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Zhao, E.

Publications and source records attributed to Zhao, E..

At least 19 recordsLinked to original sources
bioRxiv2026

Restoration of deficient IL-15 signaling enhances innate lymphoid cell immunity in brain metastasis

Natural killer (NK) cells and type 1 innate lymphoid cells (ILC1s), collectively known as group 1 innate lymphoid cells (ILCs), are critical mediators of anti-tumor immunity, yet their role in brain metastasis remains poorly defined. Using single-cell RNA sequencing and functional depletion models, we show that group 1 ILCs fail to provide adequate tumor control in breast cancer brain metastasis (BCBM). In contrast to the liver, where diverse, functionally competent group 1 ILC populations facilitate excellent metastatic control, group 1 ILCs in BCBM show limited diversity and profound tissue-specific dysfunction such that their depletion had no effect on tumor burden despite modest parenchymal infiltration. We attribute this group 1 ILC dysfunction to severely restricted IL-15 availability, with IL-15R protein detected at 500-fold lower concentration in the brain than in the liver. Local IL-15 complex administration restored group 1 ILC accumulation, cytotoxicity, and tissue residency, significantly reducing brain metastatic burden independent of T cells. Intracranial delivery of IL-15-secreting induced pluripotent stem-cell-derived (iPSC)-NK cells, already in clinical testing for non-small cell lung cancer, significantly reduced tumor burden. Collectively, these findings reveal how tissue-specific differences in cytokine availability shape group 1 ILC population diversity and disease response, establishing localized IL-15 signaling as a compelling strategy to unlock innate immunity against intracranial malignancies.

immunology↗
bioRxiv2026

EditorForge: An Active-Site-Aware Framework for Inverse-Folding-Based Protein Redesign

Inverse-folding models can rapidly generate protein sequences compatible with a supplied backbone, but unconstrained redesign is poorly suited to enzyme and genome-editor-associated domains, where catalytic, substrate-proximal, and conserved structural regions must remain protected. In this paper, we present EditorForge, a modular constraint-and-audit suite for editor-domain protein redesign that wraps fixed-backbone inverse folding with explicit design masks, fixed-position enforcement, active-site-proximity auditing, active-site-shielded regeneration, and downstream structural quality control. Using full-length Moloney murine leukemia virus reverse transcriptase structure 4MH8 (MMLV RT 4MH8) as a demonstration target, EditorForge first restricted redesign to a bounded 25-position envelope while fixing 428 residues. An initial audit detected active-site-proximal failure modes despite fixed-position integrity. Later, the Active Site Shield module then removed five unsafe design positions, replaced them with lower-contact alternatives, and regenerated candidates under stricter constraints. Post Shield Audit evaluated 24 regenerated candidates, all of which satisfied the hard sequence/mask and active-site-shield constraints. For the eight candidates that were selected or returned for structure-prediction/refolding quality control, Enhanced RefoldQC found that all 8 evaluated predicted structures passed the computational structure-QC screen. That said, the selected 8 candidates passed the computational structure-QC screen, with global C RMSD values of 1.2061-1.5555 {degrees}A, active-site C RMSD values of 0.4098-1.8397 {degrees}A, mutation-neighborhood C RMSD values of 1.3155-1.6848 {degrees}A, and average pLDDT-like confidence values of 94.87-95.11. In short, EditorForge provides a reproducible triage layer that converts general inverse-folding output into constrained and editor-specific candidate sets for downstream structural and biological review on top of existing structural prediction tools.

bioinformatics↗
bioRxiv2026

Metabolic Salvage and Acyl-chain Remodeling Support Glycosphingolipid Synthesis with the PDAC Tumor Microenvironment

