Tuning interdomain conjugation toward in situ population modification in yeasts

The ability to modify and control natural and engineered microbiomes is essential for biotechnology and biomedicine. Fungi are critical members of most microbiomes, yet technology for modifying the fungal members of a microbiome has lagged far behind that for bacteria. Interdomain conjugation (IDC) is a promising approach, as DNA transfer from bacterial cells to yeast enables in situ modification. While such genetic transfers have been known to naturally occur in a wide range of eukaryotes, and are thought to contribute to their evolution, IDC has been understudied as a technique to control fungal or fungal-bacterial consortia. One major obstacle to widespread use of IDC is its limited efficiency. In this work, we utilize interactions between genetically tractable Escherichia coli and Saccharomyces cerevisiae to control the incidence of IDC. We test the landscape of population interactions between the bacterial donors and yeast recipients to find that bacterial commensalism leads to max..., Data was collected as described in the Materials and Methods section of Stindt, et al. , MATLAB, All data here to be used in conjunction with custom MATLAB scripts found at [https://github.com/mccleanlab/Stindt_2023](https://github.com/mccleanlab/Stindt_2023) ## The following three data files contain all analyzed data used for plotted figures in Stindt et. al. 2023: \__Consolidated_Data.mat includes source data for Figures 1-3, 5-7, SI Figures 2-7, 13-20. Compiled data is from analyzed flow cytometry, fluorimetry, and image analyses, with experiments organized by Plate numbers and experiment date. Use this data with All_plots script to generate most figures. \"All_plots\" script also specifies which experimental info (plate, date) corresponds to each figure. \__Conjugation_Clump_Data.mat includes image analysis and fluorimetry data used for model fitting in Figure 4, SI Figures 8-12. For use with modeling scripts in the GitHub repository for this paper. AllFits.mat includes all saved model parameters used in Figure 4, SI Figures 8-12. For use with modeling scripts in the GitHub r...对天然与工程化微生物组进行改造与调控的能力，是生物技术与生物医学领域的核心支撑需求。真菌是绝大多数微生物组的关键组成成员，但针对微生物组中真菌类群的改造技术，其发展水平远滞后于细菌类群。域间接合（Interdomain Conjugation, IDC）是一种极具潜力的技术手段：通过将细菌细胞中的DNA转移至酵母细胞，可实现微生物组的原位改造。尽管这类遗传转移现象已被证实广泛存在于各类真核生物中，并被认为推动了真核生物的演化，但作为调控真菌或真菌-细菌群落的技术手段，IDC的相关研究仍严重不足。限制IDC大规模应用的核心障碍之一，是其较低的转化效率。本研究利用遗传操作便捷的大肠杆菌（Escherichia coli）与酿酒酵母（Saccharomyces cerevisiae）之间的相互作用，实现对IDC发生概率的精准调控。我们系统探究了细菌供体与酵母受体间的种群互作图谱，发现细菌共生作用可使IDC效率达到最大值…… 本研究的数据采集方法参照Stindt等人发表论文的材料与方法章节。所有数据需配合自定义MATLAB脚本使用，相关自定义脚本可通过以下GitHub仓库获取：https://github.com/mccleanlab/Stindt_2023。 以下三份数据文件包含了Stindt等人2023年发表论文中所有用于绘制图表的分析数据： 1. __Consolidated_Data.mat：包含图1-3、5-7以及补充材料图2-7、13-20的原始源数据。该整合数据源自流式细胞术、荧光光度法与图像分析的实验结果，实验数据按培养板编号与实验日期进行组织。配合"All_plots"脚本可生成绝大多数图表，该脚本同时标注了每张图表对应的实验信息（培养板编号、实验日期）。 2. __Conjugation_Clump_Data.mat：包含用于图4及补充材料图8-12中模型拟合的图像分析与荧光光度数据，需配合本论文GitHub仓库中的建模脚本使用。 3. AllFits.mat：包含图4及补充材料图8-12中所有已保存的模型参数，需配合本论文GitHub仓库中的建模脚本使用。