Biology subjects

Mehta, S.

Publications and source records attributed to Mehta, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Timed binary WNT activity is sufficient to model the early human neural axis

The vertebrate nervous system develops into functionally distinct domains along the body axis[1]. Developmental studies implicate multiple signaling pathways in coordinating this process, but the order of specification events, sufficiency of individual signals, and molecular basis of competency remain unclear [2,3], leaving the field without a unified model of neuraxis patterning. Enteric nervous system (ENS) and midbrain dopaminergic (mDA) progenitors have reached clinical and preclinical testing [4-11], yet neither is produced at full purity, and yields vary markedly across human pluripotent stem cell (hPSC) lines, reflecting incomplete control of axial identity. Here we use single-cell genetic fate mapping to demonstrate that hPSCs can commit to anterior or posterior cranial fate within the first 24 hours of differentiation, before neural induction. Building on this finding, we show that the duration of a single saturating WNT pulse is sufficient to specify three 'first-order' domains: anterior cranial (0 h), posterior cranial (24 h) and trunk (48 h), each pre-neural and competent for CNS, neural crest and epidermal lineages. Nested 'second-order' domains are specified within first-order domains in response to WNT, FGF, and retinoic acid. Mechanistically, an anterior gene regulatory network repressing WNT targets is irreversibly shut down during the transition to posterior identities. Applying this binary WNT patterning logic, we generate mDA neurons and ENS progenitors robustly across hPSC lines and establish an assembloid model combining first-order progenitors to produce all major regions of the nervous system.

developmental biology↗

bioRxiv2026

Combined HDAC and ATM inhibition with radiation in preclinical glioblastoma models

Background: Brain-penetrant histone deacetylase inhibitors (HDACi) are promising radiosensitizing agents for glioblastoma (GBM). HDAC inhibitors can impair DNA damage response (DDR) pathways and promote persistent DNA damage. Here, we evaluated whether combined inhibition of HDAC and the DNA repair kinase, ataxia-telangiectasia mutated (ATM), could synergize with radiation (IR) and improve therapeutic efficacy in GBM. Methods: In vitro dose-response assays were performed in patient-derived xenograft (PDX)-derived glioma stem cells (GSCs) with both inhibitors of HDAC (quisinostat, QST) and ATM (AZD1390) with or without IR treatment to assess synergy. Pharmacodynamic responses were evaluated by immunoblotting for histone acetylation, DDR signaling, and DNA damage markers. Transcriptomic analyses were performed to evaluate treatment-associated changes in DNA repair pathways. The combination of QST, AZD1390, and IR was further evaluated in an orthotopic GBM survival study. Results: Combined treatment with QST and AZD1390 produced synergistic radiosensitization in multiple GSC models in vitro. Furthermore, QST combined with IR suppressed transcription of homologous recombination (HR) pathway genes in GSCs, impairing double-strand break repair capacity through an epigenetic mechanism that does not directly inhibit ATM kinase activity. Pharmacodynamic analyses confirmed that QST increased histone acetylation while AZD1390 suppressed ATM signaling, together impairing complementary mechanisms to increase radiosensitivity of GSCs. In an orthotopic GBM model, QST combined with IR significantly prolonged survival compared with IR alone. However, the addition of AZD1390 did not provide a significant survival benefit beyond QST+IR, and AZD1390 combined with IR did not significantly improve survival relative to IR alone. Conclusions: Combined HDAC and ATM inhibition with QST and AZD1390 enhances radiosensitivity in patient-derived GBM cells, mechanistically underpinned by QST-mediated increase in DNA damage and dual disruption of DDR through complementary, non-overlapping mechanisms. However, this in vitro synergy did not confer additional survival benefit beyond QST+IR in an orthotopic GBM model under the dosing conditions tested, suggesting pharmacological limitations with the tumor microenvironment or suboptimal dosing schedules may have constrained in vivo efficacy.

cancer biology↗

bioRxiv2026

Comparative Evaluation of DDA and DIA Based Proteomic Workflows in Beryllium Related Lung Disease

