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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 397 records · Page 22Linked to original sources

bioRxiv2018

Biophysical Principles of Lineage Factor PU.1 Binding Revealed by NextPBMs

Determining the biophysical principles that shape transcription factor (TF) binding in a cell-specific manner is key to quantitative models of gene expression. High-throughput (HT) in vitro methods measuring protein-DNA binding are invaluable for relating TF binding affinity to genome-wide binding; however, the impact of cell-specific post-translational modifications (PTMs) and cofactors are not routinely assessed. To address these limitations, we describe a new HT approach, called nextPBMs (nuclear extract protein-binding microarrays), to characterize TF binding that accounts for PTMs and endogenous cofactors. We use nextPBMs to examine the DNA binding of the lineage factor PU.1/Spi1 and IRF8 in human monocytes. We identify two binding modes for PU.1 in monocytes - autonomous binding unaffected by PTMs and cooperative binding with IRF8, and identify a single cooperative mode for IRF8. We characterize the DNA binding of PU.1:IRF8 complexes, and show how nextPBMs can be used to discover cell-specific cofactors and characterize TF cooperativity at single-nucleotide resolution. We show that chromatin state and cofactors both influence the affinity requirements for PU.1 binding sites. Furthermore, we find that the influences of cooperative (IRF8) and collaborative (C/EBP) cofactors on PU.1-binding-site affinity are independent and additive.

molecular biology↗

bioRxiv2018

Biophysical basis of disruption in alpha rhythm in Alzheimer’s disease

Alpha is one of the most prominent rhythms (7.5-12.5 Hz) detected in electroencephalography (EEG) during wakeful relaxation with closed eyes. In response to elevated ambient acetylcholine levels, a subclass of thalamic pacemaker cells generate alpha. This rhythm is intrinsic to the cell and is robustly orchestrated by an interplay of hyperpolarization activated cyclic nucleotide gated channels(HCN) and calcium-ion channels. It has been shown that decreased expression of HCN channels is correlated to Alzheimer's Diseased (AD). In early stages of AD, alpha is known to be down-regulated and lowered in coherence. We use this well characterized and quantified rhythm to understand the changes in ion channel properties that lead to disruption of alpha as seen in AD in a biophysically detailed network model of the thalamo-cortical circuit that generates the alpha-rhythm. Our computational model allows us to explore the causal links between alpha rhythms, HCN channels and amyloid-beta aggregation. The most commonly used drugs(acetylcholinesterase inhibitors) in AD increase the duration and level of acetylcholine and provide temporary symptomatic relief in some cases. Our simulations show how increasing acetylcholine can provide rescue for a small range of aberrant HCN expression. We hypothesize that reduced alpha rhythm frequency and coherence is a result of down-regulated HCN expression, rather then compromised cholinergic modulation(as is currently thought). The model predicts that lowering of the alpha-rhythm can modify the network activity in the thalamo-cortical circuit and lead to an increase in the inhibitory drive to the thalamus.

neuroscience↗

bioRxiv2018

Biophysical underpinning of astroglial physiology probed with realistic cell models

Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the nanoscopic, sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we have systematically evaluated the multi-scale morphology of protoplasmic astroglia to construct a realistic multi-compartmental cell model that can be biophysically interrogated in NEURON computational environment. This approach has been implemented as an astrocyte-model builder ASTRO. As a proof of concept, we explored a hippocampal astrocyte reconstructed in silico against a battery of physiological and imaging experiments. This exploration has unveiled some basic features of astroglial physiology inaccessible empirically, such as the characteristic length of membrane voltage propagation, membrane effects of local glutamate transport, spatiotemporal dynamics of intracellular K+ redistribution, key Ca2+ buffering properties, and some basic relationships between free Ca2+ dynamics and experimental readout of fluorescent Ca2+ indicators.

neuroscience↗

bioRxiv2018

Biophysical model of axonal stimulation in epiretinal visual prostheses

Visual prostheses aim to restore vision to people blinded from degenerative photoreceptor diseases by electrically stimulating surviving neurons in the retina. However, a major challenge with epiretinal prostheses is that they may accidentally activate passing axon fibers, causing severe perceptual distortions. To investigate the effect of axonal stimulation on the retinal response, we developed a computational model of a small population of morphologically and biophysically detailed retinal ganglion cells, and simulated their response to epiretinal electrical stimulation. We found that activation thresholds of ganglion cell somas and axons varied systematically with both stimulus pulse duration and electrode-retina distance. These findings have important implications for the improvement of stimulus encoding methods for epiretinal prostheses.

