Annotation-free prediction of microbial dioxygen utilization

Abstract

Aerobes require dioxygen (O 2 ) to grow; anaerobes do not. However, nearly all microbes—aerobes, anaerobes, and facultative organisms alike—express enzymes whose substrates include O 2 , if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O 2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes encoding distinctive O 2 -utilizing enzymes, for example. These effects permit high-quality prediction of O 2 utilization from annotated genome sequences, with several models displaying ≈80% accuracy on a ternary classification task for which blind guessing is only 33% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genomic sequence content—e.g., triplets of amino acids—perform as well as intensive annotation-based classifiers, enabling rapid processing of genomes. We further show that amino acid trimers are useful because they encode information about protein composition and phylogeny. To showcase the utility of rapid prediction, we estimated the prevalence of aerobes and anaerobes in diverse natural environments cataloged in the Earth Microbiome Project. Focusing on a well-studied O 2 gradient in the Black Sea, we found quantitative correspondence between local chemistry (O 2 :sulfide concentration ratio) and the composition of microbial communities. We, therefore, suggest that statistical methods like ours might be used to estimate, or “sense,” pivotal features of the chemical environment using DNA sequencing data. IMPORTANCE We now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology—an organism’s capacity to engage metabolically with its environment—may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without annotating genes, e.g., by considering only the amino acids in protein sequences. Annotation-free algorithms enable rapid characterization of natural samples, highlighting quantitative correspondence between sequences and local O 2 levels in a data set from the Black Sea. This example suggests that DNA sequencing might be repurposed as a multi-pronged chemical sensor, estimating concentrations of O 2 and other key facets of complex natural settings.