Author
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MAUER, DEVIN - Iowa State University |
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ELLIS, CHRISTINE - Animal And Plant Health Inspection Service (APHIS) |
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Thacker, Tyler |
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RICE, SOMCHAI - Iowa State University |
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KOZIEL, JACEK - Iowa State University |
Submitted to: Meeting Abstract
Publication Type: Abstract Only Publication Acceptance Date: 5/24/2016 Publication Date: 7/3/2016 Citation: Mauer, D., Ellis, C., Thacker, T.C., Rice, S., Koziel, J. 2016. Screening of microbial volatile organic compounds for detection of disease in cattle: development of lab-scale method. Meeting Abstract. page 74. Interpretive Summary: Technical Abstract: The quest to find unique marker volatile organic compounds (VOCs) associated with human, livestock and wildlife diseases (Ellis et al., 2014) requires development of diagnostic non-invasive point-of-care tools and field surveillance technologies and strategies. The objective of this research was to (1) design, build and test a lab-scale platform for screening of microbial VOCs emitted from controlled bacterial cultures; (2) apply this method to Mycobacterium avium subsp paratuberculosis, M. bovis BCG and M. kansasii cultures, and a culture media control to demonstrate the proof-of-concept. Solid phase microextraction (SPME) was used for collection of VOCs and GC-MS was used for analyses. The lab-scale testing platform was designed for n = 4 closed-loop, forced circulating airflow sweeping bacterial cultures in Corning culture bottles (225 mL, 60 mL max fill). A series of tests to minimize background and interfering VOCs was completed. Optimized platform cleaning conditions involved: 21 h bake-out at 50 °C of the complete platform followed by 18 h bake-out at 110 °C of the pumps’ Neoprene tubing. This process reduced ~75% of background VOCs. DVB/CAR/PDMS (50/30 um, 2 cm) SPME coatings were used and 1 h was selected for collection of VOCs. Testing of n = 1 replicated trials with the 3 microbes and 1 control was conducted for 3 weeks. Chromatograms were analyzed using the XCMS multi-group comparison feature to identify peak ion abundances that differed across the all of the sample groups. Principle component analysis performed on each week’s data resulted in clear separation between cultures. A 4 class linear discriminant analysis (LDA) model was developed using all treatments and weekly time-points. The LDA model produced 100% correct classification. Preliminary results demonstrate that the lab-scale platform can be optimized for low impurity background and that the platform generates consistently significant marker compounds for each tested culture. More replicated trials are planned with statistical modeling aimed at detecting unique VOC markers. |