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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Effect of ORL-1 on Cav1.2 calcium channels

Cav1.2 is an L-type voltage-gated Ca2+ channel (VGCC) that supports Ca2+ influx in response to membrane depolarization. Ca2+ entering via Cav1.2 alters gene expression, activates Ca2+-dependent enzymes and has been implicated in synaptic plasticity. ORL-1 is a Gi/o-coupled G protein-coupled receptor (GPCR) that is expressed in the peripheral and central nervous systems. Both Cav1.2 and ORL-1 are expressed in the hippocampus, where they have been implicated in learning and memory. It is well-documented that ORL-1 interacts with another VGCC, Cav2.2. However, less is known about potential interactions between Cav1.2 and ORL-1. Here, we examine the interplay between Cav1.2 (Cav1c, Cav2{delta}-1, Cav{beta}1b) and ORL-1 co-expressed in tsA-201 cells by using biochemical, electrophysiological and confocal imaging analysis. Co-immunoprecipitations revealed that ORL-1 independently interacts with Cav1c and Cav2{delta}-1 subunits of the Cav1.2 channel complex. Electrophysiological recordings revealed that co-expression with ORL-1 reduced Cav1.2 peak current density without altering its biophysical properties. Acute perfusion with the ORL-1 receptor agonist nociceptin (1 {micro}M) did not alter Cav1.2 current density. Confocal imaging experiments revealed that ORL-1 significantly decreases Cav1.2 plasma membrane expression by disrupting forward trafficking. Interestingly, ORL-1 did not affect Cav1.2 endocytosis. Overall, our results demonstrate a previously unrecognized interaction between ORL-1 and Cav1.2 that alters Cav1.2 membrane expression without affecting biophysical properties.

neuroscience↗

bioRxiv2026

Behavioral and Functional Profiling of Acomys cahirinus Fibroblasts Reveals Enhanced Matrix Remodeling Capacity

The African spiny mouse (Acomys cahirinus) exhibits a unique capacity among mammals for scarless tissue regeneration, making it a compelling model for investigating the cellular mechanisms underlying regenerative healing. To determine how cellular heterogeneity and specific phenotypes influence fibroblast behavior, we established an immortalized Acomys fibroblast line along with a CRISPR/Cas9-mediated Col3A1 knockout variant and a DNA damage-induced senescent population. Compared with Mus musculus, NIH 3T3 fibroblasts, Acomys cells displayed distinct morphology, similar migration speeds, reduced directional persistence, and greater biophysical heterogeneity. While previous studies have linked regenerative wound healing to the elevated expression of collagen type III (Col3A1), CRISPR-mediated knockout of Col3A1 in Acomys fibroblasts yielded comparable biophysical profiles to wild-type cells in 2D culture. To examine additional contributors to the enhanced wound-like matrix environment, we established a senescence model in which Acomys fibroblasts exhibited elevated resistance to DNA-damaging agents, complete loss of proliferation, and altered single-cell morphology. In 3D collagen gel contraction assays, Col3A1 knockout attenuated matrix remodeling capacity, whereas the introduction of a small fraction of senescent cells enhanced gel contraction and remodeling dynamics, suggesting that senescent fibroblasts can modulate collective matrix behaviors. Together, these findings demonstrate that both Col3A1 expression and senescence-associated cell states contribute to fibroblast-driven matrix remodeling, highlighting Acomys fibroblasts as a valuable model for investigating how cellular heterogeneity and senescence-associated cell phenotypes could influence regenerative wound healing.

bioengineering↗

bioRxiv2026

BraiNN: A Modern Simulator for Clinically Feasible Personalized Whole-Brain Network Modeling

