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511800.9866Automated 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
511710.9864Metagenomic sequencing of mpox virus clade Ib lesions identifies possible bacterial and viral co-infections in hospitalized patients in eastern DRC. Mpox is an emerging zoonotic disease that caused two public health emergencies of international concern within two years. Less is known about the interplay of microbial organisms in mpox lesions which could result in superinfections that exacerbate outcomes or delay recovery. We utilized a unified metagenomic sequencing approach involving slow-speed centrifugation and differential lysis on 19 mpox lesion swabs of hospitalized patients in South Kivu province (eastern DRC) to characterize bacteria, antimicrobial resistance genes, mpox virus (MPXV), and viral co-infections. High-quality MPXV whole-genome sequences were obtained until a Ct value of 27. Furthermore, co-infections with other clinically relevant viruses, such as varicella zoster virus and herpes simplex virus-2, were detected and confirmed by real-time PCR. In addition, metagenomic sequence analysis of the bacterial content showed the presence of bacteria associated with skin and soft tissue infection in 10 of the 19 samples analyzed. These bacteria had a high abundance of resistance genes, with possible implications for antimicrobial treatment based on the predicted antimicrobial resistance. In conclusion, we report the presence of bacterial and viral pathogens in mpox lesions and detection of widespread resistance genes to the standard antibiotic treatment. The possibility of a co-infection, including antimicrobial resistance, should be considered when discussing treatment options, along with the determination of the case-fatality ratio.IMPORTANCEThe mpox virus clade Ib lineage emerged in the eastern Democratic Republic of the Congo owing to continuous human-to-human transmission in a vulnerable patient population. A major challenge of this ongoing outbreak is its occurrence in regions with severely limited healthcare infrastructure. As a result, less is known about co-infections in affected patients. Identifying and characterizing pathogens, including their antimicrobial resistance, is crucial for reducing infection-related complications and improving antimicrobial stewardship. In this study, we applied a unified metagenomics approach to detect and characterize bacterial and viral co-infections in mpox lesions of hospitalized mpox patients in the eastern DRC.202540445195
581920.9863Application 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
908330.9863ARGNet: 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
582540.9858Polymerase 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
259050.9858Combining stool and stories: exploring antimicrobial resistance among a longitudinal cohort of international health students. BACKGROUND: Antimicrobial resistance (AMR) is a global public health concern that requires transdisciplinary and bio-social approaches. Despite the continuous calls for a transdisciplinary understanding of this problem, there is still a lack of such studies. While microbiology generates knowledge about the biomedical nature of bacteria, social science explores various social practices related to the acquisition and spread of these bacteria. However, the two fields remain disconnected in both methodological and conceptual levels. Focusing on the acquisition of multidrug resistance genes, encoding extended-spectrum betalactamases (CTX-M) and carbapenemases (NDM-1) among a travelling population of health students, this article proposes a methodology of 'stool and stories' that combines methods of microbiology and sociology, thus proposing a way forward to a collaborative understanding of AMR. METHODS: A longitudinal study with 64 health students travelling to India was conducted in 2017. The study included multiple-choice questionnaires (n = 64); a collection of faecal swabs before travel (T0, n = 45), in the first week in India (T1, n = 44), the second week in India (T2, n = 41); and semi-structured interviews (n = 11). Stool samples were analysed by a targeted metagenomic approach. Data from semi-structured interviews were analysed using the method of thematic analysis. RESULTS: The incidence of ESBL- and carbapenemase resistance genes significantly increased during travel indicating it as a potential risk; for CTX-M from 11% before travel to 78% during travel and for NDM-1 from 2% before travel to 11% during travel. The data from semi-structured interviews showed that participants considered AMR mainly in relation to individual antibiotic use or its presence in a clinical environment but not to travelling. CONCLUSION: The microbiological analysis confirmed previous research showing that international human mobility is a risk factor for AMR acquisition. However, sociological methods demonstrated that travellers understand AMR primarily as a clinical problem and do not connect it to travelling. These findings indicate an important gap in understanding AMR as a bio-social problem raising a question about the potential effectiveness of biologically driven AMR stewardship programs among travellers. Further development of the 'stool and stories' approach is important for a transdisciplinary basis of AMR stewardship.202134579656
