# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 5163 | 0 | 0.9908 | Multi-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. | 2024 | 38429820 |
| 9076 | 1 | 0.9899 | ResiDB: 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/. | 2021 | 33495705 |
| 2541 | 2 | 0.9896 | Increased antibiotic resistance in preterm neonates under early antibiotic use. The standard use of antibiotics in newborns to empirically treat early-onset sepsis can adversely affect the neonatal gut microbiome, with potential long-term health impacts. Research into the escalating issue of antimicrobial resistance in preterm infants and antibiotic practices in neonatal intensive care units is limited. A deeper understanding of the effects of early antibiotic intervention on antibiotic resistance in preterm infants is crucial. This retrospective study employed metagenomic sequencing to evaluate antibiotic resistance genes (ARGs) in the meconium and subsequent stool samples of preterm infants enrolled in the Routine Early Antibiotic Use in Symptomatic Preterm Neonates study. Microbial metagenomics was conducted using a subset of fecal samples from 30 preterm infants for taxonomic profiling and ARG identification. All preterm infants exhibited ARGs, with 175 unique ARGs identified, predominantly associated with beta-lactam, tetracycline, and aminoglycoside resistance. Notably, 23% of ARGs was found in preterm infants without direct or intrapartum antibiotic exposure. Post-natal antibiotic exposure increases beta-lactam/tetracycline resistance while altering mechanisms that aid bacteria in withstanding antibiotic pressure. Microbial profiling revealed 774 bacterial species, with antibiotic-naive infants showing higher alpha diversity (P = 0.005) in their microbiota and resistome compared with treated infants, suggesting a more complex ecosystem. High ARG prevalence in preterm infants was observed irrespective of direct antibiotic exposure and intensifies with age. Prolonged membrane ruptures and maternal antibiotic use during gestation and delivery are linked to alterations in the preterm infant resistome and microbiome, which are pivotal in shaping the ARG profiles in the neonatal gut.This study is registered with ClinicalTrials.gov as NCT02784821. IMPORTANCE: A high burden of antibiotic resistance in preterm infants poses significant challenges to neonatal health. The presence of antibiotic resistance genes, along with alterations in signaling, energy production, and metabolic mechanisms, complicates treatment strategies for preterm infants, heightening the risk of ineffective therapy and exacerbating outcomes for these vulnerable neonates. Despite not receiving direct antibiotic treatment, preterm infants exhibit a concerning prevalence of antibiotic-resistant bacteria. This underscores the complex interplay of broader influences, including maternal antibiotic exposure during and beyond pregnancy and gestational complications like prolonged membrane ruptures. Urgent action, including cautious antibiotic practices and enhanced antenatal care, is imperative to protect neonatal health and counter the escalating threat of antimicrobial resistance in this vulnerable population. | 2024 | 39373498 |
| 9075 | 3 | 0.9895 | CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype . | 2023 | 37474912 |
| 3069 | 4 | 0.9893 | The 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. | 2025 | 40483807 |
| 5116 | 5 | 0.9892 | Prediction 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/. | 2020 | 32528441 |
| 5122 | 6 | 0.9892 | Clinical 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. | 2025 | 41087996 |
| 5110 | 7 | 0.9890 | Surveillance of carbapenem-resistant organisms using next-generation sequencing. The genomic data generated from next-generation sequencing (NGS) provides nucleotide-level resolution of bacterial genomes which is critical for disease surveillance and the implementation of prevention strategies to interrupt the spread of antimicrobial resistance (AMR) bacteria. Infection with AMR bacteria, including Gram-negative Carbapenem-Resistant Organisms (CRO), may be acute and recurrent-once they have colonized a patient, they are notoriously difficult to eradicate. Through phylogenetic tools that assess the single nucleotide polymorphisms (SNPs) within a pathogen genome dataset, public health scientists can estimate the genetic identity between isolates. This information is used as an epidemiologic proxy of a putative outbreak. Pathogens with minimal to no differences in SNPs are likely to be the same strain attributable to a common source or transmission between cases. These genomic comparisons enhance public health response by prompting targeted intervention and infection control measures. This methodology overview demonstrates the utility of phenotypic and molecular assays, antimicrobial susceptibility testing (AST), NGS, publicly available genomics databases, and open-source bioinformatics pipelines for a tiered workflow to detect resistance genes and potential clusters of illness. These methods, when used in combination, facilitate a genomic surveillance workflow for detecting potential AMR bacterial outbreaks to inform epidemiologic investigations. Use of this workflow helps to target and focus epidemiologic resources to the cases with the highest likelihood of being related. | 2023 | 37255756 |
