ABSTRACT
INTRODUCTION: The rapid identification and detection of carbapenemase-producing Klebsiella pneumoniae (CPKP) isolates is crucial to ascertain outbreaks, as well as to limit their spread. The current reference method for this purpose is multilocus sequence typing (MLST), which is laborious and expensive. Consequently, alternative typing methods are gaining attention, such as Matrix-Assisted Laser Desorption Ionization-Time Of Flight Mass Spectrometry (MALDI-TOF MS). METHODS: This study sought to analyze MALDI-TOF MS as a typing method using 44 CPKP isolates that were well characterized by MLST. The most common types of samples from which these pathogens were isolated were skin and soft tissues (32%) and urine (29%). Half of the CPKP isolates were from hospitalized patients. Two approaches were followed for the analysis of the mass peak data obtained by MALDI-TOF MS. The first using all peaks obtained and the second using a selection of 21 characteristic peaks. RESULTS: The selection of 21 characteristic peaks showed greater discrimination power for ST11 and ST101. Principal component analysis (PCA) indicated that this dataset could be efficiently grouped with lineal classifiers. A Support Vector Machine (SVM) was chosen for this purpose after checking its capacity to classify bacterial strains on the basis of MALDI-TOF MS information. CONCLUSION: SVM was able to discriminate between ST11 and ST101 with high accuracy. In conclusion, our results reveal MALDI-TOF MS as a promising alternative technique for typing of CPKP isolates.
Subject(s)
Carbapenem-Resistant Enterobacteriaceae , Klebsiella pneumoniae , Bacterial Proteins , Bacterial Typing Techniques/methods , Humans , Klebsiella pneumoniae/genetics , Multilocus Sequence Typing/methods , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods , beta-LactamasesABSTRACT
IntroductionThe rapid identification and detection of carbapenemase-producing Klebsiella pneumoniae (CPKP) isolates is crucial to ascertain outbreaks, as well as to limit their spread. The current reference method for this purpose is multilocus sequence typing (MLST), which is laborious and expensive. Consequently, alternative typing methods are gaining attention, such as Matrix-Assisted Laser Desorption IonizationTime Of Flight Mass Spectrometry (MALDI-TOF MS).MethodsThis study sought to analyze MALDI-TOF MS as a typing method using 44 CPKP isolates that were well characterized by MLST. The most common types of samples from which these pathogens were isolated were skin and soft tissues (32%) and urine (29%). Half of the CPKP isolates were from hospitalized patients. Two approaches were followed for the analysis of the mass peak data obtained by MALDI-TOF MS. The first using all peaks obtained and the second using a selection of 21 characteristic peaks.ResultsThe selection of 21 characteristic peaks showed greater discrimination power for ST11 and ST101. Principal component analysis (PCA) indicated that this dataset could be efficiently grouped with lineal classifiers. A Support Vector Machine (SVM) was chosen for this purpose after checking its capacity to classify bacterial strains on the basis of MALDI-TOF MS information.ConclusionSVM was able to discriminate between ST11 and ST101 with high accuracy. In conclusion, our results reveal MALDI-TOF MS as a promising alternative technique for typing of CPKP isolates.
IntroducciónLa rápida identificación y detección de los aislados de Klebsiella pneumoniae productores de carbapenemasas (CPKP) es crucial para identificar brotes e impedir la propagación de los aislados resistentes. El método de referencia para este propósito es el multilocus sequencing typing (MLST), que es un técnica laboriosa y cara, por lo que se buscan métodos de tipado alternativos que pueden desempeñar la misma función con menor esfuerzo. Entre las posibles técnicas se encuentra la espectrometría de masas de tiempo de vuelo MALDI-TOF.MétodosEste estudio se han utilizado el sistema MALDI-TOF MS para tipar 44 aislamientos de CPKP previamente caracterizados por MLST. Las muestras clínicas de las que proceden los aislados son principalmente piel y tejidos blandos (32%) y orina (29%). La mitad de los aislamientos de CPKP procedían de pacientes ingresados. El análisis los datos obtenidos por MALDI-TOF MS se realizó con 2 enfoques diferentes, el primero usando todos los picos obtenidos y el segundo usando una selección de picos.ResultadosLa selección de 21 picos característicos ofreció un mayor poder de discriminación entre ST11 y ST101. El análisis de componentes principales (PCA) indicó que este conjunto de datos podría agruparse eficientemente con clasificadores lineales. Para realizar este agrupamiento se escogió el algoritmo support vector machine (SVM, máquinas de vectores de soporte) para este propósito después de verificar su capacidad para clasificar las cepas bacterianas en base a la información de MALDI-TOF MS.ConclusiónSVM pudo discriminar entre ST11 y ST101 con alta precisión. En conclusión, nuestros resultados revelan MALDI-TOF MS puede ser una técnica alternativa para el tipificación de aislamientos de CPKP.
Subject(s)
Humans , Health Sciences , Klebsiella pneumoniae , Bacterial Typing Techniques , Microbiology , Communicable DiseasesABSTRACT
INTRODUCTION: The rapid identification and detection of carbapenemase-producing Klebsiella pneumoniae (CPKP) isolates is crucial to ascertain outbreaks, as well as to limit their spread. The current reference method for this purpose is multilocus sequence typing (MLST), which is laborious and expensive. Consequently, alternative typing methods are gaining attention, such as Matrix-Assisted Laser Desorption Ionization-Time Of Flight Mass Spectrometry (MALDI-TOF MS). METHODS: This study sought to analyze MALDI-TOF MS as a typing method using 44 CPKP isolates that were well characterized by MLST. The most common types of samples from which these pathogens were isolated were skin and soft tissues (32%) and urine (29%). Half of the CPKP isolates were from hospitalized patients. Two approaches were followed for the analysis of the mass peak data obtained by MALDI-TOF MS. The first using all peaks obtained and the second using a selection of 21 characteristic peaks. RESULTS: The selection of 21 characteristic peaks showed greater discrimination power for ST11 and ST101. Principal component analysis (PCA) indicated that this dataset could be efficiently grouped with lineal classifiers. A Support Vector Machine (SVM) was chosen for this purpose after checking its capacity to classify bacterial strains on the basis of MALDI-TOF MS information. CONCLUSION: SVM was able to discriminate between ST11 and ST101 with high accuracy. In conclusion, our results reveal MALDI-TOF MS as a promising alternative technique for typing of CPKP isolates.
