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511600.9911Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data. BACKGROUND: Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms. METHODS AND FINDINGS: We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets. CONCLUSION: Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/.202032528441
582610.9909Rapid 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
506820.9909Ultrasensitive Label-Free Detection of Unamplified Multidrug-Resistance Bacteria Genes with a Bimodal Waveguide Interferometric Biosensor. Infections by multidrug-resistant bacteria are becoming a major healthcare emergence with millions of reported cases every year and an increasing incidence of deaths. An advanced diagnostic platform able to directly detect and identify antimicrobial resistance in a faster way than conventional techniques could help in the adoption of early and accurate therapeutic interventions, limiting the actual negative impact on patient outcomes. With this objective, we have developed a new biosensor methodology using an ultrasensitive nanophotonic bimodal waveguide interferometer (BiMW), which allows a rapid and direct detection, without amplification, of two prevalent and clinically relevant Gram-negative antimicrobial resistance encoding sequences: the extended-spectrum betalactamase-encoding gene blaCTX-M-15 and the carbapenemase-encoding gene blaNDM-5 We demonstrate the extreme sensitivity and specificity of our biosensor methodology for the detection of both gene sequences. Our results show that the BiMW biosensor can be employed as an ultrasensitive (attomolar level) and specific diagnostic tool for rapidly (less than 30 min) identifying drug resistance. The BiMW nanobiosensor holds great promise as a powerful tool for the control and management of healthcare-associated infections by multidrug-resistant bacteria.202033086716
503530.9906Colistin and tigecycline resistance in carbapenemase-producing Gram-negative bacteria: emerging resistance mechanisms and detection methods. A literature review was undertaken to ascertain the molecular basis for tigecycline and colistin resistance mechanisms and the experimental basis for the detection and delineation of this resistance particularly in carbapenemase-producing Gram-negative bacteria. Pubmed, Google Scholar and Science Direct were searched with the keywords colistin, tigecycline, resistance mechanisms and detection methods. Trans-complementation and comparative MIC studies, mass spectrometry, chromatography, spectrofluorometry, PCR, qRT-PCR and whole genome sequencing (WGS) were commonly used to determine tigecycline and colistin resistance mechanisms, specifically modifications in the structural and regulatory efflux (acrAB, OqxAB, kpgABC adeABC-FGH-IJK, mexAB-XY-oprJM and soxS, rarA robA, ramRAB marRABC, adeLRS, mexRZ and nfxb) and lipid A (pmrHFIJFKLM, lpxA, lpxC lpxD and mgrB, pmrAB, phoPQ,) genes respectively. Mutations in the ribosomal 16S rRNA operon rrnBC, also yielded resistance to tigecycline through target site modifications. The mcr-1 gene conferring resistance to colistin was identified via WGS, trans-complementation and a murine thigh infection model studies. Common detection methods are mainly antibiotic sensitivity testing with broth microdilution while molecular identification tools are mostly PCR and WGS. Spectrofluorometry, MALDI-TOF MS, micro-array and real-time multiplex PCR hold much promise for the future as new detection tools.201627153928
511840.9904Automated 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
511950.9903ROCker 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.202541143534
976060.9902Mutations leading to ceftolozane/tazobactam and imipenem/cilastatin/relebactam resistance during in vivo exposure to ceftazidime/avibactam in Pseudomonas aeruginosa. Identifying resistance mechanisms to novel antimicrobials informs treatment strategies during infection and antimicrobial development. Studying resistance that develops during the treatment of an infection can provide the most clinically relevant mutations conferring resistance, but cross-sectional studies frequently identify multiple candidate resistance mutations without resolving the driver mutation. We performed whole-genome sequencing of longitudinal Pseudomonas aeruginosa from a patient whose P. aeruginosa developed imipenem/cilastatin/relebactam and ceftolozane/tazobactam resistance during ceftazidime/avibactam treatment. This analysis determined new mutations that arose in isolates