Personalized whole-brain modeling aims to transform treatment planning for neurological disorders by enabling patient-specific simulations of brain network dynamics. Neural mass models (NMMs) offer a tractable compromise between biophysical detail and computational cost and can be directly linked to macroscopic observables such as EEG. However, scaling NMMs to whole-brain networks with realistic connectivity, conduction delays, and cortical surface resolution--and fitting them to individual patient data--imposes computational demands that existing frameworks cannot meet at clinically relevant timescales. Here we introduce BraiNN, a JAX-based Python framework for large-scale neural mass modeling that achieves speedups of up to two to three orders of magnitude over existing tools by leveraging GPU/TPU-accelerated, XLA-compiled array computation. BraiNN combines a region-level Jansen-Rit network with a subject-specific cortical surface mesh of coupled neural mass models and biophysically grounded EEG forward modeling via reciprocity-based lead fields. Its fully differentiable computational graph enables a hybrid personalization pipeline that pairs Bayesian optimization for global parameter exploration with gradient-based refinement, completing EEG-driven spectral fitting of an eight-dimensional parameter space in approximately 2-3 hours on a single consumer GPU--compared to multiple days with conventional neural mass modeling software. Numerical verification against established benchmarks confirms that BraiNN faithfully reproduces canonical synchronization and bifurcation dynamics of Jansen-Rit networks. By reducing the time requirements for personalizing a high-detail whole-brain surface model from days to a few hours on consumer-grade hardware, BraiNN brings personalized brain network modeling closer to practical use in clinical contexts. We anticipate that BraiNN will serve as a foundation for patient-specific digital twins and EEG-guided neuromodulation planning.

neuroscience↗

bioRxiv2026

An engineered biofactory for efficient production of diverse recombinant superoxide dismutase isozymes loaded with specific metal ions for biochemical characterisation

BackgroundBiochemical, biophysical and structural characterisation of isozymes from the ubiquitous family of iron- or manganese-dependent superoxide dismutases (SodFMs) requires the purification of high-quality preparations of recombinant enzymes. Determination of their key biochemical parameter, their catalytic metal-preference, requires the comparison of the catalytic turnover of samples loaded exclusively with iron versus samples loaded exclusively with manganese. Both of these aims are inhibited by the potential contamination of recombinant preparations of SodFMs, prepared by heterologous overexpression inside Escherichia coli cells, by even low levels of endogenous SodFMs from the host, both of which show very high turnover with either manganese (E. coli MnSOD) or iron (FeSOD). To overcome this problem, we created a strain of E. coli lacking the endogenous SodFMs. Here, we characterised this E. coli BL21 (DE3) {Delta}sodA{Delta}sodB strain, determining the physiological effects of SodFM deletion and demonstrating its utility for producing recombinant SodFMs for in vitro characterisation and use. ResultsGenomic analysis verified the targeted gene deletions, without off-target effects. Growth, expression, elemental analysis, and proteomic data confirmed a lack of physiological defects of the strain except for a known inability to grow on glucose, which is overcome by heterologous SodFM expression. We demonstrate the utility of the strain for the efficient production of diverse recombinant SodFMs, including highly divergent, understudied isozymes, including the ability to precisely control the metal-loading of the heterologously expressed protein. ConclusionsThe E. coli strain described herein is a useful microbial cell factory for production of recombinant SodFMs, which should find widespread utility as expression host of choice, enabling more efficient production of protein for studies of the biochemical, biophysical and structural properties of this remarkable family of metalloenzymes.

microbiology↗

bioRxiv2026

golgi: an open-source graphical platform for image-to-recruitment modeling of peripheral nerve stimulation

