Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry
Introduction: The genus Morganella, including clinically isolated species M. sibonii and M. morganii, has a still underexplored role in clinical microbiology. Despite the clinical relevance of Morganella spp., current MALDI-TOF commercial systems fail to differentiate these species. Whole genome seq...
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Elsevier
2025-08-01
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| Series: | Journal of Mass Spectrometry and Advances in the Clinical Lab |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667145X25000185 |
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| author | Mathilde Duque Cécile Emeraud Rémy A. Bonnin Quentin Giai-Gianetto Laurent Dortet Alexandre Godmer |
| author_facet | Mathilde Duque Cécile Emeraud Rémy A. Bonnin Quentin Giai-Gianetto Laurent Dortet Alexandre Godmer |
| author_sort | Mathilde Duque |
| collection | DOAJ |
| description | Introduction: The genus Morganella, including clinically isolated species M. sibonii and M. morganii, has a still underexplored role in clinical microbiology. Despite the clinical relevance of Morganella spp., current MALDI-TOF commercial systems fail to differentiate these species. Whole genome sequencing (WGS) remains the most effective method to distinguish species. However, this method is not adapted for routine lab workflow. Enhancing MALDI-TOF’s accuracy could make it a rapid and effective approach for distinguishing Morganella species in routine laboratory diagnostics. Objectives: This study aims to improve the performance of MALDI-TOF for identifying Morganella spp. using WGS as the gold-standard reference method. Methods: We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. Whole genome sequencing was used to characterize these strains and perform phylogenetic analysis, categorizing 209 strains as M. morganii and 26 as M. sibonii. Results: The ML-based classifiers showed improved identification accuracy (44 of the 160 designed with accuracy at 1). Also, MS analysis identified 11 peaks able to discriminate between M. morganii and M. sibonii. Conclusion: Through development of a publicly-available online ML-based classifier, this study has improved the capacity of MALDI-TOF for distinguishing Morganella spp., providing a reliable, user-friendly solution suited to routine clinical diagnostics and supporting a better understanding of the roles of M. morganii and M. sibonii in human pathology. |
| format | Article |
| id | doaj-art-bf818993c2c24565b1ca6f1ff0dd7ad6 |
| institution | Kabale University |
| issn | 2667-145X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
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| series | Journal of Mass Spectrometry and Advances in the Clinical Lab |
| spelling | doaj-art-bf818993c2c24565b1ca6f1ff0dd7ad62025-08-24T05:14:57ZengElsevierJournal of Mass Spectrometry and Advances in the Clinical Lab2667-145X2025-08-013791310.1016/j.jmsacl.2025.04.011Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometryMathilde Duque0Cécile Emeraud1Rémy A. Bonnin2Quentin Giai-Gianetto3Laurent Dortet4Alexandre Godmer5Team “Resist” UMR1184 “Immunology of Viral, Auto-Immune, Hematological and Bacterial diseases (IMVA-HB)”, INSERM, Université Paris-Saclay, CEA, LabEx LERMIT, Faculty of Medicine, Le Kremlin-Bicêtre, FranceTeam “Resist” UMR1184 “Immunology of Viral, Auto-Immune, Hematological and Bacterial diseases (IMVA-HB)”, INSERM, Université Paris-Saclay, CEA, LabEx LERMIT, Faculty of Medicine, Le Kremlin-Bicêtre, France; Bacteriology-Hygiene Unit, Bicêtre Hospital, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France; Associated French National Reference Center for Antibiotic Resistance: Carbapenemase-Producing Enterobacteriaceae, Le Kremlin-Bicêtre, FranceTeam “Resist” UMR1184 “Immunology of Viral, Auto-Immune, Hematological and Bacterial diseases (IMVA-HB)”, INSERM, Université Paris-Saclay, CEA, LabEx LERMIT, Faculty of Medicine, Le Kremlin-Bicêtre, France; Bacteriology-Hygiene Unit, Bicêtre Hospital, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France; Associated French National Reference Center for Antibiotic Resistance: Carbapenemase-Producing Enterobacteriaceae, Le Kremlin-Bicêtre, FranceInstitut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics HUB, Paris, France; Institut Pasteur, Université Paris Cité, Proteomics Platform, Mass Spectrometry for Biology Unit, UAR CNRS 2024, Paris, FranceTeam “Resist” UMR1184 “Immunology of Viral, Auto-Immune, Hematological and Bacterial diseases (IMVA-HB)”, INSERM, Université Paris-Saclay, CEA, LabEx LERMIT, Faculty of Medicine, Le Kremlin-Bicêtre, France; Bacteriology-Hygiene Unit, Bicêtre Hospital, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France; Associated French National Reference Center for Antibiotic Resistance: Carbapenemase-Producing Enterobacteriaceae, Le Kremlin-Bicêtre, FranceU1135, Centre d’Immunologie et des Maladies Infectieuses (Cimi-Paris), Sorbonne Université, Paris, France; Bacteriology Deparment, Saint-Antoine Hospital, AP-HP. Sorbonne Université (Assistance Publique Hôpitaux de Paris), Paris, France; Corresponding author at: U1135, Centre d’Immunologie et des Maladies Infectieuses (Cimi-Paris), Sorbonne Université, Paris, France.Introduction: The genus Morganella, including clinically isolated species M. sibonii and M. morganii, has a still underexplored role in clinical microbiology. Despite the clinical relevance of Morganella spp., current MALDI-TOF commercial systems fail to differentiate these species. Whole genome sequencing (WGS) remains the most effective method to distinguish species. However, this method is not adapted for routine lab workflow. Enhancing MALDI-TOF’s accuracy could make it a rapid and effective approach for distinguishing Morganella species in routine laboratory diagnostics. Objectives: This study aims to improve the performance of MALDI-TOF for identifying Morganella spp. using WGS as the gold-standard reference method. Methods: We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. Whole genome sequencing was used to characterize these strains and perform phylogenetic analysis, categorizing 209 strains as M. morganii and 26 as M. sibonii. Results: The ML-based classifiers showed improved identification accuracy (44 of the 160 designed with accuracy at 1). Also, MS analysis identified 11 peaks able to discriminate between M. morganii and M. sibonii. Conclusion: Through development of a publicly-available online ML-based classifier, this study has improved the capacity of MALDI-TOF for distinguishing Morganella spp., providing a reliable, user-friendly solution suited to routine clinical diagnostics and supporting a better understanding of the roles of M. morganii and M. sibonii in human pathology.http://www.sciencedirect.com/science/article/pii/S2667145X25000185 |
| spellingShingle | Mathilde Duque Cécile Emeraud Rémy A. Bonnin Quentin Giai-Gianetto Laurent Dortet Alexandre Godmer Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry Journal of Mass Spectrometry and Advances in the Clinical Lab |
| title | Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry |
| title_full | Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry |
| title_fullStr | Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry |
| title_full_unstemmed | Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry |
| title_short | Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry |
| title_sort | enhanced identification of morganella spp using maldi tof mass spectrometry |
| url | http://www.sciencedirect.com/science/article/pii/S2667145X25000185 |
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