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908300.9903ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.202438725076
516310.9902Multi-omics data elucidate parasite-host-microbiota interactions and resistance to Haemonchus contortus in sheep. BACKGROUND: The integration of molecular data from hosts, parasites, and microbiota can enhance our understanding of the complex biological interactions underlying the resistance of hosts to parasites. Haemonchus contortus, the predominant sheep gastrointestinal parasite species in the tropics, causes significant production and economic losses, which are further compounded by the diminishing efficiency of chemical control owing to anthelmintic resistance. Knowledge of how the host responds to infection and how the parasite, in combination with microbiota, modulates host immunity can guide selection decisions to breed animals with improved parasite resistance. This understanding will help refine management practices and advance the development of new therapeutics for long-term helminth control. METHODS: Eggs per gram (EPG) of feces were obtained from Morada Nova sheep subjected to two artificial infections with H. contortus and used as a proxy to select animals with high resistance or susceptibility for transcriptome sequencing (RNA-seq) of the abomasum and 50 K single-nucleotide genotyping. Additionally, RNA-seq data for H. contortus were generated, and amplicon sequence variants (ASV) were obtained using polymerase chain reaction amplification and sequencing of bacterial and archaeal 16S ribosomal RNA genes from sheep feces and rumen content. RESULTS: The heritability estimate for EPG was 0.12. GAST, GNLY, IL13, MGRN1, FGF14, and RORC genes and transcripts were differentially expressed between resistant and susceptible animals. A genome-wide association study identified regions on chromosomes 2 and 11 that harbor candidate genes for resistance, immune response, body weight, and adaptation. Trans-expression quantitative trait loci were found between significant variants and differentially expressed transcripts. Functional co-expression modules based on sheep genes and ASVs correlated with resistance to H. contortus, showing enrichment in pathways of response to bacteria, immune and inflammatory responses, and hub features of the Christensenellaceae, Bacteroides, and Methanobrevibacter genera; Prevotellaceae family; and Verrucomicrobiota phylum. In H. contortus, some mitochondrial, collagen-, and cuticle-related genes were expressed only in parasites isolated from susceptible sheep. CONCLUSIONS: The present study identified chromosome regions, genes, transcripts, and pathways involved in the elaborate interactions between the sheep host, its gastrointestinal microbiota, and the H. contortus parasite. These findings will assist in the development of animal selection strategies for parasite resistance and interdisciplinary approaches to control H. contortus infection in sheep.202438429820
581920.9902Application of mNGS in the Etiological Analysis of Lower Respiratory Tract Infections and the Prediction of Drug Resistance. Lower respiratory tract infections (LRTIs) have high morbidity and mortality rates. However, traditional etiological detection methods have not been able to meet the needs for the clinical diagnosis and prognosis of LRTIs. The rapid development of metagenomic next-generation sequencing (mNGS) provides new insights for the diagnosis and treatment of LRTIs; however, little is known about how to interpret the application of mNGS results in LRTIs. In this study, lower respiratory tract specimens from 46 patients with suspected LRTIs were tested simultaneously using conventional microbiological detection methods and mNGS. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the logarithm of reads per kilobase per million mapped reads [lg(RPKM)], genomic coverage, and relative abundance of the organism in predicting the true-positive pathogenic bacteria. True-positive viruses were identified according to the lg(RPKM) threshold of bacteria. We also evaluated the ability to predict drug resistance genes using mNGS. Compared to that using conventional detection methods, the false-positive detection rate of pathogenic bacteria was significantly higher using mNGS. It was concluded from the ROC curves that the lg(RPKM) and genomic coverage contributed to the identification of pathogenic bacteria, with the performance of lg(RPKM) being the best (area under the curve [AUC] = 0.99). The corresponding lg(RPKM) threshold for identifying the pathogenic bacteria was -1.35. Thirty-five strains of true-positive virus were identified based on the lg(RPKM) threshold of bacteria, with the detection of human gammaherpesvirus 4 being the highest and prone to coinfection with Pseudomonas aeruginosa, Acinetobacter baumannii, and Stenotrophomonas maltophilia. Antimicrobial susceptibility tests (AST) revealed the resistance of bacteria containing drug resistance genes (detected by mNGS). However, the drug resistance genes of some multidrug-resistant bacteria were not detected. As an emerging