neuroscience↗

bioRxiv2019

Repertoire-Based Diagnostics Using Statistical Biophysics

A fundamental challenge in immunology is diagnostic classification based on repertoire sequence. We used the principle of maximum entropy (MaxEnt) to build compact representations of antibody (IgH) and T-cell receptor (TCR{beta}) CDR3 repertoires based on the statistical biophysical patterns latent in the frequency and ordering of repertoires constituent amino acids. This approach results in substantial advantages in quality, dimensionality, and training speed compared to MaxEnt models based solely on the standard 20-letter amino-acid alphabet. Descriptor-based models learn patterns that pure amino-acid-based models cannot. We demonstrate the utility of descriptor models by successfully classifying influenza vaccination status (AUC=0.97, p=4x10-3), requiring only 31 samples from 14 individuals. Descriptor-based MaxEnt modeling is a powerful new method for dissecting, encoding, and classifying complex repertoires.

immunology↗

bioRxiv2019

Biochemical, Biophysical, and Functional Analyses of Two Isoforms of the SnRK2 Inhibitor AtSCS

SNF1-related protein kinases 2 (SnRK2s) are key signaling elements that regulate abscisic acid (ABA)-dependent plant development and responses to environmental stresses. Our previous data showed that the SnRK2-interacting Calcium Sensor (SCS) is an inhibitor of SnRK2 activity. In Arabidopsis thaliana, the use of alternative transcription start sites located within AtSCS gene results in two in-frame transcripts and subsequently two proteins, which differ only by the sequence position of the N-terminus. We described the longer AtSCS-A earlier, and now we describe the shorter AtSCS-B and compare both isoforms. The two forms differ significantly in their expression profiles in plant organs and in response to environmental stresses, in calcium binding properties, and conformational dynamics in the presence and absence of Ca2+. The results show that only AtSCS-A has the features of a calcium sensor. Both forms inhibit SnRK2 activity, but differ with respect to calcium dependence, as AtSCS-A requires calcium for inhibition, while AtSCS-B does not. Analysis of Arabidopsis plants stably expressing 35S::AtSCS-A-c-myc or 35S::AtSCS-B-c-myc in the scs-1 knockout mutant revealed that in planta both forms are negative regulators of the SnRK2 activity induced in response to ABA and regulate plant defense against water deficit. Moreover, the data present biochemical, biophysical, and functional properties of EF-hand-like motifs in plant proteins.\n\nOne sentence SummaryTwo isoforms of SnRK2-interacting calcium sensor are expressed in Arabidopsis; they differ in calcium binding properties, but both of them inhibit SnRK2s and subsequently fine tune ABA signaling.

plant biology↗

bioRxiv2019

Biophysical mechanisms to relate large-scale brain network dynamics to individual-specific variability of perception

Perception necessitates interaction amongst neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Here, we propose a detailed neurobiologically realistic model that captures the neural mechanisms of inter-individual variability observed in cross-modal speech perception. From raw EEG signals recorded from human participants when they were presented with speech vocalizations of McGurk-incongruent and congruent audio-visual (AV) stimuli, we computed the global coherence metric to capture the neural variability of large-scale networks. We identified that participants McGurk susceptibility was negatively correlated to their alpha-band global coherence. The proposed biophysical model conceptualized the global coherence dynamics emerge from coupling between the interacting neural masses - representing the sensory specific auditory/visual areas and modality non-specific associative/integrative regions. Subsequently, we could predict that an extremely weak direct AV coupling result in a decrease in alpha band global coherence - mimicking the cortical dynamics of participants with higher McGurk susceptibility. Source connectivity analysis also showed decreased connectivity between sensory specific regions in participants more susceptible to McGurk effect, thus establishing an empirical validation to the prediction. Overall, our study provides an outline to link variability in structural and functional connectivity metrics to variability of performance that can be useful for several perception & action task paradigms.

neuroscience↗

bioRxiv2025

Microstructural correlates of white and gray matter in the healthy human brain: comparative analysis of diffusion biophysical models, inhomogeneous magnetization transfer, and macromolecular proton fraction

