Biology subjects

Feng, Y.

Publications and source records attributed to Feng, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

A conserved Foxp1-Pde4d axis regulates inflammatory responsiveness

Inflammatory responses are heterogeneous, even among cells exposed to a common insult, yet the regulatory mechanisms that distinguish these inflammatory states remain poorly understood. Here, we leveraged inflammatory state heterogeneity to identify a conserved Foxp1-Pde4d regulatory relationship and uncover a role for this axis in preserving inflammatory responsiveness. Genotoxic stress in adipocyte progenitors generated heterogeneous engagement of type-I interferon (IFN) and NF{kappa}B transcriptional programmes, and transcriptome-wide analysis independently identified Foxp1 and the cAMP phosphodiesterase Pde4d among genes associated with lower IFN- and higher NF{kappa}B-associated transcription. ChIP-seq revealed selective loss of Foxp1 occupancy at a Pde4d-associated regulatory element following genotoxic stress, while Foxp1 gain- and loss-of-function established Foxp1 as a positive regulator of Pde4d expression. Remarkably, coupling of Foxp1-Pde4d expression was broadly conserved across vertebrate tissues and damage models. In vivo, Foxp1 loss in zebrafish revealed a striking dissociation between inflammatory state and response capacity: basal NF{kappa}B activity was elevated, yet injury-induced NF{kappa}B activation was markedly blunted and macrophage recruitment impaired. Adenylyl cyclase inhibition restored macrophage recruitment in Foxp1-deficient zebrafish, functionally linking the Foxp1 phenotype to cAMP signalling and supporting a model in which Foxp1-Pde4d regulation preserves inflammatory responsiveness through control of cAMP. Together, these findings show how inflammatory-state heterogeneity can reveal conserved regulatory mechanisms linking basal cellular state to subsequent response capacity.

cell biology↗

bioRxiv2026

KRT7CreER Ferrets Enable Lineage Tracing of Ductal Epithelia and Pancreatic Remodeling

Keratin 7 (KRT7) is expressed in simple and ductal epithelia and is associated with epithelial plasticity during development, injury, and disease. However, the in vivo fate and regenerative behavior of KRT7 cells remain poorly defined because lineage-tracing models are lacking in species that closely recapitulate human disease. To address this gap, we generated KRT7CreER ferrets and crossed them with ROSAmTmG Cre-reporter ferrets, with or without the CFTRG551D/G551D mutation. Following tamoxifen induction, KRT7-derived EGFP cells were identified in glandular and ductal epithelia across multiple organs. Pulse-chase lineage tracing revealed that KRT7BSND cells turn over rapidly during salivary gland development. In the pancreas, KRT7 lineage-labeled cells are present at all levels of the ductal tree under homeostatic conditions. In cystic fibrosis (CF) ferrets, KRT7-derived centroacinar cells and intercalated ductal lineages persisted and underwent marked remodeling, characterized by expansion of the SOX9 centroacinar cell pod into intercalated ducts. Although acinar cells were not lineage-labeled in the healthy pancreas, EGFP/CELA3B cells emerged in the CF pancreas and in acinar-derived organoids, consistent with induction of KRT7 during acinar-to-ductal metaplasia. These findings establish KRT7CreER ferrets as a versatile model for studying ductal epithelial lineage dynamics, pancreatic remodeling, and epithelial plasticity in both homeostatic and pathological conditions.

pathology↗

bioRxiv2026

Coupled Effects of Airway Deformation and Carrier Gas on Pulmonary Gas-Particle Flow Dynamics Using an Euler-Lagrange Method with Dynamic Mesh

