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Biology subjects

Xiao, K.

Publications and source records attributed to Xiao, K..

At least 19 recordsLinked to original sources

Curvature sensing and generation by diverse membrane-tethered polymers can be explained by a unifying physical model

Cell membranes are decorated by dense, polymeric macromolecules such as proteins with bulky intrinsically disordered regions, glycosylated proteins, and glycopolymers. Experiments have illustrated that the curvature of cellular membranes, which plays a role in diverse biological processes, can be regulated by the local density of these membrane-tethered polymers and their response to environmental changes. However, the mechanisms underlying the feedback between solution conditions, membrane curvature, and the local density and molecular weight of membrane-tethered polymers remain largely unexplored. In this work, using a combination of theory and experiment, we show that the ionic strength of the surrounding solution and the degree of ionization both play important roles in regulating the conformation of the membrane-tethered polymers, which in turn influences their membrane curvature generation capabilities. Specifically, we show that for negatively charged, disordered proteins and glycans, the thickness of the polymer brush decreases with ionic strength but increases with grafting density and degree of ionization, resulting in enhanced sensitivity to membrane curvature and an amplified ability to drive membrane curvature at lower ionic concentrations. Our findings reveal a mechanism by which membrane-tethered polymers generate membrane curvature to minimize free energy, with the extent of curvature generation modulated by the polymer grafting density and layer thickness, as well as by the ionic strength of the surrounding solution. The implications of these findings are broad, suggesting that perturbations to biopolymer charge, such as sialylation of glycans and phosphorylation of disordered proteins, could actively modulate membrane curvature induction in diverse cellular processes from endocytosis to cell motility.

biophysics↗

Motif-based model of transcription predicts effects of sequence variants in AR enhancers and reveals distinct functions for AR-associated transcription factors

Androgen receptor (AR)-mediated transcription plays a central role in prostate cancer development and progression, yet the contributions of individual transcription factors (TFs) to AR-dependent enhancer activity remain incompletely understood. Here we use a biophysically motivated, interpretable motif-based model to dissect these contributions from STARR-seq data in LNCaP cells. By fitting the model separately to androgen inducibility and to baseline enhancer activity, we resolve TFs into three functional classes: hormone-dependent drivers, constitutive activators, and dual-role factors that contribute to both. These patterns suggest that inducibility is associated not only with the presence of AR and co-activator motifs, but also with the relative absence of constitutive activators that may saturate enhancer output. We validate the model against an independent saturation-mutagenesis dataset spanning 40 AR enhancers, predicting mutational effects at single-base resolution (AUC = 0.76), and show that direct fitting to these data independently recovers known AR regulators. Finally, we apply the model to prostate cancer GWAS risk alleles in AR binding site regions, prioritizing four candidate variants predicted to reduce the DHT/EtOH enhancer activity ratio at these loci.

genomics↗

Structure-aware deep learning predicts influenza antigenicity and guides vaccine strain recommendation

The continuous accumulation of genetic mutations in influenza A viruses (IAVs) drives antigenic drift, necessitating precise antigenic prediction for optimal vaccine strain selection. While sequence-based methods have advanced antigenic surveillance, they neglect the three-dimensional structural context that fundamentally dictates viral antigenicity. Here, we introduce Vir3D, which leverages ESMFold-derived structural information from amino acid sequences to precisely predict viral antigenicity and guide vaccine strain selection. Across both human H3 and highly pathogenic avian H5 subtypes, Vir3D not only accurately discriminates antigenic variants and infers pairwise antigenic distances, but also mechanistically delineates key structural residues driving viral immune evasion. In a decade-long retrospective analysis, Vir3D-prioritized vaccines consistently achieve broader antigenic coverage of circulating strains than World Health Organization (WHO) recommendations. Crucially, Vir3D successfully predicts that the emerging U.S. dairy cattle H5N1 virus (TX/24) remains antigenically stable relative to clade 2.3.4.4b vaccine strains, and subsequent wet-laboratory validation of hemagglutination inhibition (HI) assays definitively corroborates this finding. Overall, Vir3D establishes a powerful, structure-driven framework for proactive influenza surveillance and pandemic preparedness.

bioinformatics↗

Integrated ex vivo screening and transcriptomic profiling to prioritize drug combinations for rare cancers

