenced the CVM using metagenomics. Additionally, we identified studies assessing the diagnostic role of the CVM in cervical carcinogenesis by searching PubMed. We performed an environmental scan of Google and Google Scholar to review common CVM characterization techniques. Twenty-eight records presented or summarized associations between the CVM and HPV acquisition, prevalence, persistence, clearance, and cervical lesions or cancer, while three studies identified bacterial taxa detecting high-risk HPV prevalence or cervical lesions. The area under the curve ranged from 0.802 to 0.952. 16S ribosomal RNA gene sequencing and whole metagenome sequencing have sufficient resolution to study the CVM bacteriome. Bacterial communities may have important implications in cervical cancer; however, there is a need for methodological standardization for CVM characterization., (© 2024 The Author(s). Journal of Medical Virology published by Wiley Periodicals LLC.)

Introduction. The existence of a mutual relationship between gut microbiota and immune homeostasis highlights its importance in the context of kidney transplantation. Gap statement. The translational utility of gut microbiota as a biomarker for allograft injury has not been assessed before. Aim. In this study, we aimed to characterize the gut microbial diversity in kidney transplant recipients and investigate the alterations in the gut microbial composition in association with allograft injury such as histopathological graft rejection and calcineurin inhibitor toxicity. In addition, we compared the gut microbial quantitation using 16S metagenomics and quantitative PCR (qPCR) to assess its translational utility. Methodology. In this prospective longitudinal cohort study, we enrolled 38 kidney transplant recipients and collected serial faecal specimens ( n =114), once before the induction therapy, and twice after transplant, during the first and third month. We characterized the gut microbial composition through 16S rRNA sequencing and qPCR from the DNA isolates of the samples. The recipients were clinically followed up for a median of 600 days post-transplant. Histopathological evidence of allograft rejection and calcineurin inhibitor toxicity were used for the correlational analysis with gut microbial diversity. Results. Significant differences in the gut microbial diversity were observed between the pre- and post-transplant samples. Pre-transplant gut microbiota revealed a higher relative abundance of phylum Bacteroidetes in the allograft rejection group, and a higher relative abundance of phylum Firmicutes was observed in the histopathological features of calcineurin inhibitor toxicity (hCNI toxicity) group. We found a high concordance between 16S metagenomics and qPCR outputs for assessing the gut microbial diversity. Furthermore, the receiver operating characteristic curve analysis has also proven that the pre-transplant levels of gut microbial dysbiosis, as a potential predictive biomarker for allograft injury. Conclusion. Our pilot study found a strong statistical association of gut microbial dysbiosis with kidney allograft injury, highlighting the potential of gut microbiota as a predictive biomarker and that qPCR serves as a more reliable and economic tool for assessing dysbiosis paving the way for its translational utility.

We performed nanopore-based metagenomic screening on 885 ticks collected from 6 locations in Mongolia and divided the results into 68 samples: 23 individual samples and 45 pools of 2-12 tick samples each. We detected bacterial and parasitic pathogens Anaplasma ovis, Babesia microti, Coxiella burnetii, Borrelia miyamotoi, Francisella tularensis subsp. holarctica and novicida, Spiroplasma ixodetis, Theileria equi, and Rickettsia spp., including R. raoultii, R. slovaca, and R. canadensis. We identified the viral pathogens Crimean-Congo hemorrhagic fever virus (2.9%), recently described Alongshan virus (ALSV) (2.9%), and Beiji nairovirus (5.8%). We assembled ALSV genomes, and maximum-likelihood analyses revealed clustering with viruses reported in humans and ticks from China. For ALSV, we identified surface glycoprotein markers associated with isolates from Asia viruses hosted by Ixodes persulcatus ticks. We also detected 20 virus species of unknown public health impact, including a near-complete Yanggou tick virus genome. Our findings demonstrate that nanopore sequencing can aid in detecting endemic and emerging tickborne pathogens.

