Faculty of Biotechnology and Food Engineering, Technion, Haifa, Israel.
Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel.
Int J Food Microbiol. 2023 Dec 16;407:110402. doi: 10.1016/j.ijfoodmicro.2023.110402. Epub 2023 Sep 21.
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 CO 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.
酸面团发酵剂中含有微生物群落,这些微生物群落有益于最终产品的香气和体积。这些自发形成的群落的复杂性质给预测发酵表型带来了挑战。在此,我们首次在该领域展示了基因组规模代谢建模 (GEMs) 在酸面团微生物群落研究中的潜力。广泛的微生物生长计算机模拟应用于由酵母 (Saccharomyces cerevisiae) 和不同的乳酸菌 (LAB) 组成的群落,这些群落主要存在于酸面团发酵剂中。对模型代表的群落进行模拟,将特定的细菌组成与酸面团表型联系起来。基于对不同组合表型性能的排序,预测在考虑到提高 S. cerevisiae 生长能力和整体 CO 分泌率方面,肠球菌属- Lb. sakei 组的成员具有最佳效果。通量平衡分析 (FBA) 通过双向营养依赖性揭示了肠球菌属- Lb. sakei 组的成员与 S. cerevisiae 之间的相互关系,并进一步强调,与其余的 LAB 物种相比,这些细菌对营养物质的竞争程度小于酵母。进一步对挥发性化合物 (VOCs) 的产生进行建模,确定了特定物种和群落相关的 VOCs 产生特征。通过实验构建人工酸面团群落并评估发酵表型,对计算机模型的预测进行了验证。肠球菌属- Lb. sakei 组确实与酵母细胞计数和发酵率的增加有关,与群落中这些成员的代表性较低的群落相比,在最初 10 小时的发酵过程中,平均发酵率提高了 25%。总的来说,这些结果为通过定制化的特征设计新的酸面团发酵剂群落提供了一种可能的策略,包括缩短发酵时间。