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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.)

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.)

Background: Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Vital organs like the heart are affected by the occlusion of blood vessels due to atherosclerotic plaque formation. However, the role of infectious agents has always been an essential subject of investigation. This study investigated the presence of microorganisms, including nanobacteria, in atherosclerotic plaques removed from human carotid arteries by microbiological and metagenomic examination., Methods: Atheroma plaque samples were obtained from 20 patients with carotid artery stenosis who had atherectomy by surgery or percutaneous intervention. Nanobacteria were grown by culturing homogenates of the atheroma plaques. Whole genome sequencing was done for samples. Because of the high percentage of Toxoplasma gondii (T. gondii) DNA, PCR investigation was applied to detect T. gondii DNA in the samples., Results: A molecular analysis of nanobacteria revealed them to be made of human proteins, supporting the theory that they are not living organisms. According to sequencing results, samples showed that more than 50 % of the metagenomic sequences belonged to Toxoplasma gondii. PCR investigation indicated that T. gondii DNA was positive in 8 (40 %) of 20 plaques., Conclusions: Further evidence regarding the role of T. gondii in the etiology of plaque formation may help determine the strategy for prevention and treatment of infections in preventing atheroma plaque formation in the future., 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. The author is an Editorial Board Member/Editor-in-Chief/Associate Editor/Guest Editor for [Journal of Infection and Public Health] and was not involved in the editorial review or the decision to publish this article. There is no potential competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

α-Amylases and glucoamylases play a crucial role in starch degradation for various industrial applications. Further exploration of novel α-amylases and glucoamylases with diverse enzymatic characteristics is necessary. In this study, metagenomics analysis revealed a high abundance of these enzymes in the microorganisms of Daqu and Pu-erh tea, identifying 271 glucoamylases and 232 α-amylases with significant sequence identity to known enzymes. Functional studies indicated that these enzymes have broad optimal temperatures (30 °C to 70 °C) and acidic or neutral pH optima. Additionally, two novel low-temperature glucoamylases and one novel low-temperature α-amylases were characterized, demonstrating potential for use in industries operating under low temperature conditions. Further analysis suggested that fewer molecular interactions and more flexible coli regions may contribute to their high activity at low temperatures. In summary, this study not only highlights the feasibility of exploring enzymes through metagenomic approaches, but also presents a library of novel and diverse α-amylases and glucoamylases for potential industrial applications., Competing Interests: Declaration of competing interest All the authors declare that they have no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Background: Numerous studies report gut microbiome variations in bipolar disorder (BD) and schizophrenia spectrum disorders (SSD) compared to healthy individuals, though, there is limited consensus on which specific bacteria are associated with these disorders., Methods: In this study, we performed a comprehensive metagenomic shotgun sequencing analysis in 103 Dutch patients with BD/SSD and 128 healthy controls matched for age, sex, body mass index and income, while accounting for diet quality, transit time and technical confounders. To assess the replicability of the findings, we used two validation cohorts (total n = 203), including participants from a distinct population with a different metagenomic isolation protocol., Results: The gut microbiome of the patients had a significantly different β-diversity, but not α-diversity nor neuroactive potential compared to healthy controls. Initially, twenty-six bacterial taxa were identified as differentially abundant in patients. Among these, the previously reported genera Lachnoclostridium and Eggerthella were replicated in the validation cohorts. Employing the CoDaCoRe learning algorithm, we identified two bacterial balances specific to BD/SSD, which demonstrated an area under the receiver operating characteristic curve (AUC) of 0.77 in the test dataset. These balances were replicated in the validation cohorts and showed a positive association with the severity of psychiatric symptoms and antipsychotic use. Last, we showed a positive association between the relative abundance of Klebsiella and Klebsiella pneumoniae with antipsychotic use and between the Anaeromassilibacillus and lithium use., Conclusions: Our findings suggest that microbial balances could be a reproducible method for identifying BD/SSD-specific microbial signatures, with potential diagnostic and prognostic applications. Notably, Lachnoclostridium and Eggerthella emerge as frequently occurring bacteria in BD/SSD. Last, our study reaffirms the previously established link between Klebsiella and antipsychotic medication use and identifies a novel association between Anaeromassilibacillus and lithium use., 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 The Author(s). Published by Elsevier Inc. All rights reserved.)

Thermophilic anaerobic digestion (AD) of animal manure offers various environmental benefits but the process requires a microbial community acclimatized to high ammonia. In current study, a lab-scale continuous stirred tank reactor (CSTR) fed with chicken manure was operated under thermophilic condition for 450 days in total. Results showed that the volumetric methane production decreased from 445 to 328 and sharply declined to 153 mL L -1 ·d -1 with feeding total solid (TS) step increased from 5% to 7.5% and 10%, respectively. While, after a long-term stop feeding for 80 days, highly disturbed reactor was able to recover methane generation to 739 mL L -1 ·d -1 at feeding TS of 10%. Isotope analysis indicted acetate converted to methane through the syntrophic acetate oxidation and hydrogenotrophic methanogenesis (SAO-HM) pathway increased from 33% to 63% as the concentration of ammonium increased from 2493 to 6258 mg L -1 . Significant different in the genome expression of the SAO bacterial from 0.09% to 1.23%, combining with main hydrogenotrophic partners (Methanoculleus spp. and Methanothermobacter spp.) contented of 2.1% and 99.9% during inhibitory and recovery stages, respectively. The highly expressed KEGG pathway in level 3 (enzyme genes) for the Recovery sludge combining with the extraordinary high abundance of genera Halocella sp. suggested that Halocella sp. might be a highly efficient hydrolytic and acidogenic microorganism and enhance the process of SAO during carbon metabolic flow to methane. This report will be a basis for further study of AD studies on high nitrogen content of poultry manure., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dongmin Yin reports financial support was provided by Changzhou Science and Technology Bureau. Yan Li reports financial support was provided by Changzhou Science and Technology Bureau. Dongmin Yin reports financial support was provided by Changzhou University. Dongmin Yin reports a relationship with China Scholarship Council that includes: board membership. If there are other authors, they 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 Inc. All rights reserved.)

Mud volcanoes are dynamic geological features releasing methane (CH 4 ), carbon dioxide (CO 2 ), and hydrocarbons, harboring diverse methane and hydrocarbon-degrading microbes. However, the potential application of these microbial communities in chlorinated hydrocarbons bioremediation purposes such as trichloroethylene (TCE) has not yet been explored. Hence, this study investigated the mud volcano's microbial diversity functional potentiality in TCE degradation as well as their eco-physiological profiling using metabolic activity. Geochemical analysis of the mud volcano samples revealed variations in pH, temperature, and oxidation-reduction potential, indicating diverse environmental conditions. The Biolog Ecoplate™ carbon substrates utilization pattern showed that the Tween 80 was highly consumed by mud volcanic microbial community. Similarly, MicroResp® analysis results demonstrated that presence of additive C-substrates condition might enhanced the cellular respiration process within mud-volcanic microbial community. Full-length 16 S rRNA sequencing identified Proteobacteria as the dominant phylum, with genera like Pseudomonas and Hydrogenophaga associated with chloroalkane degradation, and methanotrophic bacteria such as Methylomicrobium and Methylophaga linked to methane oxidation. Functional analysis uncovered diverse metabolic functions, including sulfur and methane metabolism and hydrocarbon degradation, with specific genes involved in methane oxidation and sulfur metabolism. These findings provide insights into the microbial diversity and metabolic capabilities of mud volcano ecosystems, which could facilitate their effective application in the bioremediation of chlorinated compounds., 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 Inc.)