Abstract summaryDiffusion MRI (dMRI) and magnetization transfer (MT) rely on distinct biophysical principles and provide complementary insights into tissue microstructure. In this study, we investigated associations between microstructural metrics derived from the Standard Model (SMI) in white matter (WM) and the Standard Model with EXchange (SMEX/NEXI) in gray matter (GM) with two MT measures differing in specificity and sensitivity to myelination: the macromolecular proton fraction (MPF) and the inhomogeneous magnetization transfer ratio (ihMTR). Measurements were performed in WM and GM in ten healthy subjects scanned at 3T. In WM, the strongest significant association was observed between ihMTR and the axonal water fraction, consistent with higher myelination in regions of elevated axonal density and limited extra-axonal space. This correlation exceeded that of MPF, supporting the greater specificity of ihMTR to myelin. Interestingly, ihMTR displayed a gradient along the longitudinal axis of the corpus callosum, in agreement with previous histology measurements. Correlation between ihMTR and the extra-axonal perpendicular diffusivity (De{perp}), a putative myelination biomarker, was not significant, whereas MPF and De{perp} exhibited a moderate significant positive correlation. Since a negative correlation is expected if reflecting myelination, these results suggest that in healthy tissue De{perp} is mainly influenced by microstructural factors like fiber coherence and packing, the latter most likely affecting MPF but not ihMTR. In GM, ihMTR correlated significantly only with the exchange time (t), confirming tex as a proxy for cell membrane permeability modulated by myelin. MPF correlated exclusively with the cell-process fraction (f), suggesting the latter is modulated by total (neuronal and glial) cell membrane density. Overall, findings underscore the complementary and concurring microstructural information captured by these metrics, highlighting their potential to disentangle distinct tissue mechanisms in both healthy and pathological conditions. Future studies incorporating ground-truth histology should validate the precise sensitivity of each metric to various microstructural tissue features.

neuroscience↗

bioRxiv2026

Biophysically realistic network-level transport model of tau progression with exosome-mediated release and uptake processes

The spatiotemporal progression of tau in neurodegenerative diseases like Alzheimers follows the brains structural connectome, yet a gap exists between the macroscopic spread observed over years and the protein kinetics occurring over hours. Current models fail to reconcile this disparity or incorporate the cellular mechanisms driving transmission. Here, we advance the Network Transport Model (NTM) to bridge these scales by integrating active transport along microtubules, continuous toxic tau production, and exosome-mediated release and uptake. This framework constitutes one of the most biologically detailed models of tau spread on a whole brain to date, representing a significant innovation in how multiscale proteinopathies are simulated. Simulations on the mouse connectome demonstrate that this framework replicates empirical tau propagation patterns. Our results identify trans-neuronal release and uptake rates as the primary "bottleneck" on macroscopic spread, providing a biologically grounded explanation for the diseases slow progression. Furthermore, we find that high aggregation sequesters tau within regions, limiting global transmission, while increasing the abundance of toxic polymeric tau fibrils. Meanwhile, tau transport polarity bias (anterograde vs. retrograde) dictates spatial patterning. By linking molecular mechanics to system-wide pathology, this model provides an "in-silico" framework to evaluate how cellular-targeted interventions might alter the trajectory of tauopathic dementias. Author SummaryIn Alzheimers disease and related dementias, a protein called tau forms misfolded aggregates and gradually spreads through the brain along its neuronal wiring. A long-standing puzzle is linking the microscopic protein dynamics taking minutes to hours to the multiyear macroscopic spread of the disease. We developed a new mathematical model that is capable to explain macroscopic spreading behavior of tau as an emerging process from the underlying biophysical mechanics of tau pathology. Our simulations reproduce real patterns of tau spread, and our framework will pave the way for testing how treatments targeting specific cellular mechanisms might reshape disease progression.

neuroscience↗

bioRxiv2026

Biophysical and temporal drivers outweigh management in tropical agroforestry soil carbon sequestration