Discovering effective drug combinations requires testing many dose combinations across a diverse panel of tumor models. This approach is limited in rare cancers by a scarcity of cell lines and representative high-throughput models that would make exhaustive screening feasible and predictive. Patient-derived xenograft (PDX) models are genomically representative but too low-throughput for this purpose. Culturing PDX cells ex vivo in three-dimensional (3D) matrices offers a genomically representative and clinically relevant platform for preclinical drug testing, capturing the microenvironmental cues that shape in vivo drug response while requiring only limited tissue per assay. Toward this end, we designed and validated an experimental-computational framework, "ex vivo assessment of combination therapies" (EXACT), to enable drug combination discovery in rare tumors. Using PDX models of malignant peripheral nerve sheath tumors (MPNST), we built a platform to culture PDX cells ex vivo over multiple days, monitoring drug sensitivity and measuring transcriptomic responses to treatment. Computational analysis of these transcriptomic responses then identifies which compensatory pathway creates a unique vulnerability to a second drug. EXACT thus offers a biologically informed, scalable approach for prioritizing drug combinations in rare tumors, nominating drugs alongside biological rationale. Using this methodology, we identified a MEK inhibitor plus HDAC inhibitor combination with enhanced activity in vitro and in vivo, forming the basis of an active clinical trial. This platform could be adapted for real-time use with primary patient specimens, enabling personalized therapeutic discovery. SignificanceEXACT integrates PDX-derived 3D drug screening with biologically informed computational analysis to identify and explain effective combinations, providing a scalable strategy for therapeutic discovery in rare cancers such as MPNST.

cancer biology↗

Evolution of an Aurora Kinase A Inhibitor from an Essential tRNA Synthetase

Gene duplication is a major driver of evolution, yet how it generates fundamentally new molecular functions remains poorly understood. Here, we show how such novelty arose in KLMT-1, a selfish toxin that causes genetic incompatibilities in Caenorhabditis tropicalis. KLMT-1 evolved via duplication of an essential tRNA synthetase but, strikingly, lost its ancestral role in tRNA biology and translation. Instead, KLMT-1 localizes to centrosomes, where it targets Aurora kinase A (AIR-1). This innovation is mediated by a three-amino acid insertion that extends a {beta}-hairpin loop, enabling electrostatic interaction with a regulatory interface on the kinase. Our results demonstrate how changes in selective pressure, combined with minimal modifications in neutrally evolving regions, allow duplicated proteins to access new functional space and evolve entirely new molecular activities.

evolutionary biology↗

Integrated quantitative imaging and biomechanical modeling of early gastrulation in C. elegans

1The stereotyped internalization of two endodermal precursors during early Caenorhabditis elegans gastrulation enables quantitative dissection of cell ingression mechanics. Experimental work has shown that apical constriction drives Ea and Ep ingression, and several molecular features involved have been identified. Yet, no integrative mechanical analysis has assessed how these elements collectively produce the observed behavior. To address this, we combined biomechanical simulations with a comprehensive dataset of 3D-segmented cell meshes, some with cortical protein distributions, to analyze the mechanics of ingression in its in-vivo context. Our analysis shows the process starts shortly after birth of the ingressing cells. A cortical flow drives the formation of an E-cadherin-rich structure at the apical Ea-Ep interface, which contributes to localizing the buildup of apical tension. Simulations show that medioapical actomyosin contraction can reproduce the observed ingression movements and suggest force transmission to neighboring cells via a friction-based molecular clutch at the apical ring of contact. A series of concurrent cell divisions facilitates ingression, and their stereotyped planar orientation also contributes. Furthermore, we observe an embryo-wide movement of cells during gastrulation. This movement resembles a flow, suggesting that local force generation leads to global rearrangements via internal pressure changes. Finally, at the end of ingression, detailed microscopy shows that neighboring cells actively close the gastrulation cleft by forming a rosette-like configuration and extending actin-rich protrusions. In conclusion, our integrated mechanical description of gastrulation shows that successful ingression is driven by apical constriction and supported by localized friction-based force transmission, coordinated stereotyped cell divisions, and the resulting global tissue flow.

developmental biology↗

Effects of the Coenzyme Q10 Analog 6-Bromo-ubiquinone (6-Br-Q0C10) on Mammalian Cell Growth

