BibTex format
@article{Pizzato:2022:10.1002/mbo3.1313,
author = {Pizzato, J and Tang, W and Bernabeu, S and Bonnin, RA and Bille, E and Farfour, E and Guillard, T and Barraud, O and Cattoir, V and Plouzeau, C and Corvec, S and Shahrezaei, V and Dortet, L and Larrouy-Maumus, G},
doi = {10.1002/mbo3.1313},
journal = {MicrobiologyOpen},
pages = {1--14},
title = {Discrimination of Escherichia coli, Shigella flexneri, and Shigella sonnei using lipid profiling by MALDI-TOF mass spectrometry paired with machine learning},
url = {http://dx.doi.org/10.1002/mbo3.1313},
volume = {11},
year = {2022}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has become a staple in clinical microbiology laboratories. Protein-profiling of bacteria using this technique has accelerated the identification of pathogens in diagnostic workflows. Recently, lipid profiling has emerged as a way to complement bacterial identification where protein-based methods fail to provide accurate results. This study aimed to address the challenge of rapid discrimination between Escherichia coli and Shigella spp. using MALDI-TOF MS in the negative ion mode for lipid profiling coupled with machine learning. Both E. coli and Shigella species are closely related; they share high sequence homology, reported for 16S rRNA gene sequence similarities between E. coli and Shigella spp. exceeding 99%, and a similar protein expression pattern but are epidemiologically distinct. A bacterial collection of 45 E. coli, 48 Shigella flexneri, and 62 Shigella sonnei clinical isolates were submitted to lipid profiling in negative ion mode using the MALDI Biotyper Sirius® system after treatment with mild-acid hydrolysis (acetic acid 1% v/v for 15 min at 98°C). Spectra were then analyzed using our in-house machine learning algorithm and top-ranked features used for the discrimination of the bacterial species. Here, as a proof-of-concept, we showed that lipid profiling might have the potential to differentiate E. coli from Shigella species using the analysis of the top five ranked features obtained by MALDI-TOF MS in the negative ion mode of the MALDI Biotyper Sirius® system. Based on this new approach, MALDI-TOF MS analysis of lipids might help pave the way toward these goals.
AU - Pizzato,J
AU - Tang,W
AU - Bernabeu,S
AU - Bonnin,RA
AU - Bille,E
AU - Farfour,E
AU - Guillard,T
AU - Barraud,O
AU - Cattoir,V
AU - Plouzeau,C
AU - Corvec,S
AU - Shahrezaei,V
AU - Dortet,L
AU - Larrouy-Maumus,G
DO - 10.1002/mbo3.1313
EP - 14
PY - 2022///
SN - 2045-8827
SP - 1
TI - Discrimination of Escherichia coli, Shigella flexneri, and Shigella sonnei using lipid profiling by MALDI-TOF mass spectrometry paired with machine learning
T2 - MicrobiologyOpen
UR - http://dx.doi.org/10.1002/mbo3.1313
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000844013100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://onlinelibrary.wiley.com/doi/10.1002/mbo3.1313
UR - http://hdl.handle.net/10044/1/100902
VL - 11
ER -
