Biology subjects

Yang, P.

Publications and source records attributed to Yang, P..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Lentiviral-mediated gene complementation rescues pathogenic ABCA3 variants

The ATP-binding cassette subfamily A member 3 (ABCA3) protein on the limiting membrane of lamellar bodies in alveolar type 2 (AT2) cells transports phospholipids required for pulmonary surfactant assembly. ABCA3 deficiency results from biallelic pathogenic variants in ABCA3 and causes progressive neonatal respiratory failure or childhood interstitial lung disease (chILD). Supportive/compassionate care or lung transplantation are the only current definitive treatments for ABCA3 deficiency and progressive respiratory failure. Complementing dysfunctional ABCA3 by gene addition has therapeutic potential. Previous studies show that repairing or complementing ABCA3 in induced pluripotent stem cell (iPSC)-derived AT2 cells rescues lamellar body morphology and surfactant phospholipid composition. Pathogenic variants disrupt ABCA3 function through altered protein trafficking (type 1) or by impaired phospholipid transport (type 2) into lamellar bodies. Here we tested ABCA3 gene complementation using a human pulmonary epithelial cell line (A549) with a genomically silenced ABCA3 locus (ABCA3KO). From this line, additional cell lines that stably express individual ABCA3 variant cDNA constructs from a single genomic locus were tested: L101P (type 1), E292V (type 2), E690K (type 2), or wild-type ABCA3. Lentiviral-mediated ABCA3 delivery to each cell line partially rescued localization to LAMP3+ vesicles, lamellar body-like structure morphology, and cell proliferation. A functional assay measuring NF-{kappa}B signaling suggested that ABCA3 complementation ameliorated aberrant inflammatory signaling in E292V or E690K (type 2) mutant lines, but not in L101P (type 1) or knockout lines. These studies highlight the therapeutic potential of gene addition as well as differences between ABCA3 pathogenic variants that may influence genetic therapy outcomes.

molecular biology↗

bioRxiv2025

Interpretable deep generative ensemble learning for single-cell omics with Hydra

Single-cell omics enable the dissection of cellular heterogeneity, yet the high dimensionality, inherent noise, and sparsity present significant challenges. These challenges are amplified for rare cell populations, which are often difficult to annotate reliably but can be central to development and disease. As single-cell assays increasingly capture multiple molecular layers, the integrative analysis of such multimodal data further increases complexity. Here, we propose Hydra, a deep generative framework based on an ensemble of variational autoencoders for effective learning of unimodal and multimodal single-cell omics data. Hydra implements interpretable modules for capturing cell type-specific molecular signatures. The ensemble of such interpretable modules enables reproducible feature selection and robust cell type annotation, with particular effectiveness for rare populations. We benchmarked Hydra on a repertoire of 21 datasets, including unimodal and multimodal single-cell omics data. Our results demonstrate that Hydra offers comparable to superior performance to several state-of-the-art methods. Finally, we highlight the utility of Hydra in robustly annotating brain cellular subtypes and preserving disease-relevant signatures using our previously published dataset that profiles Alzheimers disease.

bioinformatics↗

bioRxiv2025

Refate identifies chemical compounds to target trans-regulatory networks for cellular conversion

Identifying chemical compounds that target trans-regulatory networks (TRNs) underlying molecular programs of cells for directed cellular conversion (i.e. differentiation, reprogramming, transdifferentiation, and dedifferentiation) is a key step towards advancing regenerative medicine. Recent innovations in single-cell omics technologies enabled high-resolution profiling of TRNs that govern cell identity and cell-fate decisions. Here, we introduce Refate, a computational framework that integrates large-scale multimodal single-cell atlas data to quantify cell propensity of genes, together with six drug databases, to identify chemical compounds that target TRNs for directed cellular conversion. The reconstructed TRNs, including protein-protein interactions and gene regulatory networks, alongside chemical compounds that drive the cellular conversion provide greater biological interpretability and improve efficiency and efficacy. We evaluated Refate by testing its ability to uncover known transcription factors and chemical compounds validated in experimental conversions of various cell types. Furthermore, we experimentally validated the attribute of several novel chemical compounds identified by Refate for enhancing the conversion of human embryonic stem cells to human cranial neural crest cells. Together, these findings demonstrate Refate as an effective tool for discovering chemical compounds that target TRNs to enable cellular conversion, advancing efforts towards regenerative medicine. HighlightsO_LIRefate quantifies genes for cellular conversion using multimodal single-cell atlases C_LIO_LIRefate uncovers trans-regulatory networks (TRNs) underlying cellular conversion C_LIO_LIRefate identifies chemical compounds that target TRNs for cellular conversion C_LIO_LIValidation of Refate identified chemical compounds for hESC to hCNCC conversion C_LI

developmental biology↗

bioRxiv2025

Agropyron cristatum genome provides a new insight for wheat improvement with wild relatives