| 8187 | 8 | 0.9889 | Racial disparities in metastatic colorectal cancer outcomes revealed by tumor microbiome and transcriptome analysis with bevacizumab treatment. Background: Metastatic colorectal cancer (mCRC) is a heterogeneous disease, often associated with poor outcomes and resistance to therapies. The racial variations in the molecular and microbiological profiles of mCRC patients, however, remain under-explored. Methods: Using RNA-SEQ data, we extracted and analyzed actively transcribing microbiota within the tumor milieu, ensuring that the identified bacteria were not merely transient inhabitants but engaged in the tumor ecosystem. Also, we independently acquired samples from 12 mCRC patients, specifically, 6 White individuals and 6 of Black or African American descent. These samples underwent 16S rRNA sequencing. Results: Our study revealed notable racial disparities in the molecular signatures and microbiota profiles of mCRC patients. The intersection of these data showcased the potential modulating effects of specific bacteria on gene expression. Particularly, the bacteria Helicobacter cinaedi and Sphingobium herbicidovorans emerged as significant influencers, with strong correlations to the genes SELENBP1 and SNORA38, respectively. Discussion: These findings underscore the intricate interplay between host genomics and actively transcribing tumor microbiota in mCRC's pathogenesis. The identified correlations between specific bacteria and genes highlight potential avenues for targeted therapies and a more personalized therapeutic approach. | 2023 | 38357363 |
| 2599 | 9 | 0.9889 | Evaluation 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. | 2025 | 40105741 |
| 5105 | 10 | 0.9889 | Emerging insights of Staphylococcus spp. in human mastitis. Human mastitis represents a prevalent and intricate condition that significantly challenges breastfeeding women, often exacerbated by pathogenic bacteria such as Staphylococcus aureus. A deep understanding of the interplay between human mastitis, the breast milk microbiome, and causative agents is imperative. This understanding must focus on the bacterium's virulence and resistance genes, which critically influence the severity and persistence of mastitis. Current methods for detecting these genes, including Polymerase Chain Reaction (PCR), 16S rRNA gene sequencing, shotgun metagenomic sequencing, multiplex PCR, whole genome sequencing (WGS), loop-mediated isothermal amplification (LAMP), CRISPR-based assays, and microarray technology, are vital in elucidating bacterial pathogenicity and resistance profiles. However, advanced attention is required to refine diagnostic techniques, enabling earlier detection and more effective therapeutic approaches for human mastitis. The involvement of Staphylococcus aureus in human infection should be a prime focus, especially in women's health, which deals directly with neonates. Essential virulence genes in Staphylococcus species are instrumental in infection mechanisms and antibiotic resistance, serving as potential targets for personalized treatments. Thus, this review focuses on Staphylococcusaureus-induced mastitis, examining its virulence factors and detection techniques to advance diagnostic and therapeutic strategies. | 2025 | 40349998 |
| 2269 | 11 | 0.9889 | Genomic 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. | 2025 | 39833938 |
| 9083 | 12 | 0.9888 | ARGNet: 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. | 2024 | 38725076 |
| 5119 | 13 | 0.9888 | ROCker models for reliable detection and typing of short-read sequences carrying mcr, erm, mph, and lnu antibiotic resistance genes. Quantitative monitoring of emerging antimicrobial resistance genes (ARGs) using short-read sequences remains challenging due to the high frequency of amino acid functional domains and motifs shared with related but functionally distinct (non-target) proteins. To facilitate ARG monitoring efforts using unassembled short reads, we present novel ROCker models for mcr, mph, erm, and lnu ARG families, as well as models for variants of special public health concern within these families, including mcr-1, mphA, ermB, lnuF, lnuB, and lnuG genes. For this, we curated target gene sequence sets for model training and built these models using the recently updated ROCker V2 pipeline (Gerhardt et al., in review). To validate our models, we simulated reads from the whole genome of ARG-carrying isolates spanning a range of common read lengths and used them to challenge the filtering efficacy of ROCker versus common static filtering approaches, such as similarity searches using BLASTx with various e-value thresholds or hidden Markov models. ROCker models