technology, mNGS has shown many advantages for the unbiased etiological detection and the prediction of antibiotic resistance. However, a correct understanding of mNGS results is a prerequisite for its clinical application, especially for LRTIs. IMPORTANCE LRTIs are caused by hundreds of pathogens, and they have become a great threat to human health due to the limitations of traditional etiological detection methods. As an unbiased approach to detect pathogens, mNGS overcomes such etiological diagnostic challenges. However, there is no unified standard on how to use mNGS indicators (the sequencing reads, genomic coverage, and relative abundance of each organism) to distinguish between pathogens and colonizing microorganisms or contaminant microorganisms. Here, we selected the mNGS indicator with the best identification performance and established a cutoff value for the identification of pathogens in LRTIs using ROC curves. In addition, we also evaluated the accuracy of antibiotic resistance prediction using mNGS.202235171007
509830.9899Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study. INTRODUCTION: Drug resistance (DR) of pathogens remains a global healthcare concern. In contrast to other bacteria, acquiring mutations in the core genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. Statistical machine learning (ML) methods are used to infer new genes implicated in drug resistance leveraging large collections of isolates with known whole-genome sequences and phenotypic states for different drugs. However, high correlations between the phenotypic states for commonly used drugs complicate the inference of true associations of mutations with drug phenotypes by ML approaches. METHODS: Recently, several new methods have been developed to select a small subset of reliable predictors of the dependent variable, which may help reduce the number of spurious associations identified. In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and "Hungry, Hungry SNPos" (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. We assessed their ability to select known causal mutations for resistance to a specific drug while avoiding the selection of mutations in genes associated with resistance to other drugs, thus we compared selected ML models for their applicability for MTB genome wide association studies. RESULTS AND DISCUSSION: In our analysis, ABESS significantly outperformed the other methods, selecting more relevant sets of mutations. Additionally, we demonstrated that aggregating rare mutations within protein-coding genes into markers indicative of changes in PFAM domains improved prediction quality, and these markers were predominantly selected by ABESS, suggesting their high informativeness. However, ABESS yielded lower prediction accuracy compared to logistic regression methods with regularization.202540606161
223940.9897The Direct Semi-Quantitative Detection of 18 Pathogens and Simultaneous Screening for Nine Resistance Genes in Clinical Urine Samples by a High-Throughput Multiplex Genetic Detection System. BACKGROUND: Urinary tract infections (UTIs) are one the most common infections. The rapid and accurate identification of uropathogens, and the determination of antimicrobial susceptibility, are essential aspects of the management of UTIs. However, existing detection methods are associated with certain limitations. In this study, a new urinary tract infection high-throughput multiplex genetic detection system (UTI-HMGS) was developed for the semi-quantitative detection of 18 pathogens and the simultaneously screening of nine resistance genes directly from the clinical urine sample within 4 hours. METHODS: We designed and optimized a multiplex polymerase chain reaction (PCR) involving fluorescent dye-labeled specific primers to detect 18 pathogens and nine resistance genes. The specificity of the UTI-HMGS was tested using standard strains or plasmids for each gene target. The sensitivity of the UTI-HMGS assay was tested by the detection of serial tenfold dilutions of plasmids or simulated positive urine samples. We also collected clinical urine samples and used these to perform urine culture and antimicrobial susceptibility testing (AST). Finally, all urine samples were detected by UTI-HMGS and the results were compared with both urine culture and Sanger sequencing. RESULTS: UTI-HMGS showed high levels of sensitivity and specificity for the detection of uropathogens when compared with culture and sequencing. In addition, ten species of bacteria and three species of fungi were detected semi-quantitatively to allow accurate discrimination of significant bacteriuria and candiduria. The sensitivity of the UTI-HMGS for the all the target genes could reach 50 copies per reaction. In total, 531 urine samples were collected and analyzed by UTI-HMGS, which exhibited high levels of sensitivity and specificity for the detection of uropathogens and resistance genes when compared with Sanger sequencing. The results from UTI-HMGS showed that the detection rates of 15 pathogens were significantly higher (P<0.05) than that of the culture method. In addition, there were 41(7.72%, 41/531) urine samples were positive for difficult-to-culture pathogens, which were missed detected by routine culture method. CONCLUSIONS: UTI-HMGS proved to be an efficient method for the direct semi-quantitative detection of 18 uropathogens and the simultaneously screening of nine antibiotic resistance genes in urine samples. The UTI-HMGS could represent an alternative method for the clinical detection and monitoring of antibiotic resistance.202133912478