resistant to both imipenem/cilastatin/relebactam and ceftolozane/tazobactam. Mutations in penicillin-binding protein 3 ftsI, the MexAB-OprM repressor nalD, and a virulence regulator pvdS were found in resistant isolates. Importantly, drug efflux was not increased in the resistant isolate compared to the most closely related susceptible isolates. We conclude that mutations in peptidoglycan synthesis genes can alter the efficacy of multiple antimicrobials. IMPORTANCE: Antibiotic resistance is a significant challenge for physicians trying to treat infections. The development of novel antibiotics to treat resistant infections has not been prioritized for decades, limiting treatment options for infections caused by many high-priority pathogens. Cross-resistance, when one mutation provides resistance to multiple antibiotics, is most problematic. Mutations that cause cross-resistance need to be considered when developing new antibiotics to guide developers toward drugs with different targets, and thus a better likelihood of efficacy. This work was undertaken to determine the mutation that caused resistance to three antibiotics for highly resistant Pseudomonas aeruginosa infection treatment while the bacteria were exposed to only one of these agents. The findings provide evidence that drug developers should endeavor to find effective antibiotics with new targets and that medical providers should utilize medications with different mechanisms of action in bacteria that have become resistant to even one of these three agents.202539932323
582570.9902Polymerase 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
820380.9902Intercalated cell function, kidney innate immunity, and urinary tract infections. Intercalated cells (ICs) in the kidney collecting duct have a versatile role in acid-base and electrolyte regulation along with the host immune defense. Located in the terminal kidney tubule segment, ICs are among the first kidney cells to encounter bacteria when bacteria ascend from the bladder into the kidney. ICs have developed several mechanisms to combat bacterial infections of the kidneys. For example, ICs produce antimicrobial peptides (AMPs), which have direct bactericidal activity, and in many cases are upregulated in response to infections. Some AMP genes with IC-specific kidney expression are multiallelic, and having more copies of the gene confers increased resistance to bacterial infections of the kidney and urinary tract. Similarly, studies in human children demonstrate that those with history of UTIs are more likely to have single-nucleotide polymorphisms in IC-expressed AMP genes that impair the AMP's bactericidal activity. In murine models, depleted or impaired ICs result in decreased clearance of bacterial load following transurethral challenge with uropathogenic E. coli. A 2021 study demonstrated that ICs even act as phagocytes and acidify bacteria within phagolysosomes. Several immune signaling pathways have been identified in ICs which may represent future therapeutic targets in managing kidney infections or inflammation. This review's objective is to highlight IC structure and function with an emphasis on current knowledge of IC's diverse innate immune capabilities.202438227050
506290.9901sRNA expression profile of KPC-2-producing carbapenem-resistant Klebsiella pneumoniae: Functional role of sRNA51. The emergence of carbapenem-resistant Klebsiella pneumoniae (CRKP) has significant challenges to human health and clinical treatment, with KPC-2-producing CRKP being the predominant epidemic strain. Therefore, there is an urgent need to identify new therapeutic targets and strategies. Non-coding small RNA (sRNA) is a post-transcriptional regulator of genes involved in important biological processes in bacteria and represents an emerging therapeutic strategy for antibiotic-resistant bacteria. In this study, we analyzed the transcription profile of KPC-2-producing CRKP using RNA-seq. Of the 4693 known genes detected, the expression of 307 genes was significantly different from that of carbapenem-sensitive Klebsiella pneumoniae (CSKP), including 133 up-regulated and 174 down-regulated genes. Both the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) analysis showed that these differentially expressed genes (DEGs) were mainly related to metabolism. In addition, we identified the sRNA expression profile of KPC-2-producing CRKP for the first time and detected 115 sRNAs, including 112 newly discovered sRNAs. Compared to CSKP, 43 sRNAs were differentially expressed in KPC-2-producing