Agroforestry is a cornerstone of Natural Climate Solutions, yet the hierarchical importance of its soil organic carbon (SOC) drivers remains poorly resolved across heterogeneous tropical landscapes. Current global assessments predominantly rely on categorical system typologies that mask the continuous influence of biophysical drivers, leaving the reliability of mitigation estimates unclear. Here, we synthesize 643 observations from 54 field studies in Latin America and the Caribbean to decouple the determinants of SOC sequestration using a machine-learning framework. We show that baseline soil carbon stocks and temporal kinetics override management design, collectively explaining [~]85% of sequestration variability, whereas system typology and species richness contribute marginally (R2<0.10). While the median SOC storage rate was 0.26 Mg C ha{superscript 1} yr{superscript 1}, accumulation followed a distinct non-linear trajectory: sequestration intensity peaked early before decelerating sharply after a critical inflection at year 7. Critically, sequestration is governed by a robust negative feedback from initial SOC stocks, which cross a zero-net-gain threshold at [~]80 Mg C ha{superscript 1}. Depth-resolved analyses reveal that subsoil layers (up to 55-75 cm) exhibit a cumulative relative response up to fourfold greater than surface horizons, indicating that conventional shallow monitoring could systematically underestimates long-term stabilization potential. Our findings demonstrate that current carbon accounting frameworks, rooted in generic system averages (IPCC Tier 1), are structurally limited by their inability to account for baseline-dependent saturation feedbacks and non-linear effects. Transitioning toward Tier 3 context-aware, depth-explicit modeling is therefore essential to transform agroforestry from a broad practice into a precision-based, high-integrity Natural Climate Solution. HighlightsO_LISoil carbon sequestration in tropical agroforestry is primarily controlled by baseline soil conditions and temporal dynamics rather than system typology. C_LIO_LIDepth-resolved analyses reveal long-term carbon stabilization processes overlooked by surface-based assessments. C_LIO_LICarbon accumulation is strongly front-loaded, declining sharply after system establishment. C_LIO_LIContext-dependent responses challenge generic carbon accounting frameworks and highlight the need for predictive, site-specific deployment of agroforestry. C_LI

ecology↗

bioRxiv2026

Correlations of 276 missense variants of PSEN1, PSEN2, and APP on the production of Aβ peptides against variant effect predictors and biophysical structures

INTRODUCTIONEarly-onset Alzheimers disease (EOAD) is mostly caused by mutations in Presenilin-1,2 (PSEN1, PSEN2), and amyloid precursor protein (APP) genes. Both genetic and experimental evidence has supported the PSEN-APP amyloid hypothesis as the leading pathogenesis of AD. Thus far, 276 missense variants from PSEN1,2, and APP have been identified. Herein, we report the development of algorithm platforms for predicting the degrees of pathogenic potentials of these various missense mutations in AD. METHODSWe performed extensive pairwise correlations between 276 in vitro functional assays of missense variants from PSEN1, PSEN2, and APP and clinical data among EOAD patients against 37 variant effect predictors (VEPs). Furthermore, we used the structural models of all three proteins to refine understanding of the molecular basis of missense variants in dominant negative (DN) or loss-of-function (LoF) effects. RESULTSWe found that VEPs were consistently correlated with age of onset (AAO) among EOAD patients and the A{beta}42/A{beta}40 ratio biomarker. This study also identified discrepancies in the predictor variable leading to an increase in the A{beta}42/A{beta}40 ratio; The increased A{beta}42/A{beta}40 ratio was due to decreased A{beta}40 levels by Sun et al., (2016), while Petit et al., (2022) argued that increased A{beta}42 level was the culprit. We also identified that with increased predicted pathogenicity, there were decreased A{beta}38 levels. Conversely, this study found no direct correlations with A{beta}37 and A{beta}43. Lastly, structural studies show characteristics of either DN and LoF mechanisms, as there were numerous variants located in buried hydrophobic domains and those on charged surfaces of the proteins. DISCUSSIONCurrently, a major limitation in the field is the lack of reliable approaches for predicting the pathogenicity of variants in EOAD. We show that VEPs are promising in deciphering the effects of variants in PSEN1, PSEN2, and APP genes, especially between multiple pathogenic mutations. Our study also illuminates the need for experimental studies to identify the predictor variable causing the increased A{beta}42/A{beta}40 biomarker. Our biophysical studies also identify the need to characterize whether the mechanism of pathogenesis caused by DN or LoF effects is in scope of the presenilin hypothesis. Research in ContextO_LISystematic review: The authors reviewed experimental evidence of pathogenic PSEN1, PSEN2, and APP variants in scope of the presenilin hypothesis. To our knowledge, the pathogenesis of EOAD remains incompletely understood. There are also no predictive tools/models available for informing clinical translation. C_LIO_LIInterpretation: VEPs demonstrate strong correlations with AAO and A{beta} levels from pairwise correlations with 276 missense variants. Structurally, it appears that mutations demonstrate characteristics of dominant negative and loss-of-function effects. C_LIO_LIFuture Direction: Larger studies are necessary of VEPs for realistic clinical usage. C_LI HighlightsO_LIWe performed correlations of 37 VEPs against in vitro data of EOAD variants. C_LIO_LIVEPs demonstrate potential as diagnostic tools for early onset Alzheimers disease. C_LIO_LIPathogenic missense variants show characteristics of DN and LoF effects. C_LI

