Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 199 records · Page 11Linked to original sources

bioRxiv2025

Benchmarking and optimizing Perturb-seq in differentiating human pluripotent stem cells

Perturb-seq is a powerful approach to systematically assess how genes and enhancers impact the molecular and cellular pathways of development and disease. However, technical challenges have limited its application in stem cell-based systems. Here, we benchmarked Perturb-seq across multiple CRISPRi modalities, on diverse genomic targets, in multiple human pluripotent stem cells, during directed differentiation to multiple lineages, and across multiple sgRNA delivery systems. To ensure cost-effective production of large-scale Perturb-seq datasets as part of the Impact of Genomic Variants on Function (IGVF) consortium, our optimized protocol dynamically assesses experiment quality across the weeks-long procedure. Our analysis of 1,996,260 sequenced cells across benchmarking datasets reveals shared regulatory networks linking disease-associated enhancers and genes with downstream targets during cardiomyocyte differentiation. This study establishes open tools and resources for interrogating genome function during stem cell differentiation.

genomics↗

bioRxiv2025

Genetically Defined Organoid Models Reveal Mechanisms Driving Squamous Cell Neoplastic Evolution and Identify Potential Therapeutic Vulnerabilities

Upper aerodigestive squamous cell carcinoma (UASCC) is an aggressive and lethal neoplasm, with its early neoplastic transformation mechanisms remaining poorly understood. Here, we characterize over 25 genetically-defined organoid models derived from murine and human oral/esophageal tissues harboring key driver mutations. Double knockout of TP53 and CDKN2A induced morphological dysplasia, hyperproliferation, loss of squamous differentiation, and tumorigenicity, which were further exacerbated by additional driver mutations (e.g., PIK3CA, NOTCH1, KMT2C). Single-cell analysis revealed an expansion of quiescent basal cells and proliferative squamous cells, alongside a loss of differentiated squamous cells during malignant transformation. A distinct senescence program, regulated by ANXA1, was markedly diminished during early neoplastic evolution. Mechanistically, the ANXA1-SMAD3-p27KIP1 pathway was identified as a critical regulator of this senescence program, acting to suppress neoplastic features in organoid models. Lastly, our high-throughput, single-organoid-resolution drug screens unexpectedly revealed PIK3CA-driven organoids exhibited sensitivity to Mitomycin C and Onalespib. This study provides novel mechanistic insights into early neoplastic evolution and underscores the value of genetically-defined organoid models for investigating cancer biology and identifying targeted therapies.

cancer biology↗

bioRxiv2025

spEMO: Exploring the Capacity of Foundation Models for Analyzing Spatial Multi-Omic Data

Recent advances in pathology foundation models (PFMs), which are pretrained on large-scale histopathological images, have significantly accelerated progress in disease-centered applications. In parallel, spatial multi-omic technologies collect gene and protein expression levels at high spatial resolution, offering rich understanding of tissue context. However, current models fall short in effectively integrating these complementary data modalities. To fill in this gap, we introduce spEMO, a novel computational system that unifies embeddings from pathology foundation models and large language models (LLMs) to analyze spatial multi-omic data. By incorporating multimodal representations and information, spEMO outperforms models trained on single-modality data across a broad range of downstream tasks, including spatial domain identification, spot-type classification, whole-slide disease-state prediction and interpretation, inference of multicellular interactions, and automated medical report generation. The outstanding performances of spEMO in these tasks demonstrate its strength in both biological and clinical applications. Additionally, we propose a new evaluation task, known as multi-modal alignment, to assess the information retrieval capabilities of pathology foundation models. This task provides a principled benchmark for evaluating and improving model architectures. Collectively, spEMO represents a step forward in building holistic, interpretable, and generalizable AI systems for spatial biology and pathology.