CRKP, including 39 up-regulated and 4 down-regulated sRNAs. We chose sRNA51, the most significantly differentially expressed sRNA in KPC-2-producing CRKP, as our research subject. By constructing sRNA51-overexpressing KPC-2-producing CRKP strains, we found that sRNA51 overexpression down-regulated the expression of acrA and alleviated resistance to meropenem and ertapenem in KPC-2-producing CRKP, while overexpression of acrA in sRNA51-overexpressing strains restored the reduction of resistance. Therefore, we speculated that sRNA51 could affect the resistance of KPC-2-producing CRKP by inhibiting acrA expression and affecting the formation of efflux pumps. This provides a new approach for developing antibiotic adjuvants to restore the sensitivity of CRKP.202438718038
9807100.9901Multi-label classification for multi-drug resistance prediction of Escherichia coli. Antimicrobial resistance (AMR) is a global health and development threat. In particular, multi-drug resistance (MDR) is increasingly common in pathogenic bacteria. It has become a serious problem to public health, as MDR can lead to the failure of treatment of patients. MDR is typically the result of mutations and the accumulation of multiple resistance genes within a single cell. Machine learning methods have a wide range of applications for AMR prediction. However, these approaches typically focus on single drug resistance prediction and do not incorporate information on accumulating antimicrobial resistance traits over time. Thus, identifying multi-drug resistance simultaneously and rapidly remains an open challenge. In our study, we could demonstrate that multi-label classification (MLC) methods can be used to model multi-drug resistance in pathogens. Importantly, we found the ensemble of classifier chains (ECC) model achieves accurate MDR prediction and outperforms other MLC methods. Thus, our study extends the available tools for MDR prediction and paves the way for improving diagnostics of infections in patients. Furthermore, the MLC methods we introduced here would contribute to reducing the threat of antimicrobial resistance and related deaths in the future by improving the speed and accuracy of the identification of pathogens and resistance.202235317240
8859110.9901Characterization of G-quadruplex structures in genes involved in survival and pathogenesis of Acinetobacter baumannii as a potential drug target. Acinetobacter baumannii is a notorious pathogen that commonly thrives in hospital environments and is responsible for numerous nosocomial infections in humans. The burgeoning multi-drug resistance leaves relatively minimal options for treating the bacterial infection, posing a significant problem and prompting the identification of new approaches for tackling the same. This motivated us to focus on non-canonical nucleic acid structures, mainly G-quadruplexes, as drug targets. G-quadruplexes have recently been gaining attention due to their involvement in multiple bacterial and viral pathogenesis. Herein, we sought to explore conserved putative G-quadruplex motifs in A. baumannii. In silico analysis revealed the presence of eight conserved motifs in genes involved in bacterial survival and pathogenesis. The biophysical and biomolecular analysis confirmed stable G-quadruplex formation by the motifs and showed a high binding affinity with the well-reported G-quadruplex binding ligand, BRACO-19. BRACO-19 exposure also decreased the growth of bacteria and downregulated the expression of G-quadruplex-harboring genes. The biofilm-forming ability of the bacteria was also affected by BRACO-19 addition. Taking all these observations into account, we have shown here for the first time the potential of G-quadruplex structures as a promising drug target in Acinetobacter baumannii, for addressing the challenges posed by this infamous pathogen.202438670179