neuroscience↗

bioRxiv2026

IPF AT2 cells are stuck in transition and biophysically dysfunctional

Idiopathic Pulmonary Fibrosis (IPF) is an incurable disease with extensive molecular, cellular, and organ level dysfunction. A major gap in IPF research is the lack of understanding of how short-term cellular behavior causes long-term tissue remodeling. By optimizing lung slices from explanted human lungs, we discovered foci of migratory non-canonical alveolar type 2 (AT2) cells in regions of established lung fibrosis and found that these cells are trapped in states of cellular transition that are driven by persistent developmental repair programs. Consistent with these biophysical behaviors, pharmacological activation of {beta}-catenin reproduced persistent migration, whereas YAP activation restrained it. We conclude that imbalanced developmental programs drive AT2 cell motility and lesion heterogeneity, providing a mechanistic link between short-term cellular dynamics and slowly progressive fibrosis of IPF.

cell biology↗

bioRxiv2026

Minimal biophysical rules are sufficient for the emergence of computational intelligence at the neuronal scale

How intelligence emerges from the brains complex microscopic physical system is a central question for neuroscience and artificial intelligence. Constrained by the genomic bottleneck that precludes synapse-by-synapse specification, we propose and validate a microscopic structure-function concise-constraint sufficiency hypothesis. We develop the Neuro-Informed Generative Connectome (NIGC) framework, and show that connectomes generated under a concise set of biophysical constraints (geometric embedding, node propensity modulation, a global energy budget and maximum-entropy selection) closely match the structural statistics of a measured mouse V1 microcircuit (similarities, 0.997). In parallel, using the generated connectome as the fixed reservoir of an echo state network (ESN), training only a linear readout achieves 90% accuracy on an auditory multi-classification task. Moreover, multidimensional biologically consistent functional phenotypes, such as hierarchical transmission delays and low-dimensional spatiotemporal trajectories, are obtained without fitting functional matrices or time courses. Further, by combining single-constraint ablations, pathological perturbations and cross-modal validation, we clarify how specific structural constraints map onto functional consequences. Together, these results delineate sufficient conditions for computational intelligence emergence at the microscopic scale, and provide an auditable benchmark for first-principles understanding of brain construction.

neuroscience↗

bioRxiv2026

LLMsFold: Integrating Large Language Models and Biophysical Simulations for De Novo Drug Design

The discovery of novel small molecules is challenging because of the vastness of chemical space and the complexity of protein-ligand interactions, leading to low success rates and time-consuming workflows. Here, we present LLMsFold, a computational framework that combines Large Language Models (LLMs) and biophysical foundation tools to design and validate new small molecules targeting pathogenic proteins. The pipeline starts by identifying viable binding pockets on a target protein through geometry-based pocket detection. A 70-billion-parameter transformer model from the LlaMA family then generates candidate molecules as SMILES strings under prompt constraints that enforce drug-likeness. Each molecule is evaluated by Boltz-2, a diffusion-based model for protein-ligand co-folding that predicts bound 3D structure and binding affinity. Promising candidates are iteratively optimized through a reinforcement learning loop that prioritizes high predicted affinity and synthetic accessibility. We demonstrate the approach on two challenging targets: ACVR1 (Activin A Receptor Type 1), implicated in fibrodysplasia ossificans progressiva (FOP), and CD19, a surface antigen expressed on most B-cell lymphoma and leukemia cells. Top candidates show strong in silico binding predictions and favorable drug-like profiles. All code and models are made available to support reproducibility and further development.