Computational models of peripheral nerve stimulation--coupling finite-element bioelectric fields to biophysical axon models--have become essential for designing electrodes and waveforms for neuromodulation therapies. Yet the established open tools are code-first and assume substantial modeling expertise, and several depend on commercial finite-element solvers, placing realistic nerve modeling out of reach for many experimentalists and clinicians. We present golgi, an open-source platform that takes a peripheral nerve from image to stimulated fiber population through a single graphical interface, with an equivalent scriptable Python API and command-line interface for batch studies. golgi integrates the full pipeline: image segmentation (or import of surfaces or masks), automated multi-region tetrahedral meshing, anisotropic finite-element solution of the extracellular field with explicit perineurium contact impedance, generation of realistic fiber populations and their three-dimensional trajectories (straight, or curved via a quasi-static streamline solver), and biophysical activation thresholds through interchangeable NEURON and a GPU-accelerated surrogate backend. We demonstrate golgi on extruded multifascicular swine and human cervical vagus nerves and on real three-dimensional, branching human and rabbit vagus nerves reconstructed from micro-computed tomography. It reproduces the physiological fiber-diameter recruitment order, quantifies fascicular selectivity and current steering with a multi-contact cuff electrode, and resolves anatomically defined nerve branches. Using this branch resolution, we find that selectively engaging a vagal cardiac branch from a proximal cuff depends on anatomy. In the rabbit, whose cardiac fibers are predominantly small and whose superior cardiac branch forms a discrete, spatially segregated tract, current steering isolates even its small cardiac (B-type) fibers; in the human cervical vagus only the large myelinated fibers are separable, because the high thresholds of the small cardiac fibers force stimulus currents that also recruit off-target fibers. Every study can be exported as an integrity-hashed, self-contained bundle whose finite-element-to-recruitment provenance is verifiable byte-for-byte with a single command--a reproducibility guarantee absent from existing tools. By combining non-specialist usability, anatomical realism, and verifiable reproducibility in one open package, golgi lowers the barrier to in-silico peripheral nerve stimulation modeling. golgi is freely available as open-source software. Author summaryElectrical stimulation of peripheral nerves treats a growing range of conditions, from epilepsy to inflammatory and cardiovascular disease. Deciding where to place an electrode and how to shape the stimulus increasingly relies on computer models that combine the electric field around the electrode with detailed models of how individual nerve fibers respond. We found that existing software for this is powerful but primarily designed for expert modelers: it generally requires programming, substantial modeling expertise, and sometimes expensive commercial software, which can limit its adoption by experimentalists and clinicians. We built golgi to remove that barrier. With golgi, a user can go from a nerve image all the way to predicted fiber recruitment through a single point-and-click interface, while advanced users keep full scripting control. golgi builds anatomically realistic nerve models, simulates how different fiber types and fascicles are recruited, and lets users compare electrode designs. Using golgi, we also found that whether a small but clinically important nerve branch--such as the cardiac branch of the vagus nerve--can be selectively stimulated depends on its anatomy. In a rabbit nerve, where this branch forms a discrete, spatially separated bundle and its fibers are mostly small, even its small fibers can be targeted from a cuff on the main trunk; in the human, only the large fibers can be reached selectively, because the small cardiac fibers are harder to excite and the stronger currents needed to reach them also activate off-target fibers. Critically, every result can be packaged so that anyone else can verify it reproduces exactly--making peripheral nerve stimulation models easier to build, share, and trust.

bioengineering↗

bioRxiv2026

In silico framework for benchmarking optogenetic hearing restoration

Cochlear implants (CI) partially restore hearing in profoundly hearing impaired or deaf people by electrically stimulating the auditory nerve. A bottleneck of electrical CIs is the broad spread of electrical current from each electrode that limits the transfer of spectral information, which might be overcome by future spatially confined optogenetic stimulation. Here we established an in silico framework, FraSCO, to model sound encoding in the human cochlea by an optogenetic CI (oCI) for testing the potential of optogenetic hearing restoration. The biophysical modeling framework combined an optical raytracing model implementing a human cochlea implanted with a waveguide-based oCI with a single compartment model of optogenetically modified spiral ganglion neurons (SGNs). The input was an optogenetic sound coding strategy and the quality of the neural representation was evaluated based on comparison of neurograms evoked by optogenetic and electrical stimulation to the spectrogram of the sound applied. The model aimed for technologically feasible properties of the oCIs with 64 stimulation channels. The biophysical modeling framework successfully captured essential physiological features of optogenetic SGN stimulation with a minimal set of ion channel types expressed in the SGN soma. Working with a sample of 1000 SGNs distributed along the tonotopic axis to represent sound encoding, we found that improved spectral selectivity more than compensates for lower temporal fidelity of current implementations of optogenetic stimulation. The established computational framework enables in silico investigation and benchmarking of sound encoding in the cochlea by future oCI and state-of-the-art eCI. The results indicate that optogenetic sound encoding has potential to improve speech understanding in noisy environments for CI users.