bioinformatics↗

bioRxiv2025

Cis-regulatory chromatin contacts form de novo in the absence of loop extrusion

NIPBL promotes chromatin loop extrusion by the cohesin complex until it stalls at convergently oriented CTCF sites, leading to the formation of structural loops. However, to what extent loop extrusion contributes to the establishment vs maintenance of cis-regulatory element (CRE) connectivity is poorly understood. Here, we explored the de novo establishment of chromatin folding patterns at the mitosis-to-G1-phase transition upon acute NIPBL loss. NIPBL depletion primarily impaired the formation of cohesin-mediated structural loops with NIPBL dependence being proportional to loop length. In contrast, the majority of CRE loops were established independently of loop extrusion regardless of length. However, NIPBL depletion slowed the re-formation of CRE loops with weak enhancers. Transcription of genes at NIPBL-independent loop anchors was activated normally in the absence of NIPBL. In sum, establishment of most regulatory contacts and gene transcription following mitotic exit is independent of loop extrusion.

molecular biology↗

bioRxiv2025

PSI Atlas: a comprehensive knowledgebase of plant self-incompatibility

Self-incompatibility (SI) is an important genetic mechanism in angiosperms that prevents inbreeding and promotes outcrossing. Although significant progress has been made in understanding SI, its molecular underpinnings and evolutionary origins remain elusive in many plant families. In eudicots, SI is typically regulated by a single S-locus with diverse S-haplotypes, while in grasses, it is controlled by two separate S and Z loci. Genome assemblies of SI plants have greatly facilitated the identification and evolutionary analysis of S-loci. However, a comprehensive database that integrates information on the diverse S-loci has been lacking. To fill this gap, we create the Plant Self-Incompatibility Atlas (PSIA, http://www.plantsi.cn), a knowledgebase that offers an extensive compilation of plant SI, including genomic resources for assembled SI species, the origins and evolution of S genes, and the molecular mechanisms of the eight known SI types. In our most recent release, we have obtained more than 500 genome assemblies across 469 SI species. We have also collected 1275 nucleotide and 1130 protein sequence accessions of S genes from public databases, with a total of 3095 S genes manually identified and curated. PSIA not only thoroughly explores the S-locus information of the assembled SI species but also enables users to efficiently browse, perform BLAST searches, analyze, and download S genes. Additionally, PSIA acts as a comprehensive platform for comparative genomic studies of S-loci, aiding in the exploration of the dynamic processes involved in the origin, loss, and regain of SI. Consequently, PSIA is poised to significantly enhance our understanding of angiosperm SI and offer new perspectives on plant mating systems.

plant biology↗

bioRxiv2025

A glycerol oxidase from Norway spruce (Picea abies L. Karst.) expands biochemical and structural attributes of the GMC oxidoreductase superfamily

O_LIInvestigations to uncover the versatility of carbohydrate active enzymes belonging to the auxiliary activity family 3 (AA3) have focused on microbial enzymes. Plant genomes also harbor AA3 encoding genes, and reverse genetics approaches have shown their importance in plant development. To date, however, detailed biochemical characterizations and structures of plant AA3 enzymes have not been reported. C_LIO_LIHere, we describe screening AA3 encoding genes from Norway spruce and the first in-depth biochemical and structural characterization of a plant AA3 oxidase, PaAOX1. PaAOX1 was crystallized, and the structure was solved by X-ray crystallography. C_LIO_LIPaAOX1 demonstrated highest oxidase activity against glycerol. It oxidized glycerol to glyceraldehyde, then further to glyceric acid, and showed preference for L-glyceraldehyde over D-glyceraldehyde. Despite the low sequence similarity with fungal alcohol oxidases, it had all the structural features of the typical AA3 enzyme. C_LIO_LIPaAOX1 has a novel activity among the characterized AA3s, and it showed a new structural arrangement of the region surrounding the catalytic center. Our findings contribute to a deeper understanding of the structural and functional aspects of enzymes in the GMC superfamily, and especially of alcohol oxidases, expanding the knowledge from fungi to plants. C_LI

biochemistry↗

bioRxiv2025

Using green space virtual reality to prevent stress-induced working memory impairment

