Application of mNGS in the Etiological Analysis of Lower Respiratory Tract Infections and the Prediction of Drug Resistance. - Related Documents




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581901.0000Application 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
582010.9997Sequencing Methods to Study the Microbiome with Antibiotic Resistance Genes in Patients with Pulmonary Infections. Various antibiotic-resistant bacteria (ARB) are known to induce repeated pulmonary infections and increase morbidity and mortality. A thorough knowledge of antibiotic resistance is imperative for clinical practice to treat resistant pulmonary infections. In this study, we used a reads-based method and an assembly-based method according to the metagenomic next-generation sequencing (mNGS) data to reveal the spectra of ARB and corresponding antibiotic resistance genes (ARGs) in samples from patients with pulmonary infections. A total of 151 clinical samples from 144 patients with pulmonary infections were collected for retrospective analysis. The ARB and ARGs detection performance was compared by the reads-based method and assembly-based method with the culture method and antibiotic susceptibility testing (AST), respectively. In addition, ARGs and the attribution relationship of common ARB were analyzed by the two methods. The comparison results showed that the assembly-based method could assist in determining pathogens detected by the reads-based method as true ARB and improve the predictive capabilities (46% > 13%). ARG-ARB network analysis revealed that assembly-based method could promote determining clear ARG-bacteria attribution and 101 ARGs were detected both in two methods. 25 ARB were obtained by both methods, of which the most predominant ARB and its ARGs in the samples of pulmonary infections were Acinetobacter baumannii (ade), Pseudomonas aeruginosa (mex), Klebsiella pneumoniae (emr), and Stenotrophomonas maltophilia (sme). Collectively, our findings demonstrated that the assembly-based method could be a supplement to the reads-based method and uncovered pulmonary infection-associated ARB and ARGs as potential antibiotic treatment targets.202439113195
582120.9997Direct prediction of antimicrobial resistance in Pseudomonas aeruginosa by metagenomic next-generation sequencing. OBJECTIVE: Pseudomonas aeruginosa has strong drug resistance and can tolerate a variety of antibiotics, which is a major problem in the management of antibiotic-resistant infections. Direct prediction of multi-drug resistance (MDR) resistance phenotypes of P. aeruginosa isolates and clinical samples by genotype is helpful for timely antibiotic treatment. METHODS: In the study, whole genome sequencing (WGS) data of 494 P. aeruginosa isolates were used to screen key anti-microbial resistance (AMR)-associated genes related to imipenem (IPM), meropenem (MEM), piperacillin/tazobactam (TZP), and levofloxacin (LVFX) resistance in P. aeruginosa by comparing genes with copy number differences between resistance and sensitive strains. Subsequently, for the direct prediction of the resistance of P. aeruginosa to four antibiotics by the AMR-associated features screened, we collected 74 P. aeruginosa positive sputum samples to sequence by metagenomics next-generation sequencing (mNGS), of which 1 sample with low quality was eliminated. Then, we constructed the resistance prediction model. RESULTS: We identified 93, 88, 80, 140 AMR-associated features for IPM, MEM, TZP, and LVFX resistance in P. aeruginosa. The relative abundance of AMR-associated genes was obtained by matching mNGS and WGS data. The top 20 features with importance degree for IPM, MEM, TZP, and LVFX resistance were used to model, respectively. Then, we used the random forest algorithm to construct resistance prediction models of P. aeruginosa, in which the areas under the curves of the IPM, MEM, TZP, and LVFX resistance prediction models were all greater than 0.8, suggesting these resistance prediction models had good performance. CONCLUSION: In summary, mNGS can predict the resistance of P. aeruginosa by directly detecting AMR-associated genes, which provides a reference for rapid clinical detection of drug resistance of pathogenic bacteria.202438903781
582330.9996Comparing 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
582640.9996Rapid 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
