Physical biology最新文献

Peroxiredoxins (PRXs) are antioxidant enzymes that exhibit ∼24 h redox-state oscillations across diverse life forms. These non-transcriptional rhythms operate independently of the canonical transcription-translation feedback loops, suggesting an ancient, conserved timekeeping mechanism. However, whether this redox oscillator meets the core circadian criteria-entrainment and temperature compensation-remains a key question. To address this, we developed and calibrated a mathematical model of the mitochondrial PRX/ sulfiredoxin/thioredoxin redox cycle using physiologically meaningful parameters. The model quantitatively reproduced experimental redox oscillations inA. thaliana, D. melanogaster, andM. musculus. Simulations revealed that the PRX redox oscillator possesses both temperature-compensated periodicity and the capacity for entrainment by periodic thermal and oxidative signals, thereby fulfilling the core criteria of a circadian clock. The inverse angular speed, when integrated over the closed orbit, is largely temperature-invariant, thus providing a mathematical basis for the observed period stability. The calculated phase response curves, which show phase-dependent shifts, together with the broad Arnold tongue for 1:1 resonance, demonstrate a substantial entrainment range that enables the internal rhythm to robustly lock onto periodic environmental Zeitgebers.

Bacterial growth rates are constrained by genome replication, yet the role of replication kinetics in bacterial growth rates remains incompletely understood. Here, we examine if genome size, replichore organization, and nucleotide compositional asymmetry are reasonable predictors of bacterial doubling times. In free-living bacteria, both genome size and the length of the longest replichore are found to correlate positively with doubling time, pointing to an influence of replication dynamics on bacterial growth rates. Moreover, fast-growing bacteria are shown to exhibit stronger nucleotide compositional skew. Incorporating skew into the model substantially improves predictive accuracy, suggesting that compositional asymmetry in genomes may facilitate replication fork progression and thereby enhance growth rates. Based on these observations, we speculate that nucleotide skew may play a potential adaptive role in bacterial genome replication. To assess whether the observed association between genome architecture and growth rate reflects an evolutionary signature or a mechanistic link, we reconstructed ancestral states and found that the model fits ancestral traits more strongly, with predictive strength (R²) decreasing progressively along the evolutionary tree as successive speciations occur. We speculate that this association has been stronger early in bacterial evolution and became subsequently screened as organisms diversified and increased in ecological and physiological complexity.

Metastasis is a key cause of mortality in cancer. It is driven majorly by clusters of circulating tumor cells which are often comprised of cells in a hybrid epithelial/mesenchymal (E/M) phenotype. Hybrid E/M phenotype has also been shown to be more tumor-initiating and therapy-resistant, but how cells maintain the hybrid E/M phenotype(s) remains an active area of investigation. Here, we develop a mathematical model that couples the intracellular dynamics of key players of epithelial-mesenchymal transition with the androgen receptor (AR), and cell-cell communication through Notch-Delta-Jagged signaling, in the context of prostate cancer (PCa). Our simulations show that AR can stabilize a hybrid E/M phenotype predominantly in the presence of Notch-Jagged, but not Notch-Delta, signaling. Implementing this model on a multi-cellular lattice, we observed that AR can alter the fraction of cells exhibiting a hybrid E/M and a mesenchymal phenotype. Finally, through analysis of transcriptomic and patient survival data, we found that the co-expression of AR and Jagged correlated with worse outcomes. Together, these results highlight the outcome of emergent dynamics between the Notch and AR signaling axis in promoting PCa aggressiveness.

Synthetic cationic fluorophores are widely used as probes to measure the membrane potentials of bacterial cells, eukaryotic cells, and organelles (such as mitochondria) in electrophysiology experiments and live/dead assays. We applied an external oscillating electric field to Escherichia coli using microelectrodes and observed that AC electro-osmosis caused fluorescence transients independent of bacterial electrophysiology, which could be mistaken for membrane depolarisation events. The fluorophores migrated within the microfluidic device in vortices, leading to concentration fluctuations manifested as dips in fluorescence. These fluorescent dips were universally present when using cationic fluorophores such as thioflavin-T, propidium iodide, Syto9, and Sytox Green, with or without E. coli present, whenever AC voltages were applied. Furthermore, we also demonstrate that fluorescence dips in dense bacterial communities can arise from AC electro-osmosis rather than ion-channel activity. This cautionary tale highlights how electrical stimulation experiments in microbial communities can yield misleading results if electrokinetic effects are not accounted for. We quantified the relaxation times of fluorophores under AC electro-osmosis, which depended on the community, the cells, and the dye used: PI showed the shortest relaxation time and Syto9 the longest. Removing cells resulted in longer relaxation times, and introducing dense communities did not significantly alter the relaxation times compared with single-cell experiments. Furthermore, fluorescently labelled DNA and fluorescent colloidal beads (30-130 nm) also exhibited fluorescence dips due to AC electro-osmosis, demonstrating that charged molecules and particles readily penetrate and accumulate within these assemblies. To our knowledge, this is the first study to characterize AC electro-osmosis in dense bacterial communities, revealing the high mobility of charged molecules in such systems and suggesting possible applications for enhancing antibiotic delivery.