Physiologically realistic prediction of inhaled aerosol deposition in the airways requires simultaneous consideration of airway deformation kinematics and carrier-gas properties. This study developed a 3D deformable lung airway model (DLAM) using an experimentally validated computational fluid particle dynamics (CFPD) model coupled with a dynamic mesh method. Both the DLAM and a conventional static lung airway model (SLAM) were used to predict and compare inhaled gas-particle transport and deposition in a subject-specific mouth-to-generation 9 (G9) human respiratory system. To assess the significance of airway deformation, SLAM and DLAM were compared under identical 12 breaths per minute (BPM) inhalation-exhalation waveforms for both conventional air and heliox (70% He-30% O2), considering particle sizes of 1, 5, and 10 um. The effects of carrier gas and breathing frequency were further evaluated using the more physiologically realistic DLAM, comparing air and heliox at 12, 16, and 20 BPM for the same particle sizes. Results showed that the influence of airway deformation on aerosol transport is strongly dependent on particle size. Differences between SLAM and DLAM were negligible for 1 um particles. In contrast, for 5 and 10 um particles, SLAM consistently underpredicted beyond-G9 delivery and overpredicted G0-G9 local deposition for both air and heliox. For example, for 10 um particles at 12 BPM, accounting for airway deformation kinematics increased the beyond-G9 delivery fraction by an absolute 3.76% with air and 5.55% with heliox. After establishing the size-dependent deformation effect, DLAM was further used to evaluate the influence of carrier gas and breathing frequency on inhaled aerosol transport and deposition. Compared with air, heliox reduced turbulence kinetic energy, wall shear stress, and pressure drop, and enhanced beyond-G9 delivery of larger particles. Specifically, for 10 um particles, the absolute increases in beyond-G9 delivery were 16.82%, 20.25%, and 22.90% at 12, 16, and 20 BPM, respectively. Increasing breathing frequency reduced distal delivery, particularly for 5 and 10 um particles, whereas 1 um particles maintained approximately 99% beyond-G9 penetration across all investigated conditions. These findings indicate that DLAM is essential for accurate regional dosimetry of higher-inertia aerosols, for which the static airway simplification can significantly bias predicted deposition and distal delivery.

bioengineering↗

bioRxiv2026

Synaptic microRNA dysregulation orchestrates aberrant glutamatergic transmission in a human neuronal model of 22q11.2 deletion syndrome

22q11.2 Deletion Syndrome (22q11DS), the most prevalent chromosomal microdeletion disorder in humans (incidence ~1 in 4,000 live births), arises from hemizygous deletions of a 1.5-3.0 Mb region on chromosome 22. Notably, 20-30% of individuals with 22q11DS develop schizophrenia (SCZ) by early adulthood, making 22q11DS one of the strongest genetic risk factors for SCZ. However, the mechanisms by which the 22q11.2 deletion perturbs neuronal function and drive psychotic manifestations in 22q11DS remain poorly defined. In this study, we generated human 2D cortical neurons and 3D cortical organoids from induced pluripotent stem cells (iPSCs) derived from clinically well-characterized individuals with 22q11DS. Cellular and electrophysiological analyses revealed that 22q11DS neurons exhibit hyperexcitability, reduced glutamatergic synaptic transmission, and impaired homeostatic synaptic plasticity, which are pathophysiological hallmarks that may be relevant to the clinical, cognitive, and psychophysiological profiles of patients. Transcriptomic profiling of neurons and synaptosomes from 22q11DS cortical organoids uncovered dysregulated immune pathways and synaptic gene networks. Integrative mRNA-microRNA network analysis identified synergistic downregulation of hsa-miR-185-5p and hsa-miR-128-3p at synapses, driving aberrant regulation of immune and synaptic transcripts. Experimental knockdown of these microRNAs in control neurons recapitulated the molecular and functional phenotypes of 22q11DS neurons, thereby establishing a mechanistic link to synaptic dysfunction and impaired plasticity. Collectively, these findings delineate a multimodal mechanistic framework that bridges genetic vulnerability, synaptic pathology, and psychosis penetrance in 22q11DS, positioning dysregulated synaptic microRNAs as nodal regulators of neurodevelopmental networks and potential therapeutic targets.

