Development of Surface Enhanced Raman Spectra coupled with Machine Learning Analysis for Differentiation of Closely Related Species withinEnterobacter cloacaeComplex
Abstract
Background
Enterobacter cloacaecomplex (ECC) is an important nosocomial pathogen and consists of multiple similar species. The taxonomy of ECC has been consecutively updated, adding to its identification difficulty.
Methods
A total of 92 ECC strains isolated from bloodstream infections during 2015-2020 were collected from a tertiary hospital in China. All the strains were identified by Vitek 2 Compact and Vitek MS and then subjected to whole genome sequencing (WGS) for average nucleotide identity (ANI) analysis. Surfaced-enhanced Raman spectroscopy (SERS) with a set of machine learning algorithms was applied in identifying species within ECC.
Results
Seven species were identified through ANI, including 28E. hormaecheisubsp.steigerwaltii, 17E. hormaecheisubsp.xiangfangensis, 12E. cloacae, 11 each ofE. hormaecheisubsp.hoffmanniiandE. bugandensis, sevenE. kobeiand sixE. roggenkampii. The Vitek 2 compact indistinguishably identified all the strains as ECC and Vitek MS correctly identified one strain ofE. kobeiwhile achieving ambiguous results for all the other isolates. SERS combined with XGBoost model achieved 97.75% accuracy with an area under the ROC curve value of 0.9982 in the identification of ECC.
Conclusion
SERS coupled with machine learning algorithms holds a promising potential to acquire early prediction of ECC, outperforming the capabilities of other methods.
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