Synthetic 6-Br-Q0C10 has been shown to exhibit a partial electron transfer activity of native coenzyme Q in the isolated mitochondria. It reduces energy coupling efficiency by approximately 30%, suggesting that it may be useful in modulating cell growth in tissue culture. Whether or not it behaves in the same way in the whole cells, or animal, however, has not yet been fully examined. Recently we have investigated the effect of 6-Br-Q0C10 across multiple cell lines using three detection methods. Treatment with 6-Br-Q0C10 reduces cell proliferation in all cell lines tested, with different effectiveness. Obesity-related cell lines were the most susceptible, and a pronounced inhibitory effect was also observed in cancer cell lines. These results strengthen the idea of using 6-Br-Q0C10 to manage obesity or to retard the growth of rate cancer cells and thus prolonging life.

biochemistry↗

Structural basis of caveolin-driven membrane bending

Caveolins are monotopic membrane proteins essential for caveolae formation and have key roles in signaling and lipid regulation. Caveolins assemble into amphipathic discs with a central {beta}-barrel, an architecture distinct from other membrane-remodeling proteins. These discs embed in the membrane inducing membrane curvature. However, the mechanism of disc-driven bending remains unresolved. Using cryo-electron tomography, structure-guided mutagenesis, and mammalian cell studies, we show that evolutionarily distinct caveolins differ dramatically in their ability to curve membranes despite their conserved architecture. Through computational and theoretical analyses, we demonstrate that patterning of hydrophobic residues along the rim of the disc of human Caveolin-1 induces the deformation of the surrounding leaflet, dictating membrane bending. Finally, we determine a 4.1[A] resolution structure of Caveolin-1 within heterologous caveolae in situ, showing the disc adopts a funnel-like conformation, further shaping membrane architecture. Together, these findings reveal fundamental structural principles that empower caveolins to sculpt and remodel cellular membranes.

cell biology↗

FluNexus: a versatile web platform for antigenic prediction and visualization of influenza A viruses

Influenza A viruses continuously undergo antigenic evolution to escape host immunity induced by previous infections or vaccinations, consequently causing seasonal epidemics and occasional pandemics. Antigenic prediction and visualization of influenza A viruses are crucial for precise vaccine strain selection and robust pandemic preparedness. However, a user-friendly online platform for these capabilities remains notably absent, despite widespread demand. Here, we present FluNexus (https://flunexus.com), the first-of-its-kind, one-stop-shop web platform designed to facilitate the prediction and visualization of the antigenic change in emerging variants. FluNexus features a data preprocessing module for hemagglutinin subunit 1 (HA1) and hemagglutination inhibition (HI) data across three major public health threat subtypes (H1, H3 and H5). Meanwhile, FluNexus provides an interactive interface for online antigenic prediction and offers practical guidance for researchers. Most notably, FluNexus offers the visualization of influenza A virus antigenic evolution, providing intuitive insights into its antigenic dynamics. Specially, FluNexus proposes a novel manifold-based method for positioning antigens and antisera, generating accurate antigenic cartographies even with sparse HI data. By alleviating the programming burden on biologists, FluNexus supports more informed decision-making in vaccine strain selection and strengthens surveillance and pandemic preparedness. HighlightsO_LIFluNexus features a data preprocessing module for HA1 and HI data spanning the H1, H3, and H5 subtypes. C_LIO_LIFluNexus facilitates online antigenic prediction utilizing ten state-of-the-art antigenic prediction tools, and offers practical guidance based on a comparative evaluation of their performance. C_LIO_LIFluNexus provides a visualization module for mapping antigenic evolution of influenza A viruses, incorporating a novel manifold-based method for antigenic cartography. C_LI

bioinformatics↗

Nucleoside-Modified mRNA Encoding Alpha-Galactosidase A Reverses Fabry Disease Phenotypes in Human IPSC-Derived Cardiomyocytes

The lysosomal storage disorder Fabry disease results from -galactosidase A deficiency, leading to excessive glycosphingolipid substrate accumulation, primarily globotriaosylceramide (Gb3). While the underlying molecular mechanisms remain elusive, multi-systemic complications ultimately culminate in premature death, with heart failure being the leading cause of death. Current treatment options fail to treat Fabry disease adequately and only delay its progression. Preclinical studies on an alternative approach, systemic delivery of nucleoside-modified GLA mRNA (modGLA), suggest improved effectiveness over existing therapies in reducing glycosphingolipid levels in the heart. It remains unclear whether modGLA can rescue Fabry cardiomyopathy phenotypes at the cellular level, which are not faithfully recapitulated in current animal models. To address this, we investigated characteristic phenotypes in two new models of Fabry cardiomyopathy utilizing human iPSC-derived cardiomyocytes in transcriptomic and functional analyses. These human Fabry disease cardiomyocytes displayed broad transcriptional dysregulation, apoptosis, mitochondrial dysfunction, impaired reactive oxygen species handling, as well as enhanced decay parameters of calcium transients. Mechanistically, we identified hyperphosphorylated phospholamban as a major player in this calcium dysregulation. Strikingly, modGLA therapy of Fabry cardiomyocytes restored -galactosidase A enzyme activity, reduced glycosphingolipid deposition, and normalized the observed molecular alterations, supporting modGLA therapy as a promising strategy for the treatment of Fabry disease.