Rhythmic gene expression underlies core physiological processes across organisms, from circadian timekeeping to stress responses. Recent experiments suggest that the regulation of such rhythmic dynamics involves protein compartmentalization mediated by liquid-liquid phase separation (LLPS), yet the mechanisms by which LLPS feeds back onto oscillatory behaviour remain unclear. Here we develop a minimal two-phase gene-expression model in which proteins are synthesized in the dilute phase, reversibly partition into a protein-dense droplet phase, and repress their own production via condensate-mediated regulation. In the deterministic limit, LLPS does not generate limit cycles; instead, nonlinear partitioning and timescale separation between phase separation and protein turnover convert purely relaxational dynamics into damped oscillatory transients, altering the approach to equilibrium without producing sustained oscillations. In the stochastic regime, intrinsic noise interacting with this near-focus dynamics is amplified into noise-sustained, near-periodic fluctuations with a characteristic timescale, as revealed by the power spectral density and autocorrelation functions. These results show how LLPS reshapes oscillatory signatures by encoding and filtering temporal signals in phase-specific ways, providing a quantitative framework for interpreting LLPS-rhythm coupling and for engineering biomolecular systems with tunable dynamic behaviour.

The G₄C₂hexanucleotide repeat expansion in the c9orf72 locus is a mutation associated with amyotrophic lateral sclerosis. Recent evidence suggests a link with disrupted axonal trafficking in neurons. Here, using a neuronal-like cell line without or transfected with G₄C₂repeats, we characterize the motion of lysosomes inside neurites. The neurites grew either aligned to patterned lines, or oriented freely on a 2D-substrate. Implementing time-resolved (local) mean squared displacement analysis lysosome trajectories were split into sub-diffusive, diffusive, and super-diffusive parts. Our results suggest that in the presence of the G₄C₂repeats, lysosome trafficking is hampered, exhibiting overall decreased mean squared displacement and speed, more prominently inside aligned neurites. Moreover, a prominent effect in the super-diffusive drift velocity and diffusive motion diffusion coefficient was evident when the motion occurred inside aligned neurites. Trajectories which included super-diffusive motion, exhibited a varied ratio of anterograde/retrograde/neutral for both neurite geometries in the presence of G₄C₂repeats but a similar velocity decrease for both directions in each neurite geometry. Our findings support the hypothesis that impaired axonal trafficking emerges in the presence of the G₄C₂hexanucleotide repeat expansion, and demonstrate that this effect is more prominent when the neurites are aligned.

Clustered regularly interspaced short palindromic repeat-associated proteins (CRISPR-Cas) biochemistry has been leveraged for genome editing applications in biochemical research and therapeutics. CRISPR-Cas9 and CRISPR-Cas12a are the two most widely used RNA-guided endonucleases and while Cas9 and Cas12a have a shared function, both have unique biophysical properties that alter their specificity and efficiency. The thermodynamic and kinetic properties governing their molecular interactions, recognition and binding of target DNA, and R-loop formation can differ. In some cases, these critical biophysical metrics have not been resolved. Distinctions between Cas9 and Cas12a enzymes are also prevalent in RNA:DNA hybrid binding affinities, DNA localization relative to the preferred PAM site and the DNA cleavage mechanism. In this review, we examine the thermodynamic and kinetic properties of both endonucleases, focused on the nucleic acid interactions that confer specificity and function. Complementing this biophysical overview, we discuss case studies in disparate model organisms that compare the genome editing and fidelity of Cas9 and Cas12a.