582660.9858Rapid and accurate sepsis diagnostics via a novel probe-based multiplex real-time PCR system. Sepsis is a critical clinical emergency that requires prompt diagnosis and intervention. Its prevalence has increased due to the aging population and increased antibiotic resistance. Early identification and the use of innovative technologies are crucial for improving patient outcomes. Modern methodologies are needed to minimize the turnaround time for diagnosis and improve outcomes. Rapid diagnostic tests and multiplex PCR are effective but have limitations in identifying a range of pathogens and target genes. Our study evaluated two novel probe-based multiplex real-time PCR systems: the SEPSI ID and SEPSI DR panels. These systems can quickly identify bacterial and fungal pathogens, alongside antibiotic resistance genes. The assays cover 29 microorganisms (gram-negative bacteria, gram-positive bacteria, yeast, and mold species), alongside 23 resistance genes and four virulence factors. A streamlined workflow uses 2 µL of broth from positive blood cultures (BCs) without nucleic acid extraction and provides results in approximately 1 h. We present the results from an evaluation of 228 BCs and 22 isolates previously characterized by whole-genome sequencing. In comparison to the reference methods, the SEPSI ID panel demonstrated a sensitivity of 96.88%, a specificity of 100%, and a PPV of 100%, whereas the SEPSI DR panel showed a sensitivity of 97.8%, a PPV of 89.7%, and a specificity of 96.7%. Both panels also identified additional pathogens and resistance-related targets not detected by conventional methods. This assay shows promise for rapidly and accurately diagnosing sepsis. Future studies should validate its performance in various clinical settings to enhance sepsis management and improve patient outcomes.IMPORTANCEWe present a new diagnostic method that enables the quick and precise identification of pathogens and resistance genes from positive blood cultures, eliminating the need for nucleic acid extraction. This technique can also be used on fresh pathogen cultures. It has the potential to greatly improve treatment protocols, leading to better patient outcomes, more responsible antibiotic use, and more efficient management of healthcare resources.202541025980
580370.9857Face mask sampling reveals antimicrobial resistance genes in exhaled aerosols from patients with chronic obstructive pulmonary disease and healthy volunteers. INTRODUCTION: The degree to which bacteria in the human respiratory tract are aerosolised by individuals is not established. Building on our experience sampling bacteria exhaled by individuals with pulmonary tuberculosis using face masks, we hypothesised that patients with conditions frequently treated with antimicrobials, such as chronic obstructive pulmonary disease (COPD), might exhale significant numbers of bacteria carrying antimicrobial resistance (AMR) genes and that this may constitute a previously undefined risk for the transmission of AMR. METHODS: Fifteen-minute mask samples were taken from 13 patients with COPD (five paired with contemporaneous sputum samples) and 10 healthy controls. DNA was extracted from cell pellets derived from gelatine filters mounted within the mask. Quantitative PCR analyses directed to the AMR encoding genes: blaTEM (β-lactamase), ErmB (target methylation), mefA (macrolide efflux pump) and tetM (tetracycline ribosomal protection protein) and six additional targets were investigated. Positive signals above control samples were obtained for all the listed genes; however, background signals from the gelatine precluded analysis of the additional targets. RESULTS: 9 patients with COPD (69%), aerosolised cells containing, in order of prevalence, mefA, tetM, ErmB and blaTEM, while three healthy controls (30%) gave weak positive signals including all targets except blaTEM. Maximum estimated copy numbers of AMR genes aerosolised per minute were mefA: 3010, tetM: 486, ErmB: 92 and blaTEM: 24. The profile of positive signals found in sputum was not concordant with that in aerosol in multiple instances. DISCUSSION: We identified aerosolised AMR genes in patients repeatedly exposed to antimicrobials and in healthy volunteers at lower frequencies and levels. The discrepancies between paired samples add weight to the view that sputum content does not define aerosol content. Mask sampling is a simple approach yielding samples from all subjects and information distinct from sputum analysis. Our results raise the possibility that patient-generated aerosols may be a significant means of AMR dissemination that should be assessed further and that consideration be given to related control measures.201830271606