568750.9996The effect of short-course antibiotics on the resistance profile of colonizing gut bacteria in the ICU: a prospective cohort study. BACKGROUND: The need for early antibiotics in the intensive care unit (ICU) is often balanced against the goal of antibiotic stewardship. Long-course antibiotics increase the burden of antimicrobial resistance within colonizing gut bacteria, but the dynamics of this process are not fully understood. We sought to determine how short-course antibiotics affect the antimicrobial resistance phenotype and genotype of colonizing gut bacteria in the ICU by performing a prospective cohort study with assessments of resistance at ICU admission and exactly 72 h later. METHODS: Deep rectal swabs were performed on 48 adults at the time of ICU admission and exactly 72 h later, including patients who did and did not receive antibiotics. To determine resistance phenotype, rectal swabs were cultured for methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE). In addition, Gram-negative bacterial isolates were cultured against relevant antibiotics. To determine resistance genotype, quantitative PCR (qPCR) was performed from rectal swabs for 87 established resistance genes. Within-individual changes in antimicrobial resistance were calculated based on culture and qPCR results and correlated with exposure to relevant antibiotics (e.g., did β-lactam antibiotic exposure associate with a detectable change in β-lactam resistance over this 72-h period?). RESULTS: Of 48 ICU patients, 41 (85%) received antibiotics. Overall, there was no increase in the antimicrobial resistance profile of colonizing gut bacteria during the 72-h study period. There was also no increase in antimicrobial resistance after stratification by receipt of antibiotics (i.e., no detectable increase in β-lactam, vancomycin, or macrolide resistance regardless of whether patients received those same antibiotics). This was true for both culture and PCR. Antimicrobial resistance pattern at ICU admission strongly predicted resistance pattern after 72 h. CONCLUSIONS: Short-course ICU antibiotics made little detectable difference in the antimicrobial resistance pattern of colonizing gut bacteria over 72 h in the ICU. This provides an improved understanding of the dynamics of antimicrobial resistance in the ICU and some reassurance that short-course antibiotics may not adversely impact the stewardship goal of reducing antimicrobial resistance.202032646458
224760.9996Metagenomic identification of pathogens and antimicrobial-resistant genes in bacterial positive blood cultures by nanopore sequencing. Nanopore sequencing workflows have attracted increasing attention owing to their fast, real-time, and convenient portability. Positive blood culture samples were collected from patients with bacterial bloodstream infection and tested by nanopore sequencing. This study compared the sequencing results for pathogen taxonomic profiling and antimicrobial resistance genes to those of species identification and phenotypic drug susceptibility using traditional microbiology testing. A total of 37 bacterial positive blood culture results of strain genotyping by nanopore sequencing were consistent with those of mass spectrometry. Among them, one mixed infection of bacteria and fungi was identified using nanopore sequencing and confirmatory quantitative polymerase chain reaction. The amount of sequencing data was 21.89 ± 8.46 MB for species identification, and 1.0 MB microbial strain data enabled accurate determination. Data volumes greater than or equal to 94.6 MB nearly covered all the antimicrobial resistance genes of the bacteria in our study. In addition, the results of the antimicrobial resistance genes were compared with those of phenotypic drug susceptibility testing for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. Therefore, the nanopore sequencing platform for rapid identification of causing pathogens and relevant antimicrobial resistance genes complementary to conventional blood culture outcomes may optimize antimicrobial stewardship management for patients with bacterial bloodstream infection.202338192400
224870.9996Predictive Application Value of Metagenomic Next-Generation Sequencing in the Resistance of Carbapenem-Resistant Enterobacteriaceae. Objective: Although metagenomic next-generation sequencing (mNGS) technology has achieved notable outcomes in pathogen detection, there remains a gap in the research regarding its application in predicting the antibiotic resistance of pathogenic bacteria. This study aims to analyze the clinical application value of mNGS in predicting the resistance of carbapenem-resistant Enterobacteriaceae (CRE), as well as the relevant influencing factors, thereby providing valuable insights for clinical antimicrobial therapy. Methods: Nonduplicate isolates of Enterobacterales bacteria collected from Liaocheng People's Hospital from April 2023 to June 2024 were selected, and CRE bacteria were screened. mNGS was used to detect resistance genes, and the results were compared with those of polymerase chain reaction (PCR) to evaluate the specificity and sensitivity of gene detection. Furthermore, the performance of mNGS in identifying