9758120.9901Study on collateral sensitivity of tigecycline to colistin-resistant Enterobacter cloacae complex. The past decade has witnessed the emergence and spread of carbapenem-resistant Enterobacter cloacae complex (CRECC), presenting a significant clinical challenge and urgently demanding new treatment strategies against antimicrobial resistance (AMR). This study focused on the impact of tigecycline on the susceptibility of CRECC isolates to colistin and the collateral sensitivity in CRECC. Under tigecycline pressure, the resistance of five clinically isolated CRECC strains to colistin was converted from resistance to sensitivity. These mutants exhibited significantly higher expression of acrA, acrB, and ramA genes, with mutations in the ramR gene. Overexpression of ramA in certain mutants did not alter ramR expression. No mutations were identified in lipid A synthesis genes; however, phoQ was consistently downregulated, and arnA expression varied among CRECC405-resistant mutants. Low-dose colistin and tigecycline combination therapy outperformed monotherapy in antimicrobial efficacy. Overall, collateral susceptibility to tigecycline was observed in CRECC isolates with colistin resistance. The overexpression of acrA, acrB, and ramA, due to ramR mutations, led to tigecycline resistance. Inconsistent expression levels of lipid A synthesis genes affected lipid A modification, which in turn upregulated arnA expression in CRECC405-resistant mutants. Increased sensitivity to colistin was associated with the downregulation of phoQ and arnA expression. IMPORTANCE: Antimicrobial resistance (AMR) is escalating faster than our ability to manage bacterial infections, with antibiotic-resistant bacteria emerging as a significant public health risk. Innovative strategies are urgently needed to curb AMR dissemination. Our research identified collateral sensitivity in Enterobacter cloacae complex following tigecycline (TGC) resistance, resulting in newfound sensitivity to colistin (COL), a drug to which it was once resistant. Synergistic tigecycline and colistin therapy significantly suppress bacterial growth, offering a promising approach to combat infections and curb AMR progression through the strategic pairing of antibiotics with complementary sensitivities.202540407373
9742130.9901BOCS: DNA k-mer content and scoring for rapid genetic biomarker identification at low coverage. A single, inexpensive diagnostic test capable of rapidly identifying a wide range of genetic biomarkers would prove invaluable in precision medicine. Previous work has demonstrated the potential for high-throughput, label-free detection of A-G-C-T content in DNA k-mers, providing an alternative to single-letter sequencing while also having inherent lossy data compression and massively parallel data acquisition. Here, we apply a new bioinformatics algorithm - block optical content scoring (BOCS) - capable of using the high-throughput content k-mers for rapid, broad-spectrum identification of genetic biomarkers. BOCS uses content-based sequence alignment for probabilistic mapping of k-mer contents to gene sequences within a biomarker database, resulting in a probability ranking of genes on a content score. Simulations of the BOCS algorithm reveal high accuracy for identification of single antibiotic resistance genes, even in the presence of significant sequencing errors (100% accuracy for no sequencing errors, and >90% accuracy for sequencing errors at 20%), and at well below full coverage of the genes. Simulations for detecting multiple resistance genes within a methicillin-resistant Staphylococcus aureus (MRSA) strain showed 100% accuracy at an average gene coverage of merely 0.515, when the k-mer lengths were variable and with 4% sequencing error within the k-mer blocks. Extension of BOCS to cancer and other genetic diseases met or exceeded the results for resistance genes. Combined with a high-throughput content-based sequencing technique, the BOCS algorithm potentiates a test capable of rapid diagnosis and profiling of genetic biomarkers ranging from antibiotic resistance to cancer and other genetic diseases.201931173943
9761140.9901Porphyromonas gingivalis resistance and virulence: An integrated functional network analysis. BACKGROUND: The gram-negative bacteria Porphyromonas gingivalis (PG) is the most prevalent cause of periodontal diseases and multidrug-resistant (MDR) infections. Periodontitis and MDR infections are severe due to PG's ability to efflux antimicrobial and virulence factors. This gives rise to colonisation, biofilm development, evasion, and modulation of the host defence system. Despite extensive studies on the MDR efflux pump in other pathogens, little is known about the efflux pump and its association with the virulence factor in PG. Prolonged infection of PG leads to complete loss of teeth and other systemic diseases. This necessitates the development of new therapeutic interventions to prevent and control MDR. OBJECTIVE: The study aims to identify the most indispensable proteins that regulate both resistance and virulence in PG, which could therefore be used as a target to fight against the MDR threat to antibiotics. METHODS: We have adopted a hierarchical network-based