We compared traditional data-dependent acquisition mass spectrometry (DDA-MS) with the increasingly adopted data-independent acquisition (DIA-MS) to evaluate their relative utility for large-scale quantitative biofluid proteomics of lung compartments, specifically paired bronchoalveolar lavage (BAL) cells and bronchoalveolar lavage fluid (BALF). Using beryllium-related granulomatous lung disease as a focused model, we analyzed BALF and BAL cells from beryllium-sensitized (BeS) individuals using both acquisition strategies to assess proteome depth, quantitative completeness, and analytical robustness. In BAL cells, 5,640 proteins were identified by DDA-MS and 5,227 by DIA-MS; however, DIA-MS yielded markedly improved quantitative completeness, with 5,178 proteins ([~]99%) quantified across all samples compared with 3,539 ([~]63%) quantified by DDA-MS. While 3,397 proteins were quantified by both methods, DIA-MS uniquely quantified 1,781 lower-abundance proteins. Proteins identified by both DIA and DDA-MS approaches revealed pathways associated with granulomatous inflammation, including Toll-like receptor, clathrin-mediated endocytosis, sirtuin, and C-type lectin receptor signaling, whereas DIA-MS resolved additional pathways, such as the complement cascade, coagulation system, and JAK/IL-6-type cytokine signaling. In BALF, although more proteins were identified by DDA-MS than by DIA-MS (2,069 vs 1,742), DIA-MS achieved greater quantitative completeness, with 1,695 proteins quantified across all samples compared with 1,050 using DDA-MS, underscoring its suitability for biomarker-oriented analyses in lung fluid compartments. Together, these results support DIA-MS as a robust and sensitive platform for quantitative lung proteomics and discovery of disease-relevant protein signatures.

systems biology↗

bioRxiv2026

Differential vulnerability of CA1 pyramidal neuron cell types in the 5xFAD Alzheimer's disease mouse model

Selective neuronal vulnerability is a defining feature of Alzheimers disease, yet how neurodegeneration unfolds across defined hippocampal CA1 pyramidal neuron populations remains unresolved. Previously, we showed that CA1 pyramidal neurons are organized into molecularly defined laminar cell types that form stable spatial signatures along the longitudinal axis (Pachicano et al., 2025). Here, we investigated how vulnerability manifests across CA1 pyramidal cell types in the 5xFAD mouse model of amyloid pathology. HiPlex single-molecule fluorescence in situ hybridization was used to quantify gene expression at single-cell resolution across hippocampal CA1 subregions across disease progression. Cells were classified into molecularly defined CA1 pyramidal cell types based on marker gene expression, and changes in density, proportion, and molecular state were assessed. Amyloid-associated pathology differentially affected CA1 pyramidal cell types, revealing distinct trajectories of vulnerability and resilience. Specific populations exhibited early and progressive susceptibility, while other populations demonstrated relative preservation across disease timepoints. These effects were consistent across CA1 subregions, indicating that vulnerability follows cell type identity rather than regional anatomy alone. Together, these findings demonstrate that neurodegeneration in Alzheimers disease is structured by intrinsic cell type identity and provide a cellular framework for understanding selective vulnerability.

neuroscience↗

bioRxiv2026

The Hidden Landscape of Missed Effects in Human Functional Neuroimaging

Functional neuroimaging aims to uncover brain processes underlying behavior and disease, yet studies are often underpowered to detect these effects. How this literature has shaped our understanding of brain function remains unknown, and little guidance exists for planning better powered studies. An underappreciated barrier is that commonly reported effect sizes across the brain are inflated, biasing study planning. Here, we introduce a correction for this inflation bias and show how more accurate studies can be planned using corrected effect size benchmarks from a mega-analysis of 63 typical studies across seven large datasets (52,979 participants). We find that common methods of planning studies based on uncorrected effects lead to roughly half the expected detections at typical sample sizes, with limited spatial overlap with original findings. These missed effects collectively explain meaningful additional variance in the desired outcome. We show how to recover missed effects by planning not only for power but also for a target number of detections via corrected benchmarks, or by taking a whole-brain approach with multivariate effects that individual research groups can detect (n < 50 compared to n > 1,000 for a typical univariate effect). These findings lay the groundwork for more informed study planning and a richer understanding of the widespread nature of brain effects, with implications for shared challenges (and solutions) across biomedicine.

