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Candela A, Guerrero-López A, Mateos M, Gómez-Asenjo A, Arroyo MJ, Hernandez-García M, del Campo R, Cercenado E, Cuénod A, Méndez G, Mancera L, Caballero JDD, Martínez-García L, Gijón D, Morosini MI, Ruiz-Garbajosa P, Egli A, Cantón R, Muñoz P, Rodríguez-Temporal D, Rodríguez-Sánchez B. Automatic Discrimination of Species within the Enterobacter cloacae Complex Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Supervised Algorithms. J Clin Microbiol 2023; 61:e0104922. [PMID: 37014210 PMCID: PMC10117122 DOI: 10.1128/jcm.01049-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 02/28/2023] [Indexed: 04/05/2023] Open
Abstract
The Enterobacter cloacae complex (ECC) encompasses heterogeneous clusters of species that have been associated with nosocomial outbreaks. These species may have different acquired antimicrobial resistance and virulence mechanisms, and their identification is challenging. This study aims to develop predictive models based on matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) profiles and machine learning for species-level identification. A total of 219 ECC and 118 Klebsiella aerogenes clinical isolates from three hospitals were included. The capability of the proposed method to differentiate the most common ECC species (Enterobacter asburiae, Enterobacter kobei, Enterobacter hormaechei, Enterobacter roggenkampii, Enterobacter ludwigii, and Enterobacter bugandensis) and K. aerogenes was demonstrated by applying unsupervised hierarchical clustering with principal-component analysis (PCA) preprocessing. We observed a distinctive clustering of E. hormaechei and K. aerogenes and a clear trend for the rest of the ECC species to be differentiated over the development data set. Thus, we developed supervised, nonlinear predictive models (support vector machine with radial basis function and random forest). The external validation of these models with protein spectra from two participating hospitals yielded 100% correct species-level assignment for E. asburiae, E. kobei, and E. roggenkampii and between 91.2% and 98.0% for the remaining ECC species; with data analyzed in the three participating centers, the accuracy was close to 100%. Similar results were obtained with the Mass Spectrometric Identification (MSI) database developed recently (https://msi.happy-dev.fr) except in the case of E. hormaechei, which was more accurately identified with the random forest algorithm. In short, MALDI-TOF MS combined with machine learning was demonstrated to be a rapid and accurate method for the differentiation of ECC species.
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Affiliation(s)
- Ana Candela
- Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Institute of Health Research Gregorio Marañón, Madrid, Spain
| | | | - Miriam Mateos
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Alicia Gómez-Asenjo
- Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Institute of Health Research Gregorio Marañón, Madrid, Spain
| | | | - Marta Hernandez-García
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- CIBER en Enfermedades Infecciosas, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Rosa del Campo
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- CIBER en Enfermedades Infecciosas, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Emilia Cercenado
- Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Institute of Health Research Gregorio Marañón, Madrid, Spain
- CIBER de Enfermedades Respiratorias, CIBERES CB06/06/0058, Madrid, Spain
- Medicine Department, Faculty of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - Aline Cuénod
- Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
- Division of Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
| | - Gema Méndez
- Clover Bioanalytical Software, Granada, Spain
| | | | - Juan de Dios Caballero
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- CIBER en Enfermedades Infecciosas, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Laura Martínez-García
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Desirée Gijón
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- CIBER en Enfermedades Infecciosas, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - María Isabel Morosini
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- CIBER en Enfermedades Infecciosas, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Patricia Ruiz-Garbajosa
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- CIBER en Enfermedades Infecciosas, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Adrian Egli
- Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
- Division of Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
| | - Rafael Cantón
- Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Madrid, Spain
- CIBER en Enfermedades Infecciosas, Madrid, Spain
- Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Patricia Muñoz
- Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Institute of Health Research Gregorio Marañón, Madrid, Spain
- CIBER de Enfermedades Respiratorias, CIBERES CB06/06/0058, Madrid, Spain
- Medicine Department, Faculty of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - David Rodríguez-Temporal
- Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Institute of Health Research Gregorio Marañón, Madrid, Spain
| | - Belén Rodríguez-Sánchez
- Clinical Microbiology and Infectious Diseases Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Institute of Health Research Gregorio Marañón, Madrid, Spain
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