Background: Current methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms., Method: In this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests., Results: We evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%-0.82 % respectively, and less than 10 % for the MFGC., Conclusion: The MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs., 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.)

Timely detection of Aspergillus infection is crucial given the high mortality rate of pulmonary aspergillosis (PA). Here, the diagnostic performances for PA of mycological culture, Aspergillus real-time PCR, and metagenomic next-generation sequencing (mNGS) assay from bronchoalveolar lavage fluid, were evaluated. In total, 139 patients with suspected fungal pneumonia were enrolled between December 2021 and July 2023, collecting 139 bronchoalveolar lavage fluid samples for real-time PCR and culture, with 87 undergoing mNGS assay. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve with 95% CIs of these assays for PA were as follows: 35.3% (14.2%-61.7%), 100.0% (94.0%-100.0%), 100.0% (54.1%-100.0%), 84.5% (79.3%-88.6%), and 0.676 (0.560-0.779), respectively, for culture; 82.4% (56.6%-96.2%), 98.3% (91.1%-100.0%), 93.3% (66.4%-99.0%), 95.2% (87.6%-98.2%), and 0.903 (0.815-0.959), respectively, for same diagnostic performance of real-time PCR and mNGS; and 94.1% (71.3%-99.9%), 96.7% (88.5%-99.6%), 88.9% (67.1%-96.9%), 98.3% (89.6%-99.7%), and 0.954 (0.880-0.989), respectively, for real-time PCR combining mNGS; real-time PCR, mNGS, and their combination significantly improved in area under the curve values over culture (P < 0.001), but real-time PCR testing and mNGS had no significant difference with each other and their combination. Overall, the performance of culture was limited by low sensitivity; both real-time PCR and mNGS assays as single diagnostic tests are promising compared with culture and combined tests., Competing Interests: Disclosure Statement None declared., (Copyright © 2024 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Background: Pulmonary invasive fungal infections (pIFI) disproportionately affect patients with haematological malignancies (HM). Establishing a rapid and accurate diagnosis of pIFI is challenging. Multiple guidelines recommend diagnostic testing of invasive fungal infections but lack consensus and may contribute to inconsistent diagnostic approaches., Objective: To identify key diagnostic challenges and review metagenomic sequencing data., Sources: PubMed, professional consortium, and scientific society websites search to identify relevant, published, evidence-based clinical guidelines within the past 5 years. PubMed searchs for papers describing clinically relevant novel diagnostic technologies., Content: Current guidelines for patients with HM and suspected pIFI recommend chest computed tomography imaging and specimen testing with microscopic examination (including calcofluor white stain, histopathology, cytopathology, etc.), Aspergillus galactomannan, β-D-glucan, PCR, and culture, each with certain limitations. Emerging real-world data support the adjunctive use of metagenomic sequencing-based tests for the timely diagnosis of pIFI., Implications: High-quality evidence from robust clinical trials is needed to determine whether guidelines should be updated to include novel diagnostic technologies. Trials should ask whether the combination of powerful novel diagnostics, such as pathogen-agnostic metagenomic sequencing technologies in conjunction with conventional testing can optimize the diagnostic yield for all potential pIFI pathogens that impact the health of patients with HM., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution., 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. Conflicts of interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Activated sludge comprises diverse bacteria, fungi, and other microorganisms, featuring a rich repertoire of genes involved in antibiotic resistance, pollutant degradation, and elemental cycling. In this regard, hybrid assembly technology can revolutionize metagenomics by detecting greater gene diversity in environmental samples. Nonetheless, the optimal utilization and comparability of genomic information between hybrid assembly and short- or long-read technology remain unclear. To address this gap, we compared the performance of the hybrid assembly, short- and long-read technologies, abundance and diversity of annotated genes, and taxonomic diversity by analysing 46, 161, and 45 activated sludge metagenomic datasets, respectively. The results revealed that hybrid assembly technology exhibited the best performance, generating the most contiguous and longest contigs but with a lower proportion of high-quality metagenome-assembled genomes than short-read technology. Compared with short- or long-read technologies, hybrid assembly technology can detect a greater diversity of microbiota and antibiotic resistance genes, as well as a wider range of potential hosts. However, this approach may yield lower gene abundance and pathogen detection. Our study revealed the specific advantages and disadvantages of hybrid assembly and short- and long-read applications in wastewater treatment plants, and our approach could serve as a blueprint to be extended to terrestrial environments., 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 Inc. All rights reserved.)

Objectives: Severe early childhood caries (S-ECC) is highly prevalent, affecting children's oral health. S-ECC development is closely associated with the complex oral microbial microbiome and its microorganism interactions, such as the imbalance of bacteriophages and bacteria. Till now, little is known about oral phageome on S-ECC. Therefore, this study aimed to investigate the potential role of the oral phageome in the pathogenesis of S-ECC., Methods: Unstimulated saliva (2 mL) was collected from 20 children with and without S-ECC for metagenomics analysis. Metagenomics sequencing and bioinformatic analysis were performed to determine the two groups' phageome diversity, taxonomic and functional annotations. Statistical analysis and visualization were performed with R and SPSS Statistics software., Results: 85.7 % of the extracted viral sequences were predicted from phages, in which most phages were classified into Myoviridae, Siphoviridae, and Podoviridae. Alpha diversity decreased, and Beta diversity increased in the S-ECC phageome compared to the healthy group. The abundance of Podoviridae phages increased, and the abundance of Inoviridae, Herelleviridae, and Streptococcus phages decreased in the S-ECC group. Functional annotation revealed increased annotation on glycoside hydrolases and nucleotide metabolism, decreased glycosyl transferases, carbohydrate-binding modules, and biogenic metabolism in the S-ECC phageome., Conclusions: Metagenomic analysis revealed reduced Streptococcus phages and significant changes in functional annotations within the S-ECC phageome. These findings suggest a potential weakening of the regulatory influence of oral bacteria, which may indicate the development of innovative prevention and treatment strategies for S-ECC. These implications deserve further investigation and hold promise for advancing our understanding and management of S-ECC., Clinical Significance: The findings of this study indicate that oral phageomes are associated with bacterial genomes and metabolic processes, affecting the development of S-ECC. The reduced modulatory effect of the oral phageome in counteracting S-ECC's cariogenic activity suggests a new avenue for the prevention and treatment of S-ECC., 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.)