neuroscience↗

bioRxiv2026

VDAC2 stabilizes a membrane-inserted, primed intermediate of BAX activation

BAX, a major effector of mitochondrial apoptosis, is activated through a series of conformational transitions that lead to mitochondrial outer membrane permeabilization. Genetic studies have established VDAC2 as an essential regulator of BAX-mediated apoptosis, yet the molecular basis of this regulation remains unresolved. The absence of direct structural and biochemical characterization of VDAC2-BAX interactions has prevented mechanistic understanding of how VDAC2 influences BAX activation. Here, using complementary biochemical, biophysical and structural approaches, we reconstituted and characterized a stable VDAC2-BAX complex that was not observed with VDAC1, highlighting an isoform-specific role in BAX regulation. We show that VDAC2 captures and stabilizes a primed BAX conformation displaying the hallmarks of activation, including membrane insertion, BH3 exposure, and increased accessibility of the N-terminal activation region. By integrating AlphaFold3 predictions with molecular dynamics simulations, biochemical, biophysical and structural constraints, we derive an experimentally-supported structural model in which BAX is anchored through its 9 helix while its soluble domain partially extends over the VDAC2 pore. Together, our findings support a model in which VDAC2 facilitates BAX membrane insertion and stabilizes a membrane-inserted, activation- competent BAX intermediate. Rather than serving as a structural component of apoptotic pores, VDAC2 acts as a regulatory checkpoint in BAX activation. These results provide a molecular explanation for the emerging role of VDAC2 in mitochondrial apoptosis and establish structural basis for a previously inaccessible intermediate in the BAX activation pathway.

biochemistry↗

bioRxiv2026

Geometry-based dynamics of the postsynaptic density explain protein capture by an actin-spine-geometry-dependent synaptic tag

The synaptic tagging and capture (STC) hypothesis explains how early-phase plasticity is converted into its late phase through the coincidence of synaptic tagging and plasticity-related protein (PRP) availability. Yet the biophysical basis of this process remains poorly understood. Based on the hypothesis that the interaction of actin and spine geometry implement the synaptic tag, we here investigate the associated PRP capture mechanism. We propose that capture is implemented by PSD remodelling which is gated by local membrane curvature at the PSD periphery. Using computational modelling, we show that curvature variations around the PSD that arise from long-term potentiation (LTP) inducing stimuli indeed enable a PSD growth, reproducing late-phase potentiation and the maintenance of structural LTP. We further explore how the timing of PRP availability relative to tag formation and the initial spine size determine the extent of PSD enlargement, yielding outcomes consistent with experimental findings. Hence, our results support a structural interpretation of synaptic tagging and capture in which a transient, actin-driven geometric state of the spine encodes the tag, and curvature-mediated PRP recruitment stabilises synaptic changes, and thus render spine geometry as a key biophysical regulator of memory consolidation.

neuroscience↗

bioRxiv2026

How are evolutionarily young and old proteins distributed in sequence space?

Protein sequence space is vast due to the combinatorial diversity of 20 amino acids. However, evolution has generated a limited set of "old" canonical protein families sharing evolutionary ancestry, structures and functions. It remains unclear how canonical sequences are placed in sequence space, how recently evolved "young" proteins compare to them, and whether random, young, and canonical sequences can interconvert along evolutionarily plausible paths, and which biophysical properties distinguish or link these sequences. Here, we analyse naturally occurring de novo proteins from yeast and flies, which originate from non-coding DNA and thus have experienced limited evolutionary selection. They serve as a model for examining the relationships between young de novo and intergenic proteins, older canonical proteins, and their randomized counterparts. Because de novo and randomized sequences lack detectable homology, we use an alignment-free k-mer-based distance approach. Randomization shifts distance distributions toward expected random behaviour in all classes, but natural, non-randomized sequence classes remain distinct, indicating non-random residue organization. Each class exhibits characteristic k-mer patterns, with de novo proteins clearly separated from both canonical and all randomized sequences. Sequences bridging these classes are frequently predicted to contain transmembrane helices. De novo proteins are thus not random samples of sequence space. Instead, they occupy constrained yet evolutionarily accessible regions defined by residue order and biophysical constraints, suggesting a plausible pathway for the emergence and diversification of new proteins. Significance StatementDespite the vast combinatorial potential of amino acids, evolution has produced only a limited repertoire of canonical proteins with conserved structure and function. How evolutionarily young proteins relate to older canonical proteins, and whether the sequence space between them is traversable, remain unclear. Here, we decompose canonical proteins, intergenic sequences, and recently emerged yeast and fly de novo proteins, together with randomized controls, into short, interpretable fragments (k-mers) and compare them using alignment-free distances. De novo proteins are markedly distinct from both randomized and canonical sequences. Notwithstanding their evolutionary distance, sequences are connected by stepwise paths comprising bridge sequences, often enriched for low-complexity motifs and transmembrane helices, connecting disordered and structured regions of sequence space.