bioinformatics↗

bioRxiv2026

MD Biophysics Photobiomodulation Plasma (PPT)/ Very Small Embryonic like (VSEL) Antibody Marker Trend Analysis

BackgroundPhotobiomodulation (PBM) therapy has demonstrated therapeutic potential in promoting cellular repair, modulating inflammation, and enhancing mitochondrial function. Platelet-rich plasma (PRP) is widely used in regenerative medicine due to its concentration of growth factors and cytokines. Very small embryonic-like stem cells (VSELs), a rare population of pluripotent stem cells present in adult tissues, have emerged as a potential contributor to tissue regeneration. While PBM and PRP are used in combination, how VSELs or Multi-lineage stress enduring (MUSE) cells are at play, and the biological mechanisms underlying their synergistic effects remain incompletely characterized. ObjectiveThis exploratory pilot study aimed to evaluate whether application of the MD Biophysics laser to autologous PRP is associated with measurable changes in VSEL-related antibody marker expression, and to identify directional trends to inform future controlled studies. MethodsPRP samples were collected from participants across seven test dates (July 2024 to February 2025), yielding 18 participant-session datasets. Samples were analyzed before (Pre) and after (Post) laser application using flow cytometry conducted at a UCLA Flow Cytometry Laboratory. Four VSEL-associated antibody markers were assessed: CD45-CD34+, CXCR4+, CD133+, and SSEA-4+. Analyses were descriptive and focused on paired differences and directional trends due to the exploratory design and absence of a control group. ResultsThree of four VSEL-associated markers (CXCR4+, CD133+, and SSEA-4+) demonstrated a group-level increase in median paired differences following laser application. Directional increases were observed in 12/18 sessions for CXCR4+, 10/18 for CD133+, and 9/18 for SSEA-4+. CD45-CD34+ showed a near-equal distribution of increases and decreases. Ki-67 positivity indicated the presence of viable, proliferative cells. While no findings reached statistical significance due to limited sample size, consistent directional trends were observed across multiple markers. ConclusionApplication of PBM to autologous PRP was associated with directional increases in multiple VSEL-associated antibody markers, suggesting a potential role for stem cell activation or mobilization in the mechanism of action. Although preliminary and not statistically powered, these findings provide hypothesis-generating evidence supporting further investigation. The observed trends informed iterative protocol refinement and establish a foundation for future controlled, adequately powered studies to evaluate clinical efficacy and underlying biological mechanisms.

cell biology↗

bioRxiv2026

Resolving heterogeneity of targeted lipid nanoparticles through solution-based biophysical analyses

Targeted lipid nanoparticles (tLNPs) represent the next frontier in nucleic acid therapeutics, enabling cell-specific delivery through covalent attachment of targeting ligands that drive receptor-mediated uptake. tLNPs are particularly promising for pregnancy-associated applications where precise on-target delivery is required to minimize maternal toxicity and protect fetal health. Yet, their rational design is limited by an incomplete understanding of how tLNP physicochemical properties influence biological performance. Conventional LNPs already exhibit pronounced heterogeneity in size, composition, and RNA loading, which is further amplified in tLNPs by variability in ligand attachment and surface density. Because traditional analytical methods report only ensemble-averaged properties, the nanoscale diversity of tLNPs remains unresolved. Here, we find that tLNP functional behavior is governed by previously inaccessible, structurally distinct tLNP subpopulations that are not captured by bulk measurements. We utilize asymmetric flow field-flow fractionation integrated with in-line UV spectral analysis, light scattering, and synchrotron small-angle X-ray scattering (AF4-UV-DLS-MALS-SAXS) to resolve ligand-dependent tLNP subpopulations that differ in size, shape, composition, and relative abundance. We find that protein conjugation preserves the internal lipid-RNA nanostructure of base LNPs but substantially increases particle heterogeneity, particularly for larger and multivalent targeting ligands. Despite increased heterogeneity, tLNPs functionalized with higher-avidity ligands achieve more effective targeted placental RNA delivery in mice, suggesting that binding avidity can offset the functional consequences of polydispersity. Chemometric SAXS analyses reveal that only SAXS-resolved tLNP subpopulations, not ensemble-averaged parameters, correlate with targeted placental transfection in vivo, whereas bulk-derived physicochemical metrics more strongly associate with nonspecific hepatic delivery. Together, this work harnesses a separation-coupled biophysical platform to resolve previously inaccessible tLNP subpopulations and demonstrates that subpopulation nanoscale structure, rather than bulk-averaged properties, dictates targeted RNA delivery. These insights provide a mechanistic foundation for rational engineering of next-generation precision targeted RNA LNP therapeutics.