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy where metabolic homeostasis is maintained by tumor and stromal cells within the tumor microenvironment (TME). To better assess pathways supporting macromolecule biosynthesis in PDAC tumors, we apply 13C metabolic flux analysis (MFA) to slice cultures of treatment-naive human tumors and mouse models that retain the native TME. Glycans, lipid headgroups, and very long-chain fatty acids are the most dynamic metabolic pools, while long chain fatty acids, purines, and pyrimidines are predominantly salvaged locally in situ. We use targeted pharmacological modulators to highlight the importance of recycling pathways and metabolic redundancies which mitigate changes in lipid abundances. Finally, we leverage targeted lipid fluxomics and the distinct ganglioside and globoside profiles of tumor and stromal cells, respectively, to demonstrate the role of the lipid kinase PIKfyve in supporting ganglioside homeostasis via sialic acid and ceramide salvage. These data establish application of MFA to slice cultures of PDAC tumors as an effective approach for assessing metabolic mechanisms and therapeutic responses within an intact TME.

cancer biology↗
bioRxiv2026

ARID1A deficiency unleashes centromeric RNA transcription to drive chromosomal instability and boosts PKMYT1 inhibitor efficacy via RNA sensing

Cancer gene-associated mutations and molecular hallmarks of chromosomal instability (CIN) are unexpectedly common in histologically normal cells and tissues. These emerging findings challenge the binary distinction between "normal" and "cancerous" cells and suggest that early tumorigenesis may commence against a background of widespread yet largely tolerated genomic instability. However, it remains largely unexplored how a cancer gene-associated mutation can initiate the development of CIN-like states in non-malignant cells and drive tumor evolution. ARID1A, a chromatin remodeling factor, was identified as the most frequently mutated gene in both gastric normal epithelium and tumors. This distinctive molecular convergence presents an opportunity to elucidate the mechanisms by which a cancer-associated gene facilitates the initiation of early CIN phenotypes and develop effective antitumor strategies. In the present study, using primary human gastric organoids, we employed optical genome mapping (OGM) and live-imaging technologies to demonstrate that ARID1A depletion induced a wide spectrum of structural variants (SVs), copy number variants (CNVs), and chromosomal segregation errors, characteristic features of CIN at a very early stage of gastric tumorigenesis. Mechanistically, ARID1A bound centromere repetitive satellite DNA (satDNA) sequences. Its SWI/SNF-associated chromatin remodeling activity was required for suppressing satDNA transcription and the production of -SatRNA, through restricting RNAPII elongation. Consequently, ARID1A depletion led to overexpression of -SatRNA, and a higher incidence of sister chromatid exchange (SCE), a sensitive indicator of CIN. Importantly, the elevated -SatRNA expression in ARID1A-deficient cells further established a dual therapeutic vulnerability for G2/M checkpoint blockade, such as PKMYT1 inhibitor (PKMYTi), by concurrently aggregating CIN-induced cell death and activating self-dsRNA sensing-mediated innate immune response. Notably, PKMYTi markedly promoted -SatRNA expression, aberrant release of these self-derived dsRNAs into the cytosol and a robust activation of the RIG/MDA5-MAVS-depenent type-I interferon response in ARID1A-depleted cells. As expected, PKMYTi potentiated the efficacy of immunotherapy in ARID1A-deficient gastric tumors. Together, our findings reveal that ARID1A deficiency unleashes centromeric -SatRNA transcription, which sets the molecular stage for tumor evolution and targeted therapy by coordinately inducing CIN and self-dsRNA-induced innate immune responses.