Motivation: Microbiome differential abundance analysis (DAA) remains a challenging problem despite multiple methods proposed in the literature. The excessive zeros and compositionality of metagenomics data are two main challenges for DAA., Results: We propose a novel method called "Analysis of Microbiome Differential Abundance by Pooling Tobit Models" (ADAPT) to overcome these two challenges. ADAPT interprets zero counts as left-censored observations to avoid unfounded assumptions and complex models. ADAPT also encompasses a theoretically justified way of selecting non-differentially abundant microbiome taxa as a reference to reveal differentially abundant taxa while avoiding false discoveries. We generate synthetic data using independent simulation frameworks to show that ADAPT has more consistent false discovery rate control and higher statistical power than competitors. We use ADAPT to analyze 16S rRNA sequencing of saliva samples and shotgun metagenomics sequencing of plaque samples collected from infants in the COHRA2 study. The results provide novel insights into the association between the oral microbiome and early childhood dental caries., Availability and Implementation: The R package ADAPT can be installed from Bioconductor at https://bioconductor.org/packages/release/bioc/html/ADAPT.html or from Github at https://github.com/mkbwang/ADAPT. The source codes for simulation studies and real data analysis are available at https://github.com/mkbwang/ADAPT&#95;example., (© The Author(s) 2024. Published by Oxford University Press.)

Psychrotrophic and thermoduric bacteria are the main reasons for the spoilage of dairy products. This study aims to address the composition and function of psychrotrophic and thermoduric bacteria in eight groups of raw milk samples obtained from Heilongjiang Province and Inner Mongolia (China). Microbial enumeration showed an average total bacterial count of 4.63 log c.f.u. ml -1 and psychrotrophic bacterial counts of 4.82 log c.f.u. ml -1 . The mean counts of mesophilic and thermophilic thermoduric bacteria were 3.68 log and 1.81 log c.f.u. ml -1 , respectively. Isolated psychrotrophic bacteria (26 genera and 50 species) and mesophilic thermoduric bacteria (20 genera and 32 species) showed high microbial diversity. Through metagenomic and proteomic analyses, significant disparities in the concentration and community structure of psychrotrophic and thermoduric bacteria were observed among different locations. A large number of peptidases were annotated by metagenomics, which may result in milk spoilage. They mainly come from some typical psychrotrophic and thermoduric bacteria, such as Chryseobacterium , Epilithonimonas , Pseudomonas , Psychrobacter , Acinetobacter, Lactococcus, Escherichia and Bacillus . However, the main proteins detected in fresh raw milk were associated with bacterial growth, reproduction and adaptation to cold environments. This investigation provides valuable insights into the microbial communities and protein profiles of raw milk, shedding light on the microbial factors contributing to milk deterioration.

Water scarcity has evolved into a pressing global issue, significantly impacting numerous regions worldwide. The use of treated wastewater stands out as a promising solution to this problem. However, the proliferation of various contaminants, primarily Antimicrobial Resistance Genes (ARGs), poses a significant challenge to its safe and sustainable use. In this study, we assessed the composition and abundance of 373 ARGs, corresponding to 31 different classes of antibiotics, in six wastewater treatment plants (WWTP) in Agadir city of Morocco. Influent and effluent samples were collected during the months of February and July in 2020, in addition to samples from the Atlantic Ocean. In total, 223 ARGs were uncovered, highlighting in particular resistance to aminoglycoside, macrolide lincosamide, beta-lactamase, chloramphenicol, sulfonamide, tetracycline, and other antibiotics. The mechanisms of action of these ARGs were mainly antibiotic inactivation, antibiotic target alteration, efflux pump and cellular protection. Mobile genetic elements (MGEs) were detected at high levels their co-occurrence with ARGs highlights their involvement in the acquisition and transmission of ARGs in microbial communities through horizontal gene transfer. While many wastewater treatment methods effectively reduce a large proportion of gene material and pathogens, a substantial fraction of ARGs and other contaminants persist in treated wastewater. This persistence poses potential risks to both human health and the environment, warranting the need of more effective treatment strategies., (© 2024. The Author(s).)

