Volatile metabolic diversity of Klebsiella pneumoniae in nutrient-replete conditions

Abstract

Introduction: Microorganisms catabolize carbon-containing compounds in their environment during growth, releasing a subset of metabolic byproducts as volatile compounds. However, the relationship between growth media and the production of volatile compounds has been largely unexplored to-date. Objectives: To assess the core and media-specific components of the Klebsiella pneumoniae volatile metabolome via growth in four in vitro culture media. Methods: Headspace volatiles produced by cultures of K. pneumoniae after growth to stationary phase in four rich media (brain heart infusion broth, lysogeny broth, Mueller-Hinton broth, and tryptic soy broth) were analyzed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). Differences in the composition of headspace volatiles as a function of growth media was assessed using hierarchical clustering analysis (HCA) and principal component analysis (PCA). Results: A total of 365 volatile compounds were associated with the growth of K. pneumoniae across all media, of which 36 (10 %) were common to all growth media, and 148 (41 %) were specific to a single medium. In addition, utilizing all K. pneumoniae-associated volatile compounds, strains clustered as a function of growth media, demonstrating the importance of media in determining the metabolic profile of this organism. Conclusion: K. pneumoniae produces a core suite of volatile compounds across all growth media studied, although the volatile metabolic signature of this organism is fundamentally media-dependent.

Comprehensive volatile metabolic fingerprinting of bacterial and fungal pathogen groups

“…The present study has considered the volatile metabolic profiles of 100 bacterial and fungal isolates belonging to ten distinct pathogen groups, and represents one of the most extensive analyses of the volatile metabolites produced by pathogens in vitro to-date. It additionally represents the single most extensive analyses of bacterial and fungal volatile metabolites performed using GC×GC-TOFMS, a powerful analytical technique well-suited for the characterization of complex mixtures including breath[31], as well as culture headspace metabolites[12–15, 23, 24, 32–34]. In total, we estimate that over 160 prior studies have described qualitative and/or quantitative differences in the composition of headspace volatile metabolites between different pathogen groups under either in vitro or ex vivo conditions.…”

“…However, while such an approach may optimize growth and the production of volatile metabolites for a given pathogen, one must be cautious when describing volatile metabolic differences between organisms grown on different media. Even with techniques to “subtract” the volatile molecular signature of the media itself, previous studies suggests that a microorganism’s volatile molecular signature is fundamentally media-dependent, and that alteration of growth media could result in a substantially different volatile molecular profile[23, 43, 59].…”

Dependence of the Staphylococcal Volatilome Composition on Microbial Nutrition

“…S. aureus produced the highest proportion of acids when grown in BHI, the medium with readily accessible sugars, and when grown in the no-sugar medium, NB, it produced the highest proportion of hydrocarbons. Rees and colleagues [ 36 ] characterized the volatilomes of Klebsiella pneumoniae clinical isolates cultivated in BHI, LB, MHB, and TSB. When comparing the four media by the numbers of compounds produced in each, similar chemical compositions of the K. pneumoniae volatilomes were observed.…”

SPME-GC×GC-TOF MS fingerprint of virally-infected cell culture: Sample preparation optimization and data processing evaluation

“…Some basic parameters [ i.e. agitation (250 rpm), sample volume-HS ratio (1:4), and desorption conditions (250 °C for 1 min)] were determined based on experience or previous evaluation with similar samples [34].…”

“…Differently, RF is a non-parametric approach that can deal with highly collinear data and it is resistant to different type of outliers. Some examples of the use of these normalization and data reduction techniques in the GC×GC literature are the following [9,34,41,42]).…”