Wastewater surveillance is a powerful tool to assess the risks associated with antibiotic resistance in communities. One challenge is selecting which analytical tool to deploy to measure risk indicators, such as antibiotic resistance genes (ARGs) and their respective bacterial hosts. Although metagenomics is frequently used for analyzing ARGs, few studies have compared the performance of long-read and short-read metagenomics in identifying which bacteria harbor ARGs in wastewater. Furthermore, for ARG host detection, untargeted metagenomics has not been compared to targeted methods such as epicPCR. Here, we 1) evaluated long-read and short-read metagenomics as well as epicPCR for detecting ARG hosts in wastewater, and 2) investigated the host range of ARGs across the wastewater treatment plant (WWTP) to evaluate host proliferation. Results highlighted long-read revealed a wider range of ARG hosts compared to short-read metagenomics. Nonetheless, the ARG host range detected by long-read metagenomics only represented a subset of the hosts detected by epicPCR. The ARG-host linkages across the influent and effluent of the WWTP were characterized. Results showed the ARG-host phylum linkages were relatively consistent across the WWTP, whereas new ARG-host species linkages appeared in the WWTP effluent. The ARG-host linkages of several clinically relevant species found in the effluent were identified., 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 B.V. All rights reserved.)

Objective: This study aimed to explore the clinical efficacy of metagenomic next-generation sequencing (mNGS) in diagnosing and treating suspected spinal infectious diseases., Methods: Between October 2022 to December 2023, a retrospective analysis was performed on patient records within the Department of Spinal Surgery at Guilin People's Hospital. The analysis included comprehensive data on patients with presumed spinal infectious diseases, incorporating results from mNGS tests conducted externally, conventional pathogen detection results, laboratory examination results, and imaging findings. The study aimed to assess the applicability of mNGS in the context of suspected spinal infectious lesions., Results: Twenty-seven patients were included in the final analysis. Pathogenic microorganisms were identified in 23 cases. The included cases encompassed 1 case of tuberculous spondylitis, 1 case of fungal infection, 3 cases of Brucella spondylitis, 3 cases of viral infection, 9 cases of bacterial infection, and 6 cases of mixed infections. Pathogenic microorganisms remained elusive in 4 cases. The application of the mNGS method demonstrated a significantly elevated positive detection rate compared to conventional methods (85.19% vs. 48.15%, P < 0.05). Moreover, the mNGS method detected a greater variety of pathogen species than traditional methods (Z = 10.69, P < 0.05). Additionally, the mNGS method exhibited a shorter detection time., Conclusions: mNGS demonstrated significantly higher detection rates for bacterial, fungal, viral, and mixed infections in cases of suspected spinal infectious diseases. The clinical implementation of mNGS could further enhance the efficiency of diagnosing and treating suspected spinal infectious diseases., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: Early diagnosis of infectious diseases remains a challenge. This study assessed the diagnostic value of mNGS in infections and explored the effect of various factors on the accuracy of mNGS., Methods: An electronic article search of PubMed, Cochrane Library, and Embase was performed. A total of 85 papers were eligible for inclusion and analysis. Stata 12.0 was used for statistical calculation to evaluate the efficacy of mNGS for the diagnosis of infectious diseases., Results: The AUC of 85 studies was 0.88 (95%CI, 0.85-0.90). The AUC of the clinical comprehensive diagnosis and conventional test groups was 0.92 (95%CI, 0.89-0.94) and 0.82 (95%CI, 0.78-0.85), respectively. The results of subgroup analysis indicated that the PLR and NLR were 12.67 (95%CI, 6.01-26.70) and 0.05 (95%CI, 0.03-0.10), respectively, in arthrosis infections. The PLR was 24.41 (95%CI, 5.70-104.58) in central system infections and the NLR of immunocompromised patients was 0.08 (95%CI, 0.01-0.62)., Conclusion: mNGS demonstrated satisfactory diagnostic performance for infections, especially for bone and joint infections and central system infections. Moreover, mNGS also has a high value in the exclusion of infection in immunocompromised patients., Competing Interests: Declaration of competing interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled., (Copyright © 2024. Published by Elsevier Ltd.)

Livestock facilities are widely regarded as reservoirs of infectious disease, owing to their abundance in particulate matter (PM) and microbial bioaerosols. Over the past decade, bioaerosol studies have increasingly utilised high throughput sequencing (HTS) to achieve superior throughput, taxonomic resolution, and the detection of unculturable organisms. However, the prevailing focus on amplicon sequencing has limited the identification of viruses and microbial taxa at the species-level. Herein, a literature search was conducted to identify methods capable of overcoming the aforementioned limitations. Screening 1531 international publications resulted in 29 eligible for review. Metagenomics capable of providing rich insights were identified in only three instances. Notably, long-read sequencing was not utilised for metagenomics. This review also identified that sample collection methods lack a uniform approach, highlighted by the differences in sampling equipment, flow rates and durations. Further heterogeneity was introduced by the unique sampling conditions, which makes it challenging to ground new findings within the established literature. For instance, winter was associated with increased microbial abundance and antimicrobial resistance, yet less alpha diversity. Researchers implementing metagenomics into the livestock environment should consider season, the microclimate, and livestock growth stage as influential upon their findings. Considering the increasing accessibility of long-read sequencing, future research should explore its viability within a novel uniform testing protocol for bioaerosol emissions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: SJW, CTF, EH, and PBW report financial support was provided by Fédération Équestre Internationale., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The advent of high-throughput sequencing in clinical microbiology is opening the way to new diagnostic and prognostic approaches in infectious diseases. Detection, identification and characterisation of pathogenic microorganisms are essential steps in diagnosis and implementation of appropriate antimicrobial therapy. However, standard methods of microbiological diagnosis are failing in some cases. In addition, the emergence of new infections, facilitated by international travel and global warming, requires the implementation of innovative diagnostic methods. Among the different strategies used in clinical microbiology and reviewed in this article, shotgun metagenomics is the only technique that allows today a panpathogenic and unbiased detection of all microorganisms potentially responsible for an infectious disease, including those still unknown. The aims of this article are to present the different possible strategies of high-throughput sequencing used in the microbiological diagnosis of infectious diseases and to highlight the diagnostic contribution of shotgun metagenomics in the field of central nervous system infections., (Copyright © 2023 Société Nationale Française de Médecine Interne (SNFMI). Published by Elsevier Masson SAS. All rights reserved.)

