Tn-Core: A Toolbox for Integrating Tn-seq Gene Essentiality Data and Constraint-Based Metabolic Modeling

The
design of synthetic cells requires a detailed understanding
of the relevance of genes and gene networks underlying complex cellular
phenotypes. Transposon-sequencing (Tn-seq) and constraint-based metabolic
modeling can be used to probe the core genetic and metabolic networks
underlying a biological process. Integrating these highly complementary
experimental and in silico approaches has the potential
to yield a highly comprehensive understanding of the core networks
of a cell. Specifically, it can facilitate the interpretation of Tn-seq
data sets and identify gaps in the data that could hinder the engineering
of the cellular system, while also providing refined models for the
accurate predictions of cellular metabolism. Here, we present Tn-Core,
the first easy-to-use computational pipeline specifically designed
for integrating Tn-seq data with metabolic modeling, prepared for
use by both experimental and computational biologists. Tn-Core is
a MATLAB toolbox that contains several custom functions, and it is
built upon existing functions within the COBRA Toolbox and the TIGER
Toolbox. Tn-Core takes as input a genome-scale metabolic model, Tn-seq
data, and optionally RNA-seq data, and returns: (i) a context-specific
core metabolic model; (ii) an evaluation of redundancies within core
metabolic pathways, and optionally (iii) a refined genome-scale metabolic
model. A simple, user-friendly workflow, requiring limited knowledge
of metabolic modeling, is provided that allows users to run the analyses
and export the data as easy-to-explore files of value to both experimental
and computational biologists. We demonstrate the utility of Tn-Core
using Sinorhizobium meliloti, Pseudomonas
aeruginosa, and Rhodobacter sphaeroides genome-scale
metabolic reconstructions as case studies.

合成细胞（synthetic cells）的设计需要深入理解支撑复杂细胞表型的基因与基因调控网络的相关功能。转座子测序（Transposon-sequencing, Tn-seq）与基于约束的代谢建模可用于探究生物学过程背后的核心遗传与代谢网络。将这两种高度互补的实验方法与计算机模拟（in silico）方法相结合，有望实现对细胞核心网络的全面解析。具体而言，该整合策略可助力转座子测序数据集的解读，识别可能阻碍细胞系统工程化的数据缺陷，同时还能为细胞代谢的精准预测提供优化后的模型。
在此，我们推出Tn-Core——首款专为转座子测序数据与代谢建模整合而设计的易用计算流程，可供实验生物学家与计算生物学家直接使用。Tn-Core是一款MATLAB工具箱，集成了多项自定义函数，其构建基于COBRA工具箱（COBRA Toolbox）与TIGER工具箱（TIGER Toolbox）的现有功能。Tn-Core的输入包括基因组规模代谢模型、转座子测序数据，以及可选的RNA测序（RNA-seq）数据，输出结果为：(i) 特定情境下的核心代谢模型；(ii) 核心代谢通路内的冗余性评估，以及可选的(iii) 优化后的基因组规模代谢模型。该流程操作简便、界面友好，仅需少量代谢建模相关知识即可使用，用户可完成分析并将结果导出为易于探索的文件，供实验与计算生物学家参考。
我们以苜蓿根瘤菌（Sinorhizobium meliloti）、铜绿假单胞菌（Pseudomonas aeruginosa）与球形红细菌（Rhodobacter sphaeroides）的基因组规模代谢重构模型作为案例研究，验证了Tn-Core的实用性。