neuroscience↗

bioRxiv2026

Ciliary regulation of melanogenesis unveils moonlighting roles for Joubert Syndrome proteins

We previously found that skin tone correlates with intronic polymorphisms influencing the expression of the Joubert Syndrome (JS) ciliopathy gene TMEM138, suggesting that primary cilia might regulate pigment production in skin melanocytes. Herein, we show that many ciliary proteins regulate basal pigmentation in wild-type mouse and human melanocytes by facilitating primary cilium-dependent Melanocortin-1 receptor signaling, and downstream activation of the Microphthalmia transcription factor and melanogenic gene expression. Additionally, we identify an extra-ciliary moonlighting role for TMEM138 and other JS proteins in endolysosomal organization required for the biogenesis of melanosomes, the pigment-producing lysosome-related organelle in melanocytes. Our results provide new mechanistic insights into the physiological regulation of pigmentation and suggest that JS-specific pathology might be mediated by ciliary-independent mechanisms.

cell biology↗

bioRxiv2026

UniWave-2: A Hybrid Model for Nucleic Acid Waveform Feature Extraction Enhanced by Fourier and Wavelet Transforms

Motivation: Traditional methods primarily rely on statistical features such as k-mers and GC content, making it difficult to capture complex internal relationships within sequences. Deep learning models typically rely on discrete encodings, leading to issues such as information sparsity, dimensional redun-dancy, and disruption of sequence continuity. Our previous UniWave-1 framework transforms se-quences into waveform signals; however, it lacks comprehensive physicochemical integration and com-putational efficiency. Results: We propose the UniWave-2 feature extraction framework. UniWave-2 incorporates three key advancements: (i) integration of hydrophobicity into the encoding scheme; (ii) enhancement of wave-form resolution through Fourier transform, coupled with wavelet transform to precisely capture both local details and global periodic patterns; and (iii) development of a lightweight multi-scale GRU model that facilitates cross-dimensional feature interactions, captures bidirectional temporal dependencies, and integrates long- and short-range patterns with spatial positional awareness, thereby yielding rich feature representations. UniWave-2 achieved competitive performance across multiple genomic tasks, while ISM analysis demonstrated that waveform encoding enables interpretable attribution without re-lying on gradient information. These results establish UniWave-2 as a lightweight and interpretable framework for nucleic acid sequence analysis.

bioinformatics↗

bioRxiv2026

Listeriolysin O recruits UBA1 to ubiquitinate NLRP3 and suppress inflammasome defence

The NLRP3 inflammasome is a central cytosolic defence pathway against infection, yet intracellular pathogens must restrain this response to preserve their replicative niche. Here we identify a ubiquitin-dependent immune-evasion mechanism in which L. monocytogenes uses listeriolysin O (LLO) to destabilise NLRP3. The N-terminal PEST-like sequence of LLO acted as an adaptor that recruited the host ubiquitin-activating enzyme UBA1 and promoted K48-linked polyubiquitination of NLRP3. This modification drove proteasomal NLRP3 degradation and limited caspase-1 activation, gasdermin D cleavage, IL-1{beta}/IL-18 release, and macrophage pyroptosis. Deleting the PEST region preserved basal haemolytic activity but impaired NLRP3 ubiquitination, thereby enhancing inflammasome activation during infection in macrophages and in vivo. Conversely, UBA1 overexpression suppressed NLRP3-mediated inflammatory responses and supported intracellular bacterial survival. UBA1 depletion restored NLRP3 protein levels, reduced bacterial burden and delayed lethal infection, albeit with increased inflammatory tissue injury. These findings define a ubiquitin-hijacking strategy that operates at the E1 step, whereby a bacterial toxin redirects host ubiquitin activation towards an innate immune sensor.

microbiology↗

bioRxiv2026

Multi-kingdom microbial diversity and interaction landscapes in mosquitoes revealed by 5,163 individual meta-transcriptomes