Exploring novel genetic variation for target traits is crucial for advancing wheat breeding and genetic improvement. Since 1990, we have been transferring genes from Agropyron cristatum, a promising wild relative genetic resource featuring robust spike morphology and stress tolerance, into common wheat by distant hybridization. To efficiently harness A. cristatum genes, we de novo assembled a 25.62-Gb high-quality genome of an autotetraploid accession Z559 with 28 chromosomes, and uncovered its specific genome features and an extensive repertoire of genes associated with yield and resistance to abiotic and biotic stresses. We systematically characterized A. cristatum genome introgressions by resequencing 431 wheat-A. cristatum derivatives and cloned the gene AcGNS1, which functions in the regulation of grain number per spike in wheat. Subsequently, we developed an optimized genome-selection model for wheat-A. cristatum breeding derivatives, enabling the rapid selection of new potential varieties. These findings demonstrate a genomic breeding strategy for leveraging novel genetic resources of wild relatives and advancing wheat improvement.

genomics↗

bioRxiv2025

Competing Subclones and Fitness Diversity Shape Tumor Evolution Across Cancer Types

Intratumor heterogeneity arises from ongoing somatic evolution complicating cancer diagnosis, prognosis, and treatment. Here we present TEATIME (estimating evolutionary events through single-timepoint sequencing), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric--fitness diversity--which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types.

bioinformatics↗

bioRxiv2025

Cross-species analysis of experimental PH at single cell resolution reveals prominent contributions Ednrb+ EC and Dhcr24+ macrophage populations

BackgroundAnimal models are used widely to study pulmonary hypertension (PH). The cell populations that respond to disease-inducing stimuli in these models and their relationship to human disease remain incompletely defined. Materials and methodThis study analyzed the relationship between several rodent models of PH and human disease at single-cell resolution. scRNA-seq was performed on lungs from mice exposed to hypoxia or Sugen/hypoxia, rats exposed to monocrotaline, and controls. A cross-species single-cell dataset was integrated with human lung cell atlas (HLCA) and single-cell dataset from idiopathic pulmonary arterial hypertension (IPAH) to identify overlapping cell subsets between experimental and human disease and species. ResultsHigh levels of overlap were found between species and models of PH, HLCA, and IPAH datasets. Cell subsets perturbed in rat and mouse PH were similar to those found in human disease, with macrophages and endothelial cells being most affected. A novel Dhcr24high macrophage subset harboring both tissue-remodeling and pro- inflammatory features was consistently increased across models. Several functionally diverse endothelial subtypes were found, including novel Ednrb+ and Nox2+ subpopulations, reflecting enhanced apoptosis, dysregulated angiogenesis and proliferation, and reactive oxygen species-mediated stress. These macrophage and endothelial subtypes expressed numerous PH drug target genes, and exhibited potential disease-specific intercellular interactions involving Angptl4, Cxcl12, and Sema3 signaling. Disease-associated changes in these populations were confirmed by immunofluorescence in lung tissues from animals and patients. ConclusionsWe established a comprehensive cross-species single-cell atlas of mainstream rodent PH models, highlighting several novel macrophage and endothelial subtypes and signaling motifs potentially contributing to human disease.

bioinformatics↗

bioRxiv2025

PW-GAN: Pseudo-Warping Field Guided GAN for Unsupervised Denoising of Fetal Brain MRI Images

