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Results for “Biophysics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,261 records · Page 70Linked to original sources

bioRxiv2025

S1PR3 mediates glial stimulated tumor invasion in response to interstitial fluid flow

Cellular invasion is a primary challenge to complete resection and treatment of glioblastoma, the most aggressive and deadly primary brain tumor. The brain tumor microenvironment actively stimulates glioma invasion through a multitude of cellular, chemical, and biophysical cues. We and others have shown elevated interstitial fluid flow at the tumor border is one such biophysical cue that directly stimulates invasion through tumor-intrinsic signaling and, in other tumor types, priming of cancer-associated stromal cells. It is currently unclear if interstitial flow similarly primes neuroglial cells to promote glioma cell dissemination and can be targeted for therapeutic purposes. Here, we show elevated interstitial flow upregulates expression of sphingosine-1-phosphate receptor 3 (S1PR3) in glial astrocytes and microglia, which drives glioma cell invasion via chemotaxis. Flow-induced expression of glial S1PR3 is tumor-independent and displays a biphasic relationship to fluid shear stress magnitude in vitro and flow rate in vivo. Inhibition of glial S1PR3 in a tissue engineered culture model and orthotopic mouse model abrogates flow-stimulated invasion, demonstrating a tumor-extrinsic approach to limiting glioblastoma progression. Given prior evidence of a pro-inflammatory role for glial S1PR3, identification of S1PR3 as a disease-agnostic marker of flow-stimulated glia may also have therapeutic implications across myriad neuropathologies.

cancer biology↗

bioRxiv2025

Genetic Targeting and Conductance-Based Modeling Reveal Novel Diversity Within Mouse Type II Spiral Ganglion Neurons

Spiral ganglion neurons (SGNs) transmit auditory signals from the cochlea to the brain and are divided into two main types: type I and type II, distinguished by their anatomy and connectivity. However, the function of type II SGNs remains poorly understood due to their scarcity and lack of clear physiological markers. In this study, we use two Cre-dependent fluorescent reporter mouse lines to enhance the identification and targeting of type II SGNs for whole-cell patch-clamp recordings. We reveal a set of distinguishing biophysical features, most notably, the presence of an inactivating potassium current and weaker voltage-gated sodium currents, that clearly separate type II SGNs from their type I counterparts. Additionally, we uncover greater-than-expected heterogeneity among type II SGNs, including variation in size, excitability, and ion channel expression. These features suggest the existence of distinct subtypes of type II SGNs, with potential differences in function. We find that most type II SGNs are relatively unexcitable and incapable of repetitive firing. Instead, they appear to be better suited to integrating sustained signals, potentially supporting roles in detecting cochlear damage or modulating efferent feedback. Additionally, through computational modeling, we demonstrate that removing the inactivation component of the inactivating potassium current specific to type II SGNs allowed repetitive spiking to similar levels seen in type I SGNs, suggesting a crucial role for the current in stifling type II SGN activity. Together, our findings define biophysical signatures that distinguish SGN types and subtypes, offering new insight into their contributions to normal hearing and cochlear pathology. SignificanceThe sensory neurons of the cochlea are divided into type I and type II spiral ganglion neurons. Type I spiral ganglion neurons convey the main features of sound information. The rarer type II spiral ganglion neurons appear to be putative auditory nociceptors, responding to cochlear damage. By combining genetic tools, electrical activity recordings, and computational models, we demonstrate that type I and type II spiral ganglion neurons have distinctive ion channel profiles and firing properties. Furthermore, we report previously undescribed ion channel diversity within the type II spiral ganglion neuron population, suggesting varied functions. Our results highlight the parallels between type II spiral ganglion neurons and somatosensory nociceptors and provide a framework for selectively targeting distinct auditory neuron populations.

neuroscience↗

bioRxiv2025

Ligandability Assessment of the LAG-3 D1 Domain Enables Discovery of a Small-Molecule Inhibitor