Mosquitoes are pathogen vectors embedded within diverse microbial ecosystems. However, the nature and interactions among their multi-kingdom microbiome remain poorly understood. We conducted a nationwide single-mosquito meta-transcriptomic survey of 5,163 mosquitoes representing 100 species across China, integrating viral discovery with marker-gene profiling of bacteria, archaea, fungi, and other eukaryotic microbes. From this, we identified 1,606 microbial species-level taxa, including extensive novel diversity, and revealed pronounced host species-specific organization of mosquito-associated communities. We detected 34 pathogens or potential pathogens of human or animal relevance, whose prevalence, abundance, host range, and geographic distribution defined distinct epidemiological patterns. Network analysis uncovered pervasive cross-kingdom microbial associations, including candidate antiviral relationships involving Wolbachia and other microbial taxa. Our study establishes a detailed view of the full-spectrum microbiome and provides a resource and conceptual framework for studying vector competence, pathogen emergence, and microbiome-informed mosquito-borne disease control.

microbiology↗

bioRxiv2026

DPF3a couples H3K36me2-dependent chromatin remodeling to genome architecture during myogenesis

Chromatin remodelers are generally thought to regulate gene expression through nucleosome mobilization and modulation of local chromatin accessibility. Whether these complexes can instead control transcription through higher-order chromatin organization remains poorly understood. Here we identify the BAF subunit DPF3a as an essential regulator of skeletal muscle regeneration and myogenic differentiation. DPF3a functions through association with the H3K36me2/3 reader HRP2 and preferentially localizes to a distinctive chromatin state characterized by focal depressions within broad H3K36me2 domains. Biochemical reconstitution demonstrates that H3K36 methylation directly enhances remodeling activity of the DPF3a-containing cBAF complex. Unexpectedly, despite profound transcriptional defects, loss of DPF3a produces minimal changes in local chromatin accessibility. Instead, DPF3a is required for long-range chromatin looping associated with activation of myogenic genes. Together, our findings uncover a non-canonical mechanism whereby a chromatin remodeler regulates transcription primarily through three-dimensional genome organization rather than local accessibility control, and establish histone modification-guided chromatin remodeling as a key principle in gene regulation.

molecular biology↗

bioRxiv2026

Structural modeling supports an interaction between the Drosophila Estrogen-Related Receptor and Sima/HIF-1α

The Drosophila Estrogen-Related Receptor (dERR) is an orphan nuclear receptor that regulates developmental metabolism, yet the protein cofactors that modulate its activity remain poorly defined. Here, we used the AI-based FlyPredictome platform to identify candidate dERR interaction partners and evaluate their predicted structural interfaces. Among the highest-confidence interactors is the transcription factor Sima, which represents the Drosophila ortholog of HIF1 - an interaction that was reciprocally identified in both dERR and Sima/HIF1 interaction datasets. Structural modeling predicted binding between the dERR ligand-binding domain and a conserved LXXLL motif in Sima/HIF1. Notably, the predicted dERR- Sima/HIF1 interaction interface includes residues previously shown to be essential for in vitro binding, providing independent structural support for this biologically relevant protein-protein interaction.

developmental biology↗

bioRxiv2026

Sustained Delivery of a SARS-CoV-2 Subunit Vaccine in An Adjuvanted Hydrogel Depot Enhances Vaccine Responses in Nonhuman Primates

While natural infections expose the immune system for days to weeks of inflammation and antigen presentation, immunizations with conventional bolus vaccines often lead to rapid clearance of antigens and adjuvants. Prolonged exposure to vaccines using controlled delivery devices or repeated dosing regimens has been shown to enhance germinal center reactions, leading to improved humoral responses, including increased magnitude of antibody titers and enhanced neutralizing activity. Herein, we report the use of injectable polymer-nanoparticle (PNP) hydrogels as a vaccine depot technology for sustained delivery of the clinically relevant SARS-CoV-2 Hexapro subunit antigen and a toll-like receptor agonist adjuvant. In mice, we demonstrated that PNP hydrogel vaccines enhanced germinal center responses and antibody responses relative to bolus vaccination. In nonhuman primates, hydrogel vaccines induced enhanced and durable antibody responses against wildtype and variants of concern such as Omicron BA.5 compared to bolus vaccination. We report the first use of a biomaterials-based approach for sustained delivery of vaccines in nonhuman primates, further advancing toward clinical translation.