Despite extensive research, little is known about the composition of eukaryotic protists in environmental samples. This is due to low parasite concentrations, the complexity of parasite diversity, and a lack of suitable reference databases and standardized protocols. To bridge this knowledge gap, this study used 18S rRNA short amplicon and shotgun metagenomic sequencing approaches to profile protozoan microbial communities as well as their functional pathways in treated and untreated wastewater samples collected from different regions of South Africa. Results demonstrated that protozoan diversity (Shannon index P-value = 0.03) and taxonomic composition (PERMANOVA, P-value = 0.02) was mainly driven by the type of wastewater samples (treated & untreated) and geographic location. However, these WWTPs were also found to contain a core community of protozoan parasites. The untreated wastewater samples revealed a predominant presence of free-living, parasitic, and potentially pathogenic protists typically found in humans and animals, ranging from Alveolata (27 %) phylum (Apicomplexa and Ciliophora) to Excavata (3.88 %) (Discoba and Parasalia) and Amoebozoa (2.84 %) (Entamoeba and Acanthamoeba). Shotgun metagenomics analyses in a subset of the untreated wastewater samples confirmed the presence of public health-importance protozoa, including Cryptosporidium species (3.48 %), Entamoeba hystolitica (6.58 %), Blastocystis hominis (2.91 %), Naegleria gruberi (2.37 %), Toxoplasma gondii (1.98 %), Cyclospora cayetanensis (1.30 %), and Giardia intestinalis (0.31 %). Virulent gene families linked to pathogenic protozoa, such as serine/threonine protein phosphatase and mucin-desulfating sulfatase were identified. Additionally, enriched pathways included thiamine diphosphate biosynthesis III, heme biosynthesis, Methylerythritol 4-Phosphate Pathway, methyl erythritol phosphate (MEP), and pentose phosphate pathways. These findings suggest that protozoan pathogens may possess metabolic and growth potential within WWTPs, posing a severe risk of transmission to humans and animals if inadequately disinfected before release. This study provides a baseline for the future investigation of diverse protozoal communities in wastewater, which are of public health importance., 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 © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Undigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake., Objectives: As a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake., Methods: Data were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models., Results: We identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group's respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy., Conclusions: Our findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings., Competing Interests: Conflict of interest The authors report no conflicts of interest., (Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Metagenomics and metaproteomics analyses were used to determine the microbial diversity and taxon composition, as well as the biochemical potentials of the microbiome on the sandstone of Beishiku Temple located in Northwest China. Taxonomic annotation of the metagenomic dataset revealed the predominant taxa of the stone microbiome on this cave temple with characteristics of resistance to harsh environmental conditions. Meanwhile, there were also taxa in the microbiome that showed sensitivity to environmental factors. The taxa distribution and the metabolic functional distribution patterns by the metagenome and metaproteome, respectively, showed clear differences. The high abundance of energy metabolism represented in the metaproteome suggested that there were active geomicrobiological cycles of elements within the microbiome. The taxa responsible for reactions in the nitrogen cycle from both metagenome and metaproteome supported a metabolically active nitrogen cycle, and the high activity of Comammox bacteria indicated the strong metabolic activity of ammonia oxidation to nitrate in the outdoor site. The SO X -related taxa involved in the sulfur cycle showed higher activity outdoors than indoors, and on the outdoor ground than at the outdoor cliff, as detected through metaproteomic analysis. The development of petrochemical industry in the vicinity resulting in the deposition of sulfur/oxidized sulfur via atmosphere may stimulate the physiological activity of SO X. Our findings provide metagenomic and metaproteomic evidence for microbially driven geobiochemical cycles that result in the biodeterioration of stone monuments., 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 © 2023 Elsevier B.V. All rights reserved.)

Concerns about antibiotic resistance genes (ARGs) released from wastewaters of livestock or fish farming into the natural environment are increasing, but studies on unculturable bacteria related to the dissemination of antibiotic resistance are limited. Here, we reconstructed 1100 metagenome-assembled genomes (MAGs) to assess the impact of microbial antibiotic resistome and mobilome in wastewaters discharged to Korean rivers. Our results indicate that ARGs harbored in the MAGs were disseminated from wastewater effluents into downstream rivers. Moreover, it was found that ARGs are more commonly co-localized with mobile genetic elements (MGEs) in agricultural wastewater than in river water. Among the effluent-derived phyla, uncultured members of the superphylum Patescibacteria possessed a high number of MGEs, along with co-localized ARGs. Our findings suggest that members of the Patesibacteria are a potential vector for propagating ARGs into the environmental community. Therefore, we propose that the dissemination of ARGs by uncultured bacteria should be further investigated in multiple environments., 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 © 2023 Elsevier B.V. All rights reserved.)

The antibiotic resistances in bacteria are believed to rapidly evolve over time in the anthropogenic environments which enriched with selection pressures. However, the knowledge regarding the development of antibiotic resistance in wildlife and their habitats is scarce. It is, therefore, of great interest and significance to unveil the yet-unknown antibiotic resistances in wildlife in accordance with One Health concept. To this end, we analyzed the samples taken from wildlife and surrounding environments using a functional metagenomics approach. By functional screening in combination with Illumina sequencing, a total of 32 candidate genes which encoding putative novel β-lactamase were identified. These putative β-lactamase were taxonomically assigned into bacteria of 23 genera from 7 phyla, where Proteobacteria, Actinobacteria and Firmicutes were dominant. The following functional assessment demonstrated that 4 novel β-lactamases, namely bla SSA , bla SSB1 , bla SSB2 and bla SSD , were functionally active to confer the phenotypical resistance to bacteria by increasing MICs up to 128-fold. Further analysis indicated that the novel β-lactamases identified in the current study were able to hydrolyze a broad spectrum of β-lactams including cephalosporins, and they were genetically unique comparing with known β-lactamases. The plausible transmission of some novel β-lactamase genes was supported by our results as the same gene was detected in different samples from different sites. This study shed the light on the active role of wildlife and associated environments as natural reservoirs of novel β-lactamases, implying that the antibiotic resistances might evolve in absence of selection pressure and threaten public health once spread into clinically important pathogens., 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 © 2023 Elsevier B.V. All rights reserved.)

A metagenomics-based technological framework has been proposed for evaluating the potential and utility of FEAST as an ARG profile-based source apportionment tool. To this end, a large panel of metagenomic data sets was analyzed, associating with eight source types of ARGs in environments. Totally, 1089 different ARGs were found in the 604 source metagenomes, and 396 ARG indicators were identified as the source-specific fingerprints to characterize each of the source types. With the source fingerprints, predictive performance of FEAST was checked using "leave-one-out" cross-validation strategy. Furthermore, artificial sink communities were simulated to evaluate the FEAST for source apportionment of ARGs. The prediction of FEAST showed high accuracy values (0.933 ± 0.046) and specificity values (0.959 ± 0.041), confirming its suitability to discriminate samples from different source types. The apportionment results reflected well the expected output of artificial communities which were generated with different ratios of source types to simulate various contamination levels. Finally, the validated FEAST was applied to track the sources of ARGs in river sediments. Results showed STP effluents were the main contributor of ARGs, with an average contribution of 76 %, followed by sludge (10 %) and aquaculture effluent (2.7 %), which were basically consistent with the actual environment in the area., 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 © 2022 Elsevier B.V. All rights reserved.)