516450.9897Genome sequencing analysis of the pncA, rpsA and panD genes responsible for pyrazinamide resistance of Mycobacterium tuberculosis from Indonesian isolates. BACKGROUND: Developing the most suitable treatment against tuberculosis based on resistance profiles is imperative to effectively cure tuberculosis patients. Whole-genome sequencing is a molecular method that allows for the rapid and cost-effective detection of mutations in multiple genes associated with anti-tuberculosis drug resistance. This sequencing approach addresses the limitations of culture-based methods, which may not apply to certain anti-TB drugs, such as pyrazinamide, because of their specific culture medium requirements, potentially leading to biased resistance culture results. METHODS: Thirty-four M. tuberculosis isolates were subcultured on a Lowenstein-Jensen medium. The genome of these bacteria was subsequently isolated using cetyltrimethylammonium bromide. Genome sequencing was performed with Novaseq Illumina 6000 (Illumina), and the data were analysed using the GenTB and Mykrobe applications. We also conducted a de novo analysis to compare the two methods and performed mutation analysis of other genes encoding pyrazinamide resistance, namely rpsA and panD. RESULTS: The results revealed mutations in the pncA gene, which were identified based on the databases accessed through GenTB and Mykrobe. Two discrepancies between the drug susceptibility testing and sequencing results may suggest potential instability in the drug susceptibility testing culture, specifically concerning PZA. Meanwhile, the results of the de novo analysis showed the same result of pncA mutation to the GenTB or Mykrobe; meanwhile, there were silent mutations in rpsA in several isolates and a point mutation; no mutations were found in the panD gene. However, the mutations in the genes encoding pyrazinamide require further and in-depth study to understand their relationship to the phenotypic profile. CONCLUSIONS: Compared to the conventional culture method, the whole-genome sequencing method has advantages in determining anti-tuberculosis resistance profiles, especially in reduced time and bias.202439397216
512260.9897Clinical long-read metagenomic sequencing of culture-negative infective endocarditis reveals genomic features and antimicrobial resistance. BACKGROUND: Infective endocarditis (IE) poses significant diagnostic challenges, particularly in blood culture-negative cases where fastidious bacteria evade detection. Metagenomic-based nanopore sequencing enables rapid pathogen detection and provides a new approach for the diagnosis of IE. METHOD: Two cases of blood culture-negative infective endocarditis (IE) were analyzed using nanopore sequencing with an in silico host-depletion approach. Complete genome reconstruction and antimicrobial resistance gene annotation were successfully performed. RESULTS: Within an hour of sequencing, EPI2ME classified nanopore reads, identifying Corynebacterium striatum in IE patient 1 and Granulicatella adiacens in IE patient 2. After 18 h, long-read sequencing successfully reconstructed a single circular genome of C. striatum in IE patient 1, whereas short-read sequencing was used to compare but produced fragmented assemblies. Based on these results, long-read sequencing was exclusively used for IE patient 2, allowing for the complete and accurate assembly of G. adiacens, confirming the presence of these bacteria in the clinical samples. In addition to pathogen identification, antimicrobial resistance (AMR) genes were detected in both genomes. Notably, in C. striatum, regions containing a class 1 integron and multiple novel mobile genetic elements (ISCost1, ISCost2, Tn7838 and Tn7839) were identified, collectively harbouring six AMR genes. This is the first report of such elements in C. striatum, highlighting the potential of nanopore long-read sequencing for comprehensive pathogen characterization in IE cases. CONCLUSIONS: This study highlights the effectiveness of host-depleted, long-read nanopore metagenomics for direct pathogen identification and accurate genome reconstruction, including antimicrobial resistance gene detection. The approach enables same-day diagnostic reporting within a matter of hours. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-025-11741-5.202541087996