Fetal brain magnetic resonance imaging is of great importance for prenatal neural disorder diagnosis. To improve signal-to-noise ratio and coverage, fetal brain MRI often uses thick-slice scanning to reduce motion artifacts and ensure image quality for single slices. Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects like fetal MRI. However, raw scans acquired with varying slice thicknesses present substantial disparities in quality when retrospectively reconstructed into isotropic high-resolution volumes (e.g. 0.8 mm slice thickness). In particular, thick-slice acquisitions (e.g. 5-6 mm) tend to yield suboptimal reconstruction results compared to thin-slice scans (e.g. 2-3 mm), often exhibiting residual noise, interpolation-induced artifacts, and inter-slice structural discontinuities. This challenge has highlighted the need to further reduce noise caused by reconstruction errors when the reconstructed volumes are used for downstream tasks. With the success of neural networks in computer vision, deep learning methods based on architectures such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) have achieved outstanding performance in single-image denoising. Nevertheless, CNN-based methods still heavily rely on large-scale datasets comprising noisy-clean image pairs. Although unsupervised GAN variants such as CycleGAN have been developed to mitigate this dependency, the inherent variability of GAN-generated outputs often results in tiny anatomical distortions, significantly limiting the applicability of GAN-based methods. While several approaches have introduced regularizations to address this issue, they largely focus on denoising individual slices, overlooking inter-slice structural inconsistencies that arise from treating slices independently. In this work, we propose Pseudo-Warping Field Guided Unsupervised Generative Adversarial Network (PW-GAN), which formulates post-reconstruction optimization as an unpaired style transfer problem between low-quality and high-quality MRI domains. Moreover, by incorporating a pseudo-deformation field module based on optical flow estimation, our method significantly enhances inter-slice continuity in the reconstructed volumes while effectively suppressing residual noise and interpolation artifacts introduced during the reconstruction process. Evaluations on both simulated and in vivo data demonstrate that our method outperforms existing unsupervised models and achieves performance on par with several state-of-the-art supervised methods.

neuroscience↗

bioRxiv2025

Comparative multi-omics profiling of Gossypium hirsutum and Gossypium barbadense fibers at high temporal resolution reveals key differences in polysaccharide composition and associated glycosyltransferases

Among the two allopolyploid cultivated species of cotton, Gossypium barbadense is known for its superior quality fiber compared to G. hirsutum. Length and strength are key determinants of the fiber quality. Although the mature fibers are dried cell walls consisting mainly cellulose, the dynamic remodeling of pectin, xyloglucan, and xylan polysaccharides during fiber growth significantly impact the final fiber quality. Comprehensive knowledge about polysaccharides and their biosynthesis during fiber development of the cultivated species is important for improving fiber quality. In this study, comparative large-scale glycome, transcriptome and proteome profiling were conducted daily on fibers of both cotton species covering critical stages of fiber development spanning primary cell wall synthesis and the transition to secondary wall synthesis. Interspecific comparisons revealed that a delayed accumulation of cellulose content, as well as the occurrence of lower levels and differential compositions of non-fucosylated/fucosylated xyloglucans, homogalacturonans, and highly branched Rhamnogalacturonan-I polysaccharides might be contributing to longer fiber phenotypes of G. barbadense relative to G. hirsutum. Our study also suggests, differential temporal compositions of heteroxylans might contribute to variation in cellulose microfibril arrangement and strength of fiber exists between the two species of cotton. Comparative transcriptomic analysis identified differentially expressed polysaccharide-synthesizing glycosyltransferases that might underlie fiber quality differences between the two species. Transcripts encoding many cell wall localized expansins were more abundant in G. barbadense than in G. hirsutum. Overall, these findings extend our knowledge regarding the molecular factors that contribute to fiber quality and provide insights for targeted cotton fiber improvement. SIGNIFICANCE STATEMENTComparative multi-omics profiling of two major commercial cotton species, Gossypium hirsutum and Gossypium barbadense revealed substantial differences in polysaccharide structures and expressed polysaccharide-synthesizing glycosyltransferases, that potentially contribute to differences in fiber length and strength. The molecular details elucidated in the present study contribute to the goal of improving fiber quality and its commercial value.