pathogenic microorganisms and predicting antibiotic resistance was assessed by comparing the sequencing results with those of antimicrobial susceptibility testing (AST). Results: A total of 46 isolates were confirmed as CRE through traditional AST and were further identified using the Vitek MS and Vitek 2 systems. The results indicated 27 isolates of Klebsiella pneumoniae, 14 isolates of Escherichia coli, 2 isolates of Enterobacter hormaechei, 2 isolates of Enterobacter cloacae, and 1 isolate of Citrobacter freundii. These isolates were subjected to both mNGS and PCR for detection. The calculation of the area under the receiver operating characteristic (ROC) curve demonstrated the reliability of mNGS in detecting resistance genes. Conclusion: mNGS demonstrated high sensitivity in predicting the presence of carbapenemase resistance genes in CRE, showing potential in early indication of isolate resistance information, thereby facilitating timely guidance for clinical treatment strategies.202539816186
582880.9995Target-enriched sequencing enables accurate identification of bloodstream infections in whole blood. Bloodstream infections are within the top ten causes of death globally, with a mortality rate of up to 70%. Gold standard blood culture testing is time-consuming, resulting in delayed, but accurate, treatment. Molecular methods, such as RT-qPCR, have limited targets in one run. We present a new Ampliseq detection system (ADS) combining target amplification and next-generation sequencing for accurate identification of bacteria, fungi, and antimicrobial resistance determinants directly from blood samples. In this study, we included removal of human genomic DNA during nucleic acid extraction, optimized the target sequence set and drug resistance genes, performed antimicrobial resistance profiling of clinical isolates, and evaluated mock specimens and clinical samples by ADS. ADS successfully identified pathogens at the species-level in 36 h, from nucleic acid extraction to results. Besides pathogen identification, ADS can also present drug resistance profiles. ADS enabled detection of all bacteria and accurate identification of 47 pathogens. In 20 spiked samples and 8 clinical specimens, ADS detected at least 92.81% of reads mapped to pathogens. ADS also showed consistency with the three culture-negative samples, and correctly identified pathogens in four of five culture-positive clinical blood specimens. This Ampliseq-based technology promises broad coverage and accurate pathogen identification, helping clinicians to accurately diagnose and treat bloodstream infections.202234915067
493990.9995Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. OBJECTIVES: The introduction of metagenomic sequencing to diagnostic microbiology has been hampered by slowness, cost and complexity. We explored whether MinION nanopore sequencing could accelerate diagnosis and resistance profiling, using complicated urinary tract infections as an exemplar. METHODS: Bacterial DNA was enriched from clinical urines (n = 10) and from healthy urines 'spiked' with multiresistant Escherichia coli (n = 5), then sequenced by MinION. Sequences were analysed using external databases and bioinformatic pipelines or, ultimately, using integrated real-time analysis applications. Results were compared with Illumina data and resistance phenotypes. RESULTS: MinION correctly identified pathogens without culture and, among 55 acquired resistance genes detected in the cultivated bacteria by Illumina sequencing, 51 were found by MinION sequencing directly from the urines; with three of the four failures in an early run with low genome coverage. Resistance-conferring mutations and allelic variants were not reliably identified. CONCLUSIONS: MinION sequencing comprehensively identified pathogens and acquired resistance genes from urine in a timeframe similar to PCR (4 h from sample to result). Bioinformatic pipeline optimization is needed to better detect resistances conferred by point mutations. Metagenomic-sequencing-based diagnosis will enable clinicians to adjust antimicrobial therapy before the second dose of a typical (i.e. every 8 h) antibiotic.201727667325