evolutionary biology↗

bioRxiv2026

Tuning T-cell immunological synapse by modular DNA-Nanobody engagers for precision immunotherapy

Bispecific T-cell engagers (TCEs) are a promising class of cancer immunotherapies, but their clinical use is limited by toxicity and insufficient specificity. Tuning the T cell- tumor interface through engager architecture may address these drawbacks. To this end, we engineered hybrid constructs composed of two nanobodies targeting CD3 and the model tumor antigen HER2, respectively, connected by rigid DNA linkers of variable length. Using cytotoxicity assays and hybrid biophysical platforms, we demonstrate a linker-length dependence of cell spreading on antigen, target killing and cytokine release, revealing a functional decoupling between killing and cytokine secretion, and implicating the glycocalyx as a key player. Through the addition of EGFR targeting, we also generate trispecific constructs implementing an "OR-gate" logic to address tumor heterogeneity and reduce resistance due to antigen loss. Overall, these versatile constructs show great therapeutic promise, and at the same time serve as platforms to test hypotheses on biophysical mechanisms.

synthetic biology↗

bioRxiv2026

Deep Mutational Scanning of the GPCR Rhodopsin Reveals Pleiotropic Mutational Effects and Their Roles in Modulating Light Sensitivity

Biophysical pleiotropy, the phenomenon in which a mutation affects multiple protein properties, underlies many genetic diseases and shapes protein evolution, yet remains poorly understood, limiting synthetic biology and therapeutic development. Although extensively studied in soluble proteins, little is known about how pleiotropic effects shaped the evolution of receptor protein functions. Here, we developed a cell-based assay designed to assess pleiotropic effects in G protein-coupled receptors (GPCRs), the largest receptor class in eukaryotes. Because our assay design allows for multiple GPCR molecular phenotypes (basal activity, ligand-dependent signaling, and cellular receptor abundance) to be simultaneously assessed, we applied it to study rhodopsin, a visual GPCR that evolved high light sensitivity by maximizing light-driven responses while minimizing thermally driven noise. To investigate this, we integrated our assay with deep mutational scanning of relevant rhodopsin domains to test the impacts of [~]2,000 single-residue substitutions in rhodopsin across all three molecular phenotypes ([~]6,000 measurements). By analyzing these data in the context of rhodopsins protein structures, we discovered complex pleiotropic effects that act asymmetrically to impose joint constraints, restricting mutational tolerance in rhodopsins retinal binding pocket and G protein interaction interface. A dose-response analysis of rhodopsin signaling revealed that these pleiotropic constraints arise from the dual functions of its chromophore, which acts as an agonist upon light-driven isomerization but also an inverse agonist in darkness. Because these constraints reflect mechanistically driven limitations in the evolution of receptor signaling, these findings reveal a role for biophysical pleiotropy in shaping the sensory capabilities of receptor proteins. Significance StatementUnderstanding how genetic variation impacts protein function is an important but challenging area of research as many mutations are pleiotropic, simultaneously affecting multiple protein properties, including structure, stability, and activity. Previously, genetic screens have systematically mapped pleiotropic effects in soluble proteins, but transmembrane receptor proteins embedded in cellular membranes are much more difficult to assay. These are key receptors that convert environmental stimuli into appropriate physiological responses. Here, we present a systematic study of pleiotropic effects within the receptor responsible for dim-light vision in vertebrates, revealing how light sensitivity can be mediated through differential conformational states of its chromophore. This is important not only for understanding how receptors evolved enhanced capabilities, but also for the development of tunable protein systems.

evolutionary biology↗

bioRxiv2026

Pathway-specific short-term synaptic dynamics and lateral inhibition shape frequency-dependent input integration and population-level pattern separation in the dentate gyrus

