Biology subjects

Bhatt, A.

Publications and source records attributed to Bhatt, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Varietal Evaluation of Coffee Genotype in Subtropical Nepal

Coffee (Coffea arabica L.) is an emerging high-value crop in Nepal, but national productivity remains far below genetic potential, partly due to the lack of locally validated variety recommendations. This study evaluated and compared the morphological and yield-attributing characteristics of 19 Arabica genotypes at the Horticulture Research Station, Malepatan, Pokhara (848 m above sea level) from January to June 2026 using a Randomized Complete Block Design with three replications. Yellow Caturra recorded the highest total fresh cherry yield (5.34 kg/plant), followed by Caturra Amarillo (3.56 kg/plant), Catuai Vermelo (3.19 kg/plant), and Catuai Amarillo (3.10 kg/plant). Catuai Amarillo and Caturra Amarillo produced the most bearing branches (76 and 72, respectively), while Caturra Amarillo and Yellow Caturra had the most cherries per node (7.73 and 7.47). Pacamara and Selection-10 achieved the maximum 100-cherry weight (167.67 g), while Indonesia had the largest cherry diameter (13.38 mm). Catuai Amarillo had the highest total soluble solids (25.97 degree Brix), while Tekisic had the lowest (16.80 degree Brix). Coffee leaf rust severity increased from a mean of 7.49% at 30 days after incidence (DAI) to 37.58% at 120 DAI. Selection-10 was the least affected genotype across the severity assessments and recorded the lowest area under the disease progress curve (AUDPC; 25.03 rating-days), whereas Caturra Amarillo had the highest AUDPC (347.95 rating-days). Overall, Yellow Caturra showed the strongest yield performance, while Selection-10 combined low disease development with a high 100-cherry weight. These results indicate that genotype selection should consider productivity, quality-related traits, and CLR response together.

plant biology↗

bioRxiv2026

Management of Root-Knot Nematode-Fusarium Wilt Complex with different Bio-management methods in Tomato (Solanum lycopersicum L.) var Srijana

The root-knot nematode-Fusarium complex is a major constraint to protected tomato cultivation in Nepal, particularly on the widely grown Srijana hybrid, which lacks Mi-mediated nematode resistance. The present study was conducted to identify the most effective biocontrol agent, to identify the most effective Solanum rootstock, and to compare the overall efficacy of grafting versus biocontrol agents under polyhouse conditions. For the management of the root knot nematode-Fusarium complex, a pot experiment was set up in a completely randomized design at Pokhara, Kaski. Treatments comprised Trichoderma viride, Pseudomonas fluorescens, their half-dose combination, self-grafted Srijana, and four resistant rootstocks (S. sisymbriifolium, S. melongena, S. torvum, and VNRRs-10), together with nematode-only, Fusarium-only, complex (Nematode + Fusarium), and disease-free controls. Each plant was inoculated with approximately 2,000 nematode eggs, followed by 25 ml of 10^6 spores per ml of Fusarium seven days later to mimic the field complex. Plant growth parameters and nematode infestation data were recorded and analyzed via RStudio v. 2026.04.0. The root knot nematode-Fusarium complex caused significantly greater reductions in vegetative biomass than root knot nematode and Fusarium alone (20.32 gm vs 38.40 gm vs 51.32 gm) at 45 DAT. Single-agent biocontrol gave intermediate protection (T. viride 64.9%; P. fluorescens 47.9% nematode management efficiency), whereas combined T. viride + P. fluorescens treatment produced the highest vegetative biomass but the weakest nematode suppression (320 galls, reproduction factor: 38). Grafting onto S. torvum and S. melongena resulted in the greatest nematode suppression, achieving 97.9% and 93.6% management efficiency, respectively, while S. torvum alongside VNRRs-10 maintained the highest Fusarium suppression (92.5%). Overall resistant rootstocks consistently outperformed biological control in reducing disease severity. These findings indicate that grafting onto resistant solanum rootstocks, particularly S. torvum and S. melongena, was observed as the most effective strategy for managing the root knot nematode-Fusarium complex under protected cultivation, offering a viable chemical-free management option. Keywords: Root-Knot Nematode, Fusarium oxysporum, RKN-Fusarium-complex, Solanum torvum

