Biology and Life Sciences

Abstract: Can a stellar configuration identify a particular DNA helical shape? We test a map from two Cygnus ecliptic angles to helical repeat and rise-to-radius ratio that matches canonical B-DNA within 0.92% relative RMS error. The original route uniquely wins all five retained B-DNA structures and none of the A/Z controls in the historical panel, with continuous dominance across the B-DNA region. Selecting the five brightest stars of all 88 constellations preserves the Northern Cross and the unchanged map’s first rank at the benchmark. Cygnus alone has a minimum spanning tree with four arms meeting at one star; brightness and opposition recover its axis before molecular scoring. Repeating this retrospective selection on new quintets gives 0.17–0.21% molecular-score exceedance overall and 1.08–1.46% among admissible panels under two source models. Expanded formulas and target substitution remove canonical first rank; the exploratory molecular graph extension identifies Cygnus under H2 but not H1. Historical exceedance rises from 2.7% to 55.9% when coordinate roles and formulas are released. The geometric “stellar DNA” hypothesis links an astronomically recoverable configuration to a measured B-DNA preference, with role asymmetry defining its specificity and explicit predictions for independent tests.

Abstract: Broadly neutralizing antibodies targeting the silent face of the SARS-CoV-2 receptor-binding do-main (RBD) provide essential templates for pan-sarbecovirus therapeutics, yet the precise bio-physical mechanisms dictating their neutralization breadth, energetic propensities, and escape re-silience remain unresolved. By combining all-atom and coarse-grained molecular dynamics, systematic mutational scanning, rigorous MM-GBSA binding free energy analysis, and residue interaction network modeling we delineate the comparative structural, energetic, and allosteric signatures of Site V (CC25.4, CC25.43, CC25.56, 7D6, 6D6 ) and CR3022-site (Group 1: CC25.54, CC84.2, CC84.24) antibodies. We discover that these two classes by engaging spatially opposite faces of the RBD fold produce mutually exclusive energetic hotspot modules. Site V antibodies are anchored along a surface-exposed lateral ridge by residues R355, R357, R466, I468, F464, and K462, whereas CR3022-site antibodies engage a buried β-sheet-core module comprising R408, K378, C379, Y380, F377, and Q414. Remarkably, despite these distinct epitopes, both classes converge on an ultra-conserved paratope tyrosine triad (Y99, Y100, Y49), establishing a universal structural motif for silent-face recognition. Network centrality modeling demonstrates that Site V and CR3022-site antibodies modulate ACE2 receptor interface dynamics via distinct upstream allo-steric pathways that converge on shared RBM-loop relays at Y423, Y396, and F464. Crucially, in CR3022-site antibodies, binding free energy hotspots and allosteric communication hubs coincide directly within the structurally constrained β-sheet core. This enforces a dual-constraint mecha-nism where escape mutations inherently compromise viral fold integrity. Conversely, Site V binding hotspots and allosteric networks are structurally segregated, exposing cryptic allosteric liabilities where mutations at non-contact positions (F456, R509) dynamically rewire network communication to impair neutralization without altering static interface affinity. Furthermore, within-class resilience is governed by epitope architecture: focused antibodies (CC25.56, 6D6, CC25.54) concentrate binding energy into single dominant anchors vulnerable to point mutations, whereas broad antibodies (CC84.2, CC84.24, CC25.4, CC25.43) distribute binding energetics across multi-residue contact networks. These quantitative findings align directly with experimental neutralization data. The structural immutability of the β-sheet-core module provides a mechanistic explanation for why CR3022-site antibodies CC84.2 and CC84.24 retain activity against highly evasive Omicron sublineages BA.2.86 and JN.1. A complete absence of energetic or allosteric hotspots at major Class 1/2 escape loci explains the cross-variant breadth exhibited by both si-lent-face classes. By establishing how silent-face antibodies balance potency, breadth, and resili-ence, this work offers a predictive biophysical framework for tracking viral antigenic drift and designing durable, multi-epitope antibody cocktails.