Tumor-infiltrating lymphocyte (TIL) therapy is a type of adoptive cell therapy, where the lymphocytes of a cancer patient's tumor are harvested, expandedin vitrousing IL-2 stimulation, and then infused back into the patient Rosenberg and Restifo (2015Science34862-68), Bonini and Mondino (2015Eur. J. Immunol.452457-69). However, even with the use of TIL therapy, cancer cells can survive for various reasons, such as poor lymphocyte infiltration into tumors, chronic activation of the T cell receptor and the immunosuppressive tumor microenvironment Morganet al(1976Science1931007-8). Cytokine-inducible SH2-containing (CISH) protein is a negative regulator of T cell activation, and in a recent clinical trial was knocked out in TILs to improve TIL therapy efficacy Rosenberget al(1985J. Exp. Med.1611169-88). A mechanistic signaling pathway model was developed to theoretically evaluate the efficacy ofCISHknockout (CISHKO) in T cell activation and examine potential alternative target genes that can theoretically be targeted using multiplex gene-editing or drugs to further improve T cell activation and function Donohueet al(1984J. Immunol.1322123-8). Based on the results,CISHknockout increases the transcription of activation biomarkers IL-2 and TNF-α, but also inhibitory biomarkers such as PD1 and FasL. Using global sensitivity analysis, we also found thatGSK3B, which is responsible for the deactivation of NFAT, is also predicted to further increase T cell activation when knocked out. In addition, it was predicted thatPDCD1, FASandCTLA4can be knocked out in combination withCISHto further enhance T cell activation and prevent exhaustion and apoptosis.

Protein sequence determines structure, function, and dynamics, yet the gap between sequenced proteins and experimentally determined structures continues to widen. While machine learning approaches like AlphaFold2 have transformed structural biology, they require substantial computational resources. Coevolution-based methods such as mutual information (MI) and direct coupling analysis (DCA), such as GREMLIN, offer alternatives but depend on extensive multiple sequence alignments with thousands of homologs. Here, we present a template-based pattern-matching approach that predicts protein contact maps by identifying conserved structural motifs from homologous experimental structures. Our method encodes spatial arrangements of up to five residues within 8.0 Å distance as sequence patterns, then aligns these patterns to query sequences to predict residue-residue contacts. Critically, our approach requires only a modest number of structural templates (typically 50-500) and runs on standard hardware without graphics processing units or high-performance computing clusters, processing proteins in 12-16 min regardless of length. We validated our method on 25 well-characterized protein domains, achieving correlations of 0.735-0.942 with experimental contact maps. Comparative analysis against MI and GREMLIN demonstrated that our method achieved better contact coverage while maintaining comparable accuracy. To demonstrate broader applicability, we tested on 7599 poorly annotated sequences using high-confidence AlphaFold structures as reference, achieving meanF1-score of 0.609 ± 0.095 and accuracy of 0.954 ± 0.036. Our pattern matching approach provides a computationally efficient, interpretable alternative to both deep learning and coevolution-based methods, particularly valuable for proteins with limited sequence homologs or when rapid predictions are needed.

Biological systems in general operate out of equilibrium, which brings the requirement for a constant supply of energy due to non-equilibrium entropy production. The thermodynamic uncertainty relation (TUR) essentially imposes a bound on the minimum current fluctuation the system can have given an entropy production rate. The fluctuation eventually impacts the signal-to-noise ratio, imposing an upper bound on the information transmission accuracy. In this study, we explore the role of the TUR on the information transmission capacity of a set of cellular signaling systems using coupled mathematical and machine learning approaches on experimental data in yeast under several stress conditions. Cell signaling systems are involved in sensing changes in the environment by activating a set of transcription factors (TFs), which typically diffuse inside the nucleus to trigger transcription of the required genes. However, the inherent stochasticity of the biochemical pathways severely limits the accuracy of estimating the environmental input by the TFs. The application of TUR reveals a general picture of the working principle of the TFs. We find that the activation followed by biased diffusion of TFs toward the nucleus triggers entropy production, which amplifies the magnitude of the overall TF currents toward the nucleus as well as reducing the fluctuations. These outcomes significantly improve the accuracy of information transmission carried out by the TFs following the bound imposed by TUR, leading to a correlation between accuracy and entropy production. However, TUR only imposes an upper bound on accuracy, and the correlation emerges due to the pathway being operated in the linear response regime. Thus, experimental observations coupled with TUR-based theoretical models demonstrate the role of thermodynamic fluctuation and entropy production on cellular information processing.