1482100.9995Evaluation and clinical practice of pathogens and antimicrobial resistance genes of BioFire FilmArray Pneumonia panel in lower respiratory tract infections. BACKGROUND: Existing panels for lower respiratory tract infections (LRTIs) are slow and lack quantification of important pathogens and antimicrobial resistance, which are not solely responsible for their complex etiology and antibiotic resistance. BioFire FilmArray Pneumonia (PN) panels may provide rapid information on their etiology. METHODS: The bronchoalveolar lavage fluid of 187 patients with LRTIs was simultaneously analyzed using a PN panel and cultivation, and the impact of the PN panel on clinical practice was assessed. The primary endpoint was to compare the consistency between the PN panel and conventional microbiology in terms of etiology and drug resistance, as well as to explore the clinical significance of the PN panel. The secondary endpoint was pathogen detection using the PN panel in patients with community-acquired pneumonia (CAP) or hospital-acquired pneumonia (HAP). RESULTS: Fifty-seven patients with HAP and 130 with CAP were included. The most common pathogens of HAP were Acinetobacter baumannii and Klebsiella pneumoniae, with the most prevalent antimicrobial resistance (AMR) genes being CTX-M and KPC. For CAP, the most common pathogens were Haemophilus influenzae and Staphylococcus aureus, with the most frequent AMR genes being CTX-M and VIM. Compared with routine bacterial culture, the PN panel demonstrated an 85% combined positive percent agreement (PPA) and 92% negative percent agreement (NPA) for the qualitative identification of 13 bacterial targets. PN detection of bacteria with higher levels of semi-quantitative bacteria was associated with more positive bacterial cultures. Positive concordance between phenotypic resistance and the presence of corresponding AMR determinants was 85%, with 90% positive agreement between CTX-M-type extended-spectrum beta-lactamase gene type and phenotype and 100% agreement for mecA/C and MREJ. The clinical benefit of the PN panel increased by 25.97% compared with traditional cultural tests. CONCLUSION: The bacterial pathogens and AMR identified by the PN panel were in good agreement with conventional cultivation, and the clinical benefit of the PN panel increased by 25.97% compared with traditional detection. Therefore, the PN panel is recommended for patients with CAP or HAP who require prompt pathogen diagnosis and resistance identification.202438123753
5673110.9995Antimicrobial Resistance, Genetic Lineages, and Biofilm Formation in Pseudomonas aeruginosa Isolated from Human Infections: An Emerging One Health Concern. Pseudomonas aeruginosa (PA) is a leading nosocomial pathogen and has great versatility due to a complex interplay between antimicrobial resistance and virulence factors. PA has also turned into one the most relevant model organisms for the study of biofilm-associated infections. The objective of the study focused on analyzing the antimicrobial susceptibility, resistance genes, virulence factors, and biofilm formation ability of thirty-two isolates of PA. PA isolates were characterized by the following analyses: susceptibility to 12 antimicrobial agents, the presence of resistance genes and virulence factors in PCR assays, and the quantification of biofilm production as evaluated by two distinct assays. Selected PA isolates were analyzed through multilocus sequence typing (MLST). Thirty PA isolates have a multi-resistant phenotype, and most of the isolates showed high levels of resistance to the tested antibiotics. Carbapenems showed the highest prevalence of resistance. Various virulence factors were detected and, for the quantification of biofilm production, the effectiveness of different methods was assessed. The microtiter plate method showed the highest accuracy and reproducibility for detecting biofilm-producing bacteria. MLST revealed four distinct sequence types (STs) in clinical PA, with three of them considered high-risk clones of PA, namely ST175, ST235, and ST244. These clones are associated with multidrug resistance and are prevalent in hospitals worldwide. Overall, the study highlights the high prevalence of antibiotic resistance, the presence of carbapenemase genes, the diversity of virulence factors, and the importance of biofilm formation in PA clinical isolates. Understanding these factors is crucial for effective infection control measures and the development of targeted treatment strategies.202337627668
5822120.9995Antibiotic Susceptibility, Virulome, and Clinical Outcomes in European Infants with Bloodstream Infections Caused by Enterobacterales. Mortality in neonates with Gram-negative bloodstream infections has remained unacceptably high. Very few data are available on the impact of resistance profiles, virulence factors, appropriateness of empirical treatment and clinical characteristics on patients' mortality. A survival analysis to investigate 28-day mortality probability and predictors was performed including (I) infants <90 days (II) with an available Enterobacterales blood isolate with (III) clinical, treatment and 28-day outcome data. Eighty-seven patients were included. Overall, 299 virulence genes were identified among all the pathogens. Escherichia coli had significantly more virulence genes identified compared with other species. A strong positive correlation between the number of