Abstract: Microfluidics has emerged as an important platform for manipulating and analyzing small volumes of fluids, cells, biomolecules, and particles with high precision. Its ability to control microscale flows and integrate multiple analytical steps has enabled diverse biomedical technologies, including lab-on-a-chip, point-of-care diagnostics, organ-on-chip, and microphysiological systems. This review summarizes the fundamental principles of biomedical microfluidics, including microscale fluid dynamics, laminar flow, diffusion, and key dimensionless parameters, together with major platform formats such as continuous-flow, droplet-based, and digital microfluidics. Recent fabrication approaches, including photolithography, soft lithography, molding, laser-based methods, 3D printing, micromilling, and paper- and lamination-based techniques, are also discussed. The review highlights applications in cancer and liquid biopsy, infectious disease diagnostics, cardiovascular and neurological disorders, organ-on-chip models, drug screening and toxicity testing, pharmaceutical synthesis, drug delivery, single-cell analysis, and regenerative and reproductive medicine. Particular attention is given to the growing use of microfluidics for personalized and human-relevant biomedical research. The review further examines the major challenges associated with microfluidic biomedical systems, including fabrication reproducibility, material compatibility, biological complexity, standardization, scalability and manufacturing, clinical validation, regulatory requirements, and integration with advanced computational approaches such as artificial intelligence and machine learning. Finally, future perspectives are discussed, with particular emphasis on personalized medicine, decentralized diagnostics, human-relevant drug development, multi-organ microphysiological systems, wearable and implantable microfluidics, and automated biomedical platforms.

Abstract: The traditional nanocarrier-mediated nucleic acid delivery relies on random Brownian diffusion, suffering from slow transport speed and insufficient single-cell targeting ability. In this study, we proposed a targeted siRNA delivery strategy that uses optical tweezers to deliver the siRNA-PS probes to target cells. The nanoscale inter-facial stability and trapping performance of the probes are evaluated. Delivery is achieved in less than 10 s, two orders of magnitude faster than passive-diffusion-based delivery. Using Nicotiana protoplasts as a proof-of-concept cellular model, single siRNA-PS probe target delivery tests achieved a gene-silencing efficiency of up to 92%. This work demonstrates an optical tweezers-driven targeted delivery strategy and provides experimental insights into nanophotonic manipulation of microscale colloidal assemblies.

Abstract: Questions arise about how well early bacterial models of gene regulation generalize to eukaryotes and whether metazoan repetitive sequences are merely genomic spandrels. The repeat RNAs play multiple roles in the cell through sequence-specific interactions with proteins and other RNAs. Other repeat subsets, called flipons, adopt non-B-DNA conformations under physiological conditions without any sequence change or backbone cleavage. Other low-complexity sequences encode intrinsically disordered regions (IDRs) of proteins, promoting the formation of membrane-less condensates with specific cellular functions. Here, I propose that metazoan repeats enable a fast readout of genetic information by facilitating massively parallel searches of large genomes, far exceeding what diffusion alone can achieve. The locally transcribed repeats and flipon subsets seed condensates that display information about local DNA microstates on their surfaces, creating a 2-dimensional holographic hash of their contents. The hash enables transcription factors to rapidly discover critical cognate binding sites, facilitating rapid cellular responses to perturbation. The information displayed on condensate surfaces is constantly updated by both outward- and inward-directed processes. The rewrite further optimizes responses and increases the phenotypic variability on which natural selection acts. Applying the Principle of Least Action to the Lagrangian demonstrates that this strategy minimizes search times across arbitrary volumes.

Abstract: Magnetic tweezers (MT) have become an important single-molecule manipulation tool, with several advantages over alternate methods such as optical tweezers and atomic force microscopy. MT are easy to operate, cost effective, and can apply forces ranging from sub-piconewton (~ 0.1 pN) to hundreds of piconewtons. Furthermore, they offer exceptional spatial and temporal resolution. These features position MT as a versatile and powerful tool to study many biomolecular processes at the single-molecule level. MT have played an important role in advancing our understanding of DNA–protein interactions, making significant contributions across molecular biology, enzymology, and chromatin dynamics. In this review, we start by describing principal MT geometries along with force calibration methods and then pivot to a discussion of MT-based studies of DNA mechanics, chromatin organization, nucleosomes, nucleoid-associated proteins, topoisomerases, helicases, and polymerases. We aim to provide the research community with an integrated overview of MT-based approaches to studying DNA–protein interactions, highlighting both foundational work and recent innovations that continue to shape the field of single-molecule biophysics.