LAG-3 is an emerging immune checkpoint whose extracellular D1 domain engages MHC class II through a broad protein-protein interface traditionally considered difficult to modulate with small molecules. To evaluate the ligandability of this region, we combined 100-ns molecular dynamics (MD) simulations, structure-based virtual screening, and biophysical and biochemical assays. MD sampling of the isolated D1 domain revealed a recurrent, transient cavity adjacent to the MHCII-binding surface. A representative pocket-open conformation was used to screen a [~]10,240-compound diversity library, yielding a single validated hit, N05. N05 bound the D1 domain with micromolar affinity measured by microscale thermophoresis (Kd = 59.2 {micro}M, TRIC/MST channel) and by spectral-shift detection (Kd = 56.1 {micro}M), and it partially inhibited the LAG-3/MHCII interaction (EC50 = 42.9 {micro}M; maximal inhibition [~]76%). A 30-ns MD simulation of the LAG-3-N05 complex showed stable ligand engagement within the MD-identified cavity and consistent stabilizing interactions with residues forming the pocket. These results demonstrate that the LAG-3 D1 domain possesses an accessible, dynamically formed binding site capable of accommodating small molecules, providing a structural and biophysical foundation for future exploration of LAG-3 ligandability. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/692800v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@177b446org.highwire.dtl.DTLVardef@1dc9efdorg.highwire.dtl.DTLVardef@14a2d6aorg.highwire.dtl.DTLVardef@ebca69_HPS_FORMAT_FIGEXP M_FIG C_FIG Overview of the computational and experimental workflow used to identify and validate a small-molecule binder of the LAG-3 D1 domain.

bioinformatics↗

bioRxiv2025

Structure-based design of antibody repertoires with drug-like properties

Animal immunization is the prevalent strategy for discovering antibody therapeutics1, but it is a lengthy and poorly controlled process. As an alternative, synthetic antibody repertoires deliver antibodies without animal welfare concerns, but the resulting antibodies often fail to exhibit "drug-like" biophysical properties1,2. Modern repertoires have improved developability by using a handful of frameworks with desirable biophysical properties, but at the cost of reduced structural diversity3-6. We developed a principled structure- and energy-based strategy, called CADAbRe, to navigate the complex tradeoffs between developability and structural diversity in repertoire design. The designed repertoire comprises billions of antibodies that are predicted to be stable and foldable, built from hundreds of different frameworks and hundreds of thousands of designed CDR H3s. We also developed an economical and scalable strategy for synthesizing large antibody repertoires, and as a proof of concept, designed and synthesized a 500-million variant phage display repertoire. Selections against four unrelated targets produced structurally diverse binders that exhibited drug-like properties, two of which were readily formatted as bispecifics for functional studies. Furthermore, one of the binders targets a challenging, highly charged surface. The proof-of-concept repertoire is available for academic research. We envision that the CADAbRe approach and repertoire will accelerate and rationalize antibody discovery while addressing animal-welfare concerns7.

synthetic biology↗

bioRxiv2025

Fold or flop: quality assessment of AlphaFold predictions on whole proteomes

MotivationReliability of AlphaFold2 predictions is mainly assessed using the predicted Local Distance Difference Test (pLDDT). For model organisms, 30-40% of residues fall into the low-confidence pLDDT range. Moreover, pLDDT sometimes fails to flag physically implausible structures. This raises two questions: can more robust reliability indicators be identified, and do unreliable predictions share common structural or biophysical features? ResultsWe characterize protein structures through histograms of per-residue neighbor counts, and use the Wasserstein principal component analysis to define the arity map, and lightweight and informative 2D embedding of proteins in a dataset. Using AlphaFold-DB, we show that the arity map reveals three structurally and biophysically distinct populations (well-folded proteins, intrinsically disordered proteins, and physically implausible predictions). We also use our packing based encoding at the residue level to define abstraqt (Arity-Based STRuctural Arrangement Quality assessmenT), a per-residue scoring function complementing the pLDDT, assigning low scores to hallucinated helices and distorted beta strands while correctly scoring native like predictions. AvailabilityThe code to compute arity maps is available within the Structural Bioinformatics Library. See: AlphaFold analysis, and also Documentation, Applications, Installation guide. The code and data for rerunning analyses are made available from AlphaFold analysis, and Data.

bioinformatics↗

bioRxiv2025

Tuning the open-close equilibrium of Cereblon with small molecules influences protein degradation