plant biology↗

bioRxiv2025

MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis

Analyzing animal behavior is crucial in advancing neuroscience, yet quantifying and deciphering its intricate dynamics remains a significant challenge. Traditional machine vision approaches, despite their ability to detect spontaneous behaviors, fall short due to limited interpretability and reliance on manual labeling, which restricts the exploration of the full behavioral spectrum. Here, we introduce MouseGPT, a Vision-Language Model (VLM) that integrates visual cues with natural language to revolutionize mouse behavior analysis. Built upon our first-of-its-kind dataset--incorporating pose dynamics and open-vocabulary behavioral annotations across over 42 million frames of diverse psychiatric conditions--MouseGPT provides a novel, context-rich method for comprehensive behavior interpretation. Our holistic analysis framework enables detailed behavior profiling, clustering, and novel behavior discovery, offering deep insights without the need for labor-intensive manual annotation. Evaluations reveal that MouseGPT surpasses existing models in precision, adaptability, and descriptive richness, positioning it as a transformative tool for ethology and for unraveling complex behavioral dynamics in animal models.

animal behavior and cognition↗

bioRxiv2025

Coordinated horizontal transfer of multiple genes assembles a carotenoid biosynthesis pathway in aphids

Horizontal gene transfer (HGT) plays a crucial role in genome evolution, especially when it enables the acquisition and assembly of multi-step biosynthetic pathways. Here, we investigate the evolutionary origins of carotenoid biosynthesis genes in aphids to determine whether multiple functionally related genes were acquired through HGT. We analyzed carotenoid biosynthesis genes in 23 aphid genomes based on homologs in plants, fungi, and bacteria. Phylogenetic analyses revealed that Geranylgeranyl pyrophosphate synthase (GPS), Phytoene synthase (PS), and Carotenoid desaturase (CD) were acquired via HGT from fungi by ancestral insect species, while Carotenoid cleavage oxygenase (CCO) appears to be a native insect gene. Most insect genomes contain two GPS copies, likely resulting from independent HGT events, whereas aphid genomes exhibit extensive duplication of PS and CD, a pattern uncommon in other insects. Expression analyses across aphid species with distinct pigmentation showed that these genes are broadly transcribed with substantial variability in expression levels. In Myzus persicae, comparative expression analysis between reddish and greenish clones, as well as a green-reddish clone with green and red color polymorphism, revealed that PS-4390 is a novel contributor to red pigmentation in M. persicae, in addition to CD-4400, a homolog of the tor gene in Acyrthosiphon pisum. These findings provide strong evidence that HGT can introduce multiple functionally related genes into recipient genomes, allowing them to be co-opted into a functional biosynthetic pathway.

evolutionary biology↗

bioRxiv2025

SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction

ObjectiveThe fast accumulation of vast pharmacogenomics data of cancer cell lines provide unprecedented opportunities for drug sensitivity prediction (DSP), a crucial prerequisite for the advancement of precision oncology. Recently, Generative Large Language Models (LLM) have demonstrated performance and generalization prowess across diverse tasks in the field of natural language processing (NLP). However, the structured format of the pharmacogenomics data poses challenge for the utility of LLM in DSP. Therefore, the objective of this study is multi-fold: to adapt prompt engineering for structured pharmacogenomics data toward optimizing LLMs DSP performance, to evaluate LLMs generalization in real-world DSP scenarios, and to compare LLMs DSP performance against that of state-of-the-science baselines. MethodsWe systematically investigated the capability of the Generative Pre-trained Transformer (GPT) as a DSP model on four publicly available benchmark pharmacogenomics datasets, which are stratified by five cancer tissue types of cell lines and encompass both oncology and non-oncology drugs. Essentially, the predictive landscape of GPT is assessed for effectiveness on the DSP task via four learning paradigms: zero-shot learning, few-shot learning, fine-tuning and clustering pretrained embeddings. To facilitate GPT in seamlessly processing the structured pharmacogenomics data, domain-specific novel prompt engineering is employed by implementing three prompt templates (i.e., Instruction, Instruction-Prefix, Cloze) and integrating pharmacogenomics-related features into the prompt. We validated GPTs performance in diverse real-world DSP scenarios: cross-tissue generalization, blind tests, and analyses of drug-pathway associations and top sensitive/resistant cell lines. Furthermore, we conducted a comparative evaluation of GPT against multiple Transformer-based pretrained models and existing DSP baselines. ResultsExtensive experiments on the pharmacogenomics datasets across the five tissue cohorts demonstrate that fine-tuning GPT yields the best DSP performance (28% F1 increase, p-value= 0.0003) followed by clustering pretrained GPT embeddings (26% F1 increase, p-value= 0.0005), outperforming GPT in-context learning (i.e., few-shot). However, GPT in the zero-shot setting had a big F1 gap, resulting in the worst performance. Within the scope of prompt engineering, performance enhancement was achieved by directly instructing GPT about the DSP task and resorting to a concise context format (i.e., instruction-prefix), leading to F1 performance gain of 22% (p-value=0.02); while incorporation of drug-cell line prompt context derived from genomics and/or molecular features further boosted F1 score by 2%. Compared to state-of-the-science DSP baselines, GPT significantly asserted superior mean F1 performance (16% gain, p-value<0.05) on the GDSC dataset. In the crosstissue analysis, GPT showcased comparable generalizability to the within-tissue performances on the GDSC and PRISM datasets, while statistically significant F1 performance improvements on the CCLE (8%, p-value=0.001) and DrugComb (19%, p-value=0.009) datasets. Evaluation on the challenging blind tests suggests GPTs competitiveness on the CCLE and DrugComb datasets compared to random splitting. Furthermore, analyses of the drug-pathway associations and log probabilities provided valuable insights that align with previous DSP findings. ConclusionThe diverse experiment setups and in-depth analysis underscore the importance of generative LLM, such as GPT, as a viable in silico approach to guide precision oncology. Availabilityhttps://github.com/bioIKEA/SensitiveCancerGPT