Abstract: Resonance patterns are crucial in living systems and also play a role in encoding information in modern communication technology. The DNA molecule exhibits resonant properties. We previously characterized nitrogenous bases as analogous to radio-frequency qubits. This paper explores the role of the hydrogen bond in the DNA self-synchronization mechanism. We analyze a network of n-coupled hydrogen nuclear spins between two electron-cloud spins, each individually connected to the sugar-phosphate backbone, which acts as a resonator. An external magnetic field along the z-axis affects the effective behavior of the nuclear-spin network. The symmetry breaking that enables quantum decisions in the system is governed by the hydrogen’s spin orientation. A network of interconnected spins functions as a qubit register for quantum information processing and serves as a platform for nanoscale magnetic resonance. Following the well-established proton nuclear magnetic resonance model, we describe electrons coherently traveling through a highly efficient spin filter. The article presents a highly theoretical framework. This approach has limitations, as it lacks an experimental protocol to verify the analogies between macroscopic spectroscopy and molecular biology.

Abstract: In addition to the electrogenic microorganisms, e.g. bacteria, that can be found in communities that are wired together in circuits carrying electrons in and out of biological cells, strong evidence exists that there are wireless connections between micro-organisms that pass information to one another at a distance in virtue of electromagnetic signals. One needs electromagnetic antennae to send or receive signals as they pass information back and forth between living microorganisms. We here discuss the properties of DNA and RNA long polymer molecules regarding their utility as antennae. As well as emitting or absorbing radiation photons these antennae also emit and absorb phonons, i.e. acoustic signals. The coupling between sound waves and electromagnetic waves is very appreciable due to and described by biological piezoelectricity. Biological phases of condensed matter also exhibit chiral symmetry breaking, i.e. “handedness” symmetry breaking, between right and left-handed electronic states. Describing two spin components (say up and down) times two chirality components (say left and right) ultimately requires four component electron wave functions as discovered by Dirac even if the electrons are moving at velocities much less than light speed. Coupled with the quasi-one dimensional nature of long polymer molecules this yields bound Bose condensed bound particle-hole pairs constituting a Tomonaga-Luttinger liquid similar in some ways to the Bardeen-Cooper-Schriefer Bose condensed bound electron pairs found in superconductors. One similarity is that transport weak links in the Bose condensate fluid flow yield Josephson effects described by a quantum pendulum, e.g. a sine Gordon field theory. The coherent phase in the Tomonaga-Luttinger liquid is the difference between the right- and left-handed electronic phases as first demonstrated by Coleman via the conversion of the massive Thirring model into a Josephson type pendulum model. The complete theory of wireless communication between biological organisms is only beginning to be developed. The basic DNA and RNA microbe antennae must be wired in nature as a coherent phased antenna array for appreciable communication bit rates radiated through large distances. Finally, acoustic and/or radio frequency electromagnetic radiation carrying sufficient communication information should be of use medically as a possible alternative to chemical medicines and/or vaccines in the treatment of illness; for a most recent example in the COVID-19 virus with its RNA internal antenna.