neuroscience↗

bioRxiv2026

Dynamic visualization of physiological CaMKII activity using sensitive FRET biosensors

Calcium-calmodulin (CaM)-dependent protein kinase II (CaMKII) is a key mediator of complex physiological processes throughout the body, from the brain to the reproductive system, where CaMKII translates spatiotemporally dynamic calcium elevations into specific biological functions. Directly visualizing CaMKII activity dynamics in living cells using genetically encoded fluorescent biosensors can thus provide crucial insights into the molecular regulation of health and disease. Yet the ability to sensitively and specifically monitor endogenous CaMKII activity in physiologically relevant contexts is limited by the lack of sensors that can achieve robust, quantitative visualization of CaMKII responses. Here, we leveraged a recent serine/threonine kinome-wide substrate atlas to rationally engineer a powerful suite of Forster resonance energy transfer (FRET)-based CaMKII kinase activity reporters with high specificity, sensitivity, and signal-to-noise ratio. Using these biosensors, we were able to sensitively and robustly visualize endogenous CaMKII activity dynamics in both cultured cell lines and primary cells, including cardiomyocytes, oocytes, and neurons. We further utilized 2pFLIM imaging of organotypic hippocampal slices to quantitatively track LTP-induced CaMKII activity within single dendritic spines, highlighting a major advance in the study of physiological CaMKII signaling.

bioengineering↗

bioRxiv2026

Dependencies in heterogeneous, lineage plastic patient-derived prostate cancer organoids revealed through integrated single-cell multiomics and CRISPR screening

Lineage plasticity and tumor heterogeneity limit the effectiveness of targeted therapies, yet the functional dependencies used to nominate therapeutic targets are often derived from homogeneous systems that fail to capture this complexity. Here, we establish a framework to resolve state-specific genetic vulnerabilities by integrating single-cell multiomics (RNA and ATAC) with pooled CRISPR-Cas9 screening across a large panel of patient-derived organoids (PDOs) from castrate-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC). We generate a single-cell multiome atlas spanning >190,000 cells across 22 PDOs, defining seven lineage states--including intermediate and plastic populations not resolved by bulk profiling--and demonstrate that these lineage programs robustly classify independent transcriptomic datasets from prostate cancer patient tumors. By systematically coupling this atlas to subtype-resolved CRISPR screens, we construct a functional dependency map linking cell state in heterogeneous 3D human tumor models. We show that intratumoral heterogeneity fundamentally reshapes the interpretation of gene essentiality, whereby gene-level depletion reflects the composite behavior of co-existing subpopulations, and identify a general principle in which resistant "limiting" populations disproportionately determine aggregate fitness effects. This framework reveals both canonical and previously unrecognized lineage-restricted dependencies within highly plastic tumor and NEPC states, including a therapeutically targetable dependency on the aryl hydrocarbon receptor (AHR) in a novel hybrid stem-like/ASCL1 population. Together, these data establish an extensive multi-dimensional prostate cancer resource, identify novel lineage-resolved biology, and provide a generalizable strategy for interpreting functional genomics in heterogeneous human tumors.

cancer biology↗

bioRxiv2026

Self-Interaction Nanoparticle Spectroscopy Predicts High-Concentration Viscosity of Therapeutic IgG1 Antibodies