Lignocellulose wastes stemming from agricultural residues can offer an excellent opportunity as alternative energy solutions in addition to fossil fuels. Besides, the unrestrained burning of agricultural residues can lead to the destruction of the soil microflora and associated soil sterilization. However, the difficulties associated with the biodegradation of lignocellulose biomasses remain as a formidable challenge for their sustainable management. In this respect, metagenomics can be used as an effective option to resolve such dilemma because of its potential as the next generation sequencing technology and bioinformatics tools to harness novel microbial consortia from diverse environments (e.g., soil, alpine forests, and hypersaline/acidic/hot sulfur springs). In light of the challenges associated with the bulk-scale biodegradation of lignocellulose-rich agricultural residues, this review is organized to help delineate the fundamental aspects of metagenomics towards the assessment of the microbial consortia and novel molecules (such as biocatalysts) which are otherwise unidentifiable by conventional laboratory culturing techniques. The discussion is extended further to highlight the recent advancements (e.g., from 2011 to 2022) in metagenomic approaches for the isolation and purification of lignocellulolytic microbes from different ecosystems along with the technical challenges and prospects associated with their wide implementation and scale-up. This review should thus be one of the first comprehensive reports on the metagenomics-based analysis of different environmental samples for the isolation and purification of lignocellulose degrading enzymes., 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 © 2022 Elsevier Inc. All rights reserved.)

Metagenomics has enabled culture-independent analysis of micro-organisms present in environmental samples. Metagenomics binning, which involves the grouping of contigs into bins that represent different taxonomic groups, is an important step of a typical metagenomic workflow followed after assembly. The majority of the metagenomic binning tools represent the composition and coverage information of contigs as feature vectors consisting of a large number of dimensions. However, these tools use traditional Euclidean distance or Manhattan distance metrics which become unreliable in the high dimensional space. We propose CH-Bin, a binning approach that leverages the benefits of using convex hull distance for binning contigs represented by high dimensional feature vectors. We demonstrate using experimental evidence on simulated and real datasets that the use of high dimensional feature vectors to represent contigs can preserve additional information, and result in improved binning results. We further demonstrate that the convex hull distance based binning approach can be effectively utilized in binning such high dimensional data. To the best of our knowledge, this is the first time that composition information from oligonucleotides of multiple sizes has been used in representing the composition information of contigs and a convex hull distance based binning algorithm has been used to bin metagenomic contigs. The source code of CH-Bin is available at https://github.com/kdsuneraavinash/CH-Bin., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Metabolic activities of the microbial population are important to maintain the balance of almost all the ecosystems on earth. In the human gut environment, these microbial communities play essential roles in digestion and help to maintain biochemical homeostasis by synthesizing several vital metabolic compounds. Imbalance in the microbial abundance and community structure in the human gut microbiota leads to different diseases and metabolic disorders. Studying the metabolic interplay between the microbial consortia within the host environment is the key to exploring the cause behind the development of various diseases condition. However, mapping the entire biochemical characteristic of human gut microbiota may not be feasible only through experimental approaches. Therefore, the advanced systems biology approach, i.e., metagenome-scale community metabolic modelling, is introduced for understanding the metabolic role and interaction pattern of the entire microbiome. This in silico method directly uses the metagenomic information to model the microbial communities, which mimic the metabolic behavior of the human gut microbiome. This review discusses the recent development of metagenome-scale community metabolic model reconstruction tools and their application in studying the inter-link between the human gut microbiome and health. The application of the community metabolic models to study the metabolic profile of the human gut microbiome has also been investigated. Alteration of the metabolic fluxes associated with different biochemical activities in type 1 diabetics, type 2 diabetics, inflammatory bowel diseases (IBD), gouty arthritis, colorectal cancer (CRC), etc., has also been assessed with the metagenome-scale models. Thus, modelling the microbial communities combined with advanced experimental design may lead to novel therapeutic approaches like personalized microbiome modelling for treating human disease., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The reuse of urban reclaimed water is conducive to alleviate the current serious shortage of water resources. However, antibiotic resistance genes (ARGs) in reclaimed water have received widespread attention due to their potential risks to public health. Deciphering the fate of ARGs in reclaimed water benefits the development of effective strategies to control resistome risk and guarantees the safety of water supply of reclaimed systems. In this study, the characteristics of ARGs in an integrated reclaimed water system (sewage treatment plant-constructed wetland, STP-CW) in Beijing (China) have been identified using metagenomic assembly-based analysis, as well as the combined effect of the STP-CW system on the reduction of antibiotic resistome. Results showed a total of 29 ARG types and 813 subtypes were found in the reclaimed water system. As expected, the STP-CW system improved the removal of ARGs, and about 58% of ARG subtypes were removed from the effluent of the integrated STP-CW system, which exceeded 43% for the STP system and 37% for the CW system. Although the STP-CW system had a great removal on ARGs, abundant and diverse ARGs were still found in the downstream river. Importantly, network analysis revealed the co-occurrence of ARGs, mobile genetic elements and virulence factors in the downstream water, implying potential resistome dissemination risk in the environment. Source identification with SourceTracker showed the STP-effluent was the largest contributor of ARGs in the downstream river, with a contribution of 45%. Overall, the integrated STP-CW system presented a combined effect on the reduction of antibiotic resistome, however, the resistome dissemination risk was still non-negligible in the downstream reclaimed water. This study provides a comprehensive analysis on the fate of ARGs in the STP-CW-river system, which would benefit the development of effective strategies to control resistome risk for the reuse of reclaimed water., 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 © 2022 Elsevier B.V. All rights reserved.)