bioengineering↗

bioRxiv2026

De novo design of ligand binding and sensing with a physics based generative approach

The de novo design of ligand-binding proteins has tremendous potential to revolutionize biosensor technology, yet converting these designs into functional sensors remains a major challenge due to the need for ligand-induced conformational changes or modulation of protein-protein interactions. Here, we introduce a physics-based generative approach for the de novo creation of proteins that bind small molecules and metal ions. Our method achieves customizable ligand-binding pocket formation in parallel with simulated protein folding, allowing for precise architectural control of the protein-ligand complex and facilitating the development of biosensors based on either ligand-triggered protein reassociation via split-protein reassembly or ligand-induced protein folding. We demonstrate the versatility of our computational method through successful designs targeting five small molecules, including the very small neurotransmitters serotonin and dopamine, and two metal ions. Biophysical characterization confirmed correct ligand binding, and crystal structures closely matched computational models. We demonstrated the biosensor engineering potential of these designs by constructing serotonin and dopamine sensors using a split protein strategy and explored several approaches to enhance sensor activity. Additionally, we developed a zinc sensor through a zinc-induced protein folding mechanism. Overall, our physics-based generative approach provides a robust framework for the de novo design of ligand-binding proteins, opening new avenues for the development of ligand-responsive biosensors.

bioinformatics↗

bioRxiv2026

Toward Optimizing Thalamic Deep Brain Stimulation for Cortical Modulation: A Surrogate Brain Approach

The thalamus is a central hub that interfaces with widespread cortical and subcortical nodes. Thalamic deep brain stimulation (DBS) offers a principled strategy for distributed cortical modulation: since distinct thalamic nuclei project to spatially segregated cortical territories, stimulation at a single thalamic site can influence multiple cortical nodes. Realizing this potential requires accurate subject-specific estimates of directed thalamocortical effective connectivity (EC) and a computational framework for optimizing stimulation parameters that achieve desired cortical responses. Here, we address both challenges using Neural Perturbational Inference (NPI), a surrogate-brain approach that estimates EC by applying virtual perturbations to a nonlinear dynamical model fitted to resting-state fMRI data. We extend NPI to a high-resolution thalamocortical network comprising 360 cortical regions and 442 thalamic voxels spanning 12 nuclei. We introduce two innovations in training: (i) a temporal signal-to-noise ratio (tSNR)-weighted loss accounting for signal heterogeneity, and (ii) a multi-resolution, cross-scale consistency loss that regularizes model complexity. These strategies yield improved performance in synthetic benchmarks across varying tSNR regimes. Leveraging the inferred subject-specific EC, we further formulate a constrained linear control problem to identify sparse thalamic stimulation targets that achieve desired cortical activation patterns. We validate the inferred EC structure on two independent datasets: the MacStim dataset comprising two macaque monkeys with infrared neural stimulation on medial pulvinar, and the HumanTC resting-state fMRI dataset comprising twelve human subjects. Our results reveal site-specific thalamocortical EC profiles, producing interpretable predictions that align with known ground-truth structures. Together, this work establishes a computationally grounded pathway toward personalized optimization of thalamic DBS in both human and nonhuman primates.

neuroscience↗

bioRxiv2026

Computational Fluid Particle Dynamics (CFPD)-Based Virtual Next Generation Impactor (vNGI) to Predict the Aerodynamic Particle Size Distribution (APSD) of Respiratory Drug Delivery Products: Toward New Approach Methodologies (NAMs) in Inhaler Performance Evaluation