Predicting high-concentration viscosity of monoclonal antibodies such as IgG1 is crucial for their development as therapeutics for subcutaneous delivery. Unfortunately, traditional experimental rheometry methods for assessing viscosity are low-throughput. This study evaluates Self-Interaction Nanoparticle Spectroscopy (SINS) assays--specifically charge-stabilized SINS (CS-SINS) and PEG-stabilized SINS (PS-SINS)--for high-throughput viscosity prediction. We characterized 96 IgG1 antibodies, assessing SINS against in silico descriptors and dynamic light scattering (DLS) data. CS-SINS showed strong correlation with charge, offering limited additional utility. In contrast, PS-SINS provided orthogonal information; integrating it with in silico data and DLS significantly improved random forest model accuracy for binary viscosity classification. PS-SINS measurements in multiple buffers captured complementary information, achieving comparable accuracy without DLS. Importantly, PS-SINS scores exhibited a strong logarithmic relationship (r=0.98) with high-concentration viscosity in Fc variants of clinical antibodies, suggesting a direct mechanistic link. Furthermore, PS-SINS performed reliably with one column purified (protein A) samples, supporting its early-stage application. These findings establish PS-SINS as a high-throughput tool to accelerate the developability assessment of antibody candidates.

biochemistry↗

bioRxiv2026

Elevated YBX1 mRNA expression is associated with a genomi-cally unstable and clinically aggressive cancer state: a pan-cancer analysis

Y-box binding protein 1 (YB-1; YBX1) is a multifunctional DNA- and RNA-binding protein involved in cell cycle regulation, DNA repair, stress adaptation, and therapy resistance. Elevated YBX1 mRNA expression is associated with aggressive disease across multiple cancers, yet its pan-cancer genomic and clinical correlates remain unclear. Here, we performed a comprehensive pan-cancer analysis across 53 datasets spanning 33 tumour types, integrating RNA expression, somatic mutations, copy number, hypoxia, and clinical outcomes. YBX1 was rarely mutated or amplified, indicating that oncogenic relevance is primarily driven by its expression. Tumours with high YBX1 mRNA exhibited a conserved transcriptional program enriched for cell cycle, DNA repair, and chromatin regulation pathways, and were preferentially mutated in genes involved in maintaining genomic stability, including TP53. These tumours were associated with increased mutation burden, fraction of genome altered, homologous recombination deficiency, and elevated hypoxia. Clinically, high YBX1 mRNA associated with advanced stage, higher grade, shorter progression-free survival, and reduced overall survival. Collectively, high YBX1 mRNA expression defines a conserved, genomically unstable, and clinically aggressive tumour state across multiple cancer types.

cancer biology↗

bioRxiv2026

Highly Sensitive Chemigenetic FRET-Based Kinase Biosensors

Fluorescent protein-based biosensors have transformed the study of cell physiology and pathology by enabling direct, live-cell measurements of biochemical activities with spatiotemporal precision. FRET-based biosensors offer a quantitative and well-defined readout mechanism popular among researchers, but have struggled to break free of characteristically low dynamic ranges and overall dependence on the cyan-yellow spectral region. Chemigenetic approaches that combine synthetic fluorophores with self-labeling protein tags represent an attractive solution to these longstanding constraints. Here, we pair different fluorescent protein donors with a HaloTag acceptor conjugated to a far-red fluorophore to obtain a suite of highly sensitive, chemigenetic FRET-based kinase activity biosensors with red-shifted emission and unprecedented dynamic range. We demonstrate the generalizability of this chemigenetic platform by developing biosensors for multiple kinases, as well as small GTPases and second messengers, all while maintaining high sensitivity. The high sensitivity and spectral tunability of these chemigenetic tools enabled us to perform robust multiplexed activity imaging of receptor-mediated signaling networks to quantitatively map isoform-specific coupling by GPCRs, as well as clear visualization of kinase activity in acute brain slices via two-photon fluorescence lifetime imaging. Our chemigenetic sensor toolkit thus provides the sensitivity and dimensionality needed to illuminate the spatiotemporal regulation of signaling networks in cells and tissues.