Coal, a sedimentary rock harbours a complex microbial community that plays a significant role in its formation and characteristics. However, coal metagenome sequencing and studies were less, limiting our understanding of this complex ecosystem. This study aimed to reconstruct high-quality metagenome-assembled genomes (MAGs) from the coal sample collected in the Neyveli mine to explore the unrevealed diversity of the coal microbiome. Using Illumina sequencing, we obtained high-quality raw reads in FASTQ format. Subsequently, de novo assembly and binning with metaWRAP software facilitated the reconstruction of coal MAGs. Quality assessment using CheckM identified 10 High-Quality MAGs (HQ MAGs), 7 medium-quality MAGs (MQ MAGs), and 6 low-quality MAGs (LQ MAGs). Further analysis using GTDB-Tk revealed four HQ MAGs as known species like Dermacoccus abyssi, Sphingomonas aquatilis, Acinetobacter baumannii, and Burkholderia cenocepacia. The remaining six HQ MAGs were classified as Comamonas, Arthrobacter, Noviherbaspirillum, Acidovorax, Oxalicibacterium, and Bordetella and designated as novel genomes by the validation of digital DNA-DNA hybridization (dDDH). Phylogenetic analysis and further pangenome analysis across the phylogenetic groups revealed a similar pattern with a high proportion of cloud genes. We further analysed the functional potential of these MAGs and closely related genomes using COG. The comparative functional genomics revealed that novel genomes are highly versatile, potentially reflecting adaptations to the coal environment. BlastKOALA was used to conduct a detailed analysis of the metabolic pathways associated with the MAGs. This study highlights the comparative genomic analysis of novel coal genomes with their closely related genomes to understand the evolutionary relationships and functional properties., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

The diagnosis of fracture-related infections (FRI) is challenging and requires interdisciplinary efforts. Many diagnostic approaches are based on the algorithms established for prosthetic joint infections (PJI). Data specific to FRI are limited. Microbiological diagnostics include tissue culture, sonication, and molecular methods. Novel metagenomic analyses are increasingly being used in clinical diagnostic practice. In addition to bacterial detection, the study of host tissue factors has the potential to transform the diagnostics of FRI by facilitating the assesment of clinical significance in clinical samples. The integration of host tissue analysis into microbiology reports has great potential to improve the diagnosis of FRI. This mini-review describes the potential improvement of diagnostic techniques by integrating new approaches into the diagnostic algorithm of fracture-related infections., Competing Interests: Declaration of competing interest Ada Hoffmann(1): AH received a travel grant from Gilead. Jörgen Hoffmann(2): No conflict of interest. Tamara Ruegamer(1): TR received lecture honoraria from Hikma. Norma Jung(3): NJ has received lecture fees from AbbVie, Bayer, Infectopharm, and Medacta and travel grants from Gilead and Pfizer and fees for advisory board from MSD. Ronald Man Yeung Wong(4): No conflict of interest. Volker Alt(5): No conflict of interest. Peer Eysel(2): No conflict of interest. Jonathan Jantsch(1,6): No conflict of interest., (Copyright © 2024. Published by Elsevier Ltd.)