pathology↗

bioRxiv2026

Polyketide synthase 12 is an in vivo essential source of novel mycolyl lipids in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is a major pathogen worldwide that infects and transmits only among humans, yet nearly all in vivo virulence research occurs in non-human hosts. To overcome this central challenge in tuberculosis research, we leveraged a dataset of more than 50,000 sequenced isolates to identify Mtb genes that that are functionally preserved during natural infection, disease causation, and transmission between humans. This whole-genome ranking identified polyketide synthase 12 (pks12 ) as an exceptionally in vivo essential gene in human tuberculosis, which we validated in zebrafish and mouse models. Though Pks12 produces a mycoketide lipid in only trace amounts, pks12 deletion severely altered the host-facing surface and arabinoglycan architecture of Mtb. This amplified effect was explained through the discovery of mannosyl-{beta}-1-phosphomycoketide monomycolate (MPMMM), which is synthesized from the known Pks12 product at higher abundance by antigen 85 mycolyltransferases. Thus, we used a new host-facing genomic-metabolomic-phenotypic approach to discover the functions of an in vivo essential Mtb gene, which controls the physical structure of the Mtb-host interface.

microbiology↗

bioRxiv2026

An adenylyl cyclase switch reroutes carbon from growth to virulence lipids in Mycobacterium tuberculosis

Anti-TB drugs act non-uniformly on Mycobacterium tuberculosis (Mtb) because of its distinct physiological states across diverse infection niches shaped by host-derived nutrients and stresses. Agonists of the adenylyl cyclase Rv1625c are a novel drug class that selectively inhibits Mtb growth in macrophages and cholesterol-rich conditions by an unknown mechanism. Combining condition-resolved transcriptomics, genome-scale metabolic modeling, and genetic perturbation, we show these agonists cause a blockade in the electron transport chain, which likely activates Rv1625c. The elevated cAMP in turn drives global transcriptional and post-translational remodeling of central carbon and lipid metabolism. The resulting methylcitrate cycle reversal chokes cholesterol breakdown products from entering central metabolism, diverting carbon toward cell wall and virulence-lipid (phthiocerol dimycocerosate) synthesis and inhibiting growth. These drugs thus hijack an endogenous switch that reroutes carbon from biomass to virulence-lipid production. Hence, nutrients that restore carbon flux and relieve ETC blockade reduce activity, whereas Rv1625c overexpression and ETC inhibitors potentiate drug action even in refractory conditions.

systems biology↗

bioRxiv2026

Predicting VHH-Fc Developability from Large-Scale IgG Data

The VHH-Fc antibody scaffold is an emerging therapeutic modality. No public large-scale, standardized developability VHH-Fc dataset exists. Filling that gap, we introduce GDPa5, a 160-member VHH-Fc library profiled across 10 biophysical assays on the PROPHET-Ab platform. Cross format models trained on the developability properties of 559 IgGs outperformed intra-format models trained on GDPa5 alone, which is an advantage driven by the larger scale of standardized IgG data rather than by format. The most accurately predicted properties were heparin binding (HAC, Spearman {rho}=0.82), hydrophobicity (HIC, {rho}=0.63), and self-association (AC-SINS, {rho}=0.62), all of which are largely governed by antibody surface properties. Tabular neural networks (TabICLv2, TabPFN v2.5), applied here for the first time to antibody developability prediction, outperformed conventional modeling approaches. Adding experimental HIC and HAC measurements as model inputs improved prediction of the more complex polyreactivity liability (PR-CHO, {Delta}{rho} = +0.10), supporting a tiered assay strategy that extends predictive performance while limiting experimental burden. We demonstrate through this work that IgG-trained models are a practical, data-efficient starting point for VHH-Fc developability prediction.

biophysics↗

bioRxiv2026

Decoding Bispecific Antibody Developability: Design Rules and Predictive Models from a 160-Member Library

