Scientific Reports | 2021

Comparison of bacteria disintegration methods and their influence on data analysis in metabolomics

 
 
 
 

Abstract


Metabolomic experiments usually contain many different steps, each of which can strongly influence the obtained results. In this work, metabolic analyses of six bacterial strains were performed in light of three different bacterial cell disintegration methods. Three strains were gram-negative (Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae), and three were gram-positive (Corynebacterium glutamicum, Bacillus cereus, and Enterococcus faecalis). For extraction, the methanol–water extraction method (1:1) was chosen. To compare the efficiency of different cell disintegration methods, sonication, sand mill, and tissue lyser were used. For bacterial extract metabolite analysis, 1H NMR together with univariate and multivariate analyses were applied. The obtained results showed that metabolite concentrations are strongly dependent on the cell lysing methodology used and are different for various bacterial strains. The results clearly show that one of the disruption methods gives the highest concentration for most identified compounds (e. g. sand mill for E. faecalis and tissue lyser for B. cereus). This study indicated that the comparison of samples prepared by different procedures can lead to false or imprecise results, leaving an imprint of the disintegration method. Furthermore, the presented results showed that NMR might be a useful bacterial strain identification and differentiation method. In addition to disintegration method comparison, the metabolic profiles of each elaborated strain were analyzed, and each exhibited its metabolic profile. Some metabolites were identified by the 1H NMR method in only one strain. The results of multivariate data analyses (PCA) show that regardless of the disintegration method used, the strain group can be identified. Presented results can be significant for all types of microbial studies containing the metabolomic targeted and non-targeted analysis.

Volume 11
Pages None
DOI 10.1038/s41598-021-99873-x
Language English
Journal Scientific Reports

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