cell biology↗

bioRxiv2026

Sensitive and Selective Next-Generation FRET-based PKA Biosensors

The cyclic AMP (cAMP)/protein kinase A (PKA) signaling pathway regulates diverse cellular processes through precise spatiotemporal control across subcellular compartments. Forster resonance energy transfer (FRET)-based A-kinase activity reporters (AKARs) have enabled live-cell visualization of PKA activity, but their limited dynamic range constrains detection of subtle or compartment-specific signaling events. Here, we present a suite of sixth-generation cyan/yellow FRET-based PKA sensors (the AKAR6 series) with substantially enhanced sensitivity and improved selectivity. Systematic optimization of the FRET donor-acceptor pair and sensor backbone yields superior performance versus previous best-in-class FRET-based AKARs, enabling robust detection of subtle PKA activity changes across diverse experimental modalities, including flow cytometry, fluorescence lifetime-based FRET, and two-photon imaging in brain slices. We further leverage kinome atlas data to engineer a variant with improved selectivity for more accurate visualization of nuclear PKA activity. Using the AKAR6 toolkit, we showed that in contrast to strong GPCR-induced PKA activities across all tested compartments in PC12 cells, growth factors stimulated a significant PKA activity at the trans-Golgi network but no detectable activity at cis-Golgi, signifying highly compartmentalized PKA signaling at the sub-organelle level. Furthermore, NGF and EGF induced sustained and transient PKA activity, respectively, across various intracellular compartments, including the nucleus, suggesting that growth factor-specific temporal controls are maintained across subcellular compartments. Together, the AKAR6 toolkit provides a sensitive, selective, and versatile platform for dissecting compartmentalized PKA signaling across cells and tissues.

bioengineering↗

bioRxiv2025

Flexible brain state engagement predicts cognitive control transdiagnostically

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognitive control have been consistently observed in a range of psychiatric disorders, the neural mechanisms giving rise to this behavioral variation remain elusive. Here, we tested whether the ability to flexibly recruit recurring brain activation patterns (i.e., brain states) may serve as an intermediate phenotype supporting cognitive control in individuals with a spectrum of clinical symptoms. We leveraged machine learning and external validation to explore this question in three independent, transdiagnostic datasets (N>600), including participants with anxiety disorders, schizophrenia, mood disorders, substance use disorders, post-traumatic stress disorder, obsessive-compulsive disorder, and neurodevelopmental disorders. To capture cognitive controls multifaceted nature, we assessed two of its components--inhibition and shift-- using both task-based and questionnaire data. Flexible brain state engagement predicted all cognitive control metrics in previously unseen individuals transdiagnostically, regardless of which dataset was used for model training. Connectome-based predictive modeling also revealed that shared brain networks underpinned flexible brain state engagement in a transdiagnostic manner. Leveraging brain network dynamics, we further observed that moments of more flexible brain state engagement aligned with moments of network connectivity related to better cognitive control within the same individual. This temporal alignment was replicated in all three datasets with heterogeneous samples. Altogether, this study suggests flexible engagement of brain states may support both inter- and intra-individual differences in cognitive control across individuals with diverse mental health profiles. Funding sourcesThis work was supported by F31AA032179 and R01MH121095. Data were provided in part by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410) and the Transdiagnostic Connectome Project (TCP) dataset. Datasets were accessed through OpenNeuro (https://openneuro.org/datasets/ds005237 and https://openneuro.org/datasets/ds000030/versions/00016).

neuroscience↗

bioRxiv2025

The School Cafeteria Problem: Disrupted Visuospatial Attention During Multisensory Speech-in-Noise Perception in Children with ADHD

