Background: Critical Blind Spot of Static Genomic Analysis and Metabolic Plasticity Data Bottleneck in Carbapenem-Resistant Bacterial R&D

• Conventional single-path, static genomic variant analysis guidelines possess a critical blind spot: they fail to model the variability of reversible RNA editing occurring at post‑transcriptional regulatory stages. In particular, the inability to predict host‑microenvironment adaptation and drug‑target engagement of carbapenem‑resistant Klebsiella pneumoniae (CRKP) in silico has created a persistent data bottleneck in R&D, leading to repeated failure to maintain effective target‑engagement concentrations. Under host immune oxidative stress, the tRNA deaminase TadA becomes aberrantly activated, and it edits specific adenosine residues to inosine (A‑to‑I editing) in key transcripts such as the transcription factor PncR and the response regulator DcuR, thereby conferring metabolic plasticity. Classical baseline approaches exclude this transient reprogramming and redox‑resistance feedback loops, exposing a limitation that generates false‑positive targets at scale.

Discovery: Differential Free‑Energy Scan of TadA‑RNA Complex Deamination and Single‑Cell Resolution Transcript Tensor Synchronization Demonstration

• In this study, we employed three‑dimensional RNA structure prediction algorithms together with molecular dynamics simulations to pre‑compute, in silico, the activation free‑energy changes associated with TadA binding to the target loops of PncR and DcuR, and we computationally eliminated batch effects. We also demonstrated high‑dimensional tensor synchronization using transcript editing rates as a time‑series independent variable. This platform surpasses conventional simple expression‑level models, revealing that TadA overexpression drives increased A‑to‑I editing, which in turn induces reversible alterations in the production of the antioxidant metabolite NADPH and the kinetic constants of reactions within the TCA cycle, thereby reshaping the topological trajectory of downstream transcript networks. These results validate, within a computational omics architecture, the molecular‑biological integrity of the mechanism by which dominant carbapenem‑resistant clones survive under oxidative stress.

Establishment of a Precise Stratified Model for TadA‑Mediated Redox Pathway Regulation and Reversible Physiological Homeostasis

• Leveraging the TadA editing landscape accumulated in the omics matrix of clinical isolates, we constructed a precise stratified model that quantitatively assesses the metabolic homeostasis‑tuning capacity of carbapenem‑resistant strains. The model treats the synthesis rates of edited, isoform‑producing proteins of PncR and DcuR under antibiotic and reactive oxygen species exposure as parameters. By artificially up‑clamping or down‑clamping the kinetic constants of antioxidant metabolic steps, we mapped the critical thresholds that allow bacteria to reversibly maintain viable homeostasis even under aberrant environmental stress. This genetic‑transcriptional stratification framework provides a backbone for aligning the infection behavior of specific clinical isolates with patient lineage and genotype, thereby advancing the targeting efficiency of next‑generation combination therapies aimed at overcoming drug resistance.

Outlook: Establishing a Programmable Epitranscriptomics Standard and Activating Next‑Generation IND Digital Governance

• This work resets antibiotic‑resistance control R&D governance from a static post‑hoc symptomatic system to a programmable epitranscriptomics design infrastructure built on multidimensional tensor models. During high‑throughput screening of novel drug pipelines at multinational pharmaceutical companies, TadA‑mediated gradient correction coefficients are directly integrated, creating a robust computational barrier that automates active‑ingredient selection with zero batch‑to‑batch variance. The technology meets digital‑health‑based companion diagnostic (CDx) specifications, accelerating real‑world clinical deployment. Consequently, it will automate the generation of mechanistic validation data required for IND submissions and cGMP activation approvals by agencies such as the U.S. FDA, dramatically shortening regulatory timelines and maximizing clinical success rates, thereby becoming a master digital asset.