226980.9856Genomic detection of Panton-Valentine Leucocidins encoding genes, virulence factors and distribution of antiseptic resistance determinants among Methicillin-resistant S. aureus isolates from patients attending regional referral hospitals in Tanzania. BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is a formidable public scourge causing worldwide mild to severe life-threatening infections. The ability of this strain to swiftly spread, evolve, and acquire resistance genes and virulence factors such as pvl genes has further rendered this strain difficult to treat. Of concern, is a recently recognized ability to resist antiseptic/disinfectant agents used as an essential part of treatment and infection control practices. This study aimed at detecting the presence of pvl genes and determining the distribution of antiseptic resistance genes in Methicillin-resistant Staphylococcus aureus isolates through whole genome sequencing technology. MATERIALS AND METHODS: A descriptive cross-sectional study was conducted across six regional referral hospitals-Dodoma, Songea, Kitete-Kigoma, Morogoro, and Tabora on the mainland, and Mnazi Mmoja from Zanzibar islands counterparts using the archived isolates of Staphylococcus aureus bacteria. The isolates were collected from Inpatients and Outpatients who attended these hospitals from January 2020 to Dec 2021. Bacterial analysis was carried out using classical microbiological techniques and whole genome sequencing (WGS) using the Illumina Nextseq 550 sequencer platform. Several bioinformatic tools were used, KmerFinder 3.2 was used for species identification, MLST 2.0 tool was used for Multilocus Sequence Typing and SCCmecFinder 1.2 was used for SCCmec typing. Virulence genes were detected using virulenceFinder 2.0, while resistance genes were detected by ResFinder 4.1, and phylogenetic relatedness was determined by CSI Phylogeny 1.4 tools. RESULTS: Out of the 80 MRSA isolates analyzed, 11 (14%) were found to harbor LukS-PV and LukF-PV, pvl-encoding genes in their genome; therefore pvl-positive MRSA. The majority (82%) of the MRSA isolates bearing pvl genes were also found to exhibit the antiseptic/disinfectant genes in their genome. Moreover, all (80) sequenced MRSA isolates were found to harbor SCCmec type IV subtype 2B&5. The isolates exhibited 4 different sequence types, ST8, ST88, ST789 and ST121. Notably, the predominant sequence type among the isolates was ST8 72 (90%). CONCLUSION: The notably high rate of antiseptic resistance particularly in the Methicillin-resistant S. aureus strains poses a significant challenge to infection control measures. The fact that some of these virulent strains harbor the LukS-PV and LukF-PV, the pvl encoding genes, highlight the importance of developing effective interventions to combat the spreading of these pathogenic bacterial strains. Certainly, strengthening antimicrobial resistance surveillance and stewardship will ultimately reduce the selection pressure, improve the patient's treatment outcome and public health in Tanzania.202539833938
147590.9856Evaluation of the FilmArray(®) Pneumonia Plus Panel for Rapid Diagnosis of Hospital-Acquired Pneumonia in Intensive Care Unit Patients. The FilmArray(®) Pneumonia plus Panel (FAPP) is a new multiplex molecular test for hospital-acquired pneumonia (HAP), which can rapidly detect 18 bacteria, 9 viruses, and 7 resistance genes. We aimed to compare the diagnosis performance of FAPP with conventional testing in 100 intensive care unit (ICU) patients who required mechanical ventilation, with clinically suspected HAP. A total of 237 samples [76 bronchoalveolar lavages (BAL(DS)) and 82 endotracheal aspirates (ETA(DS)) obtained at HAP diagnosis, and 79 ETA obtained during follow-up (ETA(TT))], were analyzed independently by routine microbiology testing and FAPP. 58 patients had paired BAL(DS) and ETA(DS). The positivity thresholds of semi-quantified bacteria were 10(3)-10(4) CFUs/mL or 10(4) copies/mL for BAL, and 10(5) CFUs/mL or copies/mL for ETA. Respiratory commensals (H. influenzae, S. aureus, E. coli, S. pneumoniae) were the most common pathogens. Discordant results for bacterial identification were observed in 33/76 (43.4%) BAL(DS) and 36/82 (43.9%) ETA(DS), and in most cases, FAPP identified one supplemental bacteria (23/33 BAL(DS) and 21/36 ETA(DS)). An absence of growth, or polybacterial cultures, explained almost equally the majority of the non-detections in culture. No linear relationship was observed between bin and CFUs/mL variables. Concordant results between paired BAL(DS) and ETA(DS) were obtained in 46/58 (79.3%) patients with FAPP. One of the 17 resistance genes detected with FAPP (mecA/C and MREJ) was not confirmed by conventional testing. Overall, FAPP enhanced the positivity rate of diagnostic testing, with increased recognition of coinfections. Implementing this strategy may allow clinicians to make more timely and informed decisions.202032983057