Most PROTACs and molecular glue degraders currently approved or in clinical trials recruit Cereblon (CRBN) as the ubiquitin E3 ligase. Upon binding ligands and molecular glues, CRBN undergoes a significant structural rearrangement from an open to closed state, defined by the positioning of the thalidomide-binding domain (TBD) with respect to the Lon domain. However, the exact molecular basis for this ligand-induced conformational change and its implication to neo-substrate degradation remain elusive. During our campaign to discover novel CRBN binders, we found hits exhibiting distinct biophysical behaviour from classical thalidomide-based ligands. By combining orthogonal biophysical methods of differential scanning fluorimetry, isothermal titration calorimetry, and small-angle X-ray scattering, supported by X-ray crystallography and cryo-EM structures of ligand-bound complexes, we classify CRBN binders between those that can induce CRBN closure, and those that cannot. Mutational studies identify key residues in the CRBN ligand binding pocket and N-terminal belt that are essential for the Lon and TBD domains to come together in the closed state. Finally, we show that the probability to yield active degrader molecules is greatly influenced by whether binders can or cannot induce CRBN closure. Together, our study reveals new molecular insights into the structural basis for how CRBN open-closed equilibrium is directly modulated by compound binding and impact target degradability by CRBN, with important implications to the design of PROTACs and molecular glue degraders.

biochemistry↗

bioRxiv2025

HIV Nef amplifies mechanical heterogeneity to promote immune evasion

Intracellular pathogens must evade cytotoxic immunity to establish persistent infection. Although immune escape is typically viewed through a biochemical lens, the ability of certain pathogens to alter the mechanical properties of infected cells suggests that biophysical mechanisms may also contribute to the process. Here, we show that a subset of CD4+ T cells infected with the human immunodeficiency virus (HIV) resist elimination through a soft phenotype that inhibits killing by mechanosensitive cytotoxic T lymphocytes (CTLs). This phenotype arises from the combined effects of the HIV virulence factor Nef, which remodels the actin cytoskeleton, and intrinsic heterogeneity in the basal cytoskeletal properties of infected T cells. Pharmacological or genetic perturbations that reverse Nef signaling to the cytoskeleton or that stiffen the filamentous-actin cortex sensitize infected cells to CTL-mediated lysis. Taken together, these findings define a novel, biophysical paradigm of immune evasion with implications for HIV cure strategies.

immunology↗

bioRxiv2025

Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties

1A prominent application of machine learning in therapeutic antibody design is the development of models that can generate or screen antibody candidates with a high probability of success in manufacturing and clinical trials. These models must accurately represent sequence-structure-function relationships, also known as the fitness landscape. Previous protein function benchmarks examine fitness landscapes across diverse protein families, but they exclude antibody data. Here, we introduce the second iteration of the Fitness Landscape for Antibodies (FLAb2), the largest public therapeutic antibody design benchmark to date. The datasets collected in FLAb2 contain developability assay data for over 4M antibodies across 32 studies, encompassing seven properties of therapeutic antibodies: thermostability, expression, aggregation, binding affinity, pharmacokinetics, polyreactivity, and immunogenicity. Using the curated data, we evaluate the performance of 30 artificial intelligence (AI) and biophysical models in learning these properties. Protein AI models on average do not produce statistically significant correlations for most (80%) of developability datasets. No models correlate with all properties or across multiple datasets of similar properties. Zero-shot predictions from pretrained models are incapable of accurately predicting all developability properties, although several models (IgLM, ProGen2, Chai-1, ESM2, ISM, IgFold) produce statistically significant correlations for multiple datasets for thermostability, expression, binding, or immunogenicity. Fine-tuning with at least 102 points improves performance on thermostability, aggregation, and binding, but polyreactivity and pharmacokinetics lack enough data for significance. Yet it is humbling to observe that given enough developability data (103 points), a fine-tuned one-hot encoding model can match the performance of fine-tuned billion-parameter pretrained models. Training data composition influences performance more than model architecture, and intrinsic biophysical properties (thermostability) are more readily learned than extrinsic properties (immunogenicity, pharmacokinetics). Controlling for germline distance with partial correlation reveals that protein language models draw substantially on evolutionary signal; on average, germline edit distance accounts for 40% of their apparent predictive power. FLAb2 data are accessible at https://github.com/Graylab/FLAb, together with scripts that allow researchers to benchmark, compare, and iteratively improve new AI-based developability prediction models.

