Table_2_Automating methods for estimating metabolite volatility.XLSX

The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc, we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.

代谢物的挥发性可影响其生物学功能，并为其检测的最优方法提供参考依据。然而，目前已报道的大量代谢物的挥发性信息仍难以获取，这限制了将挥发性作为代谢物核心特征的相关探索。本研究基于单分子官能团组成改进了蒸气压估算方法（SIMPOL.1），用于预测不同环境中化合物的气相分配行为。随后将上述方法整合至一款名为volcalc的开源分析流程中，该流程依托化学信息学工具，可对京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）这一关联代谢物、生物体与生化反应的大规模持续更新通路数据库内的全部代谢物自动完成挥发性估算。本研究首先以人工整理的数据集对该自动化分析流程进行基准测试，结果显示，93%的化合物可被预测为与真实类别一致的挥发性等级（如非挥发性、低挥发性、中挥发性、高挥发性）。随后，本研究展示了volcalc如何用于生成并检验关于挥发性在生物系统与生物体中作用的相关假说。具体而言，本研究估算得出：根据环境不同（分别为土壤与洁净大气），KEGG数据库中分别有3.4%与26.6%的化合物属于高挥发性类别；同时，所有生命域共享30%的核心挥发性代谢物，其中细菌界所特有的挥发性代谢物占比最高。借助volcalc，本研究为揭示挥发组（volatilome）的功能奠定了基础：该方法可便捷地与其他生物信息学流程整合，且可通过不断优化纳入更多挥发性相关维度。volcalc工具包是一款易用的工具，可助力研究者设计并检验关于挥发性代谢物及其在生物系统中独特功能的相关假说。