The dentate gyrus (DG) decorrelates overlapping entorhinal inputs into distinct granule-cell representations, a computation central to pattern separation and to reducing interference in episodic memory. The three excitatory pathways to granule cells -- the lateral perforant path (distal dendrites), the medial perforant path (middle dendrites), and proximal inputs -- carry distinct short-term synaptic dynamics, but how these combine with lateral inhibition to set the frequency dependence of DG integration and pattern separation remains unclear. Here we integrate mechanism, function, and robustness into a single computational modeling study spanning three complementary model tiers, using Tsodyks-Markram short-term synaptic parameters grounded in prior slice electrophysiology. In a biophysically detailed 37-compartment granule-cell model (Tier 1), the distal (lateral) pathway facilitates at low frequency, the middle (medial) pathway depresses, and the proximal pathway is mixed, producing frequency- and pathway-dependent integration; three-pathway summation is mildly sublinear, and a direct granule-cell-to-granule-cell lateral inhibition -- a shunting connection emulating disynaptic feedforward inhibition without an explicit interneuron -- further attenuates it. In a reduced leaky-integrate-and-fire network with the same dynamics (Tier 2), pattern separation is frequency-dependent, rising to a gamma-band maximum at 40 Hz that is reproducible across independently wired networks, whereas the basket-cell-inhibition magnitude varies with the random connectivity. In a three-layer population network (Tier 3), pattern separation is robust: although single granule-cell spike counts are highly sensitive to input noise, the population-level separation code is nearly noise-invariant (a roughly 60-fold dissociation), and separation is governed by the magnitude of local lateral inhibition rather than its targeting. Two claims that hold at the single-cell scale -- a microsecond spike-timing-precision requirement and an advantage of finely targeted inhibition -- do not survive at the network scale. Pathway-specific synaptic dynamics and lateral inhibition thus shape frequency-dependent integration and noise-robust population pattern separation in the DG. Author SummaryThe dentate gyrus (DG) performs pattern separation: it takes overlapping cortical inputs and makes their DG representations more distinct, a computation thought to reduce memory interference. How does the DG do this? We approach the question across three scales in a single computational study. First (mechanism), we show in a biophysically detailed granule-cell model that the three anatomical input pathways carry different short-term synaptic dynamics -- the distal (lateral perforant path) input facilitates, the middle (medial perforant path) input depresses, and the proximal input is mixed -- so that the cells response depends on input frequency and pathway. Second (function), in a reduced network model we show that these dynamics, combined with lateral inhibition, tune pattern separation in a frequency-selective way. Third (robustness), we find that although a single granule cells spike count is highly sensitive to input noise, the population-level separation code is nearly noise-invariant -- a "noise paradox" in which population coding rescues what is fragile at the single-cell level. We also show that two claims that appear at the single-cell scale -- a microsecond spike-timing-precision requirement, and an advantage of finely targeted inhibition -- do not survive at the network scale: what matters is the amount of local inhibition, not how it is distributed. The synaptic parameters are grounded in prior slice recordings; the model integrates the mechanism.

neuroscience↗

bioRxiv2026

Beyond air-seeding: Dynamic, multiphase interactionsreveal a two-step mechanism of embolism propagation inangiosperm xylem

BackgroundThe mechanism underlying drought-induced embolism in angiosperm xylem has been attributed to air-seeding. This concept describes the bulk flow of gas from embolised to neighbouring conduits through the penetration of gas-liquid menisci across pores in interconduit pit membranes. While there is compelling evidence for the spatial propagation of embolism, air-seeding rests on various simplifying assumptions. Among others, air-seeding presumes that xylem sap has physical properties comparable to pure water, that pit membranes can be approximated as structures with simple pores, and that embolism occurs whenever a gas-liquid interface crosses a pit membrane. ScopeRecent experimental and theoretical work demonstrates that the biophysical conditions and processes governing gas-liquid interactions at interconduit pit membranes are fundamentally more dynamic and complex than assumed by air-seeding. These phenomena include: (1) gas movement through constriction pore networks, (2) insoluble, polar lipids at conduit surfaces and interfaces, (3) dynamic surface tension of xylem sap that depends on the local packing density of interfacial lipids, (4) bubble snap-off dynamics within pit membranes, (5) surfactant-stabilized nanobubbles in sap that is oversaturated with dissolved gas, and (6) electrostatic interactions between charged interfaces. Importantly, embolism propagation involves bubble generation and embolism formation as distinct, temporarily and spatially separated processes. Embolism formation occurs when nanobubbles become unstable, whereas nanobubbles below critical stability thresholds can remain stable in sap-filled conduits. ConclusionsTogether, these findings reconfirm that pit membranes function as safety valves enabling water transport according to the cohesion-tension theory, and provide mechanistic insights into embolism propagation. They address the question why plants do not suffer constant embolism formation despite negative xylem pressures. We conclude that a revised framework explicitly accounting for the 3D structure of pit membranes, and multiphase, dynamic processes operating within them are required to explain the biophysics underlying water transport and embolism resistance in angiosperm xylem.