5098100.9855Feature 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
9076110.9855ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/.202133495705
1480120.9855Prospective observational pilot study of the T2Resistance panel in the T2Dx system for detection of resistance genes in bacterial bloodstream infections. Early initiation of antimicrobial therapy targeting resistant bacterial pathogens causing sepsis and bloodstream infections (BSIs) is critical for a successful outcome. The T2Resistance Panel (T2R) detects the following resistance genes within organisms that commonly cause BSIs directly from patient blood samples: bla(KPC), bla(CTXM-14/15), bla(NDM)/bla(/IMP)/bla(VIM), bla(AmpC), bla(OXA), vanA, vanB, and mecA/mecC. We conducted a prospective study in two major medical centers for the detection of circulating resistance genes by T2R in patients with BSIs. T2R reports were compared to antimicrobial susceptibility testing (AST), phenotypic identification, and standard molecular detection assays. Among 59 enrolled patients, 25 resistance genes were identified: bla(KPC) (n = 10), bla(NDM)/bla(/IMP)/bla(VIM) (n = 5), bla(CTXM-14/15) (n = 4), bla(AmpC) (n = 2), and mecA/mecC (n = 4). Median time-to-positive-T2R in both hospitals was 4.4 hours [interquartile range (IQR): 3.65-4.97 hours] in comparison to that for positive blood cultures with final reporting of AST of 58.34 h (IQR: 45.51-111.2 hours; P < 0.0001). The sensitivity of T2R to detect the following genes in comparison to AST was 100% for bla(CTXM-14/15), bla(NDM)/bla(/)(IMP)/bla(VIM), bla(AmpC), mecA/mecC and 87.5% for bla(KPC). When monitored for the impact of significant antimicrobial changes, there were 32 events of discontinuation of unnecessary antibiotics and 17 events of escalation of antibiotics, including initiation of ceftazidime/avibactam in six patients in response to positive T2R results for bla(KPC). In summary, T2R markers were highly sensitive for the detection of drug resistance genes in patients with bacterial BSIs, when compared with standard molecular resistance detection systems and phenotypic identification assays while significantly reducing by approximately 90% the time to detection of resistance compared to standard methodology and impacting clinical decisions for antimicrobial therapy. IMPORTANCE: This is the first reported study to our knowledge to identify key bacterial resistance genes directly from the bloodstream within 3 to 5 hours in patients with bloodstream infections and sepsis. The study further demonstrated a direct effect in modifying initial empirical antibacterial therapy in response to T2R signal to treat resistant bacteria causing bloodstream infections and sepsis.202438456690
5163130.9855Multi-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
2599140.9855Evaluation of whole-genome sequencing protocols for detection of antimicrobial resistance, virulence factors and mobile genetic elements in antimicrobial-resistant bacteria. Introduction. Antimicrobial resistance (AMR) poses a critical threat to global health, underscoring the need for rapid and accurate diagnostic tools. Methicillin-resistant Staphylococcus aureus (MRSA) and extended-spectrum beta-lactamase (ESBL)-producing Klebsiella pneumoniae (ESBL-Kp) are listed among the World Health Organization's priority pathogens.Hypothesis. A rapid nanopore-based protocol can accurately and efficiently detect AMR genes, virulence factors (VFs) and mobile genetic elements (MGEs) in MRSA and ESBL-Kp, offering performance comparable to or superior to traditional sequencing methods.Aim. Evaluate whole-genome sequencing (WGS) protocols for detecting AMR genes, VFs and MGEs in MRSA and ESBL-Kp, to identify the most accurate and efficient tool for pathogen profiling.Methodology. Five distinct WGS protocols, including a rapid nanopore-based protocol (ONT20h) and four slower sequencing methods, were evaluated for their effectiveness in detecting genetic markers. The protocols' performances were compared across AMR genes, VFs and MGEs. Additionally, phenotypic antimicrobial susceptibility testing was performed to assess concordance with the genomic findings.Results. Compared to four slower sequencing protocols, the rapid nanopore-based protocol (ONT20h) demonstrated comparable or superior performance in AMR gene detection and equivalent VF identification. Although MGE detection varied among protocols, ONT20h showed a high level of agreement with phenotypic antimicrobial susceptibility testing.Conclusion. The findings highlight the potential of rapid WGS as a valuable tool for clinical microbiology, enabling timely implementation of infection control measures and informed therapeutic decisions. However, further studies are required to optimize the clinical application of this technology, considering costs, availability of bioinformatics tools and quality of reference databases.202540105741