512470.9896Oxford nanopore long-read sequencing enables the generation of complete bacterial and plasmid genomes without short-read sequencing. INTRODUCTION: Genome-based analysis is crucial in monitoring antibiotic-resistant bacteria (ARB)and antibiotic-resistance genes (ARGs). Short-read sequencing is typically used to obtain incomplete draft genomes, while long-read sequencing can obtain genomes of multidrug resistance (MDR) plasmids and track the transmission of plasmid-borne antimicrobial resistance genes in bacteria. However, long-read sequencing suffers from low-accuracy base calling, and short-read sequencing is often required to improve genome accuracy. This increases costs and turnaround time. METHODS: In this study, a novel ONT sequencing method is described, which uses the latest ONT chemistry with improved accuracy to assemble genomes of MDR strains and plasmids from long-read sequencing data only. Three strains of Salmonella carrying MDR plasmids were sequenced using the ONT SQK-LSK114 kit with flow cell R10.4.1, and de novo genome assembly was performed with average read accuracy (Q > 10) of 98.9%. RESULTS AND DISCUSSION: For a 5-Mb-long bacterial genome, finished genome sequences with accuracy of >99.99% could be obtained at 75× sequencing coverage depth using Flye and Medaka software. Thus, this new ONT method greatly improves base-calling accuracy, allowing for the de novo assembly of high-quality finished bacterial or plasmid genomes without the need for short-read sequencing. This saves both money and time and supports the application of ONT data in critical genome-based epidemiological analyses. The novel ONT approach described in this study can take the place of traditional combination genome assembly based on short- and long-read sequencing, enabling pangenomic analyses based on high-quality complete bacterial and plasmid genomes to monitor the spread of antibiotic-resistant bacteria and antibiotic resistance genes.202337256057
476180.9896Antimicrobial resistance and biofilm formation of penile prosthesis isolates: insights from in-vitro analysis. BACKGROUND: Inflatable penile prostheses (IPPs) have been shown to harbor biofilms in the presence and absence of infection despite exposure to various antimicrobials. Microbes persisting on IPPs following antibiotic exposure have not been adequately studied to assess biofilm formation capacity and antibiotic resistance. AIM: In this study, we aimed to assess these properties of microbes obtained from explanted infected and non-infected IPPS using an in vitro model. METHODS: 35 bacterial isolates were grown and tested against various single-agent or multiple agent antibiotic regimens including: bacitracin, cefaclor, cefazolin, gentamicin, levofloxacin, trimethoprim-sulfamethoxazole, tobramycin, vancomycin, piperacillin/tazobactam, gentamicin + piperacillin/tazobactam, gentamicin + cefazolin, and gentamicin + vancomycin. Zones of inhibition were averaged for each sample site and species. Statistics were analyzed with Holm's corrected, one-sample t-tests against a null hypothesis of 0. Isolates were also allowed to form biofilms in a 96-well polyvinyl plate and absorbance was tested at 570 nm using a microplate reader. OUTCOMES: Resistance was determined via clinical guidelines or previously established literature, and the mean and standard deviation of biofilm absorbance values were calculated and normalized to the optical density600 of the bacterial inoculum. RESULTS: Every species tested was able to form robust biofilms with the exception of Staphylococcus warneri. As expected, most bacteria were resistant to common perioperative antimicrobial prophylaxis. Gentamicin dual therapy demonstrated somewhat greater efficacy. STRENGTHS AND LIMITATIONS: This study examines a broad range of antimicrobials against clinically obtained bacterial isolates. However, not all species and antibiotics tested had standardized breakpoints, requiring the use of surrogate values from the literature. The microbes included in this study and their resistance genes are expectedly biased towards those that survived antibiotic exposure, and thus reflect the types of microbes which might "survive" in vivo exposure following revisional surgery. CLINICAL TRANSLATION: Despite exposure to antimicrobials, bacteria isolated during penile prosthesis revision for both infected and non-infected cases exhibit biofilm forming capacity and extensive antibiotic resistance patterns in vitro. These microbes merit further investigation to understand when simple colonization vs re-infection might occur. CONCLUSIONS: Although increasing evidence supports the concept that all IPPs harbor biofilms, even in the absence of infection, a deeper understanding of the characteristics of bacteria that survive revisional surgery is warranted. This study demonstrated extensive biofilm forming capabilities, and resistance patterns among bacteria isolated from both non-infected and infected IPP revision surgeries. Further investigation is warranted to determine why some devices become infected while others remain colonized but non-infected.202540062463