The Next Generation Impactor (NGI) is one of the regulatory gold standards for characterizing aerodynamic particle size distributions (APSDs) of orally inhaled drug products (OIDPs); however, its reliance on complex, resource-intensive in vitro testing under tightly controlled environmental conditions limits experimental flexibility and introduces variability. In alignment with the growing regulatory emphasis on New Approach Methodologies (NAMs) for drug development, this study presents a rigorously validated computational fluid particle dynamics (CFPD)-based virtual NGI (vNGI) as an in silico method complementary to conventional testing. The vNGI replicates a significant portion of the NGI geometry and airflow physics, enabling high-resolution spatiotemporal analysis of aerosol transport and deposition mechanisms that are otherwise inaccessible experimentally. A comprehensive verification and validation framework was implemented, including mesh and particle independence studies, turbulence model assessment, and comparison of stage-wise deposition efficiencies with available in vitro data at 30 L/min. The models capabilities were further extended to low and high flow rates, and two bio-relevant mouth-throat models and polydisperse particle-laden aerosol were added. The model demonstrates strong predictive capability for a few stages and provides mechanistic insight into discrepancies in other stages, depending on the type of analysis. Importantly, this work establishes the vNGI as a fit-for-purpose according to NAM by (i) defining a clear context of use for APSD prediction and inhaler performance evaluation, (ii) capturing physically and biologically relevant air-particle interactions, and (iii) demonstrating technical robustness and reproducibility through systematic validation. The platform can potentially further enable simulation of environmental and physiological conditions, such as humidity effects, that are difficult to control experimentally, thereby improving human relevance and reducing reliance on costly and time-consuming in vitro testing. This study positions the vNGI as a scalable, regulatory-aligned NAM capable of supporting early-stage drug-device combination product development, device optimization, and an alternative bioequivalence assessment, contributing to ongoing efforts to enhance predictive performance, reduce experimental burden, and transition toward human-centric, inhalation product evaluation.

bioengineering↗

bioRxiv2026

MaxEnt-DTD: Maximum-Entropy Estimation of Diffusion Tensor Distribution for Fiber Orientation and Microstructure Characterization

Diffusion MRI (dMRI) enables noninvasive characterization of white-matter fiber orientations and tissue microstructure, but widely used approaches, such as constrained spherical deconvolution (CSD) and parametric multicompartment models, typically address these features separately. The diffusion tensor distribution (DTD) framework jointly represents fiber orientation and microstructure, but estimating DTD from finite, noisy measurements is severely ill-posed. Existing inversion methods either rely on nonnegativity constrained basis representations, which are challenging to sale to high-dimensional and high-resolution distributions, or use sampling-based approaches with limited reliability. We propose MaxEnt-DTD, a maximum-entropy algorithm for DTD estimation from finite and noisy dMRI data. By deriving the Lagrange dual formulation, we reformulate a constrained infinite-dimensional optimization problem into a finite-dimensional unconstrained convex optimization problem, substantially reducing the parameter space and enabling tractable whole-brain DTD estimation. We evaluate MaxEnt-DTD using both synthetic and in vivo data from the Human Connectome Project protocol and a second dataset using advanced B-tensor diffusion encoding. We compare MaxEnt-DTD-derived fiber orientation distributions with results from CSD and Monte-Carlo inversion methods, and assess fiber-specific microstructure measures and rotation-invariant metrics based on the cumulants of DTD. The results demonstrate that MaxEnt-DTD provides a reliable and efficient framework for joint fiber-orientation and microstructure analysis in dMRI.

