Dataset Information

Rapid Determination of Benzylpenicillin Resistance in Staphylococcus aureus Bacteraemia Model.

ABSTRACT: Rapid determination of antimicrobial susceptibility/resistance is an important factor in selecting an appropriate antimicrobial treatment and eradicating infections promptly. Conventional antimicrobial susceptibility tests (ASTs) are very time consuming. Thus, we developed a liquid chromatography-mass spectrometry (LC-MS/MS) method for rapidly determining the resistance of Staphylococcus aureus to penicillin-G in an animal-infection model. This technique will be able to detect those resistant strains whose resistance mechanism specifically controlled by penicillinase. The resistance status of S. aureus against penicillin-G was determined by conventional AST. Cultured S. aureus cells were inoculated to chicken for developing bacteraemia. The solution of penicillin-G was intravenously administered (10 mg/kg b.w.) to chickens just after infection detection. Blood samples were collected at different intervals after drug administration. The concentration of active penicillin-G and its metabolites were determined from the bacteria-free blood supernatant by utilizing the LC-MS/MS method. Evidence of infection in chicken was observed within 5 h of bacterial inoculation. The penicillinase enzyme generated by S. aureus transforms the active penicillin-G to an inactive metabolite by hydrolysis, which is evident by the mass shift from 335.10600 to 353.11579 Da as quantified using liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF/MS). The signal intensity of inactive/hydrolysed penicillin-G is several-fold greater than that of the active penicillin-G in the blood sample of chicken infected with resistant strain and treated with penicillin-G. The antimicrobial resistance index (ARI) value of resistant S. aureus strain was more than 1, demonstrating the penicillin-G-resistance pattern of that strain. This method is able to determine the extent of ?-lactam antimicrobial resistance within 1.5 h from the patient's blood and is complementary with those existing AST methods which are usually practicing in the evaluation of ?-lactam antibiotic resistance.

SUBMITTER: Kang J

PROVIDER: S-EPMC7292373 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Rapid Determination of Benzylpenicillin Resistance in <i>Staphylococcus aureus</i> Bacteraemia Model.

Kang JeongWoo J Hossain Md Akil MA Park Hae-Chul HC Kim Yong-Sang YS Park Sung-Won SW Kim Tae-Wan TW

Infection and drug resistance 20200608

Rapid determination of antimicrobial susceptibility/resistance is an important factor in selecting an appropriate antimicrobial treatment and eradicating infections promptly. Conventional antimicrobial susceptibility tests (ASTs) are very time consuming. Thus, we developed a liquid chromatography-mass spectrometry (LC-MS/MS) method for rapidly determining the resistance of <i>Staphylococcus aureus</i> to penicillin-G in an animal-infection model. This technique will be able to detect those resis ...[more]

PMID: 32606811

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Project description:Staphylococcus aureus bacteraemia is a serious infection associated with significant complications, including recurrence of bacteraemia, endocarditis and metastatic foci of infection. The management of these patients is often complex, involving appropriate source control, a thorough review and investigations to exclude metastatic foci and infective endocarditis. Additionally, a prolonged course of intravenous antibiotics is often required. As part of our quality improvement project, the following five aspects were evaluated in 56 patients with S. aureus bacteraemia at two District General Hospitals: 1) adequate and timely removal of the source of bacteraemia, 2) echocardiography to exclude endocarditis, 3) repeat blood culture to prove clearance of bacteraemia, 4) adequate duration and choice of antibiotics and 5) documentation of bacteraemia in the discharge summary. After an initial review revealed several areas for improvement, we instituted five Plan-Do-Study-Act learning cycles which involved: teaching microbiology trainees and junior doctors, improving clinical liaison and communication between the microbiology team and clinicians, as well as a clinical review of patients by the microbiology team where appropriate. The post-intervention review evaluated 24 patients with S. aureus bacteraemia between November 2012 and May 2013. The proportion of patients undergoing an echocardiogram improved from 49% to 88%. Another marked improvement was seen in the timely obtaining of clearance blood cultures, with 88% of patients having clearance blood cultures within the 2-4 day window, compared to 56% pre-intervention. 70% of patients with uncomplicated S. aureus bacteraemia received an appropriate antibiotic course post-intervention, compared with 59% pre-intervention. Documentation of the S. aureus bacteraemia in the discharge summary improved from 65% to 75%. The support of the entire microbiology team was pivotal in the successful outcome of the quality improvement project.

Project description:Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation-Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen's kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen's kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen's kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Dataset Information

Rapid Determination of Benzylpenicillin Resistance in Staphylococcus aureus Bacteraemia Model.

Publications

Rapid Determination of Benzylpenicillin Resistance in <i>Staphylococcus aureus</i> Bacteraemia Model.

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