Exposure to green space is associated with both physical and mental health benefits, including the potential to buffer acute stress responses, positioning it as a promising non-pharmacological approach to protect cognitive functions against stress. However, urban residents often face significant barriers in accessing green spaces, which are not equitably distributed. Virtual Reality (VR) technology offers a potential solution by simulating green space that could be made accessible to a broader demographic. Here, we explored whether VR-stimulated green space could dampen biological stress responses (i.e., heart rate and cortical response) and prevent stress-induced working memory (WM) impairment. Healthy young participants underwent acute stress induction followed by 15 minutes of VR-based green space (N=36) or control empty space (N=30) intervention. Although we observed the expected stress-induced increase in heart rates and elevated cortisol levels under stress, VR green space exposure failed to temper cortisol responses compared to the control VR space. Further, VR green space exposure did not bring benefits to protect working memory performance under stress across three WM tasks. Applying a Bayesian analysis approach throughout enabled us to find substantial evidence for the absence of an effect of VR-based green space exposure on biological markers of acute stress responses and working memory performance. Our findings suggest that VR-generated green space may not effectively replicate the stress-buffering effects of actual green space exposure. We discussed the implications of our findings regarding the potential and limitations of using VR or green space exposure to buffer stress responses.

neuroscience↗

bioRxiv2024

TCPGdb: A comprehensive T cell perturbation genomics database for identification of critical T cell regulators

CAR-T therapies utilizing T cells engineered with chimeric antigen receptors (CARs) have revolutionized the treatment of hematologic and immune-related malignancies. However, the anti-tumor capability of engineered CAR-T cells are often not persistent, which greatly dampen its clinical efficacy. To address this limitation, massive parallel genetic screens were widely used to identified novel targets and regulators that enhance T cell anti-tumor capability and persistence in tumor microenvironment. We hypothesized that by combining the pooled screen data from multiple independent genetic screens we could provide a systematic, comprehensive, and robust analysis of the effect of gene perturbation on T-cell based immunotherapies. After collecting data from previously published T cell screens, including CRISPR-based and ORF-based screens, through Gene Expression Omnibus (GEO), we reprocessed the gene hits summary and conducting a pathway enrichment analysis. A T cell screen perturbation score (TPS) was employed to quantifies the impact of the gene on T cell function. Additionally, gene expression data (both bulk RNA level and single-cell RNA level) from autoimmune disease and T cell-derived cancers were analyzed to pick up gene perturbations that potentially augment T cell proliferation. We integrated all data and analysis on 27 T-cells screens into our state-of-the-art T cell perturbation genomics database (TCPGdb), which is made easily accessible through our webserver and allows users to interactively explore the impact of query genes on T cell function based on prior screen data and our TPS scoring module. TCPGdb is publicly accessible at http://tcpgdb.sidichenlab.org/.

bioinformatics↗

bioRxiv2024

A poly(A) polymerase and oligo(dT)-dependent method for the purification of engineering circular RNAs that enhances protein expression in eukaryotic cells

Circular RNA (circRNA) has garnered significant attention as a promising therapeutic tool due to its remarkable stability and resistance to exonuclease degradation. However, the development of scalable and efficient purification methods remains a critical challenge. In this study, we propose and evaluate a novel purification strategy for engineered circular RNA (PAPdt), which leverages poly(A) polymerase and oligo(dT) affinity chromatography. Using poly(A) polymerase, we add poly(A) tails of over 200 nucleotides to the 3-OH ends of all linear RNA impurities in a circular RNA preparation. These linear RNAs are then separated from the circular RNA via oligo(dT) affinity chromatography. The linear impurities, bearing longer poly(A) tails, exhibit extended retention on the oligo(dT) column, whereas the circular RNA, with a shorter poly(A) sequence, elutes more quickly. By stepwise elution with ultrapure water, we effectively separate the circular RNA from the linear contaminants. This method offers a fundamental solution to the purification of circular RNA, combining high efficiency, purity, and cost-effectiveness.