molecular biology↗

Rapid neocortical network modifications via dendritic plateau potential induced plasticity

Learning in brains is associated with changes in neuronal network activity thought to be driven by synaptic plasticity. While recent work in the hippocampus has revealed some of the mechanisms involved there, less is known about how neocortical circuits adapt, especially during behavior. Here to determine if neocortical areas possess rapid plasticity mechanisms that could support online adaptations we used optical imaging and intracellular membrane potential (Vm) recordings to examine the activity of layer V neurons in a higher visual area of mice learning a task. The introduction of a novel rewarded stimulus resulted in a rapid modification of population activity that featured abrupt alterations in single neuron selectivity. Vm recordings revealed that both naturally occurring and experimentally-induced dendritic calcium plateau potentials (plateaus) rapidly alter the action potential (APs) output and Vm dynamics of neurons over many seconds of time around the plateau, in some cases from one trial to the next. Trains of high frequency APs had no effect. Finally, experimental inhibition during learning of the distal dendritic region responsible for initiating plateaus reduced the rate of population level adaptation. Our findings suggest that the deep layers of higher order visual cortex possess a rapid learning mechanism mediated by plateau-induced synaptic plasticity.

neuroscience↗

BrainBeacon: A Cross-Species Foundation Model for Single-cell Resolved Brain Spatial Transcriptomics

The brains functional complexity emerges from spatiotemporal heterogeneity in molecular, cellular, and circuit organization, shaped by evolution, aging, and disease. Single-cell spatial transcriptomics provides a transformative framework to decode this complexity, resolving cell-type-specific expression patterns, region-specific regulation, evolutionary divergence, and disease-associated disruptions. Despite recent advances in spatial transcriptomics, a unified foundation model capturing cross-species, whole-brain cellular architecture remains lacking. Here, we introduce BrainBeacon, the first cross-species foundation model for whole-brain spatial transcriptomics. Trained on 133 million spatially resolved cells covering a total area of 210194 mm2 from human, macaque, marmoset and mouse brains profiled across five major platforms, BrainBeacon integrates gene expression rankings, spatial cellular organization, and evolutionarily conserved genetic relationships into a unified transformer-based architecture. A two-stage training strategy captures both intra-cellular transcriptional dependencies and inter-cellular spatial interactions, yielding biologically grounded and interpretable representations of brain structure. Subsequently, BrainBeacon enables cross-species alignment of brain cell types and anatomical regions, facilitating integrative analyses of conserved and divergent cellular architectures. Moreover, it uncovers spatial regulatory mechanisms of aging by predicting bidirectional perturbation responses between cells and their local niche. Together, BrainBeacon serves as a foundational cross-species model for whole-brain spatial transcriptomics, enabling virtual brain atlas construction, systematic in silico perturbation studies, and mechanistic insights into brain disease pathogenesis.

bioinformatics↗

Predicting Spatial Transcriptomics from H&E Image by Pretrained Contrastive Alignment Learning

The intricate molecular landscape within tissues holds crucial information about cellular behavior and disease progression, yet capturing this complexity at a spatial level remains challenging. While Spatial transcriptomics (ST) offers valuable insights into gene expression patterns within their native tissue context, its widespread adoption is hindered by high costs and limited gene detection capabilities. Here we introduce CarHE (Contrastive Alignment of gene expRession for hematoxylin and eosin image), a method that overcomes these limitations by accurately predicting high-dimensional ST data (over 10,000 genes) solely from readily available H&E (Hematoxylin and Eosin) stained images. This novel pre-trained architecture employs contrastive learning through two mechanisms: cell-type-based transcriptomics information transfer and image-based histology information transfer. These mechanisms precisely align image features with spatial single-cell gene expressions, achieving prediction accuracies exceeding 0.7 (up to 1.7 folds compared to second best) across diverse tissue types and species. CarHEs superior performance extends to identifying subtle pathological features such as tertiary lymphoid structures in various cancers, including breast cancer, lung cancer, melanoma and ccRCC (clear cell renal cell carcinoma), and reconstructing 3D spatial transcriptomics from images alone, offering a cost-effective and robust alternative for large-scale spatial transcriptomics. We further validated CarHEs effectiveness by predicting DFS (Disease-Free Survival) from >1,600 lung cancer patients HE images, achieving a significantly higher AUC (Area Under the Receiver Operating Characteristic Curve) of 0.73 compared to state-of-the-art alternatives (0.58-0.64).