582390.9895Comparing Patient Risk Factor-, Sequence Type-, and Resistance Locus Identification-Based Approaches for Predicting Antibiotic Resistance in Escherichia coli Bloodstream Infections. Rapid diagnostic tests for antibiotic resistance that identify the presence or absence of antibiotic resistance genes/loci are increasingly being developed. However, these approaches usually neglect other sources of predictive information which could be identified over shorter time periods, including patient epidemiologic risk factors for antibiotic resistance and markers of lineage. Using a data set of 414 Escherichia coli isolates recovered from separate episodes of bacteremia at a single academic institution in Toronto, Ontario, Canada, between 2010 and 2015, we compared the potential predictive ability of three approaches (epidemiologic risk factor-, pathogen sequence type [ST]-, and resistance gene identification-based approaches) for classifying phenotypic resistance to three antibiotics representing classes of broad-spectrum antimicrobial therapy (ceftriaxone [a 3rd-generation cephalosporin], ciprofloxacin [a fluoroquinolone], and gentamicin [an aminoglycoside]). We used logistic regression models to generate model receiver operating characteristic (ROC) curves. Predictive discrimination was measured using apparent and corrected (bootstrapped) areas under the curves (AUCs). Epidemiologic risk factor-based models based on two simple risk factors (prior antibiotic exposure and recent prior susceptibility of Gram-negative bacteria) provided a modest predictive discrimination, with AUCs ranging from 0.65 to 0.74. Sequence type-based models demonstrated strong discrimination (AUCs, 0.83 to 0.94) across all three antibiotic classes. The addition of epidemiologic risk factors to sequence type significantly improved the ability to predict resistance for all antibiotics (P < 0.05). Resistance gene identification-based approaches provided the highest degree of discrimination (AUCs, 0.88 to 0.99), with no statistically significant benefit being achieved by adding the patient epidemiologic predictors. In summary, sequence type or other lineage-based approaches could produce an excellent discrimination of antibiotic resistance and may be improved by incorporating readily available patient epidemiologic predictors but are less discriminatory than identification of the presence of known resistance loci.201930894438
9081100.9894Identification and reconstruction of novel antibiotic resistance genes from metagenomes. BACKGROUND: Environmental and commensal bacteria maintain a diverse and largely unknown collection of antibiotic resistance genes (ARGs) that, over time, may be mobilized and transferred to pathogens. Metagenomics enables cultivation-independent characterization of bacterial communities but the resulting data is noisy and highly fragmented, severely hampering the identification of previously undescribed ARGs. We have therefore developed fARGene, a method for identification and reconstruction of ARGs directly from shotgun metagenomic data. RESULTS: fARGene uses optimized gene models and can therefore with high accuracy identify previously uncharacterized resistance genes, even if their sequence similarity to known ARGs is low. By performing the analysis directly on the metagenomic fragments, fARGene also circumvents the need for a high-quality assembly. To demonstrate the applicability of fARGene, we reconstructed β-lactamases from five billion metagenomic reads, resulting in 221 ARGs, of which 58 were previously not reported. Based on 38 ARGs reconstructed by fARGene, experimental verification showed that 81% provided a resistance phenotype in Escherichia coli. Compared to other methods for detecting ARGs in metagenomic data, fARGene has superior sensitivity and the ability to reconstruct previously unknown genes directly from the sequence reads. CONCLUSIONS: We conclude that fARGene provides an efficient and reliable way to explore the unknown resistome in bacterial communities. The method is applicable to any type of ARGs and is freely available via GitHub under the MIT license.201930935407
5824110.9894Evaluation of a micro/nanofluidic chip platform for the high-throughput detection of bacteria and their antibiotic resistance genes in post-neurosurgical meningitis. BACKGROUND: Post-neurosurgical meningitis (PNM) is one of the most severe hospital-acquired infections worldwide, and a large number of pathogens, especially those possessing multi-resistance genes, are related to these infections. Existing methods for detecting bacteria and measuring their response to antibiotics lack sensitivity and stability, and laboratory-based detection methods are inconvenient, requiring at least 24h to complete. Rapid identification of bacteria and the determination of their susceptibility to antibiotics are urgently needed, in order to combat the emergence of multi-resistant bacterial strains. METHODS: This study evaluated a novel, fast, and easy-to-use micro/nanofluidic chip platform (MNCP), which overcomes the difficulties of diagnosing bacterial infections in neurosurgery. This platform can identify 10 genus or species targets and 13 genetic resistance determinants within 1h, and it is very simple to operate. A total of 108 bacterium-containing cerebrospinal fluid (CSF) cultures