Author

M. Leclercq

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 2 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Research Focus:host-pathogen & immune response· 2 Organism:bacteria (other)· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

A. Droit· 2 shared A. C. Anderson· 1 shared A. P. Yeola· 1 shared A. Saint-Pol· 1 shared C. Dauly· 1 shared C. Gotti· 1 shared C. Joly-Beauparlant· 1 shared C. S. Moore· 1 shared

Activity by Year

2019-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersImmunology

A. Droit

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Antifungal activity of Cinnamomum cassia and Origanum compactum essential oils against Aspergillus oryzae

M. Leclercq, I. Commenge, M. Bouriot, F. Crusset, P. Gonzalez, J. Grimaud, F. Yates, J. Sarfati-Bert, A. Saint-Pol

This study evaluates the antifungal activity of two essential oils against Aspergillus oryzae, finding that Origanum compactum is fungistatic and Cinnamomum cassia is both fungistatic and fungicidal.

bioRxiv · Mar 24, 2022 · PreprintMicrobiology

Male sex chromosomal complement exacerbates the pathogenicity of Th17 cells in a chronic model of CNS autoimmunity.

P. M. I. A. Doss, J. Baillargeon, A. P. Yeola, J. B. Williams, M. Leclercq, C. Joly-Beauparlant, M. Alpaugh, A. C. Anderson, A. Droit, H. Lassmann, C. S. Moore, M. Rangachari

This study shows male sex chromosomal complement increases Th17 cell pathogenicity in CNS autoimmunity, independent of hormones, through downregulation of Jarid1c.

bioRxiv · Jun 13, 2019 · PreprintImmunology

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning

F. Roux-Dalvai, C. Gotti, M. Leclercq, M.-C. Helie, M. Boissinot, T. N. Arrey, C. Dauly, F. Fournier, I. Kelly, J. Marcoux, J. Bestman-Smith, M. G. Bergeron, A. Droit

This paper develops a method using LC-MS/MS and machine learning to rapidly identify bacterial species in clinical samples without culture, achieving 97% accuracy for UTI pathogens.

bioRxiv · May 10, 2019Microbiology