bioinformatics↗

Formation of extracellular vesicles depends on mechanical feedback of the cortex and the glycocalyx

Cell-secreted extracellular vesicles (EVs) play a pivotal role in local and distant cell-to-cell communication by delivering specific cargoes to other cells or to the extracellular space. In many cells, the glycocalyx, a thick sugar-rich layer at the cell surface, and the membrane-cortex attachment are crucially linked to the formation of EVs, yet it is unclear what determines the successful formation of EVs when multiple physical factors are involved. In this work, we developed a model for glycocalyx-membrane-cortex composite to investigate the effects of gly-cocalyx and membrane-cortex adhesion on the formation of EVs by combining polymer physics-based theory and Helfrich membrane theory. By performing linear stability analysis, we show that modulating the mechanical feedback among the glycocalyx, membrane-cortex attachment, and membrane curvature can give rise to two types of instabilities: a conserved Turing-type instability and a Cahn-Hilliard-type instability. Furthermore, using an equilibrium model, we identified two critical conditions for EV formation: an initial detachment of the membrane from the underlying cortex and then a sufficient driving force to induce membrane deformation for successful EV formation. We further demonstrated that there exists an optimal glycocalyx coating area at which the formation of EVs is most favorable. Finally, we use our model to predict that a heterogeneous size distribution of EVs can be generated through the regulation of glycocalyx properties, shedding insight into how EVs of different radii may be generated. Significance StatementExtracellular vesicles (EVs) are important for cell biology because they facilitate active communication between cells. Understanding the governing factors that control the formation of EVs is crucial to many cellular processes ranging from tumor progression and metastasis evolution to the disposal of unwanted biomolecules. However, whether EV secretion is a consequence of the glycocalyx and the role of membrane-cortex adhesion in the formation of EVs are still elusive. To address these issues, here we develop a biophysical model for EV formation that couples the presence of glycocalyx and membrane-cortex adhesion. We find that the glycocalyx-membrane-cortex composite system exhibits two types of instabilities utilizing stability analysis - a Turing instability and a Cahn-Hilliard type instability. Based on our proposed equilibrium model, we identified that for the initiation of membrane detachment and the formation of EVs each need to meet a critical threshold. In addition, our model predicts that the formation of EVs is most favorable when an optimal glycocalyx coating area reaches and a heterogeneous distribution EV sizes can be produced by regulating glycocalyx properties.

biophysics↗

A Novel Efficient Algorithm for Common Variants Genotyping from Low-Coverage Sequencing Data

Low-coverage whole-genome sequencing (LC-WGS) combined with imputation represents a cost-effective genotyping strategy for genome-wide association studies (GWAS) in population genetics. In this study, the Limpute algorithm was developed specifically for genotyping from low-coverage sequencing data, it extracts variant information from low-coverage sequencing data by the novel virtual probes and subsequently performs imputation through cross-reference between samples. Compared to the currently dominant algorithm for low-coverage sequencing data, GLIMPSE2, Limpute achieved similar imputation performance within common variants (r2>0.87) while the GLIMPSE2 has a runtime approximately five times longer than that of the Limpute. Furthermore, to fully evaluate the accuracy of genotype imputation by Limpute, we utilized high-coverage whole-genome sequencing data (30x), microarray data, and high-coverage whole-exome sequencing data (30x) as validation sets respectively. The results demonstrated that Limpute has a good imputation performance for common variants using low-coverage sequencing data (1x: r2 > 0.87; 3x: r2 > 0.92; 5x: r2 > 0.93). In summary, we present a highly efficient, low-cost algorithm for genotyping from low-coverage sequencing data, offering substantial support for genetic research.

bioinformatics↗

DoRIAT: A Bayesian Framework For Interpreting And Annotating Docking Runs.