resistance and virulence genes carried by each isolate was found. The cumulative probability of death obtained by the Kaplan-Meier survival analysis was 19.5%. In the descriptive analysis, early age at onset, gestational age at onset, culture positive for E. coli and number of classes of virulence genes carried by each isolate were significantly associated with mortality. By Cox multivariate regression, none of the investigated variables was significant. This pilot study has demonstrated the feasibility of investigating the association between neonatal sepsis mortality and the causative Enterobacterales isolates virulome. This relationship needs further exploration in larger studies, ideally including host immunopathological response, in order to develop a tailor-made therapeutic strategy.202134208220
5694130.9995Multiplex characterization of human pathogens including species and antibiotic-resistance gene identification. The efficient medical treatment of infections requires detailed information about the pathogens involved and potential antibiotic-resistance mechanisms. The dramatically increasing incidence of multidrug-resistant bacteria especially highlights the importance of sophisticated diagnostic tests enabling a fast patient-customized therapy. However, the current molecular detection methods are limited to either the detection of species or only a few antibiotic-resistance genes.In this work, we present a human pathogen characterization assay using a rRNA gene microarray identifying 75 species comprising bacteria and fungi. A statistical classifier was developed to facilitate the automated species identification. Additionally, the clinically most important β-lactamases were identified simultaneously in a 100-plex reaction using padlock probes and the same microarray. The specificity and sensitivity of the combined assay was determined using clinical isolates. The detection limit was 10(5) c.f.u. ml(-1), recovering 89 % of the detectable β-lactamase-encoding genes specifically. The total assay time was less than 7 hand the modular character of the antibiotic-resistance detection allows the easy integration of further genetic targets. In summary, we present a fast, highly specific and sensitive multiplex pathogen characterization assay.201626489938
5674140.9995Evaluation of Resistance by Clinically Pathogenic Bacteria to Antimicrobials and Common Disinfectants in Beijing, China. BACKGROUND: Antibiotic resistance of pathogenic bacteria is well recognized among clinicians; however, studies that directly evaluate the bacterial resistance to commonly used disinfectants in clinical settings are lacking. Currently available reports focus on the resistance of single strains to single disinfectants and do not adequately examine the degree of resistance and cross-resistance to antimicrobials in the large-scale clinical use of disinfectants. METHODS: We investigated the resistance capacity to 11 antibiotics and 7 chemical disinfectants by bacterial strains collected from body fluids of patients in 10 hospitals in Beijing, China over a 1-year period. Bacterial resistance to disinfectants was tested using minimum inhibitory concentration and minimum bactericidal concentration using agar dilution methods based on commercially available reference strains. RESULTS: A total of 1,104 pathogenic strains were identified, of which 23% were Gram-positive bacteria, 74% were Gram-negative bacteria, and 3% were fungi. Overall, resistance to antibiotics for the most common strains was significantly higher than their resistance to disinfectants. The least effective antibiotics and disinfectants were aztreonam and glutaral, respectively, exhibiting the highest overall resistance rates; while amikacin and alcohol had the lowest resistance rates. Consistently, Acinetobacter baumannii exhibited the most resistance, while Escherichia coli had the least resistance for both antibiotics and disinfectants. CONCLUSIONS: Based on the pathogen spectrum for bacterial infective pathogens evaluated in this study, as well as the status quo of their resistance to antimicrobial agents and common clinical disinfectants, it is essential for healthcare professionals to pay attention not only to the standardized use of antimicrobial agents but also to the rational application of disinfectants.201830568055