Abstract: The need of a better understanding of how sensory ion channels respond to different stimuli has led to the development of numerous experimental and theoretical approaches. In this context, the response of ion channels to external stimuli is highly complex in general, as it depends on the specific sensing mechanism, as well as the number and nature of conformational states involved. Multi-state ion channels models provide a more comprehensive physical description of their stimuli response than the simplest two-state paradigm, but can suffer from severe overparameterization, which limits physical interpretation. To address this limitation, we developed a theoretical framework demonstrating that the response of a multi-state ion channel to a given stimulus can be modeled as that of an equivalent two-state channel. This equivalence holds regardless of the number of states, conductivities, their nature, or possible transition pathways, thus overcoming overparameterization. The present equivalent model is defined by effective kinetic constants for non-conductive-to-conducting (K_eff.⁺) and conducting-to-non-conductive (K_eff.^-) transitions, which act as statistically weighted “kinetic centers” of the individual rate constants (analogous to the classical center of gravity). In the steady state, and assuming the Eyring model for the individual rates, such effective constants adopt a multiexponential dependence on the stimuli. Since the Müntz-Szász theorem for Dirichlet-to-Taylor series equivalence establishes that multiexponential functions can always fit any smooth curve, the experimental K_eff.^+/- (X) vs X curves can (in principle) always be fitted by the model proposed here. Each kinetically indistinguishable state adds a new exponential term in the fitting.

Abstract: It is generally accepted that sensory neurons transduce “information” from their environment into biologically relevant signals, allowing for adaptation and body homeostasis. However, it has not been clear what sort of information metric is generated by, or flows through, the neuron. In this work, we stated this problem based on the physical foundations of a conductance-based model of neuronal excitability, starting from the mother equations of an ensemble of N identical ion channels. For any number and nature of channel states, we show that the Fisher information in the output (membrane conductance, potential or current) about the input (the stimulus) arises as the intrinsic information metric of the gating dynamics of the ensemble. We found that the Fisher information is a primary component of the fluctuations of the neuronal membrane conductance, along with a parameter accounting for how deterministic or stochastic the ensemble is (a function of N). We also obtain a relation for the rate of transitions of the neuron, from a given configuration with n open channels to another, which is the primary origin of the intrinsic noise in the absence of artificial stimulation. This result establishes an important analytical correlation between the fluctuations of the neuron output signal, the information it carries (in Fisher’s terms), its physical and kinetic properties, and the corresponding stimuli. This might constitute a theoretical foundation for: i) designing of strategies for stochastic resonance-based computing procedures inspired by the neural intrinsic noise, and ii) modulating neural function by tuning its intrinsic Fisher information or the expected value of conductance fluctuations, with that of a binomially distributed external noise. For channels with inactive states, the model suggests the simultaneous use of two different signals of such noise. Such proposals constitute a new paradigm, which qualitatively differs from the traditional Gaussian white or Lévy noises.

Abstract: Several members of the transient receptor potential (TRP) channel superfamily are notorious for been activated by chemical, thermal and mechanical stimuli. Despite the obvious advances resulting from functional studies and structural characterizations, the mechanisms of TRP channel gating remain obscure. Amongst all, the mechanically-induced gating is probably the most intriguing activation mode of these channels. The main objective of this study was to provide a theoretical framework serving to assess the determinants of TRP channel mechanosensitivity. We hypothesized that, as for thermal stimuli and chemical agonists, the mechanically-induced activation is boosted by the weak voltage dependence of TRP channels. To assess this, we extended a gating model that was previously shown to describe the thermal activation of multiple sensory TRP channels, by considering an additional linear free energy term associated to an external mechanical stimulus. The resulting model predicts that the thermal and mechanical sensitivities are determined by the balance between an energy component associated to the protein volume and another associated to the protein-membrane surface. The model supports the hypothesis linking the mechanical- and voltage-dependent gating by predicting inverse relationships between the mechanical sensitivity and the gating valence. In addition, it serves to explain how TRP channels can act as secondary mechanosensors by being stimulated by second messengers generated upon mechanical activation of signaling pathways. Finally, we delineate experimentally testable-hypotheses that may result in further understanding of TRP channel mechanosensitivity and discuss the relevance of this property for mechanotransduction.

