Impact of <i>Lactobacillus</i>-originated metabolites on enterohemorrhagic <i>E. coli</i> in rumen fluid

Aditya, Arpita; Rahaman, Shaik O.; Biswas, Debabrata

doi:10.1093/femsec/fiac128

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…4). Although Lp producing acetic acid might inhibit Ec 29, 30, 31 , the media developed for Ec growth (M1-3_Ec) remained specificity in the presence of Lp . The results suggested that the ML-assisted medium optimization and specialization were practical, and the resultant media were robust regardless of mono- or co-culture.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

Active learning for medium optimization for selective bacterial culture

Zhang,

Aida,

Ying

2023

Preprint

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“…Nearly all of them exhibited significant specificity for the growth of the target strain, Lp or Ec, regardless of mono-or co-culture (Figure 4). Although Lp producing acetic acid might inhibit Ec [34][35][36], the media developed for Ec growth (M1-3_Ec) retained specificity in the presence of Lp. The results suggested that the ML-assisted medium optimization and specialization were practical, and the resultant media were robust regardless of mono-or co-culture.…”

Section: Active Learning Successfully Fine-tuned the Media For Select...mentioning

confidence: 99%

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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A Comprehensive Review of the Pathogenesis and Virulence Factors of E. coli

Kademane,

Dixit,

Vasundhara

2023

Salud, Ciencia y Tecnología

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Escherichia coli es un microbio muy adaptable que desempeña un papel importante en la microbiota intestinal típica de humanos y animales. Esta bacteria comensal no patógena tiene la capacidad de adquirir una variedad de componentes genéticos móviles que son completos e incluyen genes para factores de virulencia es un patógeno humano recientemente descubierto que puede causar una amplia gama de enfermedades extraintestinales y gastrointestinales. Se han caracterizado a fondo nueve patotipos distintos de E. coli entérica, que abarcan desde distintas enfermedades gastrointestinales hasta infecciones de las vías urinarias. Estos colaboran con diversos factores de virulencia y efectores que regulan su transmisión y patogenicidad alterando el funcionamiento de las células del huésped. En este artículo se ponen de relieve los recientes avances en nuestro conocimiento de los numerosos virus vinculados a los genes que emplean las distintas ecologías externas de E. coli patógenas gástricas para desencadenar problemas extraintestinales y digestivos en las personas.

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Impact of Lactobacillus-originated metabolites on enterohemorrhagic E. coli in rumen fluid

Cited by 3 publications

References 53 publications

Active learning for medium optimization for selective bacterial culture

Active learning for medium optimization for selective bacterial culture

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

A Comprehensive Review of the Pathogenesis and Virulence Factors of E. coli

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