Objectives: To evaluate the relation between metagenomic next-generation sequencing (mNGS) and the prognosis of patients with infectious diseases undergoing mechanical ventilation in the intensive care unit (ICU)., Methods: This is a single-center observational study, comparing nonrandomly assigned diagnostic approaches. We analyzed the medical records of 228 patients with suspected infectious diseases undergoing mechanical ventilation in the ICU from March 2018 to May 2020. The concordance of pathogen results was also assessed for the results of mNGS, culture, and polymerase chain reaction assays., Results: The 28-day mortality of the patients in the mNGS group was lower after the baseline difference correction (19.23% (20/104) vs 29.03% (36/124) , P = 0.039). Subgroup analysis showed that mNGS assay was associated with improved 28-day mortality of patients who are not immunosuppressed (14.06% vs 29.82%, P = 0.018). Not performing mNGS assay, higher acute physiology and chronic health evaluation II score, and hypertension are independent risk factors for 28-day mortality. The mNGS assay presented an advantage in pathogen positivity (69.8% double-positive and 25.0% mNGS-positive only), and the concordance between these two assays was 79.0%., Conclusion: mNGS survey may be associated with a better prognosis by reducing 28-day mortality of patients with infectious diseases on mechanical ventilation in the ICU. This technique presented an advantage in pathogen positivity over traditional methods., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: This study aimed to analyze the pathogenic bacteria spectrum in invasive and primary osteoarticular infection (IOI and POI) and compare the pathogen detection rate of metagenomic next-generation sequencing (mNGS) and microbial culture in IOI and POI., Methods: The suspected POI and IOI cases from 2014-2021 were included. The diagnosis of POI or IOI was made by at least two orthopedic surgeons, two infectious diseases specialists, and one senior microbiologist. Demographic characteristics, microbial culture results, and so on were recorded. The pathogenic bacteria spectrum in IOI and POI were analyzed, and the ability of mNGS and microbial culture in pathogen detection in IOI and POI were compared., Results: There were 52 POI cases and 92 IOI cases; the common pathogen in POI and IOI were both Staphylococcus aureus. There are more cases with negative microbial culture results and multiple infections in IOI, and many cases were caused by rare and fastidious bacteria. The introduction of the mNGS could significantly increase the pathogen detection rate to 92.39% in IOI, which was 8.69% higher than that of microbial culture (P = 0.007), whereas the improvement in POI was limited to about 2%., Conclusion: mNGS is an promising tool for IOI pathogen detection., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: This study aimed to evaluate the clinical value of metagenomic next-generation sequencing (mNGS) in patients with suspected lower respiratory tract infections., Methods: This retrospective study reviewed patients with suspected lower respiratory tract infections at the Wuhan Union Hospital. Data including demographic, laboratory, and radiological profiles; treatment; and outcomes were recorded and analyzed., Results: mNGS identified pathogenic microbes in 100/140 (71.4%) patients, although 135 (96.4%) had received empiric antibiotic treatment before the mNGS tests. Single bacterial infection (35/100, 35%) was the most common type of infection in patients with positive mNGS results, followed by single fungal infection (14/100, 14%), bacterial-viral coinfection (14/100, 14%), single viral infection (12/100, 12%), bacterial-fungal coinfection (9/100, 9%), fungal-viral coinfection (9/100, 9%), and bacterial-fungal-viral coinfection (7/100, 7%). Moreover, compared with culture test, mNGS showed higher sensitivity (63/85, 74.1% vs 22/85, 25.9% P = 0.001) and lower processing time (24 hours vs 48 hours). Antibiotic treatment was adjusted or confirmed based on the mNGS results in 123 (87.9%) patients, including five (3.6%), 33 (23.6%), and 85 (60.7%) patients, in whom treatment was downgraded, upgraded, and unchanged, respectively. Almost all patients, regardless of escalation, de-escalation, or no change in treatment, showed significant improvement in clinical symptoms and inflammatory indicators. In addition, 17 (12.1%) patients were referred to Wuhan Pulmonary Hospital for further treatment because of confirmed or suspected tuberculosis., Conclusion: mNGS could be a promising technique for microbiological diagnosis and antibiotic management, potentially improving outcomes for patients., (Copyright © 2022. Published by Elsevier Ltd.)

Machine learning or deep learning models have been widely used for taxonomic classification of metagenomic sequences and many studies reported high classification accuracy. Such models are usually trained based on sequences in several training classes in hope of accurately classifying unknown sequences into these classes. However, when deploying the classification models on real testing data sets, sequences that do not belong to any of the training classes may be present and are falsely assigned to one of the training classes with high confidence. Such sequences are referred to as out-of-distribution (OOD) sequences and are ubiquitous in metagenomic studies. To address this problem, we develop a deep generative model-based method, MLR-OOD, that measures the probability of a testing sequencing belonging to OOD by the likelihood ratio of the maximum of the in-distribution (ID) class conditional likelihoods and the Markov chain likelihood of the testing sequence measuring the sequence complexity. We compose three different microbial data sets consisting of bacterial, viral, and plasmid sequences for comprehensively benchmarking OOD detection methods. We show that MLR-OOD achieves the state-of-the-art performance demonstrating the generality of MLR-OOD to various types of microbial data sets. It is also shown that MLR-OOD is robust to the GC content, which is a major confounding effect for OOD detection of genomic sequences. In conclusion, MLR-OOD will greatly reduce false positives caused by OOD sequences in metagenomic sequence classification., 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 © 2022 Elsevier Ltd. All rights reserved.)

Microbiology has long studied the ways in which subtle genetic differences between closely related microbial strains can have profound impacts on their phenotypes and those of their surrounding environments and communities. Despite the growth in high-throughput microbial community profiling, however, such strain-level differences remain challenging to detect. Once detected, few quantitative approaches have been well-validated for associating strain variants from microbial communities with phenotypes of interest, such as medication usage, treatment efficacy, host environment, or health. First, the term "strain" itself is not used consistently when defining a highly-resolved taxonomic or genomic unit from within a microbial community. Second, computational methods for identifying such strains directly from shotgun metagenomics are difficult, with several possible reference- and assembly-based approaches available, each with different sensitivity/specificity tradeoffs. Finally, statistical challenges exist in using any of the resulting strain profiles for downstream analyses, which can include strain tracking, phylogenetic analysis, or genetic association studies. We provide an in depth discussion of recently available computational tools that can be applied for this task, as well as statistical models and gaps in performing and interpreting any of these three main types of studies using strain-resolved shotgun metagenomic profiling of microbial communities., 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 © 2022. Published by Elsevier Ltd.)