bioinformatics↗

bioRxiv2026

Neuronal excitability and parameter variability in the Hodgkin-Huxley model

Biophysically detailed neuron models are often built as a one-way pipeline in which voltage-clamp data are reduced to a single set of best-fit channel parameters, which are then combined into a deterministic spiking model. This practice discards experimentally observed variability and obscures the mechanisms by which robustness and degeneracy arise in excitable systems. Here, we reintroduce parameter variability into the Hodgkin-Huxley model and embed uncertainty and global sensitivity analysis into model construction. We digitized sodium and potassium rate constant data from the original Hodgkin and Huxley figures and used bootstrap resampling to estimate the variability of the voltage-dependent kinetic parameters. We then propagated these uncertainty estimates through a spatially extended squid axon cable model using large-scale Monte Carlo simulations. At the channel level, first-order Sobol sensitivity indices revealed that all kinetic parameters contribute to output variance in a strongly time-dependent manner, with distinct parameters controlling transient and steady-state behavior for potassium and sodium conductances. At the level of neuronal excitability, sampling hundreds of thousands of parameter sets produced a heterogeneous population of firing behaviors, including non-firing, phasic, regular, and spontaneous activity. Across stimulus amplitudes, the dominant firing mode was a single spike at stimulus onset. At the same time, the regularly firing subpopulation exhibited a broad distribution of firing rates, with a mean that matched the classic Hodgkin-Huxley prediction. In the phasic subpopulation, action potential propagation and conduction velocity varied widely yet remained consistent with experimental ranges. Finally, global sensitivity analysis during spiking shows uniformly small first-order indices but large total-order indices, indicating that excitability is primarily governed by strong interactions among parameters rather than by any single conductance or kinetic parameter. These results support a population-based view of conductance-based modeling in which biologically relevant behavior emerges from structured regions of parameter space. Author summaryNeurons are often modelled by first fitting ion-channel data to a few parameters, then integrating them into a single-neuron model. This typical method masks the fact that actual experiments show variability and that many different parameter sets can yield similar electrical behavior. In this research, we explored what happens when we keep rather than average out that variability. We revisited Hodgkin and Huxleys classic squid giant axon studies and derived ranges for sodium and potassium channel parameters by resampling digitized points from the original Hodgkin-Huxley figures using bootstrap resampling. We then ran simulations of hundreds of thousands of squid axon models, each with a unique, experimentally grounded parameter set, and analyzed the collective results. This showed that the most common response was a single action potential rather than repetitive firing, aligning with the axons role in the rapid escape response. Additionally, we discovered that no single parameter alone controls spiking; rather, it depends on interactions among multiple parameters. Our findings advocate a practical change in biophysical modeling: instead of hunting for a single best-fit model, researchers should estimate parameter uncertainty directly from data, create large ensembles that sample this uncertainty, and perform sensitivity analyses on these ensembles before choosing any model for further study.

neuroscience↗

bioRxiv2026

Neurophysiological excitation/inhibition imbalance in young adults burdened with childhood interpersonal trauma

Adverse childhood experiences, such as violence, abuse, and neglect, are increasingly recognized as significant modifiers of brain development. Here, we tested whether adults with histories of childhood trauma, but without psychiatric comorbidities exhibit altered excitation/inhibition (E/I) balance, as indicated by electroencephalography (EEG) signatures. Participants, divided into low- trauma and high-trauma groups, underwent two experimental conditions: eyes-closed resting-state recording and a reaction-time task with visual stimuli. From these data, we computed 1/f spectral slopes, a widely used electrophysiological marker of E/I balance; we complemented these analyses with a leaky integrate-and-fire (LIF) microcircuit model combined with a biophysically grounded forward-modeling approach to simulate realistic brain signals and derive E/I balance estimates. Group comparisons for both slopes and E/I estimates revealed significant resting-state differences, characterized by a shift toward increased neuronal excitation in the high-trauma group. The high- trauma group exhibited altered stimulus-related 1/f slope dynamics relative to the pre-stimulus baseline, reflecting attenuated neuronal inhibition. E/I ratio measures were not significantly correlated with participants transient affective states. Together, these findings suggest that childhood trauma is associated with enduring, trait-like alterations in cortical E/I balance that extend beyond affective state and manifest across both resting and task-related brain dynamics. Significance StatementChildhood trauma is a major risk factor for mental illness, yet its lasting effects on basic brain physiology remain poorly understood. Using electroencephalography combined with biophysically grounded neural circuit modeling, we show that young adults with a history of childhood interpersonal trauma exhibit a persistent shift toward cortical hyperexcitation at rest and a reduced ability to engage inhibitory control during cognitive processing. These effects occur even in individuals without psychiatric diagnoses and are independent of current anxiety or depression, indicating a trait-like neurophysiological footprint of early adversity. This excitation-inhibition imbalance may represent a transdiagnostic vulnerability linking childhood trauma to later psychopathology.

