VOC fingerprints: metabolomic signatures of biothreat agents with and without antibiotic resistance

Abstract

Category A and B biothreat agents are deemed to be of great concern by the US Centers for Disease Control and Prevention (CDC) and include the bacteria Francisella tularensis, Yersinia pestis, Burkholderia mallei, and Brucella species. Underscored by the impact of the 2020 SARS-CoV-2 outbreak, 2016 Zika pandemic, 2014 Ebola outbreak, 2001 anthrax letter attacks, and 1984 Rajneeshee Salmonella attacks, the threat of future epidemics/pandemics and/or terrorist/criminal use of pathogenic organisms warrants continued exploration and development of both classic and alternative methods of detecting biothreat agents. Volatile organic compounds (VOCs) comprise a large and highly diverse group of carbon-based molecules, generally related by their volatility at ambient temperature. Recently, the diagnostic potential of VOCs has been realized, as correlations between the microbial VOC metabolome and specific bacterial pathogens have been identified. Herein, we describe the use of microbial VOC profiles as fingerprints for the identification of biothreatrelevant microbes, and for differentiating between a kanamycin susceptible and resistant strain. Additionally, we demonstrate microbial VOC profiling using a rapid-throughput VOC metabolomics method we refer to as 'simultaneous multifiber headspace solid-phase microextraction' (simulti-hSPME). Finally, through VOC analysis, we illustrate a rapid non-invasive approach to the diagnosis of BALB/c mice infected with either F. tularensis SCHU S4 or Y. pestis CO92. Category A and B biothreat agents include several bacteria, viruses, and toxins deemed to be of great concern by the US Centers for Disease Control and Prevention (CDC), due to their ease of dissemination/transmission, morbidity/mortality rates, and ability to cause public panic and disruption. Underscored by the impact of the 2020 SARS-CoV-2 outbreak, the 2016 Zika pandemic, the 2014 Ebola outbreak, the 2001 anthrax letter attacks, and the 1984 Rajneeshee Salmonella attacks, the threat of future epidemics/pandemics and/or terrorist use of pathogenic organisms warrants continued exploration and development of both classic and alternative methods of rapidly detecting agents of threat. Although sensitive and selective detection techniques such as polymerase chain reaction (PCR), microbial dichotomous keying, and/or enzyme-linked immunosorbent assays (ELISA) are well established 1 , these techniques are typically time consuming, laborious, and costly. Conversely, bacterial metabolome fingerprints, such as those derived through the use of techniques like Matrix-Assisted Laser Desorption Ionization (MALDI) coupled with mass spectrometry (MS), are rapid, reproducible, and cost effective 2,3. Using gas chromatography coupled with mass spectrometry (GC-MS), we have previously demonstrated the diagnostic potential of volatile organic compounds (VOCs) emanating from biological samples 4-7. VOCs comprise a large and highly diverse group of carbon-based molecules, generally related by their volatility at ambient temperature. A VOC-b...

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

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Non-destructive mid-IR spectroscopy with quantum cascade laser can detect ethylene gas dynamics of apple cultivar ‘Fuji’ in real time

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