Nanopore- and AI-empowered metagenomic viability inference

Abstract

The ability to differentiate between viable and dead microorganisms in metagenomic data is crucial for various microbial inferences, ranging from assessing ecosystem functions of environmental microbiomes to inferring the virulence of potential pathogens from metagenomic analysis. While established viability-resolved genomic approaches are labor-intensive as well as biased and lacking in sensitivity, we here introduce a new fully computational framework that leverages nanopore sequencing technology to assess microbial viability directly from freely available nanopore signal data. Our approach utilizes deep neural networks to learn features from such raw nanopore signal data that can distinguish DNA from viable and dead microorganisms in a controlled experimental setting of UV-induced Escherichia cell death. The application of explainable AI tools then allows us to pinpoint the signal patterns in the nanopore raw data that allow the model to make viability predictions at high accuracy. Using the model predictions as well as explainable AI, we show that our framework can be leveraged in a real-world application to estimate the viability of obligate intracellular Chlamydia, where traditional culture-based methods suffer from inherently high false negative rates. This application shows that our viability model captures predictive patterns in the nanopore signal that can be utilized to predict viability across taxonomic boundaries. We finally show the limits of our models generalizability through antibiotic exposure of a simple mock microbial community, where a new model specific to the killing method had to be trained to obtain accurate viability predictions. While the potential of our computational frameworks generalizability and applicability to metagenomic studies needs to be assessed in more detail, we here demonstrate for the first time the analysis of freely available nanopore signal data to infer the viability of microorganisms, with many potential applications in environmental, veterinary, and clinical settings. Author summaryMetagenomics investigates the entirety of DNA isolated from an environment or a sample to holistically understand microbial diversity in terms of known and newly discovered microorganisms and their ecosystem functions. Unlike traditional culturing of microorganisms, genomic approaches are not able to differentiate between viable and dead microorganisms since DNA might persist under different environmental circumstances. The viability of microorganisms is, however, of importance when making inferences about a microorganisms metabolic potential, a pathogens virulence, or an entire microbiomes impact on its environment. As existing viability-resolved genomic approaches are labor-intensive, expensive, and lack sensitivity, we here investigate our hypothesis if freely available nanopore sequencing signal dat that captures DNA molecule information beyond the DNA sequence might be leveraged to infer such viability. This hypothesis assumes that DNA from dead microorganisms accumulates certain damage signatures that reflect microbial viability and can be read from nanopore signal data using fully computational frameworks. We here show first evidence that such a computational framework might be feasible by training a deep model on controlled experimental data to predict viability at high accuracy, exploring what the model has learned, and using it in a real-world application by application to a bacterial species of veterinary relevance. We finally show that a specific model has to be trained to accurately predict viability after antibiotic exposure of a mock microbial community. While the generalizability of our computational framework therefore needs to be assessed in much more detail, we here demonstrate that freely available data might be usable for relevant viability inferences in environmental, veterinary, and clinical settings.