4941150.9995BacCapSeq: a Platform for Diagnosis and Characterization of Bacterial Infections. We report a platform that increases the sensitivity of high-throughput sequencing for detection and characterization of bacteria, virulence determinants, and antimicrobial resistance (AMR) genes. The system uses a probe set comprised of 4.2 million oligonucleotides based on the Pathosystems Resource Integration Center (PATRIC) database, the Comprehensive Antibiotic Resistance Database (CARD), and the Virulence Factor Database (VFDB), representing 307 bacterial species that include all known human-pathogenic species, known antimicrobial resistance genes, and known virulence factors, respectively. The use of bacterial capture sequencing (BacCapSeq) resulted in an up to 1,000-fold increase in bacterial reads from blood samples and lowered the limit of detection by 1 to 2 orders of magnitude compared to conventional unbiased high-throughput sequencing, down to a level comparable to that of agent-specific real-time PCR with as few as 5 million total reads generated per sample. It detected not only the presence of AMR genes but also biomarkers for AMR that included both constitutive and differentially expressed transcripts.IMPORTANCE BacCapSeq is a method for differential diagnosis of bacterial infections and defining antimicrobial sensitivity profiles that has the potential to reduce morbidity and mortality, health care costs, and the inappropriate use of antibiotics that contributes to the development of antimicrobial resistance.201830352937
5658160.9995Molecular identification and biofilm formation of aerobic and anaerobic coinfection bacterial isolated from cystic fibrosis patients in southwest Iran from 2014 to 2022. BACKGROUND: Coinfections and resistant bacterial infections are more likely to occur in cystic fibrosis patients because their immune systems are weak. The purpose of this study was to identify by molecular means as well as the formation of biofilm of aerobic and anaerobic coinfection bacteria isolated from cystic fibrosis patients in southwest Iran from 2014 to 2022. METHODS: In this investigation, 130 clinical specimens were collected from 130 CF patients by universal primer. Biofilm formation was investigated using the microtiter plate method. Antibiotic resistance was measured using Vitec 2 device. In addition, identification of methicillin-resistant Staphylococcus aureus using genes mecA was performed. MAIN FINDINGS: In aerobic bacteria, Pseudomonas aeruginosa was detected in (32%) of samples. In anaerobic bacteria (16%) Prevotella spp. was the most frequently isolated anaerobe bacteria found in of the CF patients. In this study, 75% of the bacteria could form biofilms, while 23% were unable to biofilm formation. CONCLUSION: In conclusion, P. aeruginosa was found to be the most frequently isolated bacterium from patients with CF, and many of these bacteria could form biofilms. Additionally, the high prevalence of antibiotic resistance indicates the urgent need for increased attention to antibiotic preparation and patient screening concerning bacterial coinfections and the virulence and adhesion factors of these bacteria. Furthermore, the present study demonstrates that the coinfection of bacteria with high antibiotic resistance and a high capacity for biofilm formation can pose a life-threatening risk to CF patients, mainly due to their weakened immune systems.202337566205
5817170.9995Comparative genomics reveals the correlations of stress response genes and bacteriophages in developing antibiotic resistance of Staphylococcus saprophyticus. Staphylococcus saprophyticus is the second most common bacteria associated with urinary tract infections (UTIs) in women. The antimicrobial treatment regimen for uncomplicated UTI is normally nitrofurantoin, trimethoprim-sulfamethoxazole (TMP-SMX), or a fluoroquinolone without routine susceptibility testing of S. saprophyticus recovered from urine specimens. However, TMP-SMX-resistant S. saprophyticus has been detected recently in UTI patients, as well as in our cohort. Herein, we investigated the understudied resistance patterns of this pathogenic species by linking genomic antibiotic resistance gene (ARG) content to susceptibility phenotypes. We describe ARG associations with known and novel SCCmec configurations as well as phage elements in S. saprophyticus, which may serve as intervention or diagnostic targets to limit resistance transmission. Our analyses yielded a comprehensive database of phenotypic data associated with the ARG sequence in clinical S. saprophyticus isolates, which will be crucial for resistance surveillance and prediction to enable precise diagnosis and effective treatment of S. saprophyticus UTIs.202338051037