Bispecific antibodies deliver functional outcomes that monospecific antibodies cannot, yet emergent self-association, polyreactivity, and aggregation often degrade their developability relative to their parental arms. Whether bispecific developability inherits from the parents or is driven by the format has not been tested at scale. We characterized 160 bispecific antibodies and their 65 parental arms on a uniform knobs-into-holes CrossMab IgG1 scaffold across 10 assays on the PROPHET-Ab high-throughput platform. Bispecific developability separates into three classes of inheritance. Hydrophobicity and surface charge inherit cleanly from the parents (Spearman {rho} {approx} 0.85 to 0.95), so parental-level screening predicts bispecific fate. Self-association and polyreactivity inherit partially ({rho} {approx} 0.60 to 0.88), with mechanistically interpretable emergent outliers driven in part by Fv-Fv charge complementarity and a parental biophysical ceiling on the hydrophobicity (HIC) by surface-charge (HAC) plane. Thermostability is poorly predicted from parental antibodies ({rho} < 0.4), so it requires bispecific-level testing. The class framework yields actionable selection rules: triage hydrophobicity and charge at the parental level, avoid pairing two high-HIC x high-HAC arms, pair opposite-sign Fv charges to suppress self-association but re-validate at the formulation buffer, and measure thermostability on the bispecific itself. This work charts a tractable path from monospecific sequence to bispecific developability prediction. SignificanceBispecific antibodies are a fast-growing therapeutic class, yet the rational design of well-behaving bispecific antibodies from validated monospecific antibody building blocks remains challenging. A key bottleneck is the lack of comprehensive, high-quality public datasets linking parental antibody developability properties to corresponding bispecific antibody developability properties. We address this gap by releasing a dataset comprising 160 bispecific antibodies and the 65 parental monospecific antibodies profiled in 10 developability assays. The data show that bispecific antibody developability is complex. Some properties are easily predictable from the parents, whereas others emerge in the bispecific format or from the bispecific format itself. The factors that govern each property can be identified empirically and used to make practical selection decisions. The mechanistic explanations and predictive models reported here establish a compact set of actionable rules. Together, they define a framework for using computational pipelines to convert monospecific antibodies into bispecific antibodies with drug-like developability properties, enabling faster and more effective generation of high-quality bispecific antibodies for diverse therapeutic applications.

biophysics↗

bioRxiv2026

Yoda molecules agonize PIEZO2

PIEZO proteins (PIEZO1 and PIEZO2) are essential mechanosensitive channels. PIEZO1 is thought to be selectively activated by Yoda molecules (Yoda1 and Yoda2). Although a structural framework for PIEZO1 activation by Yoda1 exists, a molecular mechanism underlying this selective activation is lacking. Here, using electrophysiology and calcium imaging, we show that Yoda1 increases PIEZO2 open probability and stretch sensitivity as efficaciously as PIEZO1 but elicits weaker PIEZO2-dependent calcium entry, rationalizing why its effect on PIEZO2 has been overlooked. Both Yoda1 and its more potent Yoda2 analog slow down inactivation of PIEZO2 currents with potency similar to PIEZO1 but with lower efficacy. Using mutagenesis and molecular dynamics simulations, we further show that Yoda2s benzoic acid group forms a transient salt bridge with a conserved arginine in the Yoda binding site, providing a molecular basis for Yoda2s increased potency. Our study cautions a reevaluation of studies using these molecules to untangle biological functions mediated by PIEZO channels.

biophysics↗

bioRxiv2026

CD8⁺ T cells induce interstrand crosslinking-associated DNA damage in neurons

Viral pathogens cause neurologic sequelae during acute and post-acute phases of infection. CD8+ T cells are hypothesized to contribute to these effects, but the mechanisms through which they act are poorly understood. We posited that viral infections and/or antiviral immune responses induce DNA damage, which may underlie neuronal dysfunction. Using a model of neurotropic flavivirus infection, we found that genes associated with interstrand crosslinking (ICL) DNA damage were upregulated post-infection, temporally congruent with T cell infiltration. Using an in vitro co-culture system, our results demonstrate that CD8+ T cells induced ICL-like damage in primary neurons, independent of antigen-specific interactions or direct contact. Human transcriptomic data also showed overexpression of genes associated with ICL damage in the brains of people with Parkinsons disease, Alzheimers disease, and multiple sclerosis, which are neurologic diseases characterized by neuroinflammation. Together, these data indicate that CD8+ T cells cause genotoxic DNA damage in neurons, which may underlie the neurologic dysfunction seen in neurodegenerative conditions. SummaryResults indicate that CD8+ T cells induce interstrand crosslinking-like DNA damage in neurons independent of antigen-specificity in a mouse model of viral infection, in vitro primary cell culture system, and human neurologic diseases. These findings provide insight on the mechanistic connection between neuroinflammation and neurologic dysfunction.

immunology↗

bioRxiv2026

CholBindNet: Interpretable Neural Networks for Cholesterol Binding Site Prediction