neuroscience↗

bioRxiv2026

Virus-human protein-protein interactions predict viral phenotypes

Viral phenotypes such as host and tissue tropism are critical determinants of viral infection and transmission. Inferring viral phenotypes presents unique challenges compared to cellular organisms, as viruses rely entirely on host machinery for replication and survival. Current methods for predicting viral phenotypes mainly rely on viral genomic data, often overlooking host-related information. Here, we evaluated the utility of predicted virus-human protein-protein interactions (PPIs) in inferring diverse viral phenotypes using machine-learning algorithms. For predicting human infectivity, a PPI-based machine learning model outperformed both virus genomic and protein sequence-based models that used large language model embeddings. It also surpassed previous methods that incorporated both viral and host genomic data. The human proteins identified by the model were significantly enriched in functions related to viral infection and immune response. In predicting various phenotypes of human RNA viruses, PPI-based models performed better than virus sequence-based models in forecasting virulence, human transmissibility and transmission routes, while showing comparable performance to genomic sequence-based models in predicting tissue tropism. Finally, we demonstrated that a PPI-based model could distinguish high-risk HPV genotypes from low-risk ones. Proteins associated with high-risk HPV were involved in apoptosis and immune regulation, whereas those linked to low-risk HPV were enriched in telomere maintenance and DNA repair. Collectively, this study is the first to demonstrate the value of predicted virus-human PPIs in inferring viral phenotypes, thereby enhancing our understanding of the molecular mechanisms underlying these phenotypes. It also provides effective tools for risk assessment of emerging viruses, contributing to improved pandemic preparedness.

bioinformatics↗

bioRxiv2026

Balancing of immune activation and suppression during phage infection

Signaling-based anti-bacteriophage systems such as CBASS and Thoeris synthesize infection-triggered nucleotide signals that activate anti-phage effectors1,2. However, the phage features sensed by these systems and the mechanisms phages use to evade signaling immunity remain poorly understood. Here, studying clinically relevant Pseudomonas aeruginosa phages from the Migulavirinae family3, we show that closely related phages encode subtle allelic variation in side tail fiber proteins that determine sensitivity to type II Thoeris. In parallel, these same phages encode an "anti-defense hotspot" that contains three adjacent genes that are each sufficient to facilitate phage evasion of both CBASS and Thoeris defenses, counter-balancing the activating proteins. Comparative analysis of this anti-signaling hotspot across the broader family of related N4-like phages uncovered a new Thoeris anti-defense (Tad) protein that sponges NAD-derived molecules (e.g. gcADPR) and exhibits sequence and structural similarity to a poorly characterized nucleotide-binding region of the human ryanodine receptor. Together, these findings reveal how the balance between immune activation and antagonism shifts phage outcomes and reveals a surprising similarity between a phage molecular sponge and an important human protein.

microbiology↗

bioRxiv2026

Enhancing adeno-associated virus cellular entry through receptor engineering

Adeno-associated viruses (AAV) are approved for gene therapy of several genetic disorders; however, key aspects of AAV cellular entry remain poorly understood. We previously identified carboxypeptidase D (AAVR2) as an AAV receptor distinct from the multi-serotype AAV receptor KIAA0319L (AAVR). In this study, we investigate the molecular mechanisms and biological roles of AAVR and AAVR2 in mediating AAV gene transfer. Using proximity-dependent biotin identification (BioID), we defined the interactome of AAVR in the presence or absence of AAV8 and identified various proteins involved in viral entry including AAVR2. We confirmed a direct physical interaction between AAVR and AAVR2, mediated by non-AAV interacting regions in the C-termini. Further, we identified functional motifs within the carboxy-terminal tails of both receptors to facilitate the engineering of chimeric receptors with enhanced activity. Functional assays demonstrated that the overexpression of AAVR or AAVR2 enhances the cellular attachment and entry of AAV in a serotype-specific manner. Finally, we generated a stable cell line expressing a minimal functional AAVR2 with increased sensitivity for in vitro potency testing for AAVR2-engaging serotypes like AAV8. Collectively, these findings reveal significant functional similarities in AAV receptor biology and establish a framework for engineering receptor-guided modalities.

molecular biology↗