2522150.9855Identification and specificity validation of unique and antimicrobial resistance genes to trace suspected pathogenic AMR bacteria and to monitor the development of AMR in non-AMR strains in the environment and clinical settings. The detection of developing antimicrobial resistance (AMR) has become a global issue. The detection of developing antimicrobial resistance has become a global issue. The growing number of AMR bacteria poses a new threat to public health. Therefore, a less laborious and quick confirmatory test becomes important for further investigations into developing AMR in the environment and in clinical settings. This study aims to present a comprehensive analysis and validation of unique and antimicrobial-resistant strains from the WHO priority list of antimicrobial-resistant bacteria and previously reported AMR strains such as Acinetobacter baumannii, Aeromonas spp., Anaeromonas frigoriresistens, Anaeromonas gelatinfytica, Bacillus spp., Campylobacter jejuni subsp. jejuni, Enterococcus faecalis, Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Klebsiella pneumonia subsp. pneumoniae, Pseudomonas aeruginosa, Salmonella enterica subsp. enterica serovar Typhimurium, Thermanaeromonas toyohensis, and Vibrio proteolyticus. Using in-house designed gene-specific primers, 18 different antibiotic resistance genes (algJ, alpB, AQU-1, CEPH-A3, ciaB, CMY-1-MOX-7, CMY-1-MOX-9, CMY-1/MOX, cphA2, cphA5, cphA7, ebpA, ECP_4655, fliC, OXA-51, RfbU, ThiU2, and tolB) from 46 strains were selected and validated. Hence, this study provides insight into the identification of strain-specific, unique antimicrobial resistance genes. Targeted amplification and verification using selected unique marker genes have been reported. Thus, the present detection and validation use a robust method for the entire experiment. Results also highlight the presence of another set of 18 antibiotic-resistant and unique genes (Aqu1, cphA2, cphA3, cphA5, cphA7, cmy1/mox7, cmy1/mox9, asaI, ascV, asoB, oxa-12, acr-2, pepA, uo65, pliI, dr0274, tapY2, and cpeT). Of these sets of genes, 15 were found to be suitable for the detection of pathogenic strains belonging to the genera Aeromonas, Pseudomonas, Helicobacter, Campylobacter, Enterococcus, Klebsiella, Acinetobacter, Salmonella, Haemophilus, and Bacillus. Thus, we have detected and verified sets of unique and antimicrobial resistance genes in bacteria on the WHO Priority List and from published reports on AMR bacteria. This study offers advantages for confirming antimicrobial resistance in all suspected AMR bacteria and monitoring the development of AMR in non-AMR bacteria, in the environment, and in clinical settings.202338058762
5194160.9854Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis. We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.201930429253
3069170.9854The hospital sink drain biofilm resistome is independent of the corresponding microbiota, the environment and disinfection measures. In hospitals, the transmission of antibiotic-resistant bacteria (ARB) may occur via biofilms present in sink drains, which can lead to infections. Despite the potential role of sink drains in the transmission of ARB in nosocomial infections, routine surveillance of these drains is lacking in most hospitals. As a result, there is currently no comprehensive understanding of the transmission of ARB and the dissemination of antimicrobial resistance genes (ARGs) and associated mobile genetic elements (MGEs) via sink drains. This study employed a multifaceted approach to monitor the total aerobic bacteria as well as the presence of carbapenemase-producing Enterobacterales (CPEs), the microbiota and the resistome of sink drain biofilms (SDBs) and hospital wastewater (WW) of two separate intensive care units (ICUs) in the same healthcare facility in France. Samples of SDB and WW were collected on a monthly basis, from January to April 2023, in the neonatal (NICU) and the adult (AICU) ICUs of Grenoble Alpes University Hospital. In the NICU, sink drain disinfection with surfactants was performed routinely. In the AICU, routine disinfection is not carried out. Culturable aerobic bacteria were quantified on non-selective media, and CPEs were screened using two selective agars. Isolates were identified by MALDI-TOF