were tested using the MNCP for the identification of bacteria and determinants of genetic resistance. The results were compared to those obtained with conventional identification and antimicrobial susceptibility testing methods. RESULTS: For the 108 CSF cultures, the concordance rate between the MNCP and the conventional identification method was 94.44%; six species attained 100% consistency. For the production of carbapenemase- and extended-spectrum beta-lactamase (ESBL)-related antibiotic resistance genes, both the sensitivity and specificity of the MNCP tests were high (>90.0%) and could fully meet the requirements of clinical diagnosis. CONCLUSIONS: The MNCP is fast, accurate, and easy to use, and has great clinical potential in the treatment of post-neurosurgical meningitis.201829559366
2540120.9894Equine sinusitis aetiology is linked to sinus microbiome by amplicon sequencing. BACKGROUND: Information regarding the microbiome in sinusitis using genetic sequencing is lacking and more-in-depth understanding of the microbiome could improve antimicrobial selection and treatment outcomes for cases of primary sinusitis. OBJECTIVES: To describe sinus microbiota in samples from horses with sinusitis and compare microbiota and the presence of antimicrobial resistance genes between primary, dental-related and other secondary causes of sinusitis. STUDY DESIGN: Retrospective case series. METHODS: Records of equine sinusitis from 2017 to 2021 were reviewed and historical microbial amplicon sequence data were obtained from clinical diagnostic testing of sinus secretions. Following bioinformatic processing of bacterial and fungal sequence data, the sinus microbiota and importance of sinusitis aetiology among other factors were investigated from the perspectives of alpha diversity (e.g., number of operational taxonomic units [OTUs], Hill1 Diversity), beta diversity, and differentially abundant taxa. Quantitative PCR allowed for comparisons of estimated bacterial abundance and detection rate of common antibiotic resistance-associated genes. In a smaller subset, longitudinal analysis was performed to evaluate similarity in samples over time. RESULTS: Of 81 samples analysed from 70 horses, the bacterial microbiome was characterised in 66, and fungal in five. Only sinusitis aetiology was shown to significantly influence microbiome diversity and composition (p < 0.05). Dental-related sinusitis (n = 44) was associated with a significantly higher proportion of obligate anaerobic bacteria, whereas primary sinusitis (n = 12) and other (n = 10) groups were associated with fewer bacteria and higher proportions of facultative anaerobic and aerobic genera. Antimicrobial resistance genes and fungal components were exclusively identified in dental-related sinusitis. MAIN LIMITATIONS: Retrospective nature, incomplete prior antimicrobial administration data. CONCLUSIONS: Molecular characterisation in sinusitis identifies microbial species which may be difficult to isolate via culture, and microbiome profiling can differentiate sinusitis aetiology, which may inform further treatment, including antimicrobial therapy.202336199163
2267130.9893MOLECULAR CHARACTERIZATION AND DETECTION OF MULTIDRUGRESISTANT GENE IN BACTERIAL ISOLATES CAUSING LOWER RESPIRATORY TRACT INFECTIONS (LRTI) AMONG HIV/AIDS PATIENTS ON HIGHLY ACTIVE ANTIRETROVIRAL THERAPY (HAART) IN UYO, SOUTH-SOUTH NIGERIA. BACKGROUND: Antibiotic-resistant genes (ARGs) pose a significant challenge in modern medicine, rendering infections increasingly difficult to treat as bacteria acquire mechanisms to resist antibiotics. Addressing ARGs necessitates a multifaceted approach, encompassing surveillance efforts to monitor their presence and the development of strategies aimed at managing and curbing the spread of antibiotic resistance. Hence, this study characterized the genetic determinants of antibiotic resistance among isolates responsible for Lower Respiratory Tract Infections (LRTIs) in People Living with HIV/AIDS (PLWHA) in Uyo. METHODS: Sputum samples were collected from 61 LRTI suspects, with bacterial isolates identified using VITEK-2 technology. Polymerase chain reaction assays were employed to detect resistance genes within the isolates. RESULTS: Results revealed a bacterial etiology in 39.3% of the samples, with a majority (79.2%) originating from St. Luke Hospital, Anua (SLHA), and the remainder (20.8%) from the University of Uyo Teaching Hospital (UUTH). Staphylococcus aureus emerged as the predominant isolate (46.6%), while resistance was notably high against Gentamicin and Sulphamethazole/Trimethoprim. Conversely, Azithromycin, imipenem, clindamycin, erythromycin, and ceftriaxone displayed relatively lower resistance levels across all isolates. Notably, four resistance genes CTX-M, Aac, KPC, and MecA were identified, with CTX-M detected in all multidrug-resistant isolates. This underscores the predominantly community-acquired nature of resistance as conferred by CTX-M. CONCLUSION: In conclusion, this study underscores the critical importance of continued vigilance and proactive measures in combating antibiotic resistance, particularly within vulnerable populations such as PLWHA. By elucidating the genetic mechanisms underlying antibiotic resistance, informed targeted interventions can be mitigated to curb threats posed by multidrug-resistant bacteria in clinical settings.202440385712