Objective: Fitz-Hugh-Curtis Syndrome (FHCS) is an inflammation of the liver capsule as a complication of pelvic inflammatory disease (PID) in sexually active women, mostly associated with Chlamydia trachomatis (C. trachomatis) and Neisseria gonorrhoeae. Classically presenting as sharp right upper quadrant pain, usually accompanied salpingitis and ascites. With nonspecific clinical presentation and poor specificity, definitive diagnosis needs tissue biopsy and culture by laparoscopy., Case Report: We report the case of a 22-year-old female with a 2-month history of abdominal pain and distention. Symptomatic relief when supportive treatments were given, with the ultrasound and PET-CT suggested advanced bilateral ovarian cancer. After metagenomic next-generation sequencing (mNGS) detected C. trachomatis in ascitic fluid. Following anti-infective medication, clinical improvement was satisfactory and the patient was discharged., Conclusion: FHCS with distention was rare and challenging to diagnose. The mNGS would be a potent, non-invasive pathogen detection method with significant sensitivity and specificity., Competing Interests: Declaration of competing interest The authors have no conflicts of interest relevant to this article., (Copyright © 2024. Published by Elsevier B.V.)

Background: The influence of the gut microbiota on long-term immune checkpoint inhibitor (ICI) efficacy and immune-related adverse events (irAEs) is poorly understood, as are the underlying mechanisms., Methods: We performed gut metagenome and metabolome sequencing of gut microbiotas from patients with lung cancer initially treated with anti-PD-1/PD-L1 therapy and explored the underlying mechanisms mediating long-term (median follow-up 1167 days) ICI responses and immune-related adverse events (irAEs). Results were validated in external, publicly-available datasets (Routy, Lee, and McCulloch cohorts)., Findings: The ICI benefit group was enriched for propionate (P = 0.01) and butyrate/isobutyrate (P = 0.12) compared with the resistance group, which was validated in the McCulloch cohort (propionate P < 0.001, butyrate/isobutyrate P = 0.002). The acetyl-CoA pathway (P = 0.02) in beneficial species mainly mediated butyrate production. Microbiota sequences from irAE patients aligned with antigenic epitopes found in autoimmune diseases. Microbiotas of responsive patients contained more lung cancer-related antigens (P = 0.07), which was validated in the Routy cohort (P = 0.02). Escherichia coli and SGB15342 of Faecalibacterium prausnitzii showed strain-level variations corresponding to clinical phenotypes. Metabolome validation reviewed more abundant acetic acid (P = 0.03), propionic acid (P = 0.09), and butyric acid (P = 0.02) in the benefit group than the resistance group, and patients with higher acetic, propionic, and butyric acid levels had a longer progression-free survival and lower risk of tumor progression after adjusting for histopathological subtype and stage (P < 0.05)., Interpretation: Long-term ICI survivors have coevolved a compact microbial community with high butyrate production, and molecular mimicry of autoimmune and tumor antigens by microbiota contribute to outcomes. These results not only characterize the gut microbiotas of patients who benefit long term from ICIs but pave the way for "smart" fecal microbiota transplantation. Registered in the Chinese Clinical Trial Registry (ChiCTR2000032088)., Funding: This work was supported by Beijing Natural Science Foundation (7232110), National High Level Hospital Clinical Research Funding (2022-PUMCH-A-072, 2023-PUMCH-C-054), CAMS Innovation Fund for Medical Sciences (CIFMS) (2022-I2M-C&T-B-010)., Competing Interests: Declaration of interests Authors declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Bacterial species in microbial communities are often represented by mixtures of strains, distinguished by small variations in their genomes. Short-read approaches can be used to detect small-scale variation between strains but fail to phase these variants into contiguous haplotypes. Long-read metagenome assemblers can generate contiguous bacterial chromosomes but often suppress strain-level variation in favor of species-level consensus. Here we present Strainy, an algorithm for strain-level metagenome assembly and phasing from Nanopore and PacBio reads. Strainy takes a de novo metagenomic assembly as input and identifies strain variants, which are then phased and assembled into contiguous haplotypes. Using simulated and mock Nanopore