plant biology↗

bioRxiv2026

Human development and inequality shape global threat patterns for terrestrial biodiversity

Biodiversity loss is driven by unsustainable human activities, yet the contextual conditions and underlying drivers of threats remain poorly understood. We assessed how protected areas, socioeconomic conditions, and biophysical factors explain global patterns of threat probabilities across six major threat types and four vertebrate taxa. We identified key explanatory variables and their associations with threats using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Socioeconomic conditions, specifically human development and income inequality, were the strongest predictors. Their associations were complex and non-linear: notably, high human development index (HDI) was associated with both higher and lower threat probabilities, depending on inequality and regional context. Second, land cover and biophysical variables, such as shrubland cover, tree cover, and elevation range, explained additional, but taxon-specific variation. Finally, protected areas showed limited ability to explain threat patterns. By linking threat probabilities to their contextual and socioecological conditions, we aim to build a better understanding of the systemic drivers of biodiversity loss.

ecology↗

bioRxiv2026

Formation of an RNA-mediated nuclear compartment

Mammalian nuclei are spatially compartmentalized so that active and inactive segments of the genome occupy separate sub-nuclear neighborhoods. Compartments are often found in association with nuclear structures such as the nuclear lamina, nucleoli, and nuclear speckles (speckles), suggesting links between them. The molecular mechanisms by which compartments form remain largely unknown. Speckles are nuclear bodies that contain high concentrations of RNA splicing factors and associated chromatin has a high density of highly expressed genes. Combining liquid chromatin Hi-C to quantify chromatin interaction lifetimes genome-wide, immunofluorescence, fluorescence in situ hybridization, live cell imaging, and nascent transcript analysis, we have determined the biophysical and molecular basis of the speckle-associated chromosomal compartment. We find that genomic regions making up this compartment are stably glued together. Surprisingly, removal of speckles by rapid depletion of SON and SRRM2 that form the structural scaffold of these bodies shows that this stable association is not dependent on the speckle itself. Instead, we find that RNA molecules are the molecular glue that forms the biophysical basis of the speckle chromatin compartment. Based on observations that promoters and enhancers are also engaged in stable long-lived chromatin interactions that are independent of RNA, we propose a pathway for formation of the speckle chromosomal compartment: Initial stable clustering of promoters and enhancers is followed by production of (nascent) RNA. The exceptionally high density of GC-rich RNA emerging from speckle-associated loci forms a glue that holds these loci together and facilitates recruitment of speckle components. The result is a structurally stable nuclear compartment that facilitates efficient splicing.

genomics↗

bioRxiv2026

Synthesis cost is a hidden driver of convergent amino acid composition in plastid ribosomal proteins

Protein evolution is a walk in the evolutionary space directed by mutation and selection. While functional and structural constraints serve as the main determinant of amino acid substitution in most proteins, synthesis cost and mutational bias can also alter the direction and rate of amino acid evolution, especially in systems experiencing relaxed selection. Here, we focused on the highly expressed plastid ribosomal proteins (PRP), which comprise 58 conserved proteins encoded by both plastid and nuclear genomes. Relaxed selection has been repeatedly identified in three distantly related plant lineages, providing a valuable comparative framework to investigate the significance of synthesis cost and mutation. We first demonstrated that the hemiparasitic tribe Cymbarieae (Orobanchaceae) represented a new case where concerted cyto-nuclear rate elevation occurs in their PRP. Further investigation revealed convergent shifts in amino acid composition in all four plant lineages attributable to arginine-to-lysine and methionine-to-isoleucine/valine/leucine substitutions. The replacement residues were biophysically similar but had lower molecular weight and shorter side chains, which significantly destabilized protein folding as demonstrated by protein structure modeling. We found that the composition shifts ran counter to the expectation of mutational bias but were consistent with the expectation of synthesis cost minimization, which is potentially adaptive for highly expressed PRP. Further, cost minimization significantly influenced all conservative substitutions between biophysically similar amino acids but was absent in non-conservative substitutions. We thus propose cost minimization as a secondary selective drive for protein evolution in PRP, unmasked in lineages and sites with relaxed selection on their function.