bioinformatics↗

bioRxiv2025

Multi-view gene panel characterization for spatially resolved omics

Spatially resolved transcriptomics has transformed our ability to study complex tissues at the cellular and subcellular resolution. However, targeted spatial technologies require pre-selected gene panels, which are typically curated based on existing biological knowledge and prior research hypotheses. While current methods often prioritize capturing cell type information, we argue that an effective gene panel should also capture cell type diversity, cell states, pathway-level information, and minimize redundancy. To address these broader requirements, we developed a gene panel characterization platform that characterizes panels across multiple perspectives, thus allowing us to compare panels comprehensively. Notably, computationally constructed gene panels performed competitively in capturing major cell types when compared to our in-house manually curated panel. However, refined manual curation offered distinct advantages, particularly in capturing minor and rare cell types and exhibited lower information redundancy comparatively. Building on this framework, we integrated these metrics into a deep learning platform, panelScope, leveraging them as a loss function to design holistic gene panels. Using an acute myeloid leukemia (AML) dataset with 42 well-defined cell types and the 5K Xenium panel from 10X Genomics, we demonstrate the utility of our framework in comprehensively characterizing gene panels, enabling the design of tailored panels that address diverse research needs.

bioinformatics↗

bioRxiv2025

Ultradeep N-glycoproteome Atlas of Mouse Reveals Spatiotemporal Signatures of Brain Aging and Neurodegenerative Diseases

The current depth of site-specific N-glycoproteomics is insufficient to fully characterize glycosylation events in biological samples. Herein, we achieved an ultradeep and precision analysis of the N-glycoproteome of mouse tissues by integrating multiple workflows. The largest N-glycoproteomic dataset to date was established on mice, which contained 91,972 precursor glycopeptides, 62,216 glycoforms, 8,939 glycosites and 4,563 glycoproteins. The database consisted of 6.8 million glyco-spectra (containing oxonium ions), among which 160,928 were high-quality spectra with confident N-glycopeptide identifications. The large-scale and high-quality dataset enhanced the performance of current artificial intelligence models for glycopeptide tandem spectrum prediction. Using this ultradeep dataset, we observed tissue specific microheterogeneity and functional implications of protein glycosylation in mice. Furthermore, the region-resolved brain N-glycoproteomes for Alzheimers Diseases, Parkinson Disease and aging mice revealed the spatiotemporal signatures and distinct pathological functions of the N-glycoproteins. A comprehensive database resource of experimental N-glycoproteomic data from this study and previous literatures were further established. This N-glycoproteome atlas serves as a promising tool for revealing the role of protein glycosylation in biological systems.

neuroscience↗

bioRxiv2025

Proteomics-based models of gene expression and cellular control of cotton fiber development