and PacBio metagenome data, we show that Strainy assembles accurate and complete strain haplotypes, outperforming current Nanopore-based methods and comparable with PacBio-based algorithms in completeness and accuracy. We then use Strainy to assemble strain haplotypes of a complex environmental metagenome, revealing distinct strain distribution and mutational patterns in bacterial species., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2' 3' cyclic 3' phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Phenol, quinoline, and pyridine, commonly found in industrial wastewater, disrupt the nitrification process, leading to nitrite accumulation. This study explores the potential mechanisms through which these biotoxic organic compounds affect nitrite accumulation, using metagenomic and molecular docking analyses. Despite increasing concentrations of these compounds from 40 to 160 mg/L, ammonia nitrogen removal was not hindered, and stable nitrite accumulation rates exceeding 90 % were maintained. Additionally, these compounds inhibited nitrite-oxidizing bacteria (NOB) and enriched ammonia-oxidizing bacteria (AOB) in situ. As the concentration of these compounds rose, protein (PN) and polysaccharide (PS) concentrations also increased, along with a higher PN/PS ratio. Metagenomic analysis further revealed an increase in hao relative abundance, while microbial community analysis showed increased Nitrosomonas abundance, which contributed to nitrite accumulation stability. Molecular docking indicated that these compounds have lower binding energy with hydroxylamine oxidoreductase (HAO) and nitrate reductase (NAR), theoretically supporting the observed sustained nitrite accumulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The bioleaching utilizing indigenous microbial inoculation can continuously improve the dewaterability of sludge. In this study, metagenomic analysis was innovative employed to identify the key microorganisms and functional genes that affect the dewatering performance of sludge in the bioleaching conditioning process. The results demonstrated that long-term repeated inoculation of acidified sludge resulted in increased abundance of many functional genes associated with the transport of carbohydrate and amino acid. Additionally, genes encoding key iron transport proteins (such as afuA, fhuC, and fhuD) and genes related to electron transfer carriers in ferrous iron oxidation process (such as rus and cyc2) were significantly enriched, thereby promoting the improvement of sludge dewatering performance through enhanced iron oxidation. Notably, Acidithiobacillus, Betaproteobacteria, and Hyphomicrobium were the major sources of functional genes. This study reveals the microscopic mechanisms underlying the improvement of sludge dewaterability through bioleaching based on mixed culture from a novel perspective of gene metabolism., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Early and accurate identification of pathogens in pulmonary infections after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is critically important. The clinical usefulness of metagenomic next-generation sequencing (mNGS) in the diagnosis of pulmonary infections after allo-HSCT remains under discussion. This multicenter retrospective study was conducted to compare mNGS and conventional microbiological tests (CMTs) in identifying the pathogens of pulmonary infections in allo-HSCT recipients. One hundred forty allo-HSCT recipients with suspected pulmonary infections who underwent bronchoscopy were included. mNGS and CMTs performed on bronchoalveolar lavage fluid specimens showed 71.4% positivity on mNGS compared to 55.0% positivity on CMTs. mNGS identified 182 pathogens, including bacteria (n = 88), fungi (n = 35) and viruses (n = 59), compared to 106 pathogens detected by CMTs (bacteria, n = 31; fungi, n = 24; viruses, n = 51). Pulmonary infection was finally diagnosed in 98 patients, including 22 bacterial, 7 fungal, 18 viral, and 48 mixed infections and 3 infections with an unknown pathogen. Mixed infections were identified in 50.5% of the patients with pulmonary infection. The sensitivity of mNGS and CMTs for diagnosing pulmonary infections was 88.8% and 69.4%, respectively (P = .001), and the specificity were 81.0% and 85.7%, respectively (P = .688). Our findings suggest that mNGS may be a promising technology for diagnosing pulmonary infections in allo-HSCT recipients., (Copyright © 2024 The American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved.)

