Literature DB >> 28947683

Headspace volatile organic compounds from bacteria implicated in ventilator-associated pneumonia analysed by TD-GC/MS.

Oluwasola Lawal1, Howbeer Muhamadali, Waqar M Ahmed, Iain R White, Tamara M E Nijsen, Royston Goodacre, Stephen J Fowler.   

Abstract

Ventilator-associated pneumonia (VAP) is a healthcare-acquired infection arising from the invasion of the lower respiratory tract by opportunistic pathogens in ventilated patients. The current method of diagnosis requires the culture of an airway sample such as bronchoalveolar lavage, which is invasive to obtain and may take up to seven days to identify a causal pathogen, or indeed rule out infection. While awaiting results, patients are administered empirical antibiotics; risks of this approach include lack of effect on the causal pathogen, contribution to the development of antibiotic resistance and downstream effects such as increased length of intensive care stay, cost, morbidity and mortality. Specific biomarkers which could identify causal pathogens in a timely manner are needed as they would allow judicious use of the most appropriate antimicrobial therapy. Volatile organic compound (VOC) analysis in exhaled breath is proposed as an alternative due to its non-invasive nature and its potential to provide rapid diagnosis at the patient's bedside. VOCs in exhaled breath originate from exogenous, endogenous, as well as microbial sources. To identify potential markers, VAP-associated pathogens Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus were cultured in both artificial sputum medium and nutrient broth, and their headspaces were sampled and analysed for VOCs. Previously reported volatile markers were identified in this study, including indole and 1-undecene, alongside compounds that are novel to this investigation, cyclopentanone and 1-hexanol. We further investigated media components (substrates) to identify those that are essential for indole and cyclopentanone production, with potential implications for understanding microbial metabolism in the lung.

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Year:  2018        PMID: 28947683     DOI: 10.1088/1752-7163/aa8efc

Source DB:  PubMed          Journal:  J Breath Res        ISSN: 1752-7155            Impact factor:   3.262


  7 in total

Review 1.  Breathomics for the clinician: the use of volatile organic compounds in respiratory diseases.

Authors:  Wadah Ibrahim; Liesl Carr; Rebecca Cordell; Michael J Wilde; Dahlia Salman; Paul S Monks; Paul Thomas; Chris E Brightling; Salman Siddiqui; Neil J Greening
Journal:  Thorax       Date:  2021-01-07       Impact factor: 9.139

2.  Discovery of Volatile Biomarkers of Parkinson's Disease from Sebum.

Authors:  Drupad K Trivedi; Eleanor Sinclair; Yun Xu; Depanjan Sarkar; Caitlin Walton-Doyle; Camilla Liscio; Phine Banks; Joy Milne; Monty Silverdale; Tilo Kunath; Royston Goodacre; Perdita Barran
Journal:  ACS Cent Sci       Date:  2019-03-20       Impact factor: 14.553

Review 3.  Metabolomic studies of Pseudomonas aeruginosa.

Authors:  Karolina Anna Mielko; Sławomir Jan Jabłoński; Justyna Milczewska; Dorota Sands; Marcin Łukaszewicz; Piotr Młynarz
Journal:  World J Microbiol Biotechnol       Date:  2019-11-07       Impact factor: 3.312

Review 4.  Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile.

Authors:  Valentin-Mihai Dospinescu; Akira Tiele; James A Covington
Journal:  Biosensors (Basel)       Date:  2020-07-23

5.  Use of GC-IMS for detection of volatile organic compounds to identify mixed bacterial culture medium.

Authors:  Yanyi Lu; Weiping Lu; Lin Zeng; Min Li; Bowen Yan; Dandan Gao; Bangfu Zhou; Qinghua He
Journal:  AMB Express       Date:  2022-03-04       Impact factor: 3.298

6.  TD/GC-MS analysis of volatile markers emitted from mono- and co-cultures of Enterobacter cloacae and Pseudomonas aeruginosa in artificial sputum.

Authors:  Oluwasola Lawal; Hugo Knobel; Hans Weda; Tamara M E Nijsen; Royston Goodacre; Stephen J Fowler
Journal:  Metabolomics       Date:  2018-04-26       Impact factor: 4.290

7.  On-Line Mixture Quantification to Track Temporal Change of Composition Using FAIMS.

Authors:  Yasufumi Yokoshiki; Takamichi Nakamoto
Journal:  Sensors (Basel)       Date:  2019-12-10       Impact factor: 3.576

  7 in total

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