Cholesterol is a key modulator of membrane protein structure and function, yet predicting cholesterol binding sites remains challenging due to its undrug-like physicochemical properties. Here, we curated more than 800 high-resolution transmembrane protein structures containing cholesterol and developed an interpretable, atom-based deep-learning framework, CholBindNet, comprising four model architectures: a 3D convolutional neural network, a graph neural network, a graph attention network, and a graph convolutional network. A Positive-Unlabeled (PU) training strategy was employed to address the scarcity of true negative samples resulting from the promiscuous nature of cholesterol binding. We show that CholBindNet substantially outperforms existing deep-learning models trained on general ligand-binding datasets. The performance and generalizability of the model were further demonstrated by rapidly assessing strong, median, and weak cholesterol-binding sites in the PIEZO2 ion channel in excellent agreement with computationally expensive all-atom molecular dynamics (MD) simulations. Additionally, strong model interpretability was achieved for CholBindNet through atom-level feature encoding, Grad-CAM visualization, and attention-based scoring analysis. Overall, CholBindNet provides an efficient and scalable approach for predicting cholesterol binding sites on membrane proteins, achieving performance comparable to MD simulations while offering mechanistic biophysical insights beyond amino-acid sequence. This work hence lays the foundation for future development of deep-learning models targeting membrane protein drug-binding sites and cholesterol-modulated therapeutics. Significance StatementDeep-learning models for ligand-binding prediction have advanced rapidly, yet those trained on soluble proteins perform poorly for membrane proteins, particularly for cholesterol binding. We introduce CholBindNet, a set of neural network models specifically designed to identify cholesterol-binding sites in transmembrane proteins. CholBindNet substantially outperforms existing deep-learning approaches and accurately ranks strong, intermediate, and weak cholesterol-binding sites in close agreement with computationally intensive all-atom molecular dynamics simulations. This work provides a practical and scalable alternative to long-timescale simulations for studying cholesterol-protein interactions. The curated cholesterol benchmark and open-source models will enable broader adoption of deep learning for investigating lipid regulation of membrane proteins and for guiding the design of drugs targeting membrane-embedded binding sites.

biophysics↗

bioRxiv2025

VadK, a Non-Canonical Kinase that Regulates the Methylcitrate Cycle and is Essential for Mycobacterium tuberculosis Virulence

The evolution of new enzymatic functions is constrained and guided by the architecture of an organisms metabolic and regulatory networks as well as by environmental constraints. Here, we identify a previously uncharacterized kinase that has evolved from pyruvate phosphate dikinase (PPDK). Through biochemical and systems-level analyses, we show that this enzyme, encoded by Rv1127c in Mycobacterium tuberculosis (Mtb), has diverged from its ancestral role in central carbon metabolism to function as a histidine kinase in pathogenic mycobacteria and related species. We designate this enzyme Virulence Associated DiKinase (VadK), reflecting its ability to autophosphorylate and role in coordinating metabolism and virulence. VadK is essential for the utilization of exogenous carbon sources critical for survival within the host and is required for Mtb pathogenicity in murine models of tuberculosis. Furthermore, VadK interacts with key enzymes of the methylcitrate cycle, and 13C-metabolic flux analysis indicates that it fine-tunes flux through this pathway, with elevated flux proving growth limiting. Together, these findings identify VadK as a previously unrecognized regulatory kinase that integrates metabolic control with virulence in Mtb, revealing a new facet of metabolic regulation in bacterial pathogenesis and a potential target for therapeutic intervention.

microbiology↗

bioRxiv2025

Isoform-Level Analysis of 10x Genomics Single-Cell cDNA Libraries from Cultured K562 Cells Using Long-Read Sequencing

Integration of Oxford Nanopore Technologies (ONT) long-read sequencing with 10x Genomics single-cell cDNA libraries enables novel transcript detection, isoform analysis and captures full-length gene body coverage. The purpose of the study was the comparison of three approaches for sequencing 10x Genomics Chromium Single Cell cDNA libraries using long-read sequencing: single-cell full-length transcript sequencing by sampling (FLT-seq), the cDNA-PCR Sequencing Kit (SQK-PCS111) and the PCR Expansion Kit (EXP-PCA001). Our aim was to evaluate their efficiency in enriching full-length cDNA fragments, identifying barcodes, detecting novel isoforms and mutations, and characterizing transcript coverage profiles.