Abstract: In a companion paper we developed a theoretical framework for polymodal gating of TRP channels predicting that their sensitivity to thermal and mechanical stimuli depends on a balance between volume and surface energy components of the protein. This result can be generalized to other channel types and is clearly apparent in Piezo channels, for which the propel-like structures have a high surface-to-volume ratios. However, Piezo1 and Piezo2 channels have quite similar morphologies and structures, yet fairly different responses to lateral membrane tension. This fact suggests that, along with surface-to-volume ratio, the particular mechanistic and the effective stimulus transmission from the sensing to the pore gates are also relevant. Here, we to introduce in a new biophysical framework implicating the interactions coupling between the perturbating mechanical stimulus and the gating mechanism. We found that when considering a “non-ideal coupling”, the classical relation for the maximum sensitivity differs from ∆A/(4k_BT), which is in turn identified as the particular solution for small proteins with relatively simple structures. Although the mathematical solution of the problem is not straightforward, the ‘coupling factor’ (as defined here) of such interaction can be obtained experimentally in a simple way. Additionally, our model predicts that the membrane tension applied on the surface of the channel can be amplified in the pore surface in real cases (a lever-like effect). These ideas can be extrapolated to other non-mechanical perturbations, as well as to combined perturbations of different nature.

Abstract: Breast cancer remains a major cause of cancer-related mortality, and improved molecular characterization of pathological progression may support more objective tissue assessment. Raman spectroscopy (RS) provides a label-free and non-destructive approach for probing the intrinsic biochemical composition of biological tissues. In this study, we establish a Raman-based framework for characterizing biochemical remodeling across representative human breast pathologies and for exploring subtype-associated spectral differences between invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC). Formalin-fixed, paraffin-embedded (FFPE) tissues from five patients, representing normal breast tissue, intraductal papilloma, high-grade ductal carcinoma in situ (DCIS), IDC, and ILC, were analyzed using 785 nm excitation over the fingerprint region of 500–2000 cm⁻¹. Spectral preprocessing combining Savitzky–Golay smoothing, airPLS baseline correction, and vector normalization substantially reduced fluorescence-related background contributions and improved the resolution of biochemical Raman features. The spectra revealed progressive attenuation of lipid-associated bands near ~1063, ~1300, and ~1445 cm⁻¹, accompanied by increased relative contributions from protein- and nucleic-acid-associated features near ~1003, ~1420, and 1600–1650 cm⁻¹. The phenylalanine-to-lipid intensity ratio (I₁₀₀₃/I₁₄₄₅) increased from ≤0.2 in normal tissue to ≥1.5 in invasive lesions, providing an interpretable quantitative descriptor of the shift from a lipid-dominant toward a relatively protein-enriched Raman phenotype. IDC and ILC also exhibited distinct spectral characteristics, with IDC showing a prominent ~1618 cm⁻¹ aromatic C=C-associated feature and ILC exhibiting an enhanced ~890 cm⁻¹ carbohydrate-associated contribution. Principal component analysis (PCA) demonstrated clear multivariate organization of the training spectra, while supervised classification using LDA, SVM, random forest, and KNN confirmed that the Raman-derived features contained substantial discriminative information. Among the evaluated models, the RBF-SVM achieved the highest training accuracy of 93.4% and retained an accuracy of 90.0%, sensitivity of 85.7%, and specificity of 100% on a held-out test set. Collectively, these findings support a proof-of-concept framework in which breast pathological progression is associated with coordinated biochemical Raman changes that can be quantified through interpretable spectral descriptors and subsequently exploited for machine-learning classification. Larger patient-level and multi-center studies are required to validate the robustness and clinical generalizability of the proposed approach.

Abstract: Tumors, especially malignant tumors, are diseases of uncontrolled proliferation. The most fundamental properties of malignant tumors, pervasive nongenetic heterogeneity, the necessity of supporting tissues, rarity of occurrence, and inevitable therapy resistant relapses, remain unexplained by mutation-centric models. Here, we provide a multiscale tumor model rooted in first principles. The central premise of the model is that globally arbitrary accesses of chromatins in a cell unleash a repertoire of stochastic gene expression modules, including proliferation, death, stemness, EMT, angiogenesis, neurogenesis, fibrogenesis, and inflammation, that compete for finite transcriptional resources and are antagonized by the differentiation program that drive the tumor cell along its differentiation pathway from what it originates. A tumor can only become clinically diseases when it successfully self-organizes a complete supporting stroma of blood vessels, nerves, connective tissues, and an inflammation-driven clearance cycle, thereby locking into a self-constructed, noise-driven, evolvable dissipative structure. The model visualizes extremely low probability of tumor formation across pediatric and adult contexts, and the post-treatment recurrence with progressive dedifferentiation and can display testable predictions, such as prognostically favorable and poor tumor cell subpopulations and therapeutic taxic. By providing a unified physical theory of tumors, the model transforms tumor biology from a descriptive science into a predictive one.