approach to construct a protein interaction network. Firstly, individual networks of four major efflux pump proteins and two virulence regulatory proteins were constructed, followed by integrating them into one. The relationship between proteins was investigated using a combination of centrality scores, k-core network decomposition, and functional annotation, to computationally identify the indispensable proteins. RESULTS: Our study identified four topologically significant genes, PG_0538, PG_0539, PG_0285, and PG_1797, as potential pharmacological targets. PG_0539 and PG_1797 were identified to have significant associations between the efflux pump and virulence genes. This type of underpinning research may help in narrowing the drug spectrum used for treating periodontal diseases, and may also be exploited to look into antibiotic resistance and pathogenicity in bacteria other than PG.202235835406
9744150.9900PARGT: a software tool for predicting antimicrobial resistance in bacteria. With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called 'features' in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.202032620856
8837160.9900Phage resistance formation and fitness costs of hypervirulent Klebsiella pneumoniae mediated by K2 capsule-specific phage and the corresponding mechanisms. INTRODUCTION: Phage is promising for the treatment of hypervirulent Klebsiella pneumoniae (hvKP) infections. Although phage resistance seems inevitable, we found that there still was optimization space in phage therapy for hvKP infection. METHODS: The clinical isolate K. pneumoniae FK1979 was used to recover the lysis phage ΦFK1979 from hospital sewage. Phage-resistant bacteria were obtained on LB agar and used to isolate phages from sewage. The plaque assay, transmission electron microscopy (TEM), multiplicity of infection test, one-step growth curve assay, and genome analysis were performed to characterize the phages. Colony morphology, precipitation test and scanning electron microscope were used to characterize the bacteria. The absorption test, spot test and efficiency of plating (EOP) assay were used to identify the sensitivity of bacteria to phages. Whole genome sequencing (WGS) was used to identify gene mutations of phage-resistant bacteria. The gene expression levels were detected by RT-qPCR. Genes knockout and complementation of the mutant genes were performed. The change of capsules was detected by capsule quantification and TEM. The growth kinetics, serum resistance, biofilm formation, adhesion and invasion to A549 and RAW 264.7 cells, as well as G. mellonella and mice infection models, were used to evaluate the fitness and virulence of bacteria. RESULTS AND DISCUSSION: Here, we demonstrated that K2 capsule type sequence type 86 hvKP FK1979, one of the main pandemic lineages of hvKP with thick capsule, rapidly developed resistance to a K2-specific lysis phage ΦFK1979 which was well-studied in this work to possess polysaccharide depolymerase. The phage-resistant mutants showed a marked decrease in capsule expression. WGS revealed single nucleotide polymorphism (SNP) in genes encoding RfaH, galU, sugar glycosyltransferase, and polysaccharide deacetylase family protein in the mutants. RfaH and galU were further identified as being required for capsule production and phage sensitivity. Expressions of genes involved in the biosynthesis or regulation of capsule and/or lipopolysaccharide significantly decreased in the mutants. Despite the rapid and frequent development of phage resistance being a disadvantage, the attenuation of virulence and fitness in vitro and in vivo indicated that phage-resistant mutants of hvKP were more susceptible to the immunity system. Interestingly, the newly isolated phages targeting mutants changed significantly in their plaque and virus particle morphology. Their genomes were much larger than and significantly different from that of ΦFK1979. They possessed much more functional proteins and strikingly broader host spectrums than ΦFK1979. Our study suggests that K2-specific phage has the potential to function as an antivirulence agent, or a part of phage cocktails combined with phages targeting phage-resistant bacteria, against hvKP-relevant infections.202337538841