5167140.9893Decreased Antimicrobial Resistance Gene Richness Following Fecal Microbiota, Live-jslm (REBYOTA®) Administration: Post Hoc Analysis of PUNCH CD3. BACKGROUND: The human gastrointestinal microbiome helps maintain vital functions related to overall health, including resistance to pathogen colonization. Disruption of the microbiome, leading to loss of colonization resistance, can be caused by multiple factors, including antimicrobial use. The loss of colonization resistance may lead to establishment or proliferation of opportunistic bacteria that carry genes associated with antimicrobial resistance, potentially increasing the risk of infection by such antimicrobial-resistant bacteria. A potential approach to mitigating this risk involves restoration of healthier microbiota and pathogen colonization resistance. METHODS: A metagenomic sequencing method was used to conduct a post hoc analysis of antibiotic resistance gene richness among fecal samples from participants administered fecal microbiota, live-jslm (REBYOTA; abbreviated as RBL) or placebo in the PUNCH CD3 study (NCT03244644) for the prevention of recurrent Clostridioides difficile infection. RESULTS: At baseline, participants had higher antibiotic resistance gene richness than a representative healthy cohort. Over time, RBL responders had lower antibiotic resistance gene richness at the class, group, and mechanism levels as compared with placebo responders. These differences were evident as early as 1 week after administration and sustained for at least 6 months. RBL responders also had decreased richness of antibiotic resistance genes deemed high risk based on designated bacterial public health threats. CONCLUSIONS: These data support a model in which microbiota-based products, including RBL, may reduce antibiotic resistance gene richness, thereby possibly reducing the risk of antimicrobial-resistant organism infection. TRIAL REGISTRATION: NCT03244644 (https://clinicaltrials.gov/study/NCT03244644; 9 August 2017).202540672762
5118150.9892Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.202235134132
5116160.9892Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data. BACKGROUND: Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms. METHODS AND FINDINGS: We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets. CONCLUSION: Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/.202032528441
5825170.9892Polymerase Chain Reaction (PCR) Profiling of Extensively Drug-Resistant (XDR) Pathogenic Bacteria in Pulmonary Tuberculosis Patients. Introduction Pulmonary tuberculosis (TB) remains a global health concern, exacerbated by the emergence of extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis. This study employs advanced molecular techniques, specifically polymerase chain reaction (PCR) profiling, to comprehensively characterize the genetic landscape of XDR pathogenic bacteria in patients diagnosed with pulmonary TB. The objective of the study is to elucidate the genes that are associated with drug resistance in pulmonary TB strains through the application of PCR and analyze specific genetic loci that contribute to the development of resistance against multiple drugs. Materials and methods A total of 116 clinical samples suspected of TB were collected from the tertiary healthcare setting of Saveetha Medical College and Hospitals for the identification of MTB, which includes sputum (n = 35), nasal swabs (n = 17), blood (n = 44), and bronchoalveolar lavage (BAL) (n = 20). The collected specimens were processed and subjected to DNA extraction. As per the protocol, reconstitution of the DNA pellet was carried out. The reconstituted DNA was stored at -20 °C for the PCR assay. From the obtained positive sample specimens, XDR pulmonary TB specimens were focused on the targeted genes, specifically the rpoB gene for rifampicin resistance, inhA, and katG gene for thepromoter region for isoniazid resistance. Results Out of a total of 116 samples obtained, 53 tested positive for pulmonary TB, indicative of a mycobacterial infection. Among these positive cases, 43 patients underwent treatment at a tertiary healthcare facility. Subsequently, a PCR assay was performed with the extracted DNA for the target genes rpoB, inhA, and katG. Specifically, 22 sputum samples exhibited gene expression for rpoB, inhA, and katG, while nine nasal swabs showed expression of the rpoB and inhA genes. Additionally, rpoB gene expression was detected in seven blood specimens, and both rpoB and inhA genes were expressed in five BAL samples. Conclusion The swift diagnosis and efficient treatment of XDR-TB can be facilitated by employing advanced and rapid molecular tests and oral medication regimens. Utilizing both newly developed and repurposed anti-TB drugs like pretomanid, bedaquiline, linezolid, and ethionamide. Adhering to these current recommendations holds promise for managing XDR-TB effectively. Nevertheless, it is significant to conduct well-designed clinical trials and studies to further evaluate the efficacy of new agents and shorter treatment regimens, thus ensuring continuous improvement in the management of this challenging condition.202438953074