bioengineering↗

bioRxiv2024

A new class of penicillin-binding protein inhibitors to address drug-resistant Neisseria gonorrhoeae

{beta}-Lactams are the most widely used antibiotics for the treatment of bacterial infections because of their proven track record of safety and efficacy. However, susceptibility to {beta}-lactam antibiotics is continually eroded by resistance mechanisms. Emerging multidrug-resistant (MDR) Neisseria gonorrhoeae strains possessing altered penA alleles (encoding PBP2) pose a global health emergency as they threaten the utility of ceftriaxone, the last remaining outpatient antibiotic. Here we disclose a novel benzoxaborinine-based penicillin-binding protein inhibitor series (boro-PBPi) that is envisioned to address penA-mediated resistance while offering protection against evolution and expansion of {beta}-lactamases. Optimization of boro-PBPi led to the identification of compound 21 (VNRX-14079) that exhibits potent antibacterial activity against MDR N. gonorrhoeae achieved by high affinity binding to the PBP2 target. Boro-PBPi/PBP2 complex structures confirmed covalent interaction of the boron atom with Ser310 and the importance of the {beta}3-{beta}4 loop for improved affinity. 21 elicits bactericidal activity, a low frequency of resistance, a good safety profile, suitable pharmacokinetic properties, and in vivo efficacy in a murine infection model against ceftriaxone-resistant N. gonorrhoeae. 21 is a promising anti-gonorrhea agent poised for further advancement.

microbiology↗

bioRxiv2024

Outer membrane tube formation by Francisella novicida involves extensive envelope modifications and is linked with type VI secretion and alterations to the host phagosomal membrane

Francisella tularensis is a Gram-negative, intracellular pathogen that causes the zoonotic disease tularemia. Due to its ease of dissemination and high lethality, F. tularensis is classified as a Tier 1 Select Agent with potential for misuse as a bioweapon. The mechanisms by which Francisella replicates intracellularly and interacts with the host during infection are not well understood. Francisella produces spherical outer membrane vesicles (OMV) and novel tubular extensions of its cell surface that are also released extracellularly. These OMV and outer membrane tubes (OMT) contain Francisella virulence factors and are produced in response to amino acid starvation and during infection of macrophages. To investigate how the OMT are formed, we used cryogenic electron tomography to examine the model Francisella species, F. novicida, during in vitro culture and within the macrophage phagosome. OMT formation involved progressive alterations of the bacterial envelope, resulting in extensions of both the inner and outer membranes. A dynamic cytoplasmic structure was present at the base of the OMT that extended into the tubes during elongation, together with cytoplasmic material. OMT produced within the macrophage phagosome were associated with changes to the phagosomal membrane, suggesting a role in phagosomal escape. Consistent with this, using confocal microscopy, we observed colocalization of the Francisella type VI secretion system with the OMT, both within bacteria and in released tubular vesicles. These findings reveal the cellular transformations that occur during membrane tubulation by Francisella and provide insights into the function of membrane-derived structures during host-pathogen interactions.

microbiology↗

bioRxiv2024

Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA

Understanding cellular responses to genetic perturbations is essential for understanding gene regulation and phenotype formation. While high-throughput single-cell RNA-sequencing has facilitated detailed profiling of heterogeneous transcriptional responses to perturbations at the single-cell level, there remains a pressing need for computational models that can decode the mechanisms driving these responses and accurately predict outcomes to prioritize target genes for experimental design. Here, we present scLAMBDA, a deep generative learning framework designed to model and predict single-cell transcriptional responses to genetic perturbations, including single-gene and combinatorial multi-gene perturbations. By leveraging gene embeddings derived from large language models, scLAMBDA effectively integrates prior biological knowledge and disentangles basal cell states from perturbation-specific salient representations. Through comprehensive evaluations on multiple single-cell CRISPR Perturb-seq datasets, scLAMBDA consistently outperformed state-of-the-art methods in predicting perturbation outcomes, achieving higher prediction accuracy. Notably, scLAMBDA demonstrated robust generalization to unseen target genes and perturbations, and its predictions captured both average expression changes and the heterogeneity of single-cell responses. Furthermore, its predictions enable diverse downstream analyses, including the identification of differentially expressed genes and the exploration of genetic interactions, demonstrating its utility and versatility.

