Gram-negative and -positive bacteria differentiation in blood culture samples by headspace volatile compound analysis. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Dolch, Michael; Janitza, Silke; Boulesteix, Anne-Laure; Graßmann-Lichtenauer, Carola; Praun, Siegfried; Denzer, Wolfgang; Schelling, Gustav; Schubert, Sören
• Is Part Of: Journal of biological research. Volume 23:Issue 1(2016)
• Page Start: 1
• Page End: 8
• Publication Date: 2016-12
• Subjects: Mass spectrometry -- Chemical ionization -- Volatile compound -- Blood culture -- Prediction rule -- Gram identification; Biology -- Periodicals; Biology, Experimental -- Periodicals; Biology -- Periodicals; Biological Sciences -- Periodicals; Biology; Biology, Experimental; Periodicals; 570.72
• DOI: 10.1186/s40709-016-0040-0
• Languages: English
• ISSNs: 2241-5793

Abstract

Abstract Background Identification of microorganisms in positive blood cultures still relies on standard techniques such as Gram staining followed by culturing with definite microorganism identification. Alternatively, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or the analysis of headspace volatile compound (VC) composition produced by cultures can help to differentiate between microorganisms under experimental conditions. This study assessed the efficacy of volatile compound based microorganism differentiation into Gram-negatives and -positives in unselected positive blood culture samples from patients. Methods Headspace gas samples of positive blood culture samples were transferred to sterilized, sealed, and evacuated 20 ml glass vials and stored at −30 °C until batch analysis. Headspace gas VC content analysis was carried out via an auto sampler connected to an ion–molecule reaction mass spectrometer (IMR-MS). Measurements covered a mass range from 16 to 135 u including CO2, H2, N2, and O2 . Prediction rules for microorganism identification based on VC composition were derived using a training data set and evaluated using a validation data set within a random split validation procedure. Results One-hundred-fifty-two aerobic samples growing 27 Gram-negatives, 106 Gram-positives, and 19 fungi and 130 anaerobic samples growing 37 Gram-negatives, 91 Gram-positives, and two fungi were analysed. In anaerobic samples, ten discriminators were identified by the random forest method allowing for bacteria differentiation into Gram-negative and -positive (error rate: 16.7 % in validation data set). For aerobic samples the error rate was not better than random. Conclusions In anaerobic blood culture samples of patients IMR-MS based headspace VC composition analysis facilitates bacteria differentiation into Gram-negative and -positive.