bioengineering↗

bioRxiv2026

Enzymatic and Biophysical Analysis of two Highly Related Cytochrome P450 Reductases from Artemisia annua Reveals Differences in Their Ligand Interactions and Domain Motions

Artemisinin is an effective antimalarial drug sourced from Artemisia annua, but its low and variable yields require enhancement either semi-synthetically or in-planta to meet the global demand for treatment. Though essential enzymes have been identified in the artemisinin biosynthetic pathway, including an essential Cytochrome P450 monooxygenase (CYP71AV1), there are still many unknowns. Cytochrome P450 reductase 1 (herein, AaCPR1), has been experimentally confirmed as an electron transfer partner for CYP71AV1 in its three step oxygenation of key artemisinin precursors. However, the recent discovery of a highly related CPR, herein AaCPR2, introduces the possibility that another, potentially more catalytically favourable interaction, could exist for CYP71AV1. Therefore, enzyme kinetics and differential scanning fluorimetry (DSF) were used in the characterisation of both AaCPR1 and AaCPR2 to determine the existence and source of their catalytic differences. Tested enzyme activity under cytochrome c and NADPH concentrations revealed that AaCPR1 had lower Km and higher kcat/Km values, while AaCPR2 had higher Vmax and kcat values. This suggests that AaCPR1 is more effective at reducing cytochrome c when substrate conditions are limiting, whereas AaCPR2 is more effective than AaCPR1 at reducing cytochrome c when substrate conditions are saturating. This implies a functional partitioning of the two enzymes on the basis of substrate availability. The DSF results provided deeper insight into the different protein-ligand interactions between the two enzymes. AaCPR2 reached lower maximum melting temperatures across all tested conditions, whereas AaCPR1 had higher maximum melting temperatures. Thus, AaCPR1 exhibits higher thermal stability and has a higher temperature threshold than AaCPR2. This contributes to the notion that the AaCPRs are functionally divergent also on the basis of temperature. The cumulative differences in melting behaviour between the two enzymes led to the hypothesis that AaCPR1 and AaCPR2 exhibit different domain motions that may lead to preferential catalysis for one redox partner over another. This was further supported by the prediction of a highly variable loop region between the two enzymes at the connecting domain just after the flexible hinge. If such loops are highly mobile, as predicted, then the residue differences therein could provide a bio-structural basis for the kinetic and thermal/biophysical differences observed between AaCPR1 and AaCPR2. These data support that AaCPR1 and AaCPR2 possess fundamental biophysical differences despite their high degree of relatedness. Ultimately, these differences suggest differential metabolic functions of the two enzyme in artemisinin biosynthesis and/or other important secondary metabolic processes.

plant biology↗

bioRxiv2026

A biophysical corticostriatal model predicts learning-dependent 7T fMRI dynamics and individual reward bias in humans

Circuit-level computational models can do more than explain existing data; they can generate novel hypotheses and capture individual differences in human populations. We demonstrate this using a biophysical corticostriatal model, transforming simulated neuronal activity from local field potential (LFP) into functional magnetic resonance imaging (fMRI) signals via the balloon model, and finally generating behavioral outcomes. We then validate the results against those obtained from human subjects. The model generates a counterintuitive yet testable prediction. Although prefrontal-striatal coherence demonstrates an increase at the neural LFP scale during learning, the same circuit-- passed through the hemodynamic transform---predicts a decrease in prefrontal-striatal BOLD correlation, driven by category-selective representations that emerge in cortex but not striatum. This prediction is confirmed in fMRI data optimized for single-subject-level detection sensitivity. The model further enables single-subject fitting of a behavioral outcome, classifying individuals into those differentially responding to positive versus negative reward bias. Established biomarkers, including activation measured by the amplitude of low-frequency fluctuations and dopaminergic effects on hemodynamic latency, are also conserved across LFP and fMRI scales. These findings reposition circuit models as generative tools for human neuroscience, capable of producing mechanistically grounded hypotheses and parsing individual variation in ways inaccessible to data-driven approaches alone.

neuroscience↗