bioinformatics↗

bioRxiv2024

The comprehensive detection of hemoglobinopathy variants via long-read sequencing

BACKGROUNDThe genetic complexity of hemoglobin genes, characterized by high GC content and homologous sequences, poses significant challenges for detecting hemoglobin variants in clinical settings. METHODSA long-read indexed PCR method utilizing the novel CycloneSEQ nanopore sequencing platform was developed to detect all variant types, including single nucleotide variants (SNVs), deletions, structural variants (SVs) in HBA, HBB, HBD, and HBG genes. The method was validated using 507 clinical samples to assess its performance. RESULTSThe long-read indexed PCR system employed 13 primers targeting the hemoglobin gene clusters. This design enabled the detection of 37 types of HBA deletions, 5 SV (3 multicopies (, anti3.7, anti4.2) and 2 fusion allele (HK and anti-HK)), 37 HBB deletions, and all SNVs in the targeted regions. Validation across 507 samples (84 with HBA variants, 60 with HBB variants, 256 with both HBA and HBB variants, and 107 with no known variants) demonstrated 100.0% sensitivity and specificity. Additionally, the long-read sequencing enabled phasing of variants within hemoglobin genes, providing insights critical for clinical interpretation. CONCLUSIONSThe long-read indexed PCR method, combined with the CycloneSEQ nanopore sequencing platform, proved to be a robust and efficient solution for detecting hemoglobinopathy variants. The integration of indexed primers and barcoding enhances scalability, making this method ideal for large-scale population screening programs in the future.

genomics↗

bioRxiv2024

Molecular Insights into the Regulation of GNPTAB by TMEM251

In vertebrates, newly synthesized lysosomal enzymes traffick to lysosomes through the mannose-6-phosphate (M6P) pathway. The Golgi membrane protein TMEM251 was recently discovered to regulate lysosome biogenesis by controlling the level of GlcNAc-1-phosphotransferase (GNPT). However, its precise function remained unclear. In this study, we demonstrate that TMEM251 is a two-transmembrane protein indispensable for GNPT stability, cleavage by Site-1-Protease (S1P), and enzymatic activity. We reconcile conflicting models by showing that TMEM251 enhances GNPT cleavage and prevents its mislocalization to lysosomes for degradation. We further establish that TMEM251 achieves this by interacting with GOLPH3 and retromer complexes to anchor the TMEM251-GNPT complex at the Golgi. Alanine mutagenesis identified F4XXR7 motif in TMEM251s N-tail for GOLPH3 binding. Together, our findings uncover TMEM251s multi-faceted role in stabilizing GNPT, retaining it at the Golgi, and ensuring the fidelity of the M6P pathway, thereby providing insights into its molecular function.

cell biology↗

bioRxiv2024

Predicting inbred parent synchrony at flowering for maize hybrid seed production by integrating crop growth model with whole genome prediction

One of the challenges of maize hybrid seed production is to ensure synchrony at flowering of the two inbred parents of a hybrid, which depends on the specific parental combination and environmental conditions of the production field. Maize flowering can be simulated using a mechanistic crop growth model that converts thermal time accumulation to leaf numbers based on inbred specific physiological parameter values. Heretofore, these inbred specific physiological parameters need to be measured or assigned based on prior knowledge. Here, we leverage genetic, environmental and management data to predict physiological parameters and simulate flowering phenotypes by using whole genome prediction methodology combined with a crop growth model (CGM-WGP) as part of in-field in-season inbred growth development. We use two estimation sets that differ in terms of management and weather information to test the robustness of our approach. As part of our findings, we demonstrate the importance of defining informative priors to generate biologically meaningful predictions of unobserved physiological parameters. Our CGM-WGP infrastructure is efficient at simulating flowering phenotypes. An important practical application of our method is the ability to recommend differential planting intervals for male and female maize inbreds used in commercial seed production fields to synchronize male and female flowering. Core ideasO_LISynchrony at flowering of maize inbred parents is crucial for optimal pollination and consequently seed yield. C_LIO_LIIntegrating WGP with CGM can accurately predict physiological parameters and simulate maize flowering phenotypes. C_LIO_LICGM-WGP infrastructure can be used to optimize field operations for large scale maize hybrid seed production. C_LI