1482180.9892Evaluation and clinical practice of pathogens and antimicrobial resistance genes of BioFire FilmArray Pneumonia panel in lower respiratory tract infections. BACKGROUND: Existing panels for lower respiratory tract infections (LRTIs) are slow and lack quantification of important pathogens and antimicrobial resistance, which are not solely responsible for their complex etiology and antibiotic resistance. BioFire FilmArray Pneumonia (PN) panels may provide rapid information on their etiology. METHODS: The bronchoalveolar lavage fluid of 187 patients with LRTIs was simultaneously analyzed using a PN panel and cultivation, and the impact of the PN panel on clinical practice was assessed. The primary endpoint was to compare the consistency between the PN panel and conventional microbiology in terms of etiology and drug resistance, as well as to explore the clinical significance of the PN panel. The secondary endpoint was pathogen detection using the PN panel in patients with community-acquired pneumonia (CAP) or hospital-acquired pneumonia (HAP). RESULTS: Fifty-seven patients with HAP and 130 with CAP were included. The most common pathogens of HAP were Acinetobacter baumannii and Klebsiella pneumoniae, with the most prevalent antimicrobial resistance (AMR) genes being CTX-M and KPC. For CAP, the most common pathogens were Haemophilus influenzae and Staphylococcus aureus, with the most frequent AMR genes being CTX-M and VIM. Compared with routine bacterial culture, the PN panel demonstrated an 85% combined positive percent agreement (PPA) and 92% negative percent agreement (NPA) for the qualitative identification of 13 bacterial targets. PN detection of bacteria with higher levels of semi-quantitative bacteria was associated with more positive bacterial cultures. Positive concordance between phenotypic resistance and the presence of corresponding AMR determinants was 85%, with 90% positive agreement between CTX-M-type extended-spectrum beta-lactamase gene type and phenotype and 100% agreement for mecA/C and MREJ. The clinical benefit of the PN panel increased by 25.97% compared with traditional cultural tests. CONCLUSION: The bacterial pathogens and AMR identified by the PN panel were in good agreement with conventional cultivation, and the clinical benefit of the PN panel increased by 25.97% compared with traditional detection. Therefore, the PN panel is recommended for patients with CAP or HAP who require prompt pathogen diagnosis and resistance identification.202438123753
2553190.9892Early-life gut microbiome modulation reduces the abundance of antibiotic-resistant bacteria. BACKGROUND: Antibiotic-resistant (AR) bacteria are a global threat. AR bacteria can be acquired in early life and have long-term sequelae. Limiting the spread of antibiotic resistance without triggering the development of additional resistance mechanisms is of immense clinical value. Here, we show how the infant gut microbiome can be modified, resulting in a significant reduction of AR genes (ARGs) and the potentially pathogenic bacteria that harbor them. METHODS: The gut microbiome was characterized using shotgun metagenomics of fecal samples from two groups of healthy, term breastfed infants. One group was fed B. infantis EVC001 in addition to receiving lactation support (n = 29, EVC001-fed), while the other received lactation support alone (n = 31, controls). Coliforms were isolated from fecal samples and genome sequenced, as well as tested for minimal inhibitory concentrations against clinically relevant antibiotics. RESULTS: Infants fed B. infantis EVC001 exhibited a change to the gut microbiome, resulting in a 90% lower level of ARGs compared to controls. ARGs that differed significantly between groups were predicted to confer resistance to beta lactams, fluoroquinolones, or multiple drug classes, the majority of which belonged to Escherichia, Clostridium, and Staphylococcus. Minimal inhibitory concentration assays confirmed the resistance phenotypes among isolates with these genes. Notably, we found extended-spectrum beta lactamases among healthy, vaginally delivered breastfed infants who had never been exposed to antibiotics. CONCLUSIONS: Colonization of the gut of breastfed infants by a single strain of B. longum subsp. infantis had a profound impact on the fecal metagenome, including a reduction in ARGs. This highlights the importance of developing novel approaches to limit the spread of these genes among clinically relevant bacteria. Future studies are needed to determine whether colonization with B. infantis EVC001 decreases the incidence of AR infections in breastfed infants. TRIAL REGISTRATION: This clinical trial was registered at ClinicalTrials.gov, NCT02457338.201931423298