MS, and antibiotic susceptibility testing (AST) was performed on Enterobacterales and P. aeruginosa. The resistome was analyzed by high-throughput qPCR targeting >80 ARGs and MGEs. The overall bacterial microbiota was assessed via full-length 16S rRNA sequencing. No CPEs were isolated from SDBs in either ICU by bacterial culture. Culture-independent approaches revealed an overall distinct microbiota composition of the SDBs in the two ICUs. The AICU SDBs were dominated by pathogens containing Gram-negative bacterial genera including Pseudomonas, Stenotrophomona, Klebsiella, and Gram-positive Staphylococcus, while the NICU SDBs were dominated by the Gram-negative genera Achromobacter, Serratia, and Acidovorax, as well as the Gram-positive genera Weisella and Lactiplantibacillus. In contrast, the resistome of the SDBs exhibited no significant differences between the two ICUs, indicating that the abundance of ARGs and MGEs is independent of microbiota composition and disinfection practices. The AICU WW exhibited more distinct aerobic bacteria than the NICU WW. In addition, the AICU WW yielded 15 CPEs, whereas the NICU WW yielded a single CPE. All the CPEs were characterized at the species level. The microbiota of the NICU and AICU WW samples differed from their respective SDBs and exhibited distinct variations over the four-month period:the AICU WW contained a greater number of genes conferring resistance to quinolones and integron integrase genes, whereas the NICU WW exhibited a higher abundance of streptogramin resistance genes. Our study demonstrated that the resistome of the hospital SDBs in the two ICUs of the investigated healthcare institute is independent of the microbiota, the environment, and the local disinfection measures. However, the prevalence of CPEs in the WW pipes collecting the waste from the investigated drains differed. These findings offer valuable insights into the resilience of resistance genes in SDBs in ICUs, underscoring the necessity for innovative strategies to combat antimicrobial resistance in clinical environments.202540483807
5823180.9854Comparing 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
5467190.9854Whole genome sequencing-based classification of human-related Haemophilus species and detection of antimicrobial resistance genes. BACKGROUND: Bacteria belonging to the genus Haemophilus cause a wide range of diseases in humans. Recently, H. influenzae was classified by the WHO as priority pathogen due to the wide spread of ampicillin resistant strains. However, other Haemophilus spp. are often misclassified as H. influenzae. Therefore, we established an accurate and rapid whole genome sequencing (WGS) based classification and serotyping algorithm and combined it with the detection of resistance genes. METHODS: A gene presence/absence-based classification algorithm was developed, which employs the open-source gene-detection tool SRST2 and a new classification database comprising 36 genes, including capsule loci for serotyping. These genes were identified using a comparative genome analysis of 215 strains belonging to ten human-related Haemophilus (sub)species (training dataset). The algorithm was evaluated on 1329 public short read datasets (evaluation dataset) and used to reclassify 262 clinical Haemophilus spp. isolates from 250 patients (German cohort). In addition, the presence of antibiotic resistance genes within the German dataset was evaluated with SRST2 and correlated with results of traditional phenotyping assays. RESULTS: The newly developed algorithm can differentiate between clinically relevant Haemophilus species including, but not limited to, H. influenzae, H. haemolyticus, and H. parainfluenzae. It can also identify putative haemin-independent H. haemolyticus strains and determine the serotype of typeable Haemophilus strains. The algorithm performed excellently in the evaluation dataset (99.6% concordance with reported species classification and 99.5% with reported serotype) and revealed several misclassifications. Additionally, 83 out of 262 (31.7%) suspected H. influenzae strains from the German cohort were in fact H. haemolyticus strains, some of which associated with mouth abscesses and lower respiratory tract infections. Resistance genes were detected in 16 out of 262 datasets from the German cohort. Prediction of ampicillin resistance, associated with bla(TEM-1D), and tetracycline resistance, associated with tetB, correlated well with available phenotypic data. CONCLUSIONS: Our new classification database and algorithm have the potential to improve diagnosis and surveillance of Haemophilus spp. and can easily be coupled with other public genotyping and antimicrobial resistance databases. Our data also point towards a possible pathogenic role of H. haemolyticus strains, which needs to be further investigated.202235139905