Bayesian modeling of longitudinal metatranscriptomes of broiler meat spoilage microbiomes shows shared predictive signature associated with spoilage at refrigerated temperatures

Abstract

Microbial spoilage of packaged meat is driven by complex microbial succession and related metabolic activity, yet conventional shelf-life assessment is mainly based on shelf-life studies relying on culturing and sensory analysis. In routine quality assurance, results are obtained retrospectively, and they are only indirectly linked to the metabolic activity related to sensory deterioration. Functional, time informative approaches that capture the active metabolic state of the spoilage microbiome and predict the rate of spoilage are lacking. We developed a censoring-aware Gaussian process (CAGP) framework to model longitudinal pathway expression profiles from broiler meat metatranscriptomes collected over consecutive storage days at 4 or 6{degrees}C. Samples were annotated using odor-based sensory scores defining fresh, early-spoilage, and late-spoilage phases. Because observed zeros in pathway-level data may reflect non-detection rather than true absence, the model treats low values as left-censored observations below a soft detection threshold while estimating smooth temporal trajectories with uncertainty. In leave-one-out prediction within the 4{degrees}C time-series, predicted sampling days differed from the true days by an average of 0.43 days, and predicted spoilage phases agreed with the sensory classification. Trajectories learned at 4{degrees}C also transferred to an independent 6{degrees}C time-series at the spoilage-phase level, suggesting that shared functional spoilage programs are preserved despite temperature-dependent changes in spoilage rate. Cross-entropy ranking further identified pathway modules carrying time- and phase-informative signals across temperatures. Overall, this framework provides a probabilistic approach for linking metatranscriptomic functional dynamics to sensory spoilage progression, supporting shelf-life assessment beyond retrospective microbial enumeration. IMPORTANCEShelf-life evaluation of meat products still relies heavily on microbial counts, targeted detection of spoilage organisms, and sensory panels. However, microbial abundance and species-level composition do not always predict when a product becomes unacceptable, because spoilage depends on the active metabolic state of the microbiome and can vary between strains, production lots, and storage conditions. This study shows that longitudinal metatranscriptomics, combined with censoring-aware Bayesian time-series modeling, can recover functional pathway trajectories aligned with sensory spoilage progression. By identifying pathway-level signatures that transfer across refrigeration temperatures, the approach moves shelf-life assessment from retrospective enumeration toward predictive, function-based monitoring. In this study, a spoilage signature refers to a set of microbial pathway trajectories whose expression patterns are informative of storage time and sensory spoilage phase. These signatures could support future tools for earlier spoilage detection, better shelf-life estimation, and improved control of product quality in meat production.