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Microbial metabolic functions are increasingly conceptualized as fitness-regulating traits. However, volatile compounds (the volatilome), despite their key roles in metabolism and ecology, are often overlooked in trait-based frameworks. We propose that volatile traits not only reflect ecological strategies but also shape them by mediating responses to selection pressures. Their volatility affects diffusion, substrate access, and interactions across space, conferring selective advantages as resources or waste products. We outline approaches to incorporate volatile traits into predictive models to improve understanding of microbial selection and community dynamics. This integration enables a more holistic view of microbial life by accounting for the ecological and evolutionary consequences of volatile-mediated processes. 

Abstract Biogenic volatile organic compounds (VOCs) constitute a significant portion of gas-phase metabolites in modern ecosystems and have unique roles in moderating atmospheric oxidative capacity, solar radiation balance, and aerosol formation. It has been theorized that VOCs may account for observed geological and evolutionary phenomena during the Archaean, but the direct contribution of biology to early non-methane VOC cycling remains unexplored. Here, we provide an assessment of all potential VOCs metabolized by the last universal common ancestor (LUCA). We identify enzyme functions linked to LUCA orthologous protein groups across eight literature sources and estimate the volatility of all associated substrates to identify ancient volatile metabolites. We hone in on volatile metabolites with confirmed modern emissions that exist in conserved metabolic pathways and produce a curated list of the most likely LUCA VOCs. We introduce volatile organic metabolites associated with early life and discuss their potential influence on early carbon cycling and atmospheric chemistry. 

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 calledvolcalcthat 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 howvolcalcmight 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. Withvolcalc, 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. Thevolcalcpackage is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.