Despite the growing interest in the microbiome in recent years, the study of the virome, the major part of which is made up of bacteriophages, is relatively underdeveloped compared with their bacterial counterparts. This is due in part to the lack of a universally conserved marker such as the 16S rRNA gene. For this reason, the development of metagenomic approaches was a major milestone in the study of the viruses in the microbiome or virome. However, it has become increasingly clear that these wet-lab methods have not yet been able to detect the full range of viruses present, and our understanding of the composition of the virome remains incomplete. In recent years, a range of new technologies has been developed to further our understanding. Direct RNA-Seq technologies bypass the need for cDNA synthesis, thus avoiding biases subjected to this step, which further expands our understanding of RNA viruses. The new generation of amplification methods could solve the low biomass issue relevant to most virome samples while reducing the error rate and biases caused by whole genome amplification. The application of long-read sequencing to virome samples can resolve the shortcomings of short-read sequencing in generating complete viral genomes and avoid the biases introduced by the assembly. Novel experimental methods developed to measure viruses' host range can help overcome the challenges of assigning hosts to many phages, specifically unculturable ones., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Metagenomics is a discipline that studies the genetic material of all tiny organisms in the biological environment. In recent years, the interaction between metagenomic microbial communities, the transfer of horizontal genes, and the dynamic changes of microbial ecosystems have attracted more and more attention. It is of great significance to use the community detection algorithm to divide the metagenomic microbes into modules, and it has a positive guiding role for the follow-up research on human, drug, microbial interaction study and drug prediction and development. At present, there are challenges in mining the effective information hidden in large-scale microbial sequence data. The non-linear characteristics and non-scalability of microbial sequence data still bother people. This paper proposes an end-to-end unsupervised GCN learning model OTUCD (Operational Classification Unit Community Detection), which divides large-scale metagenomic sequence data into potential gene modules. We construct an OTU network, and then performs subsequent nonoverlapping community detection task with graph convolutional networks. Experimental scores show that the community detection effect of this method is better than other latest metagenomic algorithms., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Combined sewage overflows (CSOs) have become an important source of antibiotic resistance genes (ARGs) in the environment, while the distribution and dynamics of antibiotic resistome in the CSOs events have not been well understood. This study deciphered the profiles of antibiotic resitome in the CSOs based on metagenomics analysis from reads to metagenome assembly genomes (MAGs), and the dynamical changes of ARGs were clarified through continuous monitoring of the CSO event. Results showed that antibiotic inactivation was the dominant resistance mechanism, and sulfonamide, aminoglycoside along with multidrug resistance were the dominant antibiotic resistance types. It was speculated that the antibiotic resistome were generally determined by sewer sediment flushed out along with the CSOs not domestic sewage in the pipes. The host range and mobility of the antibiotic resistome were determined at contigs level, and the hosts mainly belonged to the Proteobacteria with the genus of Pseudomonas, Escherichia, Enterobacter and Aeromonas being dominant. The transposase (tnpA), IS91 and integrons were mobile genetic elements (MGEs) located together with ARGs, and a MAG carrying 32 ARGs and 140 VFGs was assembled. Although microbial community contributed most to the changes of antibiotic resistome in the CSOs directly, the risks caused by the MGEs should be paid more attention., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Viruses are diverse biological entities that influence all life. Even with limited genome sizes, viruses can manipulate, drive, steal from, and kill their hosts. The field of virus genomics, using sequencing data to understand viral capabilities, has seen significant innovations in recent years. However, with advancements in metagenomic sequencing and related technologies, the bottleneck to discovering and employing the virosphere has become the analysis of genomes rather than generation. With metagenomics rapidly expanding available data, vital components of virus genomes and features are being overlooked, with the issue compounded by lagging databases and bioinformatics methods. Despite the field moving in a positive direction, there are noteworthy points to keep in mind, from how software-based virus genome predictions are interpreted to what information is overlooked by current standards. In this review, we discuss conventions and ideologies that likely need to be revised while continuing forward in the study of virus genomics., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: There is an abundant link between the gut microbiota and human health and it plays a critical role in the clinic. It is recognized that microbial dysregulation contributes to the pathogenesis of tuberculosis (TB) but the underlying mechanisms remain unclear. In this study, we investigated the association of gut microbiome composition with TB as well as its possible roles in the development of this disease., Methods: Fecal samples were collected from 10 TB patients and 20 healthy control samples. DNA extracted from fecal samples was subjected to 16S rDNA gene sequencing analysis on the Illumina MiSeq platform., Results: Compared with healthy control samples, the gut microbiome of patients with TB was characterized by the decreased Alpha diversity. Perhaps, the decrease of microbial diversity which results in microbial dysregulation is the reason for clinical patients with more symptoms. The PTB group showed the most unique microbiota by higher abundance of Bifidobacteriaceae, Bifidobacteriales, Coriobacteriaceae, Coriobacteriales, Actinobacteria, Caulobacteraceae, Phyllobacteriaceae, Rhizobiales, Burkholderiaceae, Burkholderiaceae. Inflammatory status in PTB patients may be associated with the increased abundance of Clostridia and decreased abundance of Prevotella. We found that the abundance of Solobacterium and Actinobacteria was higher in the patients. There were 4 significant differences (p < 0.05) in the two groups which belonged to four metabolic categories, including endocytosis, phosphotransferase system (PTS), toluene degradation, and amoebiasis., Conclusion: We applied the approach of metagenomic sequencing to characterize the features of gut microbiota in PTB patients. The present study provided a detailed analysis of the characterization of the gut microbiota in patients based on the clinic. According to the metagenome analysis, our results indicated that the gut microbiota in PTB patients was significantly different from healthy control samples as characterized by the bacteria and metabolic pathway. The richness of the gut microbiota in patients was revealed. It was hypothesized that the above-mentioned changes of the gut microbiota could exert an impact on the development of PTB through the downstream regulation of the immune status of the host by way of the gut-lung axis., Competing Interests: Declaration of competing interest The authors declare that they have no competing interest., (Copyright © 2021 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Abstract: Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole genome sequencing. This approach is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, next-generation sequencing is still anchored in the traditional laboratory practice of the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding and shotgun and long-read metagenomics, are part of the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed alongside current limitations and knowledge gaps and new opportunities arising from the use of this technology., (Copyright ©, International Association for Food Protection.)

To analyze the application of the metagenomics method in the identification of viral infectious agents that lead to diarrhea outbreaks. This study is a systematic review, which looked for publications on the following platforms: PubMed, Scientific Electronic Library Online (SciELO), LILACS and CAPES periodicals, conducted according to the PRISMA methodology, investigating in the literary composition studies related to metagenomics applied in the identification of viral infectious agents, which lead to diarrhea in humans. 1198 publications were identified. Of these, after analyzes and exclusions at different stages, 18 studies remained, which directly corresponded to the theme. Diarrhea was presented as a universal health concern. Despite the emergence of vaccines, cases of diarrhea remain persistent in poor populations. In this context, metagenomics emerges as a primary tool in detecting enteric viruses and identifying new viruses, revolutionizing health diagnoses, knowledge of viral diversity, and health surveillance, contributing to the correct etiology of infectious agents that would never be identified by conventional methods. The 18 articles studied point to advances in research in viral metagenomics of diarrheal samples, contributing to the discernment of diarrhea outbreaks, and properly associating with their etiological agents, they are presented in an innovative way for studies on the understanding of viral diversity., (Copyright © 2021. Published by Elsevier B.V.)

Objective: To investigate the application value of metagenomic next-generation sequencing (mNGS) in children with hematological diseases presenting with Febrile Neutropenia (FN)., Methods: We retrospectively analyzed the clinical data of 49 hematological children with FN, and compared the results of mNGS with those of traditional pathogen detection (TPD) and the prognoses of mNGS positive group and negative group., Results: A total of 77 pathogenic strains were identified, of which 70 strains were detected by mNGS, 19 strains by TPD, and Aspergillus and G- bacterias were the predominant strains in FN children who developed bloodstream infections. 42 cases were in the mNGS-positive group, of which 17 were simple infections, 25 were mixed infections, and 7 were in the negative group; the TPD-positive group contained 19 cases, all of which were simple infections. The detection rate of total and mixed pathogens was higher than that of TPD, and the difference was statistically significant (P<0.05). The mNGS positive group was detected earlier than the negative group, and with lower mortality and drug-related adverse events (DRAE), and the difference was statistically significant (P<0.05)., Conclusion: For FN children with hematological diseases, early mNGS can effectively improve the efficacy of pathogen detection, and precise treatment after clarifying the pathogens can reduce mortality and avoid antibiotic abuse., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

MDG, a polysaccharide derived from Ophiopogon japonicus, displays a protective effect against obesity and non-alcoholic fatty liver disease (NAFLD). However, there is no definitive evidence proving the specific mechanism of MDG against NAFLD. The results showed MDG supplementation ameliorated lipid accumulation, liver steatosis, and chronic inflammation in high-fat diet-induced NAFLD mice. Besides, MDG increased the abundance and diversity of microbial communities in the gut. These effects were mediated by the colonization of fecal microbiota. Further investigation revealed that Akkermansia muciniphila levels correlated negatively with NAFLD development, and lipid metabolism-related signaling might be the key regulator. Our study suggested that MDG treatment could inhibit obesity and the NAFLD process by modulating lipid-related pathways via altering the structure and diversity of gut microbiota. In addition, Akkermansia miniciphila might be a promising candidate in future research into NAFLD., (Copyright © 2021. Published by Elsevier B.V.)