neuroscience↗

bioRxiv2026

Dynamics-aware Evolutionary Profiling Uncouples Structural Rigidity from Functional Motion to Enable Enhanced Variant Interpretation

Evolutionary conservation is a powerful part of mutational intolerance prediction, yet traditional pathogenicity metrics frequently conflate two distinct biophysical constraints: structural stability (rigidity) and functional mechanics (dynamics). We introduce Dynamics-Aware Evolutionary Profiling to resolve this ambiguity, integrating Molecular Dynamics with evolutionary conservation and coupling analysis across human/cross-species-proteome of 151 protein structures. By mathematically uncoupling biophysical forces, we define orthogonal metrics; the Rigid Conserved Score (RCS) for the structural scaffold, and the Dynamic Conserved Score (DCS) for flexible residues. Our analysis reveals a fundamental bifurcation in pathogenicity. RCS serves as a filter for lethal structural failure, isolating hydrophobic core residues whose mutation triggers unfolding. In contrast, DCS identified a rare population of residues that are evolutionarily highly-conserved but structurally mobile; these Dynamic-Conserved sites exhibit intermediate pathogenicity and are enriched in flexible hinge residues (Gly, Pro). Validation against 737 human variants from ClinVar demonstrates that DCS captures a distinct pathogenic mechanism regarding essential protein motion. Notably, DCS and RCS correctly flagged some pathogenic variants of NARS1 and PGK1 that were misclassified as benign or ambiguous by AlphaMissense. These results indicate that while the rigid core represents a stability bottleneck, DCS isolates functional sites likely driving allosteric regulation. We provide an open-access web interface (ADEPT) for these metrics. By isolating dynamic-conserved residues, this framework refines the interpretation of Variants of Uncertain Significance in dynamic regions and reveals tunable targets for rational drug design, moving beyond the static optimization of the folded state.

bioinformatics↗

bioRxiv2026

BioGraphX: Bridging the Sequence-Structure Gap via PhysicochemicalGraph Encoding for Explainable Subcellular Localization Prediction

Computational approaches for protein subcellular localization prediction are important for understanding cellular mechanisms and developing treatments for complex diseases. However, a critical limitation of current methods is their lack of interpretability: while they can predict where a protein localizes, they fail to explain why the protein is assigned to a specific location. Moreover, understanding protein behavior traditionally requires knowledge of three-dimensional structure, which is a costly and time-consuming process. Here, we propose BioGraphX, a novel encoding framework that constructs protein interaction graphs directly from protein sequences using biochemical rules. This approach provides a constraint-based structural proxy directly from sequence, reducing the dependency on experimentally determined three-dimensional structures. Building upon this representation, BioGraphX-Net demonstrates superior performance on the DeepLoc 2.0 benchmark by integrating ESM-2 embeddings with the proposed features via a gating mechanism. Gating analysis shows that although ESM-2 embeddings provide strong contributions, BioGraphX features function as high-precision filters. SHAP analysis reveals feature importance patterns consistent with a sophisticated biophysical logic: sequence signals act as universal exclusion filters, while organelle-specific combinations of biophysical features enable precise compartment discrimination. Notably, Frustration features help resolve targeting ambiguities in complex compartments, reflecting evolutionary constraints while preventing mislocalization from sequence mimicry. It has the additional advantage of promoting Green AI in bioinformatics, achieving performance comparable to the state-of-the-art while maintaining a minimal parameter count of 13.46 million. In summary, BioGraphX not only provides accurate predictions but also offers new insights into the language of life.