consistently showed F1 scores up to 10× higher (31% higher on average) and lower false-positive (by 30%, on average) and false-negative (by 16%, on average) rates based on 250 bp reads compared to alternative methods. The ROCker models and all related reference materials and data are freely available through http://enve-omics.ce.gatech.edu/rocker/models, further expanding the available model collection previously developed for other genes. Their application to short-read metagenomes, metatranscriptomes, and PCR amplicon data should facilitate more accurate classification and quantification of unassembled short-read sequences for these ARG families and specific genes.IMPORTANCEAntimicrobial resistance gene families encoding erm and mph genes confer resistance to the macrolide class of antimicrobials, which are used to treat a wide range of infections. Similarly, the mcr gene family confers resistance to polymyxin E (colistin), a drug of last resort for many serious drug-resistant bacterial infections, and the lnu gene family confers resistance to lincomycin, which is reserved for patients allergic to penicillin or where bacteria have developed resistance to other antimicrobials. Assessing the prevalence of these genes in clinical or environmental samples and monitoring their spread to new pathogens are thus important for quantifying the associated public health risk. However, detecting these and other resistance genes in short-read sequence data is technically challenging. Our ROCker bioinformatic pipeline achieves reliable detection and typing of broad-range target gene sequences in complex data sets, thus contributing toward solving an important problem in ongoing surveillance efforts of antimicrobial resistance. | 2025 | 41143534 |
| 5103 | 14 | 0.9888 | Revolutionising bacteriology to improve treatment outcomes and antibiotic stewardship. LABORATORY INVESTIGATION OF BACTERIAL INFECTIONS GENERALLY TAKES TWO DAYS: one to grow the bacteria and another to identify them and to test their susceptibility. Meanwhile the patient is treated empirically, based on likely pathogens and local resistance rates. Many patients are over-treated to prevent under-treatment of a few, compromising antibiotic stewardship. Molecular diagnostics have potential to improve this situation by accelerating precise diagnoses and the early refinement of antibiotic therapy. They include: (i) the use of 'biomarkers' to swiftly distinguish patients with bacterial infection, and (ii) molecular bacteriology to identify pathogens and their resistance genes in clinical specimens, without culture. Biomarker interest centres on procalcitonin, which has given good results particularly for pneumonias, though broader biomarker arrays may prove superior in the future. PCRs already are widely used to diagnose a few infections (e.g. tuberculosis) whilst multiplexes are becoming available for bacteraemia, pneumonia and gastrointestinal infection. These detect likely pathogens, but are not comprehensive, particularly for resistance genes; there is also the challenge of linking pathogens and resistance genes when multiple organisms are present in a sample. Next-generation sequencing offers more comprehensive profiling, but obstacles include sensitivity when the bacterial load is low, as in bacteraemia, and the imperfect correlation of genotype and phenotype. In short, rapid molecular bacteriology presents great potential to improve patient treatments and antibiotic stewardship but faces many technical challenges; moreover it runs counter to the current nostrum of defining resistance in pharmacodynamic terms, rather than by the presence of a mechanism, and the policy of centralising bacteriology services. | 2013 | 24265945 |
| 5125 | 15 | 0.9888 | Do we still need Illumina sequencing data? Evaluating Oxford Nanopore Technologies R10.4.1 flow cells and the Rapid v14 library prep kit for Gram negative bacteria whole genome assemblies. The best whole genome assemblies are currently built from a combination of highly accurate short-read sequencing data and long-read sequencing data that can bridge repetitive and problematic regions. Oxford Nanopore Technologies (ONT) produce long-read sequencing platforms and they are continually improving their technology to obtain higher quality read data that is approaching the quality obtained from short-read platforms such as Illumina. As these innovations continue, we evaluated how much ONT read coverage produced by the Rapid Barcoding Kit v14 (SQK-RBK114) is necessary to generate high-quality hybrid and long-read-only genome assemblies for a panel of carbapenemase-producing Enterobacterales bacterial isolates. We found that 30× long-read coverage is sufficient if Illumina data are available, and that more (at least 100× long-read coverage is recommended for long-read-only assemblies. Illumina polishing is still improving single nucleotide variants (SNVs) and INDELs in long-read-only assemblies. We also examined if antimicrobial resistance genes could be accurately identified in long-read-only data, and found that Flye assemblies regardless of ONT coverage detected >96% of resistance genes at 100% identity and length. Overall, the Rapid Barcoding Kit v14 and long-read-only assemblies can be an optimal sequencing strategy (i.e., plasmid characterization and AMR detection) but finer-scale analyses (i.e., SNV) still benefit from short-read data. | 2024 | 38354391 |