The shapes and material properties of cotton seed coat trichoblasts are the basis of a multibillion-dollar natural fiber industry. As such, these highly specialized cells are low-hanging fruit for intentional trait engineering. However, broad successes will require more mechanistic knowledge about their systems-level cellular controls. This time-series study integrates daily measurements of purified fiber transcriptomes and proteomes with multiscale fiber phenotyping datasets that span the same developmental interval. Abundance profiles of the subcellular proteomes are the foundation of the analyses. This resource article provides direct information concerning which homoeologs operate and informative depictions of how compartmentalized cellular systems change during developmental transitions. Prediction accuracy was partially validated by analysis of the protein expression group 11, which contained multiple known secondary cell wall cellulose synthases and dozens of unknown proteins and an averaged profile that was strongly correlated with a sharp state transition in cellulose microfibril alignment and increased cellulose content. The dataset as a whole can serve as a hypothesis-generating machine to guide future experiments that relate to cell shape and growth rate control, reversible tissue formation, and cell wall remodeling. Integration of mRNA and protein abundance revealed widespread evidence for post-transcriptional control. In addition, there were hundreds of transcriptionally controlled genes with differing timepoints of transition. This latter gene set can be used to more reliably analyze transcriptional control networks and to generate collections of gene expression drivers for cotton fiber research. The protein and transcript data are organized into user-friendly tables and a web interface that can be searched using any plant ortholog of interest based on developmental time, abundance, annotations, or phenotypic association.

plant biology↗

bioRxiv2025

Histone methyltransferase PRDM9 promotes survival of drug-tolerant persister cells in glioblastoma

Chemotherapy often kills a large fraction of cancer cells but leaves behind a small population of drug- tolerant persister cells. These persister cells survive drug treatments through reversible, non-genetic mechanisms and cause tumour recurrence upon cessation of therapy. Here, we report a drug tolerance mechanism regulated by the germ-cell-specific H3K4 methyltransferase PRDM9. Through histone proteomic, transcriptomic, lipidomic, and ChIP-sequencing studies combined with CRISPR knockout and phenotypic drug screen, we identified that chemotherapy-induced PRDM9 upregulation promotes metabolic rewiring in glioblastoma stem cells, leading to chemotherapy tolerance. Mechanistically, PRDM9-dependent H3K4me3 at cholesterol biosynthesis genes enhances cholesterol biosynthesis, which persister cells rely on to maintain homeostasis under chemotherapy- induced oxidative stress and lipid peroxidation. PRDM9 inhibition, combined with chemotherapy, resulted in strong anti-cancer efficacy in preclinical glioblastoma models, significantly enhancing the magnitude and duration of the antitumor response by eliminating persisters. These findings demonstrate a previously unknown role of PRDM9 in promoting metabolic reprogramming that enables the survival of drug-tolerant persister cells.

cancer biology↗

bioRxiv2025

Molecular basis for shifted receptor recognition by an encephalitic arbovirus

After decades of inactivity throughout the Americas, western equine encephalitis virus (WEEV) recently re-emerged in South America, causing a large-scale outbreak in humans and horses. WEEV binds protocadherin 10 (PCDH10) as a receptor; however, nonpathogenic strains no longer bind human or equine PCDH10 but retain the ability to bind avian receptors. Highly virulent WEEV strains can also bind the very low-density lipoprotein receptor (VLDLR) and apolipoprotein E receptor 2 (ApoER2) as alternative receptors. Here, by determining cryo-electron microscopy structures of WEEV strains isolated from 1941-2005 bound to mammalian receptors, we identify polymorphisms in the WEEV spike protein that explain shifts in receptor dependencies and that can allow nonpathogenic strains to infect primary cortical neurons. We predict the receptor dependencies of additional strains and of a related North American alphavirus. Our findings have implications for outbreak preparedness and enhance understanding of arbovirus neurovirulence through virus receptor binding patterns.

microbiology↗

bioRxiv2024

Neurodevelopmental deviations in schizophrenia: Evidences from multimodal connectome-based brain ages