genomics↗

bioRxiv2025

Form Follows Function: A New Paradigm of Pancreatic Cancer Progression

Pancreatic ductal adenocarcinoma (PDAC) exhibits a distinctive propensity to invade nearby organs and infiltrate large blood vessels, even in the absence of distant metastasis. While the genetic and transcriptomic drivers of PDAC progression have been well studied, the mechanisms by which these molecular changes translate into functional, invasive behavior remain largely unknown. Here, we uncover a striking level of tissue organization, characterized by previously unrecognized spatial and geometric properties within and among tumor structures. Leveraging the first large-scale, AI-assisted, human-curated PDAC atlas from hematoxylin and eosin (H&E) images, we annotated, classified, and characterized 144,474 malignant and normal structures from treatment-naive (n=118) and neoadjuvant-treated PDAC patients (n=50). Additionally, we developed a new computational tool, SHAPE, to investigate PDAC aggressiveness through a comprehensive "geometrization" of cancer progression. Using traditional H&E-stained slides and three-dimensional (3D) tissue reconstruction experiments, we observed that invading tumor structures display an eccentric morphology with pronounced local angular coherence. These geometric and spatial properties revealed coherent architectural patterns, with invasive structures closely tracking vessels and nerves as they infiltrate surrounding tissue. Mechanistically, integration of morphological features from 39,045 annotated tumor structures with whole-genome and RNA sequencing data revealed that PDACs with numerous eccentric structures exhibit increased copy number alterations (CNAs), loss of heterozygosity (LoH) on the p-arm of chromosome 17, and a quasi-mesenchymal/basal-like molecular subtype. Spatial transcriptomic analysis of 1,650 tumor structures from six additional PDAC patients further confirmed upregulation of invasive cellular programs within highly eccentric structures, such as epithelial-to-mesenchymal transition (EMT), angiogenesis, coagulation, and complement pathways, underscoring their infiltrative nature. Finally, cross-validation of our AI-based method enabled a fully automated, highly interpretable computational approach to assist pathologists and clinicians in evaluating neoadjuvant chemotherapy response, predicting patient survival, and guiding chemotherapy in adjuvant settings. Collectively, these findings deepen our understanding of PDAC progression, identify a new hallmark of tumor architecture, and pave the way for full integration of AI-driven morphology-based approaches into clinical workflows to improve the management of PDAC patients.

cancer biology↗

bioRxiv2025

Prediction of bacterial protein-compound interactions with only positive samples

Prediction of Compound-Protein Interactions (CPI) in bacteria is crucial to advance various pharmaceutical and chemical engineering fields, including bio-catalysis, drug discovery, and industrial processing. However, current CPI models cannot be applied for bacterial CPI prediction due to the lack of curated negative interaction samples. This paper introduces a novel Positive-Unlabeled (PU) learning framework, named BIN-PU, to address this limitation. BIN-PU generates pseudo positive and negative labels from known positive interaction data, enabling effective training of deep learning models for CPI prediction. We also propose a weighted positive loss function that weights to truly positive samples. We have validated BIN-PU with multiple CPI backbone models, comparing the performance with the existing PU model using bacterial cytochrome P450 (CYP) data. Extensive experiments demonstrate the superiority of BIN-PU over the benchmark model in predicting CPIs with only truly positive samples. Furthermore, we have validated BIN-PU on additional bacterial proteins obtained from literature review, human CYP datasets, and uncurated data for its reproducibility. We have also validated the CPI prediction for the uncurated CYP data with biological and biophysical experiments. BIN-PU represents a significant advancement in CPI prediction for bacterial proteins, opening new possibilities for improving predictive models in related biological interaction tasks.

bioinformatics↗

bioRxiv2025

Pupil responses to social stimuli are associated with adaptive behaviors across the first 24 months of life