| 5098 | 16 | 0.9888 | Feature 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. | 2025 | 40606161 |
| 6691 | 17 | 0.9887 | The antimicrobial resistance monitoring and research (ARMoR) program: the US Department of Defense response to escalating antimicrobial resistance. Responding to escalating antimicrobial resistance (AMR), the US Department of Defense implemented an enterprise-wide collaboration, the Antimicrobial Resistance Monitoring and Research Program, to aid in infection prevention and control. It consists of a network of epidemiologists, bioinformaticists, microbiology researchers, policy makers, hospital-based infection preventionists, and healthcare providers who collaborate to collect relevant AMR data, conduct centralized molecular characterization, and use AMR characterization feedback to implement appropriate infection prevention and control measures and influence policy. A particularly concerning type of AMR, carbapenem-resistant Enterobacteriaceae, significantly declined after the program was launched. Similarly, there have been no further reports or outbreaks of another concerning type of AMR, colistin resistance in Acinetobacter, in the Department of Defense since the program was initiated. However, bacteria containing AMR-encoding genes are increasing. To update program stakeholders and other healthcare systems facing such challenges, we describe the processes and impact of the program. | 2014 | 24795331 |
| 2600 | 18 | 0.9887 | Genomic characterization of antimicrobial resistance in 61 aquatic bacterial isolates. Antimicrobial resistance (AMR) in pathogens important to aquatic animal health is of increasing concern but vastly understudied. Antimicrobial therapy is used to both treat and prevent bacterial disease in fish and is critical for a viable aquaculture industry and for maintenance of wild fish populations. Unfortunately, phenotypic antimicrobial susceptibility testing is technically difficult for bacteria recovered from aquatic animal hosts resulting in challenges in resistance monitoring using traditional methods. Whole-genome sequencing provides an appealing methodology for investigation of putative resistance. As part of the ongoing efforts of the FDA CVM Vet-LIRN to monitor AMR, source laboratories cultured and preliminarily identified pathogenic bacteria isolated from various fish species collected in 2019 from across the United States. Sixty-one bacterial isolates were evaluated using whole-genome sequencing. We present here the assembled draft genomes, AMR genes, predicted resistance phenotypes, and virulence factors of the 61 isolates and discuss concurrence of the identifications made by source laboratories using matrix-assisted laser desorption/time-of-flight mass spectrometry. | 2024 | 38566327 |
| 5161 | 19 | 0.9887 | Genomic analysis of contaminant Stenotrophomonas maltophilia, from placental swab culture, carrying antibiotic resistance: a potential hospital laboratory contaminant. Acute chorioamnionitis has been considered as reflective of amniotic fluid infection. Standard microbiological work ups for causative microorganism of intra-amniotic infection is based on microbial identification. However, frequency of positive placental culture is varied depending on placental sampling techniques, contaminations, methods of microbiologic work ups or comprehensive microbiologic work ups. In this report, we performed a hybrid whole genome sequencing of a proven bacterial contaminant obtained from placental culture in a patient with preterm labor and acute chorioamnionitis. This is to unveil genetic characterization of contaminant Stenotrophomonas maltophilia habouring antibiotic resistance genes. Stenotrophomonas maltiphilia was proven to be bacterial contaminant since Ureaplasma urealyticum was subsequently demonstrated in amniotic fluid by 16 S rRNA gene Sanger sequencing. Cultivation results from other sources were no growth. We identified Stenotrophomonas maltiphilia strain RAOG732 which carried several antibiotic resistance genes, including aminoglycoside, fluoroquiolone and beta-lactam. Biofilm production genes were also identified in this genome. We firstly utilized a hybrid sequencing approach to investigate the genome of S. maltiphilia in the patient with preterm and acute chorioamnionitis, a proven bacterial laboratory contaminant. The analysis provided several antibiotic resistance-associated and genes biofilm-associated genes. The detection of S. maltiphilia raised the awareness of the colonization of biofilm-producing bacteria in hospitals, where surveillance for decontamination is necessary. | 2025 | 40594762 |