5683180.9995Association between antimicrobial resistance among Enterobacteriaceae and burden of environmental bacteria in hospital acquired infections: analysis of clinical studies and national reports. BACKGROUND: WHO has named three groups of gram-negative bacteria "our critical antimicrobial resistance-related problems globally". It is thus a priority to unveil any important covariation of variables behind this three-headed epidemic, which has gained alarming proportions in Low Income Countries, and spreads rapidly. Environmental bacteria including Acinetobacter spp. are common nosocomial pathogens in institutions that have high rates of antimicrobial resistance among other groups of gram-negative bacteria. METHODS: Based on two different data sources, we calculated the correlation coefficient (Pearson's r) between pathogenic burden of Acinetobacter spp. and antimicrobial resistance among Enterobacteriaceae in European and African nosocomial cohorts. CLINICAL REPORTS: Database search for studies on nosocomial sepsis in Europe and Africa was followed by a PRISMA-guided selection process. NATIONAL REPORTS: Data from Point prevalence survey of healthcare-associated infections published by European Centre for Disease Prevention and Control were used to study the correlation between prevalence of Acinetobacter spp. and antimicrobial resistance among K. pneumoniae in blood culture isolates. FINDINGS: The two approaches both revealed a strong association between prevalence of Acinetobacter spp. and rates of resistance against 3. generation cephalosporins among Enterobacteriaceae. In the study of clinical reports (13 selected studies included), r was 0.96 (0.80-0.99) when calculated by proportions on log scale. Based on national reports, r was 0.80 (0.56-0.92) for the correlation between resistance rates of K. pneumoniae and proportion of Acinetobacter spp. INTERPRETATION: The critical antimicrobial resistance-related epidemics that concern enteric and environmental gram-negative bacteria are not independent epidemics; they have a common promoting factor, or they are mutually supportive. Further, accumulation of antimicrobial resistance in nosocomial settings depends on the therapeutic environment. Burden of Acinetobacter spp. as defined here is a candidate measure for this dependence.201931372534
5002190.9995Genomic Diversity of Hospital-Acquired Infections Revealed through Prospective Whole-Genome Sequencing-Based Surveillance. Healthcare-associated infections (HAIs) cause mortality, morbidity, and waste of health care resources. HAIs are also an important driver of antimicrobial resistance, which is increasing around the world. Beginning in November 2016, we instituted an initiative to detect outbreaks of HAIs using prospective whole-genome sequencing-based surveillance of bacterial pathogens collected from hospitalized patients. Here, we describe the diversity of bacteria sampled from hospitalized patients at a single center, as revealed through systematic analysis of bacterial isolate genomes. We sequenced the genomes of 3,004 bacterial isolates from hospitalized patients collected over a 25-month period. We identified bacteria belonging to 97 distinct species, which were distributed among 14 groups of related species. Within these groups, isolates could be distinguished from one another by both average nucleotide identity (ANI) and principal-component analysis of accessory genes (PCA-A). Core genome genetic distances and rates of evolution varied among species, which has practical implications for defining shared ancestry during outbreaks and for our broader understanding of the origins of bacterial strains and species. Finally, antimicrobial resistance genes and putative mobile genetic elements were frequently observed, and our systematic analysis revealed patterns of occurrence across the different species sampled from our hospital. Overall, this study shows how understanding the population structure of diverse pathogens circulating in a single health care setting can improve the discriminatory power of genomic epidemiology studies and can help define the processes leading to strain and species differentiation. IMPORTANCE Hospitalized patients are at increased risk of becoming infected with antibiotic-resistant organisms. We used whole-genome sequencing to survey and compare over 3,000 clinical bacterial isolates collected from hospitalized patients at a large medical center over a 2-year period. We identified nearly 100 different bacterial species, which we divided into 14 different groups of related species. When we examined how genetic relatedness differed between species, we found that different species were likely evolving at different rates within our hospital. This is significant because the identification of bacterial outbreaks in the hospital currently relies on genetic similarity cutoffs, which are often applied uniformly across organisms. Finally, we found that antibiotic resistance genes and mobile genetic elements were abundant and were shared among the bacterial isolates we sampled. Overall, this study provides an in-depth view of the genomic diversity and evolutionary processes of bacteria sampled from hospitalized patients, as well as genetic similarity estimates that can inform hospital outbreak detection and prevention efforts.202235695507