bioinformatics↗

bioRxiv2026

Stabilizing Plasmodium falciparum Proteins for Small Molecule Drug Discovery

Early-stage drug discovery relies on the availability of stable protein for reliable biophysical characterization of ligand binding. However, many Plasmodium falciparum proteins are challenging to produce in heterologous systems, which limits their experimental utility. To address this, we tested whether ProteinMPNN-guided sequence design could generate stabilized surrogate constructs that retain wild-type-like structure and binding thermodynamics. Designs were generated with constraints to maintain conserved and binding-site residues for three therapeutically relevant targets: PfBDP1-BRD, PfBDP4-BRD, and PfK13-KREP. The resulting constructs showed markedly increased thermal stability. Using PfBDP1-BRD as a benchmark, isothermal titration calorimetry confirmed that the stabilized variants retained wild-type-like binding thermodynamics with a known ligand. Extending this approach to other targets, a PfK13-KREP construct led to an apo structure with a binding pocket closely matching the wild type, and a stabilized PfBDP4-BRD surrogate - a previously unstable target - enabled the identification of PfBDP4-BRD binders and a 1.25 [A] co-crystal structure with a newly found inhibitor. Our findings demonstrate that computationally stabilized surrogates are practical and effective tools for robust biophysics and structure-enabled drug discovery against otherwise challenging malaria proteins.

biochemistry↗

bioRxiv2026

3D Cell-Matrix Mechanical Interaction Models for Cancer Invasion and Drug Evaluation

Cancer cells breach the extracellular matrix (ECM) using both protease-driven degradation and force-driven physical remodeling, yet most anti-metastatic drug screens still rely on biochemical assays that overlook cell-matrix mechanical reciprocity. Here, we present a fully synthetic 3D invasion platform based on cellular force-responsive polyisocyanide (PIC) hydrogels that isolates biophysical invasion mechanisms. Cell-generated forces align and densify the PIC fibrous network, reproducing hallmark matrix remodeling seen in the tumor microenvironment. A constitutive model, parameterized by the critical stress for strain stiffening effect, links matrix nonlinear elasticity to pericellular stiffening, long-range mechanotransmission, and intercellular coupling. Using this system, we show that breast cancer cells invade by pulling and pushing the network even when matrix metalloproteinases are inhibited, revealing a physical bypass of protease blockade. Accordingly, broad-spectrum metalloproteinase inhibitors that suppress invasion in Matrigel fail to inhibit invasion here, exposing a limitation of current drug-evaluation pipelines. In co-culture, cancer-associated fibroblasts markedly accelerate invasion by generating aligned fiber tracks through higher contractility, implicating CAF-driven mechanical remodeling as a key route for breaching barriers during metastasis. The platform is thermoresponsive, compatible with standard Transwell formats, enables direct imaging of fiber architecture and invasion fronts, and decouples biophysical from biochemical cues for mechanism-aware, animal-free assessment of anti-metastatic therapies.

cancer biology↗

bioRxiv2026

Environmental and biotic drivers of Aedes albopictus spatiotemporal distribution in the Argentina-Brasil-Paraguay subtropical triple border: The key role of periurban and disturbed wild environments

There is empirical evidence that biophysical factors determine the spatio-temporal distribution of mosquito vectors, and identifying the variables that shape their ecology allows decision-makers to design effective surveillance and control strategies. This study evaluated the spatiotemporal distribution of Aedes albopictus in relation to environmental and biotic variables in the Iguazu Department, Misiones Province, Argentina, within the tri-border region shared with Brazil and Paraguay. Environmental characterization integrated field data and remotely sensed biophysical variables, and vector occurrence was analyzed at micro- and meso-spatial scales using generalized linear mixed models. Eleven sampling sessions were conducted between April 2019 and February 2020 at 81 sites representing urban, periurban, and wild environments. A total of 1,614 Ae. albopictus and 4,358 Ae. aegypti specimens were identified. Rainfall, minimum temperature, exposure days, and land cover were the main predictors of Ae. albopictus presence, showing nonlinear responses to precipitation and vegetation. The selected model explained 67% of the variance. The species exhibited clear spatiotemporal stratification, with periurban and disturbed wild areas functioning as ecotones favorable to its establishment. These findings provide key insights to guide preventive actions and strengthen integrated vector management strategies in the region.

