Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

Aida, Honoka; Ying, Bei-Wen

doi:10.3390/biology12091170

Cited by 7 publications

(4 citation statements)

References 107 publications

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“…The present study first demonstrated ML-assisted medium specialization for differentiated bacterial growth. ML was remarkably significant in medium optimization for microbial and mammalian cells [26,31], which could be widely applied to synthetic construction and production [48,49]. The present study provides alternative applications in medium development for selective cultures.…”

Section: Discussionmentioning

confidence: 75%

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

Zhang,

Aida,

Ying

2023

Applied Microbiology

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Medium optimization and development for selective bacterial cultures are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to fine-tune the medium components for the selective culture of two divergent bacteria, i.e., Lactobacillus plantarum and Escherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the fine-tuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. Both r and K showed maximized differentiation between the two strains. A further analysis of all the data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated, determinative manner of growth decisions of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective cultures and offered novel insights into the contribution of the chemical components to specific bacterial growth.

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Section: Discussionmentioning

confidence: 75%

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

Zhang,

Aida,

Ying

2023

Applied Microbiology

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“…The present study first demonstrated ML-assisted medium specialization for differentiated bacterial growth. ML was remarkably significant in medium optimization for microbial and mammalian cells 21, 26 , which could be widely applied to synthetic construction and production 43, 44 . The present study provided alternative applications in medium development for selective culture.…”

Section: Discussionmentioning

confidence: 99%

Active learning for medium optimization for selective bacterial culture

Zhang,

Aida,

Ying

2023

Preprint

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Medium optimization and development for selective bacterial culture are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to finetune the medium components for the selective culture of two divergent bacteria, i.e.,Lactobacillus plantarumandEscherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the finetuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. BothrandKshowed maximized differentiation between the two strains. Further analysis of all data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated determinative manner of growth decision of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective culture and offered novel insights into the contribution of the chemical components to specific bacterial growth.

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“…To determine the essential genomic sequence of modern cells, removing redundant DNA sequences from the wild-type genome in bacteria, so-called genome reduction, has been challenged to a large extent ( Kotaka et al, 2023 ; Kato and Hashimoto, 2007 ; Pósfai et al, 2006 ; Hashimoto et al, 2005 ). These efforts have been made to discover the minimal genetic requirement for a free-living organism growing under the defined conditions ( Aida and Ying, 2023 ; Breuer et al, 2019 ; Hutchison et al, 2016 ). It resulted in the finding of the coordination of genome with cell growth, that is, genome reduction significantly decreased the growth rate of Escherichia coli cells independent of culture media or growth forms ( Hitomi et al, 2022 ; Xue et al, 2021 ; Kurokawa et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Experimental evolution for the recovery of growth loss due to genome reduction

Hitomi,

Ishii,

Ying

2024

eLife

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As the genome encodes the information crucial for cell growth, a sizeable genomic deficiency often causes a significant decrease in growth fitness. Whether and how the decreased growth fitness caused by genome reduction could be compensated by evolution was investigated here. Experimental evolution with an Escherichia coli strain carrying a reduced genome was conducted in multiple lineages for approximately 1,000 generations. The growth rate, which was largely declined due to genome reduction, was considerably recovered, associated with the improved carrying capacity. Genome mutations accumulated during evolution were significantly varied across the evolutionary lineages and were randomly localized on the reduced genome. Transcriptome reorganization showed a common evolutionary direction and conserved the chromosomal periodicity, regardless of highly diversified gene categories, regulons, and pathways enriched in the differentially expressed genes. Genome mutations and transcriptome reorganization caused by evolution, which were found to be dissimilar to those caused by genome reduction, must have followed divergent mechanisms in individual evolutionary lineages. Gene network reconstruction successfully identified three gene modules functionally differentiated, which were responsible for the evolutionary changes of the reduced genome in growth fitness, genome mutation, and gene expression, respectively. The diversity in evolutionary approaches improved the growth fitness associated with the homeostatic transcriptome architecture as if the evolutionary compensation for genome reduction was like all roads leading to Rome.

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Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

Cited by 7 publications

References 107 publications

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

Active learning for medium optimization for selective bacterial culture

Experimental evolution for the recovery of growth loss due to genome reduction

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