BackgroundPathologic schizophrenia process originate early in brain development, leading to detectable brain alterations via structural and functional magnetic resonance imaging (MRI). Recent MRI studies have sought to characterize disease effects from a brain age perspective, but developmental deviations from the typical brain age trajectory in youths with schizophrenia remain unestablished. AimThis study investigated brain development deviations in early-onset schizophrenia (EOS) patients by applying machine learning algorithms to structural and functional MRI data. MethodsMultimodal MRI data, including T1-weighted MRI (T1w-MRI), diffusion MRI, and resting-state functional MRI (rs-fMRI) data, were collected from 80 antipsychotic-naive first-episode EOS patients and 91 typically developing (TD) controls. The morphometric similarity connectome (MSC), structural connectome (SC), and functional connectome (FC) were separately constructed by using these three modalities. According to these connectivity features, eight brain age estimation models were first trained with the TD group, the best of which was then used to predict brain ages in patients. Individual brain age gaps (BAGs) were assessed as brain ages minus chronological ages. ResultsBoth the SC and MSC features performed well in brain age estimation, whereas the FC features did not. Compared with the TD controls, the EOS patients presented widened structural BAGs, with opposite trends between childhood and adolescence. These increased absolute BAG scores for EOS patients were positively correlated with the severity of their clinical symptoms. ConclusionThese findings from a multimodal brain age perspective suggest that advanced BAGs exist early in youths with schizophrenia.

neuroscience↗

bioRxiv2024

Anti-Cancer Immune Priming with Beta-Radioligand Therapy and Isoform-Selective Targeting of 4Ig-B7-H3

Radioligand therapy (RLT), a re-emerging oncologic strategy using molecularly-targeted therapeutic radioisotopes, clinically reduces tumor burden and enhances survival for select patients with otherwise unresponsive advanced prostate cancer and neuroendocrine tumors. Developing new approaches to next generation targets and a better understanding of systemic immune effects could broaden the impact of RLT. Aside from contributions to immune checkpoint, B7-H3 (CD276) is an attractive oncologic target because of its widespread and high differential expression across a variety of solid tumors compared to normal tissues. However, B7-H3 has two isoforms: a 4Ig-B7-H3 isoform, the dominant transmembrane protein expressed on tumors and tumor immune microenvironments (TIME), and a 2Ig-B7-H3 isoform, a soluble ectodomain protein, representing a circulating, and in the context of RLT, significant shed (decoy) antigen. To enhance tumor-specific binding and circumvent confounding soluble 2Ig-B7-H3, a novel IgG2a monoclonal antibody (MIL33B) was generated with high affinity for 4Ig-B7-H3 (72 picomolar) and 8- to 18-fold selectivity over soluble 2Ig-B7-H3. Live cell fluorescence microscopy using AF594-labeled MIL33B demonstrated strong membranous localization and target specificity. PET-CT imaging with 89Zr-labeled MIL33B confirmed robust tumor-selective target binding in vivo in murine xenograft (HeLa cervical) and syngeneic tumor models (4T1 breast, B16F10 melanoma, and CT26 colorectal) expressing human 4Ig-B7-H3. As a single dose beta-emitting systemic RLT therapeutic, 90Y-labeled MIL33B (100 Ci) produced 53% long-term survival in a 4Ig-B7-H3-dependent manner in an otherwise fatal established CT26 colorectal tumor model. Immunologic analysis showed that 90Y-MIL33B RLT functioned as an immune priming event, engaging downstream CD8+ T-cell activation and inducing immunological memory in vivo, thus illustrating the potential of systemic beta-RLT to target both primary and metastatic sites. Thus, MIL33B showcases a strategy to selectively target 4Ig-B7-H3 for beta-RLT, warranting further investigation as an immune priming tactic alone or in combination for cancer therapy. Statement of significanceModest antigen expression levels, even if target tissue-selective, combined with ectodomain shedding (soluble decoy antigens) can generally hinder targeted diagnostic and therapeutic strategies, but are especially challenging for radioligand therapy, PET imaging, and in vivo diagnostics wherein high specific activity radioisotopes necessitate use of low masses of biocarrier. Binding, absorption and non-specific tissue deposition of radiolabeled biocarriers by decoy antigens can significantly misdirect systemic radiation, reducing therapeutic efficacy. An antibody development process with a focus aimed at on-target affinity for folded proteins on live cells resulted in a novel picomolar affinity antibody selectively targeting membranous 4Ig-B7-H3 over soluble decoy 2Ig-B7-H3. This antibody shows promise as a transformative systemic beta-radioligand therapy platform for immune priming applications in oncology, and potentially in cardiology, rheumatology, and autoimmunity.

cancer biology↗