Abstract: A physiological variable held by a controller with integral action cannot be moved by a sustained input. This is a theorem, not an observation: integral action drives the sensitivity function to zero at zero frequency, so a constant drug input produces zero steady-state deviation at any receptor occupancy. Potency cannot overcome it. What can move such a variable is a signal placed where the loop’s rejection is weakest, and Bode’s sensitivity integral guarantees that such a band exists. This paper derives that bound and shows by simulation that the drug-to-output path is a bandpass peaking at the loop’s natural frequency. Under an amplitude ceiling the effect of a pulsatile input peaks at a dosing period that is a fixed multiple of the loop’s natural period, the multiplier set by damping; the peak is broad, so what the model constrains sharply is the loss incurred by dosing faster than the loop. Four independent literatures retrodict the result: parathyroid hormone, gonadotropin-releasing hormone, adaptive deep brain stimulation, and conditioned dose reduction. Each shows a regulated system responding to the temporal pattern of exposure rather than its integral. The framework treats chronic symptom disorders as failures of active inference. That interpretation is assessed against what has actually been tested, which is less than the field’s confidence implies. Four predictions are stated, each with a criterion for failure.

Abstract: Background. In the final steps of the coagulation cascade, thrombin converts fibrinogen to fibrin, which polymerizes into a fibrin network that stabilizes blood clots. The concentration of these two proteins can affect network structure. The fibrin network is dissolved by plasmin, by cleaving fibrin peptide chains. In internal fibrinolysis, tissue plasminogen activator (tPA) activates endogenous plasminogen to plasmin, with activation efficiency much enhanced when tPA and plasminogen are bound to fibrin. The effect of varying fibrinogen and thrombin concentrations and the effect of the resulting varying network structure on fibrinolysis are convoluted, with contradictory trends reported in the literature. Objectives. Investigate how fibrinogen and thrombin concentration quantitatively affect fibrin network structure, and how fibrinogen, thrombin concentrations and the resulting, altered network structure affect fibrinolysis, at a given, rate-limiting tPA concentration. Methods. Fibrin clots were generated from diluted, platelet-poor plasma and purified PEAK 1 fibrinogen, at varying fibrinogen and exogenous thrombin concentrations. The effects of varying fibrinogen and thrombin concentrations on network structure were quantified using non-linear power-law equations derived from confocal images. Internal clot lysis time (CLT) was determined using turbidity-based assays as a function of fibrinogen and thrombin concentrations and network structure at fixed, rate-limiting tPA, and therefore fixed plasmin concentration. Results and Conclusions. Network structural features (fiber density, pore size) and CLT fit well to empirical equations of the form k([Fgn]^α× [Thr]^β). Generally, increasing fibrinogen resulted in higher fiber density and thicker fibers, whereas increasing thrombin resulted in higher fiber density and thinner fibers. Internal CLT showed strong dependence on fibrinogen, and negligible dependence on thrombin. This implies that fibrinogen is the main determinant of internal CLT in these systems, while thrombin and, thus, thrombin-dependent formation kinetics and network structure, per se, have a near-negligible effect.

Abstract: This article questions whether the predictive success of algorithmic descriptions warrants treating them as complete accounts of physical organisation. It distinguishes stability from organisation and argues that genuinely random fluctuations cannot, by themselves, explain the persistence of information-bearing structure whose coherence depends on information not already contained in the governing dynamics. Fluctuations treated as noise at one descriptive level may contain correlations or structure that become causally relevant at another. This possibility does not challenge established physical laws, but it questions the completeness of algorithmic explanation: predictive adequacy within a model should not be equated with the ontological completeness of the description. This framework is exemplified at the molecular scale, where Levinthal’s paradox and interfacial water dynamics suggest that protein folding relies on phase-correlated environmental fluctuations rather than isotropic stochastic search.