BackgroundMany children with attention deficit hyperactivity disorder (ADHD) have difficulty distinguishing speech in crowded scenarios with competing conversations and noise. This can impact learning and community participation. Prior work has implicated audiovisual integration of lip movements with speech sounds as a potential underlying mechanism, but difficulty in accurately deploying spatial attention is an important, often overlooked, factor that may impact the accuracy of speech perception in noisy environments. ObjectiveEvaluate patterns of audiovisual integration and allocation of visuospatial attention during speech processing in a multiple-talker scenario among school-age children with ADHD and typically developing children (TD). MethodsWe recruited 7-12-year-old children with ADHD to participate in an integrated virtual reality and electrophysiologic (EEG) speech-in-noise perception paradigm. In virtual reality, children were asked to press a button whenever a centrally-located target character spoke a target monosyllabic word, while ignoring any words spoken by two flanking distractor characters. We manipulated 1) audiovisual (AV) content by intermixing trials containing either audio, visual, or multisensory AV representations of the three characters and 2) spatial attention demands by including trials with and without interference from the distracting speakers. We measured AV gain in target detection accuracy. Simultaneously, steady-state visual evoked potentials (SSVEP) were continuously recorded via a 128-channel electrode array as an index of visuospatial attention allocation to each speaker. To elicit SSVEP, the target and distractor speakers were tagged with different visual frequency oscillations (23hz and 15hz, respectively). EEG signal-to-noise ratio (SNR) in the corresponding first and second harmonics for those frequency bands was measured over the occipital scalp. ResultsChildren with ADHD showed reduced AV gain relative to TD controls. TD controls also demonstrated more robust SSVEP at 23Hz and 46Hz (target speaker) than at 15Hz (distractor speakers). Compared to TD children, children with ADHD had reduced selective activation in response to the target. As a result, children with ADHD showed more equivalent allocation of visuospatial attention between 23Hz target and 15Hz distractor frequencies. ConclusionsChildren with ADHD demonstrate a reduction in the typical perceptual benefit afforded by exposure to multisensory compared to unisensory speech stimuli, accompanied by substantial differences in the allocation of visuospatial attention to the relevant speaker. Thus, dysregulation of selective visuospatial attention may impair their ability to effectively perceive language in noisy settings.

neuroscience↗

bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

neuroscience↗

bioRxiv2025

Fetal network controllability co-develops with synaptic density and synchronizes with maternal network controllability during pregnancy

White matter undergoes rapid changes during the fetal period that are foundational for future cognitive functions. However, how these changes contribute to the brains capacity to support its dynamic activities--its controllability--remains largely unknown. Here, we apply network control theory (NCT) to investigate the developmental trajectory of controllability from the second trimester through the first postnatal month. We analyzed structural connectivity data from fetuses and infants as part of the developing Human Connectome Project. We identified a robust, nonlinear U-shaped developmental curve of whole-brain controllability across the perinatal period, with a minimum at approximately 35 weeks of gestation. Preterm birth disrupted these trajectories, leading to greater controllability and earlier minimums compared to age-matched fetuses. Using gene expression microarray data from 18 fetal post-mortem brains, we identified genes implicated in synaptic functions that co-develop with changes in controllability during the fetal period. We then used positron emission tomography in seven pregnant rhesus macaques to quantify changes in fetal synaptic density. Increased synaptic density in non-human primates (NHPs) co-occurred with periods of reduced controllability in humans. Finally, using longitudinal scans of a pregnant woman, we mapped the trajectory of changes in maternal controllability during pregnancy. This trajectory exhibited a U-shaped pattern that inversely correlated with the fetal trajectory, reaching a maximum around 36 weeks. Together, fetal controllability follows a nonlinear trajectory that co-develops with synaptic functions and synchronizes with maternal changes in controllability during pregnancy.

neuroscience↗

bioRxiv2025

The role of aging on endothelial cell-cell junctions and pulmonary microvascular permeability