5827170.9900Duplex dPCR System for Rapid Identification of Gram-Negative Pathogens in the Blood of Patients with Bloodstream Infection: A Culture-Independent Approach. Early and accurate detection of pathogens is important to improve clinical outcomes of bloodstream infections (BSI), especially in the case of drug-resistant pathogens. In this study, we aimed to develop a culture-independent digital PCR (dPCR) system for multiplex detection of major sepsiscausing gram-negative pathogens and antimicrobial resistance genes using plasma DNA from BSI patients. Our duplex dPCR system successfully detected nine targets (five bacteria-specific targets and four antimicrobial resistance genes) through five reactions within 3 hours. The minimum detection limit was 50 ag of bacterial DNA, suggesting that 1 CFU/ml of bacteria in the blood can be detected. To validate the clinical applicability, cell-free DNA samples from febrile patients were tested with our system and confirmed high consistency with conventional blood culture. This system can support early identification of some drug-resistant gram-negative pathogens, which can help improving treatment outcomes of BSI.202134528911
5069180.9900MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples. Recently, urinary tract infection (UTI) triggered by bacteria carrying pan-drug-resistant genes, including carbapenem resistance gene bla(NDM) and bla(KPC), colistin resistance gene mcr-1, and tet(X) for tigecycline resistance, have been reported, posing a serious challenge to the treatment of clinical UTI. Therefore, point-of-care (POC) detection of these genes in UTI samples without the need for pre-culturing is urgently needed. Based on PEG 200-enhanced recombinase polymerase amplification (RPA) and a refined Chelex-100 lysis method with HRP-catalyzed lateral flow immunoassay (LFIA), we developed an MCL-PRPA-HLFIA cascade assay system for detecting these genes in UTI samples. The refined Chelex-100 lysis method extracts target DNA from UTI samples in 20 min without high-speed centrifugation or pre-incubation of urine samples. Following optimization, the cascade detection system achieved an LOD of 10(2) CFU/mL with satisfactory specificity and could detect these genes in both simulated and actual UTI samples. It takes less than an hour to complete the process without the use of high-speed centrifuges or other specialized equipment, such as PCR amplifiers. The MCL-PRPA-HLFIA cascade assay system provides new ideas for the construction of rapid detection methods for pan-drug-resistant genes in clinical UTI samples and provides the necessary medication guidance for UTI treatment.202337047757
5164190.9899Genome sequencing analysis of the pncA, rpsA and panD genes responsible for pyrazinamide resistance of Mycobacterium tuberculosis from Indonesian isolates. BACKGROUND: Developing the most suitable treatment against tuberculosis based on resistance profiles is imperative to effectively cure tuberculosis patients. Whole-genome sequencing is a molecular method that allows for the rapid and cost-effective detection of mutations in multiple genes associated with anti-tuberculosis drug resistance. This sequencing approach addresses the limitations of culture-based methods, which may not apply to certain anti-TB drugs, such as pyrazinamide, because of their specific culture medium requirements, potentially leading to biased resistance culture results. METHODS: Thirty-four M. tuberculosis isolates were subcultured on a Lowenstein-Jensen medium. The genome of these bacteria was subsequently isolated using cetyltrimethylammonium bromide. Genome sequencing was performed with Novaseq Illumina 6000 (Illumina), and the data were analysed using the GenTB and Mykrobe applications. We also conducted a de novo analysis to compare the two methods and performed mutation analysis of other genes encoding pyrazinamide resistance, namely rpsA and panD. RESULTS: The results revealed mutations in the pncA gene, which were identified based on the databases accessed through GenTB and Mykrobe. Two discrepancies between the drug susceptibility testing and sequencing results may suggest potential instability in the drug susceptibility testing culture, specifically concerning PZA. Meanwhile, the results of the de novo analysis showed the same result of pncA mutation to the GenTB or Mykrobe; meanwhile, there were silent mutations in rpsA in several isolates and a point mutation; no mutations were found in the panD gene. However, the mutations in the genes encoding pyrazinamide require further and in-depth study to understand their relationship to the phenotypic profile. CONCLUSIONS: Compared to the conventional culture method, the whole-genome sequencing method has advantages in determining anti-tuberculosis resistance profiles, especially in reduced time and bias.202439397216