Abstract: Synaptobrevin (Syb/VAMP) is the vesicular SNARE that drives synaptic vesicle fusion. Before fusion, Syb is located in the vesicle membrane. After fusion, Syb is translocated to the presynaptic plasma membrane and exposed to a different lipid environment. How its membrane-proximal juxtamembrane and transmembrane domains (JMD–TMDs) organ-ize these different surrounding membrane environments remains incompletely under-stood. Here, we used MARTINI coarse-grained molecular dynamics simulations to in-vestigate Syb1/2 membrane organization and collective behavior. Syb1 and Syb2 induced similar and robust local lipid disorder, whereas the spatial organization of lipid disorder and membrane mismatch depended more strongly on membrane lipid composition. Higher order Syb clustering was promoted by the presence of PIP₂ in the membrane, which is decreased in its absence and insensitive to PS depletion. Consistently, Syb cluster formation was associated with pronounced PIP₂ redistribution and enrichment, while PS showed substantially weaker enrichment and decreased association with Syb in the presence of PIP₂. Clustering spatially overlapped otherwise localized lipid-disordered regions without proportionally amplifying their magnitude. Together, these results dis-tinguish robust local membrane disorder from composition-dependent geometric re-modeling and identify PIP₂ as a prominent correlate of higher-order Syb organization that may contribute to post-fusion Syb reorganization before endocytic retrieval.

Abstract: Chromatin spatial organization is commonly described through compaction, contact frequencies, chromosome territories, loops and polymer dynamics, yet these measures do not address how nuclear confinement shapes accessible genome configurations. We introduce the concept of chromatin congestion to describe changes in the geometrically, topologically and mechanically admissible chromatin configurations within a confined nucleus, integrating directional geometric constraints, topological obstruction, stochastic exploration, mechanical deformation and long-range dependence into a common description. We performed stochastic simulations of 480 nuclei across six microchannel widths from 14 to 4 micrometers, keeping chromatin content constant and quantifying collision-free path length, locus displacement, topological encounters, curvature, bending work, perturbation propagation and DNA-damage burden. As confinement progressed, chromatin displacement initially increased, consistent with exploration of the configurations still available, but declined under stronger restriction. Topological encounters, curvature and mechanical work increased, while perturbations extended across progressively larger nuclear regions and were associated with greater damage burden. Our simulations suggest that progressive confinement can drive transition from locally buffered chromatin dynamics to increasingly collective genome behaviour, characterized by stronger spatial interdependence among genomic regions. Potential applications include microfluidic migration experiments, single-cell nuclear phenotyping and the integration of imaging, mechanical and genome-organization measurements.

Abstract: The small GTPase Ras, a central switch in signal transduction of cell growth, is a crucial element in the development of many forms of cancer. Recent studies have shown that membrane association of Ras is essential to its downstream effector recruitment and signal transduction. Here we investigate the membrane association and interaction of K-Ras4B, the most frequently found Ras isoform in tumor cells, using all-atom molecular dynamics simulations totaling 8.5 µs. We find that the isoform-specific, farnesylated hypervariable region (HVR) of Ras plays an important role in the organization and oligomerization of its globular domain (G-domain) on the surface of the membrane. We show that the overall pose of the Ras G-domain on the membrane can be determined by its HVR. The HVR thus can control downstream effector binding by positioning the G-domain on the membrane in a specific orientation. Furthermore, we observe that the HVR alone already transiently dimerizes in the membrane and recruits negatively charged phosphatidylserine lipids around the K-Ras isoform specific poly-lysine region. The observed HVR dimerization potentially can initiate the dimerization of the full K-Ras, which is essential for downstream effector signaling via various pathways. These atomistic details behind the molecular mechanism of the HVR provide novel insight into Ras interaction with membrane and its availability for downstream effectors.