Background: The airway microbiome in severe asthma has not been characterised at species-level by metagenomic sequencing, nor have the relationships between specific species and mucosal immune responses in 'type-2 low', neutrophilic asthma been defined. We performed an integrated species-level metagenomic data with inflammatory mediators to characterise prevalence of dominant potentially pathogenic organisms and host immune responses., Methods: Sputum and nasal lavage samples were analysed using long-read metagenomic sequencing with Nanopore and qPCR in two cross-sectional adult severe asthma cohorts, Wessex (n = 66) and Oxford (n = 30). We integrated species-level data with clinical parameters and 39 selected airway proteins measured by immunoassay and O-link., Results: The sputum microbiome in health and mild asthma displayed comparable microbial diversity. By contrast, 23% (19/81) of severe asthma microbiomes were dominated by a single respiratory pathogen, namely H. influenzae (n = 10), M. catarrhalis (n = 4), S. pneumoniae (n = 4) and P. aeruginosa (n = 1). Neutrophilic asthma was associated with H. influenzae, M. catarrhalis, S. pneumoniae and T. whipplei with elevated type-1 cytokines and proteases; eosinophilic asthma with higher M. catarrhalis, but lower H. influenzae, and S. pneumoniae abundance. H. influenzae load correlated with Eosinophil Cationic Protein, elastase and IL-10. R. mucilaginosa associated positively with IL-6 and negatively with FGF. Bayesian network analysis also revealed close and distinct relationships of H. influenzae and M. catarrhalis with type-1 airway inflammation. The microbiomes and cytokine milieu were distinct between upper and lower airways., Conclusions: This species-level integrated analysis reveals central, but distinct associations between potentially pathogenic bacteria and airways inflammation in severe asthma., (© 2024 The Author(s). Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.)

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ ., (© 2024. Springer Nature Limited.)

Purpose: To analyze the trend of intraocular viral load after antiviral treatment in patients with varicella-zoster virus-induced acute retinal necrosis and to explore the effect of viral genotypes on clinical manifestations., Methods: In this case series, viral load was detected using polymerase chain reaction from aqueous humor during treatment; viral load curves were fitted, and the time required to reach the inflection point between plateau phase and logarithmic reduction phase was estimated. Variations in viral genomes were detected by metagenomic sequencing., Results: Twenty eyes of 20 patients were included. The median (interquartile range) initial viral load was 5.9 × 10 7 (1.1 × 10 7 -1.1 × 10 8 ) copies/mL. The average duration of retinitis was 5 ± 3 weeks. The average time required to reach the inflection point was 4.2 ± 1.6 days. Time required to reach the inflection point was correlated with the duration of retinitis ( P = 0.025). Patients with varicella-zoster virus carrying the p.S715* variation in ribonucleotide reductase ( RNR ) subunit 1 gene had lower initial viral loads (median 1.3 × 10 7 copies/mL) than those without (median 1.1 × 10 8 copies/mL; adjusted P = 0.030)., Conclusions: The inflection of viral load curve is helpful to estimate the length of plateau phase and the duration of retinitis during antiviral treatment in patients with acute retinal necrosis. Loss-of-function variation in RNR gene might be correlated with lower virulence of varicella-zoster virus., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Opthalmic Communications Society, Inc.)

To elucidate the mechanism of biochar addition on carbon and nitrogen retention during distilled grain (DGW) composting, this study investigated the losses of carbon and nitrogen and functional genes related to carbon and nitrogen metabolisms between biochar-treated and control composts. The addition of biochar significantly increased carbon and nitrogen retention by 13.5% and 33.8%, respectively. The difference in core carbon metabolism genes indicated that biochar addition inhibited CO 2 release and promoted carbon fixation during the later composting phase, leading to improved carbon retention. Nitrogen metabolism analysis indicated that biochar addition suppressed early-phase ammoniation and late-phase denitrification and promoted nitrification and ammonia assimilation during the later stages of composting, thereby preserving nitrogen. During the later composting phase, biochar addition enhanced carbon-nitrogen coupling metabolism activity, leading to the synchronous retention of carbon and nitrogen. These findings elucidate the mechanism of biochar addition on carbon and nitrogen retention during DGW composting., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)