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פותח על ידי קלירמאש פתרונות בע"מ -
The rise of the sourdough: Genome-scale metabolic modeling-based approach to design sourdough starter communities with tailored-made properties
Year:
2023
Authors :
פרייליך, שירי
;
.
Volume :
Co-Authors:

Omer Sabach
Keren Buhnik-Rosenblau
Inbar Kesten
Shay Freilich
Shiri Freilich
 Yechezkel Kashi .

Facilitators :
From page:
0
To page:
0
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Total pages:
1
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Abstract:

Sourdough starters harbor microbial consortia that benefit the final product's aroma and volume. The complex nature of these spontaneously developed communities raises challenges in predicting the fermentation phenotypes. Herein, we demonstrated for the first time in this field the potential of genome-scale metabolic modeling (GEMs) in the study of sourdough microbial communities. Broad in-silico modeling of microbial growth was applied on communities composed of yeast (Saccharomyces cerevisiae) and different Lactic Acid Bacteria (LAB) species, which mainly predominate in sourdough starters. Simulations of model-represented communities associated specific bacterial compositions with sourdough phenotypes. Based on ranking the phenotypic performances of different combinations, Pediococcus spp. - Lb. sakei group members were predicted to have an optimal effect considering the increase in S. cerevisiae growth abilities and overall CO2 secretion rates. Flux Balance Analysis (FBA) revealed mutual relationships between the Pediococcus spp. - Lb. sakei group members and S. cerevisiae through bidirectional nutrient dependencies, and further underlined that these bacteria compete with the yeast over nutrients to a lesser extent than the rest LAB species. Volatile compounds (VOCs) production was further modeled, identifying species-specific and community-related VOCs production profiles. The in-silico models' predictions were validated by experimentally building synthetic sourdough communities and assessing the fermentation phenotypes. The Pediococcus spp. - Lb. sakei group was indeed associated with increased yeast cell counts and fermentation rates, demonstrating a 25 % increase in the average leavening rates during the first 10 fermentation hours compared to communities with a lower representation of these group members. Overall, these results provide a possible novel strategy towards the de-novo design of sourdough starter communities with tailored-made characterizations, including a shortened leavening period.

Note:
Related Files :
Flux balance analysis
In-silico modeling
lactic acid bacteria
Leavening
microbial ecology
Saccharomyces cerevisiae
עוד תגיות
תוכן קשור
More details
DOI :
10.1016/j.ijfoodmicro.2023.110402
Article number:
0
Affiliations:
Database:
PubMed
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
65701
Last updated date:
08/10/2023 21:17
Creation date:
08/10/2023 21:12
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Scientific Publication
The rise of the sourdough: Genome-scale metabolic modeling-based approach to design sourdough starter communities with tailored-made properties

Omer Sabach
Keren Buhnik-Rosenblau
Inbar Kesten
Shay Freilich
Shiri Freilich
 Yechezkel Kashi .

The rise of the sourdough: Genome-scale metabolic modeling-based approach to design sourdough starter communities with tailored-made properties

Sourdough starters harbor microbial consortia that benefit the final product's aroma and volume. The complex nature of these spontaneously developed communities raises challenges in predicting the fermentation phenotypes. Herein, we demonstrated for the first time in this field the potential of genome-scale metabolic modeling (GEMs) in the study of sourdough microbial communities. Broad in-silico modeling of microbial growth was applied on communities composed of yeast (Saccharomyces cerevisiae) and different Lactic Acid Bacteria (LAB) species, which mainly predominate in sourdough starters. Simulations of model-represented communities associated specific bacterial compositions with sourdough phenotypes. Based on ranking the phenotypic performances of different combinations, Pediococcus spp. - Lb. sakei group members were predicted to have an optimal effect considering the increase in S. cerevisiae growth abilities and overall CO2 secretion rates. Flux Balance Analysis (FBA) revealed mutual relationships between the Pediococcus spp. - Lb. sakei group members and S. cerevisiae through bidirectional nutrient dependencies, and further underlined that these bacteria compete with the yeast over nutrients to a lesser extent than the rest LAB species. Volatile compounds (VOCs) production was further modeled, identifying species-specific and community-related VOCs production profiles. The in-silico models' predictions were validated by experimentally building synthetic sourdough communities and assessing the fermentation phenotypes. The Pediococcus spp. - Lb. sakei group was indeed associated with increased yeast cell counts and fermentation rates, demonstrating a 25 % increase in the average leavening rates during the first 10 fermentation hours compared to communities with a lower representation of these group members. Overall, these results provide a possible novel strategy towards the de-novo design of sourdough starter communities with tailored-made characterizations, including a shortened leavening period.

Scientific Publication
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