Rapid and accurate pathogen identification is necessary for appropriate treatment of pneumonia. Here, we describe the use of shotgun metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage for pathogen identification in pneumonia in a large-scale multicenter prospective study with 159 patients enrolled. The results of mNGS were compared with standard methods including culture, staining, and targeted PCR, and the clinical impact of mNGS was evaluated. A positive impact was defined by a definitive diagnosis made using the mNGS results, or change of management because of the mNGS results, leading to a favorable clinical outcome. Overall, mNGS identified more organisms than standard methods (117 versus 72), detected 17 pathogens that consistently were missed in all cases by standard methods, and had an overall positive clinical impact in 40.3% (64 of 159) of cases. mNGS was especially useful in identification of fastidious and atypical organisms causing pneumonia, contributing to detection of definitive pathogens in 45 (28.3%) cases in which standard results were either negative or insufficient. mNGS also helped reassure antibiotic de-escalation in 19 (11.9%) cases. Overall, mNGS led to a change of treatment in 59 (37.1%) cases, including antibiotic de-escalation in 40 (25.2%) cases. This study showed the significant value of mNGS of bronchoalveolar lavage for improving the diagnosis of pneumonia and contributing to better patient care., (Copyright © 2021 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

The mosquito microbiome is critical to multiple facets of their biology, including larval development and disease transmission. For mosquitoes that reside in temperate regions, periods of diapause are critical to overwintering survival, but how the microbiome impacts this state is unknown. In this study, we compared the midgut microbial communities of diapausing and non-diapausing Culex pipiens and assessed how a reduced midgut microbiome influences diapause preparation. High community variability was found within and between non-diapausing and diapausing individuals, but no specific diapause-based microbiome was noted. Emergence of adult, diapausing mosquitoes under sterile conditions generated low bacterial load (LBL) lines with nearly a 1000-fold reduction in bacteria levels. This reduction in bacterial content resulted in significantly lower survival of diapausing females after two weeks, indicating acquisition of the microbiome in adult females is critical for survival throughout diapause. LBL diapausing females had high carbohydrate levels, but did not accumulate lipid reserves, suggesting an inability to process ingested sugars necessary for diapause-associated lipid accumulation. Expression patterns of select genes associated with mosquito lipid metabolism during diapause showed no significant differences between LBL and control lines, suggesting transcriptional changes may not underlie impaired lipid accumulation. Overall, a diverse, adult-acquired microbiome is critical for diapause in C. pipiens to process sugar reserves and accumulate lipids that are necessary to survive prolonged overwintering., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Despite the impact of childhood diarrhea on morbidity and mortality, our understanding of its sequelae has been significantly hampered by the lack of studies that examine samples across the entire intestinal tract. Infant rhesus macaques are naturally susceptible to human enteric pathogens and recapitulate the hallmarks of diarrheal disease such as intestinal inflammation and growth faltering. Here, we examined intestinal biopsies, lamina propria leukocytes, luminal contents, and fecal samples from healthy infants and those experiencing growth faltering with distant acute or chronic active diarrhea. We show that growth faltering in the presence or absence of active diarrhea is associated with a heightened systemic and mucosal pro-inflammatory state centered in the colon. Moreover, polyclonal stimulation of colonic lamina propria leukocytes resulted in a dampened cytokine response, indicative of immune exhaustion. We also detected a functional and taxonomic shift in the luminal microbiome across multiple gut sites including the migration of Streptococcus and Prevotella species between the small and large intestine, suggesting a decompartmentalization of gut microbial communities. Our studies provide valuable insight into the outcomes of diarrheal diseases and growth faltering not attainable in humans and lays the groundwork to test interventions in a controlled and reproducible setting., (© 2021. The Author(s).)

Saliva is a biofluid that maintains the health of oral tissues and the homeostasis of oral microbiota. Studies have demonstrated that Oral squamous cell carcinoma (OSCC) patients have different salivary microbiota than healthy individuals. However, the relationship between these microbial differences and clinicopathological outcomes is still far from conclusive. Herein, we investigate the capability of using metagenomic and metaproteomic saliva profiles to distinguish between Control (C), OSCC without active lesion (L0), and OSCC with active lesion (L1) patients. The results show that there are significantly distinct taxonomies and functional changes in L1 patients compared to C and L0 patients, suggesting compositional modulation of the oral microbiome, as the relative abundances of Centipeda, Veillonella, and Gemella suggested by metagenomics are correlated with tumor size, clinical stage, and active lesion. Metagenomics results also demonstrated that poor overall patient survival is associated with a higher relative abundance of Stenophotromonas, Staphylococcus, Centipeda, Selenomonas, Alloscordovia, and Acitenobacter. Finally, compositional and functional differences in the saliva content by metaproteomics analysis can distinguish healthy individuals from OSCC patients. In summary, our study suggests that oral microbiota and their protein abundance have potential diagnosis and prognosis value for oral cancer patients. Further studies are necessary to understand the role of uniquely detected metaproteins in the microbiota of healthy and OSCC patients as well as the crosstalk between saliva host proteins and the oral microbiome present in OSCC., (Copyright © 2021. Published by Elsevier B.V.)

The urinary tract consists of the bladder, ureters, and kidneys, and is an essential organ system for filtration and excretion of waste products and maintaining systemic homeostasis. In this capacity, the urinary tract is impacted by its interactions with other mucosal sites, including the genitourinary and gastrointestinal systems. Each of these sites harbors diverse ecosystems of microbes termed the microbiota, that regulates complex interactions with the local and systemic immune system. It remains unclear whether changes in the microbiota and associated metabolites may be a consequence or a driver of urinary tract diseases. Here, we review the current literature, investigating the impact of the microbiota on the urinary tract in homeostasis and disease including urinary stones, acute kidney injury, chronic kidney disease, and urinary tract infection. We propose new avenues for exploration of the urinary microbiome using emerging technology and discuss the potential of microbiome-based medicine for urinary tract conditions.

Due to the importance of using lignocellulosic biomass, it is always important to find an effective novel enzyme or enzyme cocktail or fusion enzymes. Identification of bifunctional enzymes through a metagenomic approach is an efficient method for converting agricultural residues and a beneficial way to reduce the cost of enzyme cocktail and fusion enzyme production. In this study, a novel stable bifunctional cellulase/xylanase, PersiCelXyn1 was identified from the rumen microbiota by the multi-stage in-silico screening pipeline and computationally assisted methodology. The enzyme exhibited the optimal activity at pH 5 and 50°C. Analyzing the enzyme activity at extreme temperature, pH, long-term storage, and presence of inhibitors and metal ions, confirmed the stability of the bifunctional enzyme under harsh conditions. Hydrolysis of the rice straw by PersiCelXyn1 showed its capability to degrade both cellulose and hemicellulose polymers. Also, the enzyme improved the degradation of various biomass substrates after 168 h of hydrolysis. Our results demonstrated the power of the multi-stage in-silico screening to identify bifunctional enzymes from metagenomic big data for effective bioconversion of lignocellulosic biomass., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2018. Published by Elsevier B.V.)