physiology↗

bioRxiv2024

Cardiolipin-mimic lipid nanoparticles without antibody modification delivered senolytic in-vivo CAR-T therapy for inflamm-aging

mRNA-based in vivo CAR T cell engineering offers advantages over ex vivo therapies, including streamlined manufacturing and transient expression. However, current delivery requires antibody-modified vehicles with manufacturing challenges. In this study, inspired by cardiolipin, we identified a cardiolipin-like di-phosphoramide lipid that improved T cell transfection without targeting ligands, both in vivo and in vitro. The T cell-favored tropism is likely due to the lipids packing, shape, and rigidity. Encapsulating circular RNA further prolonged mRNA expression in the spleen and T cells. Using PL40 lipid nanoparticles, we delivered mRNA encoding a CAR targeting the senolytic and inflammatory antigen urokinase-type plasminogen activator receptor (uPAR), alleviating uPAR-related liver fibrosis and rheumatoid arthritis (RA). Single cell sequencing in humans confirmed uPARs relevance to senescence and inflammation in RA. To further enhance clinical translation, we screened and humanized scFvs against uPAR, establishing PL40 mRNA encoding a circular human uPAR CAR, with potential for treating aging-inflamed disorders. One Sentence SummaryWeve developed a unique class of Cardiolipin-mimic lipids that facilitate mRNA delivery to T cells in vivo without the need for antibody modification, enhancing the treatment of liver fibrosis and rheumatoid arthritis through circular CAR uPAR RNA and propelling the clinical application of humanized CAR against human uPAR.

bioengineering↗

bioRxiv2024

Beyond Integration: SuperGLUE Facilitates Explainable Training Framework for Multi-modal Data Analysis

Single-cell Multi-modal Data Integration has been an area of active research in recent years. However, it is difficult to unify the integration process of different omics in a pipeline, and evaluate the contributions of data integration. In this manuscript, we revisit the definition and contributions of multi-modal data integration, and propose a novel and scalable method based on probabilistic deep learning with an explainable framework powered by statistical modeling to extract meaningful information after data integration. Our proposed method is capable of integrating different types of omic and sensing data. It offers an approach to discovering important relationships among biological features or cell states. We demonstrate that our method outperforms other baseline models in preserving both local and global structures and perform a comprehensive analysis for mining structural relationships in complex biological systems, including inference of gene regulatory networks, extraction of significant biological linkages, and analysis of differentially regulatory relationships.

bioinformatics↗

bioRxiv2024

RobustCell: A Model Attack-Defense Framework for Robust Transcriptomic Data Analysis

Computational methods should be accurate and robust for tasks in biology and medicine, especially when facing different types of attacks, defined as perturbations of benign data that can cause a significant drop in method performance. Therefore, there is a need for robust models that can defend attacks. In this manuscript, we propose a novel framework named RobustCell to analyze attack-defense methods in single-cell and spatial transcriptomic data analysis. In this biological context, we consider three types of attacks as well as two types of defenses in our framework and systemically evaluate the performances of the existing methods on their performance of both clustering and annotating single cells and spatial transcriptomic data. Our evaluations show that successful attacks can impair the performances of various methods, including single-cell foundation models. A good defense policy can protect the models from performance drops. Finally, we analyze the contributions of specific genes toward the cell-type annotation task by running the single-gene and group-genes attack methods. Overall, RobustCell is a user-friendly and extension-flexible framework for analyzing the risks and safety of analyzing transcriptomic data under different attacks.

bioinformatics↗