evolutionary biology↗

bioRxiv2026

Stepping-stone population structure and sidespread clonality of the mesophotic octocoral Swiftia exserta in the warm temperate northwest Atlantic

Isolated mesophotic banks form spatially discrete networks of patchy ecosystems along continental shelves. Their long-term persistence depends on connectivity among populations of the dominant coral species that structure these communities. Yet the scale of larval-mediated gene flow across these networks remains poorly characterized. Similarly, the extent to which clonal propagation shapes local population dynamics is unresolved, leaving the relative contributions of sexual and asexual reproduction to population maintenance an open question. Here, population genomics and high-resolution (1km) biophysical larval dispersal modeling are integrated to resolve the genetic connectivity and clonal dynamics of Swiftia exserta, a key habitat-forming octocoral, across 13 mesophotic banks spanning the U.S. Gulf and eastern coast within the Warm Temperate Northwest Atlantic (WTNWA) coastal and shelf biogeographic province. Genome-wide SNP markers derived from Restriction-site Associated Sequencing (RADseq) were genotyped across 288 individuals. About 40% of individuals belonged to clonal genets confined to single banks within meters of one another, indicating that asexual propagation sustains local density but plays no detectable role in inter-bank connectivity. Population genetic analyses pointed to isolation by distance as the primary structuring force, with the majority of molecular variance residing within rather than among populations. The west-most population, East Flower Garden Banks, was the most differentiated population in the WTNWA Northern Gulf ecoregion, reflecting its peripheral position within the bank network. The Edisto population from the WTNWA Carolinian ecoregion, was distinct from all others, implicating the Florida Peninsula as a phylogeographic barrier. Biophysical particle-tracking simulations independently recovered the same spatial structure, with modeled larval exchange consistent with the genomic signal across the bank network. These findings reveal that S. exserta persists across the northern WTNWA province through a dual reproductive strategy of clonality at the bank scale and episodic larval exchange at the network scale, with implications for the conservation and management of mesophotic octocoral metacommunities.

ecology↗

bioRxiv2026

OCTOPUS: A versatile open-source tool creating realistic numericalbrain cells

Brain cell morphology plays a crucial role in function and pathology. Biophysical models of diffusion MRI (dMRI) quantify cell morphology in vivo, enabling the design of novel biomarkers. These models represent cells by simplified geometries, such as spheres and randomly oriented cylinders, for which analytical signal expressions exist. However, dMRI signals are sensitive to morphological features, such as branching, tapering, undulation, beading, and protrusions, unaccounted for in most models, as they often render analytical solutions impossible. Simulations of the dMRI signal in synthetically generated cells offer a powerful tool to explore how microstructural morphology impacts the dMRI signal. Nevertheless, no tool to generate digital replicas of brain cells is available openly. To address this gap, we introduce the OCTOPUS toolbox, which generates cells featuring all geometrical features described above. OCTOPUS, provided via the Python interface OCTOpool, enables accessible, efficient creation of cells with complex geometries. We recreated histologically reconstructed neuronal and glial cells, including pyramidal, GABAergic and glutamatergic neurons, and astro- and microglia. To illustrate the plausibility of OCTOPUS-generated cells, we reproduced established properties of dMRI signals from brain tissue, such as the signatures of short-range disorder, branching and protrusions, and a high-b-value power law. By comparing the geometries and dMRI signals of generated and original cells, we found different growth strategies adequate for more isotropic and more anisotropic cells. We anticipate that realistic cell substrates created by OCTOPUS will help validate biophysical models, design dMRI sequences sensitive to fine-grained cell morphology beyond analytical models, and generate realistic numerical substrates of brain tissue.

neuroscience↗