The advent of sequence-to-structure deeplearning models have transformed protein engineering landscape by providing an accurate and cost effective way to determine crystal structures. Despite their accuracy, deep-learning predictions tend to give limited insights around protein dynamics. To improve conformation exploration we have developed a machine learning pipeline that combines deep-learning predictions with molecular docking. In this report, we propose Docking Run Intepretation and Annotation Tool (DoRIAT). In contrast to frameworks that score models based on interface interactions, DoRIAT uses a set of parameters that summarize binding conformation. We use DoRIAT to score output from docking runs, identify complexes close to the native structure and create ensembles of models with similar binding conformations. Our results demonstrate that the single structural model DoRIAT selects to be the closest representation of the crystal structure lies within the top 10 of docked models, ranked by Root Mean Squared Distance(RMSD), in around 80% of cases.

systems biology↗

Glycocalyx-induced formation of membrane tubes

Tubular membrane structures are ubiquitous in cells and in the membranes of intracellular organelles such as the Golgi complex and the endoplasmic reticulum. Tubulation plays essential roles in numerous biological processes, including filopodia growth, trafficking, ion transport, and cellular motility. Understanding the fundamental mechanism of the formation of membrane tubes is thus an important problem in the fields of biology and biophysics. Though extensive studies have shown that tubes can be formed due to localized forces acting on the membrane or by the curvature induced by membrane-bound proteins, little is known about how membrane tubes are induced by glycocalyx, a sugar-rich layer at the cell surface. In this work, we develop a biophysical model that combines polymer physics theory and the Canham-Helfrich membrane theory to investigate how the glycocalyx generates cylindrical tubular protrusions on the cell membrane. Our results show that the glycocalyx alone can induce the formation of tubular membrane structures. This tube formation involves a first-order shape transition without any externally applied force or other curvature-inducing mechanisms. We also find that critical values of glycocalyx grafting density and glycopolymer length are needed to induce the formation of tubular structures. The presence of vertical actin force, line tension, and spontaneous curvature reduces the critical grafting density and length of polymer that triggers the formation of membrane tube, which suggests that the glycocalyx makes tube formation energetically more favorable when combined with an actin force, line tension, and spontaneous curvature. Significance StatementIn many cells, the existence of glycocalyx, a thick layer of polymer meshwork comprising proteins and complex sugar chains coating the outside of the cell membrane, regulates the formation of membrane tubes. Here, we propose a theoretical model that combines polymer physics theory and the Canham-Helfrich membrane theory to study the formation of cylindrical tubular protrusions induced by the glycocalyx. Our findings indicate that glycocalyx plays an important role in the formation of membrane tubes. We find that there exists critical grafting density and length of polymer that triggers the formation of membrane tubes, and the glycocalyx-induced tube formation is facilitated when combined with actin forces, line tension, and spontaneous curvature. Our theoretical model has implications for understanding how biological membranes may form tubular structures.

biophysics↗

Machine learning engineered PoLixNano nanoparticles overcome delivery barriers for nebulized mRNA therapeutics

There continues to be a dearth of competent inhalable mRNA delivery although it holds great potential for addressing a wide variety of refractory diseases. The huge advances seen with parenteral-administered lipid nanoparticle (LNP) have not been translated into nebulized mRNA delivery due to the aggressive nebulization process and insurmountable barriers inherent to respiratory mucosa. Here, we show amphiphilic block copolymers revealed by machine learning (ML) can spontaneously form stabilized nanoparticles (PoLixNano) with the lipids components of LNP and simultaneously impart the PoLixNano with "shield" (shear force-resistant) and "spear" (pulmonary barriers-penetrating abilities) capabilities. We present a ML approach that leverages physicochemical properties and inhaled mRNA transfection profiles of a chemically diverse library of polymeric components to validate the integration of "shield" and "spear" properties as highly predictive indicators of transfection efficiency. This quantitative structure-mRNA transfection prediction (QSMTP) model identifies top-performing amphiphilic-copolymers from more than 10000 candidates and suggests their mucus-penetrating ability outweights the shear force-resistant property in contributing to efficient mRNA transfection. The optimized PoLixNano substantially outperforms the LNP counterpart and mediates up to 1114-times higher levels of mRNA transfection in animal models with negligible toxicities. The PoLixNano promotes overwhelming SARS-CoV-2 antigen-specific sIgA antibody secretion and expansion of TRM cells which collectively confers 100% protection in mice against lethal SARS-CoV-2 challenges and blocks the transmission of Omicron variant between hamsters. PoLixNano also displays versatile therapeutic potential in lung carcinoma and cystic fibrosis models. Our study provides new insights for designing delivery platforms of aerosol-inhaled mRNA therapeutics with clinical translation potential.

synthetic biology↗