ecology↗

bioRxiv2026

Electrostatic facilitation of odorant capture in insects

Olfaction is a sensory modality common to most organisms. In insects, the primary olfactory organ is the antenna, where sensilla house olfactory receptor neurons adapted to detect volatile organic compounds (VOCs). Whilst olfaction is well-understood at molecular and neural levels, questions remain as to how, biophysically, airborne VOCs reach sensilla. Transport through passive diffusion and active antennal motion is empirically supported but cannot entirely explain the remarkably rapid VOC sampling rates. We present evidence that the insect antennae exploits electrostatic forces that amplify VOC transfer from bulk air to sensilla. In effect, charged antennae capture more ambient VOCs than neutral ones, also evoking an enhanced electrophysiological (EAG) response to VOCs. Experimentally altering the charge of isolated antennae modulates EAG responses and olfactory sensitivity. Multiphysics modelling incorporating electrostatic and fluid dynamic mechanisms supports empirical evidence. Altogether, this work reveals the existence of a previously unknown and complementary biophysical mechanism supporting olfaction. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/703773v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1917cc3org.highwire.dtl.DTLVardef@962075org.highwire.dtl.DTLVardef@2ce4b3org.highwire.dtl.DTLVardef@15ada3a_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2026

Noise Decorrelation as a Hypothetical Mechanism for Phase-Specific Neurometabolic Outcomes in HIV Infection: A Computational Framework

Despite direct neurotoxic assault and cytokine storm during acute HIV infection, over 90% of individuals maintain normal neurocognitive function with preserved N-acetylaspartate (NAA)--a clinical paradox that has resisted mechanistic explanation for 35 years. Here we show that environmental noise correlation length ({xi})--a quantum biophysical parameter inferred across photosynthesis, magnetoreception, and now neuronal metabolism--distinguishes protected from vulnerable neurometabolic states. Using hierarchical Bayesian inference on the largest consolidated neuro-metabolic dataset to date (13 group-level observations aggregating ~220-296 patients across 4 independent studies), we find shorter noise correlation during acute infection ({xi}acute = 0.425 {+/-} 0.065 nm) compared to chronic infection ({xi}chronic = 0.790 {+/-} 0.065 nm), with non-overlapping 95% highest density intervals. The inferred protection exponent {beta}{xi} = 2.33 {+/-} 0.51 (95% HDI: 1.49-3.26) indicates superlinear scaling of metabolic protection with decreasing correlation length. Independent validation via enzyme kinetics modeling yields concordant results (protection ratio = 1.28 {+/-} 0.17), and the inferred{xi} values (0.42-0.81 nm) converge with noise correlation scales in photosynthetic energy transfer and avian magnetoreception--systems where{xi} is likewise inferred from functional data rather than directly measured. The phase-specific and regionally structured modulation observed implicates a conserved mechanism for maintaining neuronal metabolic integrity during acute inflammatory stress, with cross-system convergence of noise correlation scales providing independent biophysical validation. All primary data have been deposited in an open-source repository (Zenodo DOI: 10.5281/zenodo.18685010), constituting the first publicly available consolidated HIV neuro-metabolic MRS dataset.

neuroscience↗

bioRxiv2026

Validating Neurite EXchange Imaging (NEXI) using diffusion Monte Carlo simulations in realistic numerical gray matter substrates

NEXI is a gray matter (GM) microstructural model designed to probe brain tissue microstructure in vivo using diffusion MRI. NEXI describes GM as two exchanging Gaussian compartments - neurites, modeled as randomly oriented, infinitely long sticks, and the extracellular space - allowing the estimation of biophysically interpretable parameters related to neurite microstructure and intercompartmental exchange. While modeling cell processes as sticks and each compartment as Gaussian are common assumptions for brain biophysical models of diffusion, neurite structural irregularities and the presence of somas, particularly in GM, may violate them and bias NEXI parameter estimates. Furthermore, the barrier-limited exchange assumed in the Karger model that underlies NEXI may also be violated in realistic conditions. Therefore, in this work, we evaluate NEXIs accuracy in numerical substrates that incorporate realistic GM features and membrane permeability. To this end, we generated several GM-like substrates with neurite beading, undulation, orientation dispersion, and somas across a range of membrane permeabilities. Diffusion signals were generated with Monte Carlo simulations of water diffusion and subsequently fitted with NEXI. Overall, NEXI accurately recovered exchange times across permeability levels and successfully disentangled exchange effects from other microstructural features, showing only minor bias in estimates from the realistic geometries. These results support its potential for in vivo GM microstructure mapping and studies of brain disorders.

neuroscience↗