Koh, H. R. | Search bioRxiv

bioRxiv2026

Cellular Chemical Dynamics Governing Signal Transduction and Adaptive Gene Expression: Beyond Classical Kinetics

Cellular adaptation is inherently nonstationary processes with complex stochastic dynamics1-5. Despite remarkable progress in quantitative biology6-11, a quantitative understanding of the cell adaptation dynamics in terms of the underlying cellular network remains elusive. Here, we present the next-generation chemical dynamics model and theory for cellular networks, providing an effective, quantitative description of the adaptive gene expression dynamics in living cells responding to external stimuli. Unlike conventional kinetics, chemical dynamics of cellular network modules are characterized by their reaction-time distributions, rather than by rate coefficients12. For a general model of cell signal transduction and adaptive gene expression, we derive exact analytical expressions for the time-dependent mean and variance of protein numbers produced in response to external stimuli, validated by accurate stochastic simulations. These results provide a unified, quantitative explanation of the stochastic responses of diverse E. coli genes to antibiotic stress and transcriptional induction. Our analysis reveals existence of a general quadratic relationship between the mean and variance of activation times across diverse genes. The gene activation process influences transient dynamics of downstream protein levels, but not their steady-state levels. In contrast, post-translational maturation process affects both transient dynamics and steady-state variability of mature protein levels. This finding indicates that the gene expression variability measured by fluorescent reporter proteins depends on the maturation time of the reporters. This work suggests a new direction for the development of digital twins of living cells.

biophysics↗

bioRxiv2025

High-fidelity CRISPR genome editing of single-nucleotide mutation with near-complementary guide RNA via enhanced target binding kinetics

The CRISPR-Cas9 system is a powerful genome editing tool capable of precisely recognizing and cleaving specific DNA sequences, and has been extensively investigated as a strategy for correcting mutations associated with genetic diseases and cancer. However, conventional CRISPR genome engineering often fail to discriminate single-nucleotide mutations from wild-type alleles when the mutation is located outside the protospacer adjacent motif (PAM) sequence. To address this limitation, we developed a RNA engineering approach for designing near-complementary single guide RNA (sgRNA) that contain intentional mismatches within the seed region of the sgRNA. Single molecule kinetic analyses showed that the near-complementary sgRNA selectively reduces the binding affinity of CRISPR ribonucleoprotein complex by via differentiated increment in the dissociation rates to the wild-type target DNA compared to the mutant allele. The engineered kinetic characteristics of near-complementary sgRNAs enable highly specific genome editing of single-base mutations without reliance on PAM proximity. We demonstrate the application of the strategy to the a cancer-specific single-nucleotide G228A (-124C > T) mutation in the TERT promoter, frequently found in glioblastomas and other tumors, that does not generate a canonical PAM sequence. Our near-complementary sgRNA successfully induced selective editing of the mutant allele while sparing the wild-type sequence. Furthermore, single-molecule fluorescence resonance energy transfer (smFRET) analyss revealed distinct differences in binding kinetics between mutant and wild-type DNA, providing kinetic insight into the discrimination process. We conclude that the near-complementary sgRNA CRISPR editing strategy facilitates precise PAM-independent targeting of single-nucleotide mutations without protein engineering and offers a molecular framework for expanding the specificity and applicability of CRISPR-based genome and epigenome editing technologies.

biochemistry↗