Praopim Limsakul, Natcha Rasitanon, Sahban Da-Oh, Amornrat Phongdara, Aekkaraj Nualla-Ong, Krit Charupanit
Here, we present an end-to-end computational framework for Record-seq data. To address the primary computational bottleneck of spacer sequence extraction, we implemented a wavefront alignment approach for efficient quasi-local pattern matching, achieving an approximately 30-fold speedup. We introduce transcription unit-based feature counting as an alternative to gene-body quantification to better represent prokaryotic transcription and increase statistical power by capturing signal from untranslated regions, which are spacer acquisition hotspots. For downstream analyses, we incorporate multiple normalization strategies and a nonparametric differential expression testing framework designed for sparse datasets. Further, we analyze spacer acquisition patterns and train sequence-based neural models that predict acquisition propensity from genomic sequence and annotations, providing a framework for assessing whether acquisition rules generalize as Record-seq is extended to new microbial hosts.
Efficient regulation of recombinant enzyme expression is an important consideration for the development of microbial biocatalysts. Heat-inducible promoters regulated by the alternative sigma factor σ32 provide an inducer-free strategy for controlling gene expression in Escherichia coli. In this study, four σ32-dependent promoters (PdnaK, PgrpE, PibpA, and PclpB) were comparatively characterized using the PET-degrading enzyme FAST-PETase fused to superfolder green fluorescent protein as a model recombinant protein. Promoter performance was evaluated based on basal leakage, induction kinetics, and expression strength following heat induction. Among the promoters examined, PdnaK exhibited the strongest heat-inducible expression and was dissected to examine the autonomous and combinatorial behavior of its promoter-derived elements. Molecular docking analysis further supported the experimental observations by showing qualitative agreement between predicted σ32-DNA interactions and promoter performance. Together, these findings provide a comparative characterization of σ32-dependent promoters and identify promoter architectures that may facilitate the development of heat-inducible recombinant enzyme expression systems in E. coli.