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Mol Cell Proteomics. 2019 Oct 4. pii: mcp.TIR119.001559. doi: 10.1074/mcp.TIR119.001559. [Epub ahead of print]

Fast and accurate bacterial species identification in urine specimens using LC-MS/MS mass spectrometry and machine learning.

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Proteomics platform, CHU de Quebec, Universite Laval Research Center, Quebec City, Quebec, Canada.
Computational Biology Laboratory, CHU de Quebec, Universite Laval Research Center, Quebec City, QC.
Infectiology Research Center, CHU de Quebec, Universite Laval Research Center, Quebec City, Quebec,.
Infectiology Research Center, CHU de Quebec, Universite Laval Research Center, Quebec City, Quebec.
ThermoFisher Scientific, Bremen, Germany.
Thermo Fisher Scientific.
Laboratoire de microbiologie infectiologie, CHU de Quebec,Universite Laval, Quebec City, QC, Canada.
CHU de Quebec - Universite Laval, Canada


Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture prior to analysis (24h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures. In the first training step, libraries of peptides are obtained on pure bacterial colonies in DDA mode, their detection in urine is then verified in DIA mode, followed by the use of machine learning classifiers (NaiveBayes, BayesNet and Hoeffding tree) to define a peptidic signature to distinguish each bacterial species from the others. Then, in the second step, this signature is monitored in unknown urine samples using targeted proteomics. This method, allowing bacterial identification in less than 4h, has been applied to fifteen species representing 84% of all Urinary Tract Infections. More than 31000 peptides in 190 samples were quantified by DIA and classified by machine learning to determine an 82 peptides signature and build a prediction model. This signature was validated for its use in routine using Parallel Reaction Monitoring on two different instruments. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the standard threshold. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.


Bacteria; LC-MS/MS; Machine Learning; Microbiology; SWATH-MS; Tandem Mass Spectrometry; Targeted mass spectrometry; Urine analysis

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