Inheritance of extraordinary metabolic activity from parental bacteria individuals

Many phenotypic traits, such as fermentation activity, have been shown to be instable due to stochastic gene expression and environmental influence. While previous studies only have obtained understanding at the level of the microbial community, the fate of extraordinary traits of an individual through generations of reproduction has yet to be adequately investigated. This work uses the lactic acid bacteri <i>Lactiplantibacillus plantarum</i> as a research model to study the activity inheritance between parental generations and filial generations. An integrated single-cell manipulation strategy is established, including fluorescent screening using an extracellular pH probe and a microwell array, micropicking using a micropipette, and amplifying an individual bacterium via single-cell culture. Consequently, it is found that daughter bacteria can well inherit the strong acid-producing activity from their parental bacterial individuals, although as the reproduction proceeds over 30 generations, the offspring gradually regresses to the mediocre, thus setting a caveat for the limiting generations for desired inheritance. This is likely due to the deterioration of the cell living environment. This work illustrates the inheritable features of bacterial metabolic traits at the level of individual bacteria and is therefore fundamentally insightful for biotechnological applications like bioenergy production that require consistent or at least predictable metabolic performance.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-05-09
DOI
https://doi.org/10.1073/pnas.2502818122
Citations
4
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
3.22

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article

Inheritance of extraordinary metabolic activity from parental bacteria individuals

Yuyang Lu, Yaoyao Zhang, Ruo‐Chen Xie, Jia Gao et al.
4 citations
Proceedings of the National Academy of Sciences
Gene Regulatory Network Analysis
3.22
article

Inheritance of extraordinary metabolic activity from parental bacteria individuals

Yuyang Lu, Yaoyao Zhang, Ruo‐Chen Xie, Jia Gao, Wei Wang, Hua Su
article en
4 citations

Abstract

Many phenotypic traits, such as fermentation activity, have been shown to be instable due to stochastic gene expression and environmental influence. While previous studies only have obtained understanding at the level of the microbial community, the fate of extraordinary traits of an individual through generations of reproduction has yet to be adequately investigated. This work uses the lactic acid bacteri <i>Lactiplantibacillus plantarum</i> as a research model to study the activity inheritance between parental generations and filial generations. An integrated single-cell manipulation strategy is established, including fluorescent screening using an extracellular pH probe and a microwell array, micropicking using a micropipette, and amplifying an individual bacterium via single-cell culture. Consequently, it is found that daughter bacteria can well inherit the strong acid-producing activity from their parental bacterial individuals, although as the reproduction proceeds over 30 generations, the offspring gradually regresses to the mediocre, thus setting a caveat for the limiting generations for desired inheritance. This is likely due to the deterioration of the cell living environment. This work illustrates the inheritable features of bacterial metabolic traits at the level of individual bacteria and is therefore fundamentally insightful for biotechnological applications like bioenergy production that require consistent or at least predictable metabolic performance.

Proceedings of the National Academy of SciencesVol. 122(19)
Nanjing University of Chinese Medicine (CN), State Key Laboratory of Analytical Chemistry for Life Science (CN), Nanjing University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province
Responsible consumption and production
Openalex Percentile: Top 8%
Gene Regulatory Network Analysis
3.22
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