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Metabolite-based cell sorting workflow for identifying microbes producing carbonyls in tobacco leaves
Tianfei Zheng1,2,3, Qianying Zhang4, Zheng Peng1,2,3
1School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, Jiangsu, China.
Applied Microbiology and Biotechnology
|May 22, 2022
Summary
Researchers identified key microbes producing aldehyde and ketone flavor compounds in fermented cigar tobacco leaves using a novel metabolite labeling with fluorescence-activated cell sorting (ML-FACS) workflow. This method efficiently isolates functional microorganisms from complex communities.
Area of Science:
- Microbiology
- Biotechnology
- Food Science
Background:
- Carbonyl compounds like aldehydes and ketones significantly influence tobacco flavor.
- Microbial activity during tobacco fermentation is a primary source of these flavor compounds.
- Identifying specific microbial producers is crucial for enhancing tobacco quality.
Purpose of the Study:
- To develop and apply an efficient workflow for isolating and identifying microorganisms responsible for producing aldehydes and ketones in fermented cigar tobacco leaves (FCTL).
- To characterize the flavor-related carbonyl compounds produced by identified microbes.
- To establish a method for capturing functional microbes from complex environments in pure cultures.
Main Methods:
- Development of a workflow combining metabolite labeling with fluorescence-activated cell sorting (ML-FACS).
- Utilized 16S rRNA gene sequencing for microbial identification.
- Employed microbial culturing techniques to isolate and verify functional microbes.
- Separation of microbes using flow cytometry after labeling with cyanine5 hydrazide.
Main Results:
- Successfully identified four microbial genera (Acinetobacter, Sphingomonas, Solibacillus, and Lysinibacillus) as major producers of carbonyl compounds in FCTL.
- Confirmed the production of specific flavor-related aldehydes and ketones (e.g., benzaldehyde, phenylacetaldehyde) by these isolates in a synthetic medium.
- Demonstrated the efficacy of ML-FACS in isolating target microorganisms from complex microbial communities.
Conclusions:
- The developed ML-FACS workflow provides an efficient method for rapidly isolating and identifying aldehyde/ketone-producing microorganisms.
- This research successfully identified key microbial players contributing to the flavor profile of fermented cigar tobacco.
- The ML-FACS approach holds potential for identifying other compound-producing microorganisms in diverse environmental systems.