BackgroundPupil changes in response to well-controlled stimuli can be used to understand processes that regulate attention, learning, and arousal. This study investigates whether pupil dynamics to social stimuli are associated with concurrent adaptive behavior in typically developing infants. To accomplish this, we developed and assessed pupillary responses to Stimuli for Early Social Arousal and Motivation in Infants (SESAMI). MethodsA sample of forty-six typically developing children aged six to twenty-four months were exposed to SESAMI. Infants were presented with either dynamic social faces or non-social stimuli that controlled for luminance, motion, and auditory exposure. A multi-level mixed effects model was used to fit pupillary response functions (PRFs) that measure the change in pupil size over time as the infants fixate on either a socially dynamic face or a non-social control. This model produces separate social and non-social PRFs for both the population and each individual. An average individual deviation score from the population PRF was calculated separately for social and non-social trials yielding the pupil response index (PRI). Vineland Adaptive Behavior Scales (VABS) were regressed on social and non-social individual PRIs while controlling for age and average fixation time. We tested whether the social PRI was a statistically significant predictor of adaptive behavior by comparing the model predicted VABS scores with observed scores. ResultsAn increase in PRI to social stimuli was significantly associated with better adaptive behaviors in typically developing children between 6 and 24 months of age. ConclusionsSESAMI combined with pupillometry and multi-level mixed effects modeling, provides a novel and scalable framework for quantifying individual differences in pupil changes in response to social stimuli relative to a population-level baseline. By demonstrating that pupil response indices during social fixations predict adaptive behaviors, we lay the foundation to test how these measures may help identify infants with intellectual and developmental disorders.

physiology↗

bioRxiv2025

A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training

Antibodies must bind their targets with high affinity and specificity to achieve useful therapeutic activity. They must also possess suitable developability properties (e.g., thermostability, solubility, viscosity, polyreactivity) to ensure favorable manufacturing, formulation, and in vivo performance. Both binding and developability properties are inherent to a given antibody amino acid sequence. Identification or selection of antibodies possessing suitable binding characteristics is now routine, and de novo computational design models, trained on extensive complementarity-determining region sequence and structural data, are rapidly improving. Developability properties, however, remain difficult to predict largely due to insufficient training data, with empirical testing being heavily used to avoid challenges in late-stage antibody development. To fill this gap, we built a high-throughput antibody developability assay platform designed to generate the large datasets needed to train improved machine learning (ML) models. We optimized and automated known developability assays [Jain et al., 2017], and developed a robust integrated data analytics pipeline. Here we report data on 246 antibodies--representing 106 approved, 135 clinical-stage, and 5 preregistration/withdrawn molecules--across a panel of 10 developability assays, in a "tidy data" format suitable for AI/ML modeling. We used these data to develop an XGBoost [Chen et al., 2016] ML model that better predicts similarity to approved antibodies compared to conventional use of developability warning thresholds. Additionally, we confirm that preliminary predictive models do improve with more training data. Our high-throughput PROPHET-Ab platform enables data generation at the scale needed to develop improved ML models to predict antibody developability. SignificanceSuccessful antibody drugs exhibit important "developability" properties, beyond tight and specific binding to their target, including high expressibility, high stability and solubility, low aggregation propensity, low viscosity, low polyreactivity, and long in vivo half-life. Collectively, developability properties predict favorable manufacturing, storage, administration, and safety, and deficiencies in these properties increase risk for clinical failure. Despite progress in developing machine learning models to predict structure and binding, antibody developability models lag, largely due to a lack of sufficiently large training datasets. We have built a high-throughput platform, PROPHET-Ab, that enables data generation at the scale needed to train improved AI/ML models to predict antibody developability.

biophysics↗

bioRxiv2025

A two-step clockwork mechanism opens a proteo-lipidic pore in PIEZO2 channels

Mechanosensitive PIEZO channels are thought to open their pore through tension-induced flattening of large transmembrane arm domains. Yet, the structural basis of this activation remains unclear. Here, we uncover the conformational coupling between arm flattening and pore opening in PIEZO2 by capturing protein motions across length scales using hybrid-resolution molecular dynamics simulations. Sampling multiple microsecond-long trajectories under physiological activation tension show that arm flattening correlates with anticlockwise rotation of the pore domain and with clockwise twisting of inner pore helices, enabling dilation and hydration of a transmembrane pore gate. These clockwork motions enable PIEZO2 to populate two open states with distinct conductance depending on applied membrane tension, in agreement with single-channel electrophysiology. Pore opening is accompanied by the separation of pore helices, creating interhelical gaps which become filled with lipids, resulting in the fully conducting pore being walled by both lipids and protein. The fully open PIEZO2 state recapitulates minimal pore size, conductance, ion selectivity, and outward rectification of chloride currents observed electrophysiologically. This work reveals how tension-induced large-scale rearrangements of the PIEZO2 arms funnel into subtle and dynamic gating motions, providing invaluable structural insights for future structure-function and drug discovery studies.

