Evidence map›Paper›PMID 38483624›Full record

ArticleArchives of microbiology2024

Metabolomics-based study of chemical compositions in cellulase additives derived from a tobacco-origin Bacillus subtilis and their impact on tobacco sensory attributes.

Xiaofeng Chen, Teng Long, Shixin Huang, Yiqiang Chen, Hongliang Lu, Zhenkun Jiang, Cheng Cheng, Jingjing Li, Shanyi Chen, Wei He and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Archives of microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Journal of microbiology and biotechnology · 2026
    Article
  2. Article
  3. Isolation of indigenousFrontiers in microbiology · 2025
    Article
  4. Article
  5. Influence of specific tobacco endophyticFrontiers in microbiology · 2024
    Article
  6. Analysis of the effects ofFrontiers in bioengineering and biotechnology · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Xiaofeng Chen *Technical Innovation Center for Utilization of Marine Biological Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, China.
Teng Long *Technology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China.
Shixin HuangTechnical Innovation Center for Utilization of Marine Biological Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, China.
Yiqiang ChenTechnology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China.
Hongliang LuTechnology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China.
Zhenkun JiangTechnology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China.
Cheng ChengTechnology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China.
Jingjing LiTechnology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China.
Shanyi ChenTechnology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China.
Wei HeTechnology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China.
Xu TangTechnical Innovation Center for Utilization of Marine Biological Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, China. tangxu@tio.org.cn.
Jianqiang FanTechnology Center, China Tobacco Fujian Industrial Co., Ltd., Xiamen, China. fjq10393@fjtic.cn.

Funding

National Natural Science Foundation of China 42188102the Opening Foundation of Fujian Key Laboratory of Marine Carbon Sequestration Research Fund FKLMCS2023005the Opening Foundation of Fujian Key Laboratory of Marine Carbon Sequestration Research Fund FKLMCS2023006the Science and Technology Project of China National Tobacco Corporation 110202202022
6 · The paper itself

Abstract

To enhance the quality of tobacco leaves and optimize the smoking experience, diverse strains of functional bacteria and their associated metabolites have been used in tobacco aging. Exogenous cellulase additives are frequently employed to facilitate the degradation of cellulose and other macromolecular matrices and enhance the quality of the tobacco product. However, little is known about how microbial metabolites present in exogenous enzyme additives affect tobacco quality. In this study, crude cellulase solutions, produced by a tobacco-originating bacterium Bacillus subtilis FX-1 were employed on flue-cured tobacco. The incorporation of cellulase solutions resulted in the reduction of cellulose crystallinity in tobacco and the enhancement of the overall sensory quality of tobacco. Notably, tobacco treated with cellulase obtained from laboratory flask fermentation demonstrated superior scent and flavor attributes in comparison to tobacco treated with enzymes derived from industrial bioreactor fermentation. The targeted and untargeted metabolomic analysis revealed the presence of diverse flavor-related precursors and components in the cellulase additives, encompassing sugars, alcohols, amino acids, organic acids, and others. The majority of these metabolites exhibited significantly higher levels in the flask group compared to the bioreactor group, probably contributing to a pronounced enhancement in the sensory quality of tobacco. Our findings suggest that the utilization of metabolic products derived from B. subtilis FX-1 as additives in flue-cured tobacco holds promise as a viable approach for enhancing sensory attributes, establishing a solid theoretical foundation for the potential development of innovative tobacco aging additives.

Indexed as

Bacillus subtilisCellulaseCelluloseCellulaseCelluloseBacillus subtilisCellulase additivesSensory evaluationTargeted and untargeted metabolomicsTobacco

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.