Lung injury leads to pulmonary microvascular endothelial cell (PMVEC) damage, disruption of cell-cell junctions, and increased permeability. Previously, we demonstrated in a mechanical ventilation-induced model of lung injury that aging exacerbated pulmonary microvascular permeability. Based on this, we hypothesized that aging was associated with increased PMVEC barrier dysfunction due to impaired cell-cell junction integrity. PMVEC were isolated from young and aged mice and cultured to confluence in vitro. Barrier function and cell-cell junction integrity were assessed through electric cell-substrate impedance sensing, XPerT permeability assay, immunofluorescence, and western blot analysis. Further studies were conducted to examine alterations in the proteome, markers of inflammation, and actin cytoskeleton organization. To model injurious conditions, PMVEC were stimulated with inflammatory cytokines; permeability and actin cytoskeletal alterations were subsequently assessed. We observed increased basal permeability in PMVEC from aged mice, which was associated with disrupted cell-surface localization of the adherens junction protein, vascular endothelial (VE)-cadherin. Protein abundance of VE-cadherin was significantly increased with age; however, levels of the adapter protein,{gamma} -catenin, and the tight junction protein, claudin-5, were decreased. Measures of inflammation, including cytokine expression and cell surface abundance of adhesion molecules, did not differ with age. Alterations in actin cytoskeleton organization, characterized by augmented presence of actin stress fibers, were observed in aged PMVEC. Under inflammatory conditions, permeability and actin stress fiber formation were exacerbated with age. It is concluded that aging predisposes PMVEC to elevated injury, due to inherent deficiencies in cell-cell junctions and barrier function, potentially mediated through altered actin cytoskeleton organization. New and NoteworthyCompared with pulmonary microvascular endothelial cells (PMVEC) from young mice, PMVEC isolated from aged mice had higher permeability, which was directly associated with impairments in cell-cell junctions. The higher permeability in aged PMVEC was not associated with augmented inflammatory signaling but was associated with actin cytoskeletal alterations. Following an inflammatory insult, PMVEC from aged mice had further exacerbated permeability. These findings may begin to highlight why older patients exhibit higher mortality during lung injury.

cell biology↗

bioRxiv2025

Oscillatory signal decoding within the ERK cascade

Extracellular-signal-regulated kinase (ERK) integrates multiple growth factor and hormone stimuli to control essential cellular processes such as proliferation, survival, and migration. In electrically excitable cells, the ERK pathway also interfaces with intracellular Ca2+ dynamics to achieve non-canonical, cell-type specific functions, having been implicated in neuronal synaptic plasticity, cardiac hypertrophy, and pancreatic insulin secretion. Yet how the classical Ras/MEK/ERK cascade responds to and decodes dynamic Ca2+ signals at its multiple levels to regulate cellular function is poorly understood. Here, we investigated the dynamics of Ca2+-induced ERK pathway activation in a pancreatic {beta}-cell line using genetically encoded fluorescent biosensors. By carefully manipulating Ca2+ input signals and directly monitoring the activity dynamics of individual ERK pathway components, we reveal that {beta}-cell Ca2+ oscillations undergo sequential signal processing along the ERK cascade, mediated by the characteristic response kinetics at each pathway step. We further demonstrate that the ERK cascade and possibly other Ca2+-responsive pathways operate within a hybrid network architecture to achieve both hierarchical and parallel processing of {beta}-cell Ca2+ oscillations, providing important insights into dynamic signal decoding by this crucial signaling network.

cell biology↗

bioRxiv2025

Rational design of oxidation-resistant antibodies through local electrostatic modulation

Oxidation is a significant degradation pathway in proteins, particularly therapeutic antibodies, that can impair function, efficacy, and stability. Understanding the sequence and structural factors that drive oxidation susceptibility is critical for incorporating chemical stability into early-stage antibody design. Here, we present a machine learning classifier trained on expert-guided structural features to assess tryptophan (Trp) oxidation risks in the complementarity-determining regions (CDR) of 187 antibodies produced internally at Genentech. The model reveals a strong correlation between local negative electrostatic potential and Trp oxidation susceptibility. A simplified two-parameter model derived from these insights achieves 79% accuracy in classifying oxidation risk, compared to 84% accuracy with the full-feature classifier. Beyond our internal dataset, the two-parameter model successfully predicted oxidation risk for all CDR Trp sites in a blind subset of eight clinical-stage antibodies. In addition, we show that modulating the electrostatic potential around the Trp side-chains through distant charge-altering mutations can significantly reduce oxidation rates. In four out of five re-engineered antibodies, oxidation rates decreased by approximately 50%, while half of these maintained binding affinity. Finally, we demonstrate that this approach can guide multi-property optimization by balancing oxidation resistance and affinity in an anti-CD33 antibody. These results establish a strong link between local electrostatic environments and Trp oxidation susceptibility, and provide a practical framework for designing oxidation resistant biotherapeutics.

biochemistry↗