biophysics↗

bioRxiv2024

Mammalian PIEZO channels rectify anionic currents

Under physiological conditions, mammalian PIEZO channels (PIEZO1 and PIEZO2) elicit transient currents mostly carried by monovalent and divalent cations. PIEZO1 is also known to permeate chloride ions, with a Cl- / Na+ permeability ratio of about 0.2. Yet, little is known about how anions permeate PIEZO channels. Here, by separately measuring sodium and chloride currents using non-permanent counter-ions, we show that both PIEZO1 and PIEZO2 rectify chloride currents outwardly, favoring entry of chloride ions at voltages above their reversal potential, whereas little to no rectification was observed for sodium currents. Interestingly, chloride currents elicited by 9K, an anion-selective PIEZO1 mutant harboring multiple positive residues along intracellular pore fenestrations, also rectify but in the inward direction. Molecular dynamics simulation indicate that the inward rectification of chloride currents in 9K correlates with the largely positive electrostatic potential at the intracellular pore entrance, suggesting that rectification can be tuned by pore polarity. These results demonstrate that the pore of mammalian PIEZO channels inherently rectifies chloride currents. Statement of significanceMechanosensitive PIEZO ion channels play many important roles across cells and tissues. Their open pore facilitates the flow of cations down their electrochemical gradients, eliciting sodium-driven membrane depolarization and calcium-dependent signaling under physiological conditions. Yet, these channels also permeate chloride ions. In this study, we show that the two mammalian PIEZO channel homologs preferentially permeate chloride ions into the cells at voltages more positive than the chloride reversal potential. Although PIEZOs permeate cations more effectively than chloride ions, the influx of chloride ions mediated by PIEZOs could participate in certain physiological processes.

biophysics↗

bioRxiv2024

Pannexin 1 and Pannexin 3 differentially regulate the tumorigenic properties of cutaneous squamous cell carcinoma

Pannexin (PANX) channels are present in skin and facilitate the movement of signalling molecules during cellular communication. PANX1 and PANX3 function in skin homeostasis and keratinocyte differentiation but were previously reduced in a small cohort of human cutaneous squamous cell carcinoma (cSCC) tumours compared to normal epidermis. In our study, we used SCC-13 cells, limited publicly available RNA-seq data and a larger cohort of cSCC patient-matched samples to analyze PANX1 and PANX3 expression and determine the association between their dysregulation and the malignant properties of cSCC. In a bioinformatics analysis, PANX1 transcripts were increased in cSCC and head and neck SCC tumours compared to normal tissues, but PANX3 mRNA showed no differences. However, in our own cohort, PANX3 transcripts were decreased in cSCC compared to patient-matched aged skin, whereas PANX1 was upregulated in cSCC. PANX1 localized to all regions within the cSCC tumour microenvironment and increased levels were associated with larger tumour dimensions. To investigate PANX1 function in SCC-13 cells, we deleted PANX1 via CRISPR/Cas9 and treated with PANX1 inhibitors which markedly reduced cell growth and migration. To assess PANX3 function in cutaneous carcinogenesis, we employed the DMBA/TPA model using our global Panx3 knockout (KO) mice, where 60% of wildtype and 100% of KO mice formed pre-cancerous papillomas. Average papilloma volumes at endpoint were significantly increased in KO mice and showed moderate evidence of increases in KO mice over time. Collectively, these findings suggest PANX1 and PANX3 dysregulation may have potential tumour promoting and suppressive effects for keratinocyte transformation, respectively. Key points summaryO_LIPannexin 1 and pannexin 3 are channel-forming proteins which are critical in the normal maintenance and function of keratinocytes in the skin but may become altered in cutaneous squamous cell carcinoma (cSCC) tumours. C_LIO_LIIn this study, we used a combination of culture models, mouse models and patient-derived tissues. We found pannexin 1 levels are increased in cSCC tumours and present in all tumour regions, functioning to promote cSCC cell growth and migration. C_LIO_LIConversely, pannexin 3 levels are decreased in cSCC tumours and this protein reduces the incidence and growth of pre-cancerous lesions. C_LIO_LITaken together, our data indicates that in cSCC these pannexin family members seem to have opposite effects, where pannexin 1 is pro-tumorigenic and pannexin 3 is anti-tumorigenic. C_LIO_LIThese results help us to better understand the mechanisms of malignant transformation of keratinocytes and offer a new potential therapeutic target for the treatment of advanced cSCC. C_LI

cancer biology↗