cancer biology↗
bioRxiv2026

Nutrient microenvironments reprogram RPE metabolism

Induced pluripotent stem cell-derived retinal pigment epithelium (iPSC RPE) has become a widely used model for studying the mechanisms of age-related macular degeneration (AMD). However, the nutrient composition of currently used RPE culture media is highly variable, posing a major challenge to reproducibility in RPE metabolism and phenotype. We systematically investigate how six distinct nutrient microenvironments shape RPE phenotype, function and metabolism in both iPSC RPE and fetal RPE (fRPE). These included MEM, DMEM-HG/F12 basal media, physiological human plasma-like medium (HPLM) supplemented with FBS or B27, and X-VIVO 10. Although canonical RPE markers were expressed across all conditions, B27 supplementation and X-VIVO 10 increased RPE cell size, hexagonality, and transepithelial resistance. Culture in HPLM+FBS induced accumulation of lipid droplets and sub-RPE deposits, whereas X-VIVO 10 resulted in the formation of large intracellular vacuoles. B27 supplementation enhanced basal respiration, while X-VIVO 10 increased glycolytic capacity. Amino acid consumption was broadly conserved across media types, including complete depletion of proline in all conditions by 48 hours; however, lipid and nucleotide metabolism varied substantially between conditions. Notably, B27 supplementation in specific media types reversed the net direction of several metabolites, with creatine, serine and taurine shifting from consumption to production, while riboflavin and guanine shifted from production to consumption. These findings establish the nutrient environment as a key determinant of RPE phenotype, function and metabolism. Our work provides a valuable resource for media selection and interpretation of cellular and metabolic phenotypes relevant to RPE disease modeling.

cell biology↗
bioRxiv2025

Spatial transcriptomics reveals heterogeneous cell-cell interactions among brain regions in a cuprizone model consistent with multiple sclerosis lesions

The cuprizone (CPZ) model is widely used for modeling demyelination in multiple sclerosis (MS) and for testing potential remyelination therapies. We integrated single-cell and spatial transcriptomics (ST) to fine map the spatial cellular and molecular responses during de and remyelination. ST revealed global demyelination and neuroinflammation in the brain beyond the corpus callosum, with region-specific differences. We identified oligodendroglia and microglia as two major cell types with significant transcriptomic changes in the model. Ligand receptor pairing analyses predicted growth factor and phagocytic pathway enrichment during demyelination, which is consistent with changes in MS lesions. During remyelination, while mature oligodendrocytes nearly reversed their phenotype back to the control state, microglia remained associated with the demyelination phenotype. Finally, astrocytes in the CPZ model had the greatest preservation of disease-associated modules to MS lesions, while the MOL, OPC, and microglia showed moderate to low preservation, which overall suggested that the CPZ model had moderate translatability to chronically active MS lesions.

neuroscience↗
bioRxiv2024

Zinc and Metformin Co-Functionalized Polyetheretherketone: A Novel Dental Implant Material Tailored for the Elderly

This study focuses on addressing the challenges of dental implants in the geriatric population by enhancing the bioactivity of polyetheretherketone (PEEK) through surface modification. PEEK, with its elastic modulus close to alveolar bone, mitigates stress shielding but faces limitations in osseointegration due to low bioactivity. We introduced zinc (Zn) and metformin (MF) onto PEEK surfaces via a dopamine-assisted physical adhesion method, creating a functionalized derivative called ZnMF@PEEK. This combination targets diminished osteogenic potential, persistent inflammation, and cell senescence, which are common issues in elderly patients. Comprehensive physicochemical characterizations confirmed the successful preparation of ZnMF@PEEK, and in vitro and in vivo experiments systematically evaluated its biocompatibility and bioactivity. The results indicate that ZnMF@PEEK holds promise as a dental implant material tailored to the specific needs of the elderly, addressing multifaceted challenges in osseointegration.

bioengineering↗
bioRxiv2024

Genome-Wide Identification and Expression Pattern of the N-acetylserotonin deacetylase (ASDAC) Gene Family in Orchidaceae

Orchids are a kind of horticultural plant with highly ornamental and medical value. N-acetylserotonin deacetylase (ASDAC) is the only reverse enzyme of the melatonin biosynthesis pathway, and plays an important role in regulating the balance of melatonin. Melatonin as a multifunctional molecule, is typically involved in plant growth and development regulation, as well as abiotic stress tolerance. Here, we aimed at identifying ASDAC genes from the orchid genome to provide valuable information for further study of the role of melatonin in orchids. In this study, a total of 7 ASDAC genes were identified from the 7 orchid genome with one member in each species. The 7 orchid ASDACs have an HDAC functional domain, and cluster together with functionally confirmed OsHDAC10 and AtHDAC14, it shows that these members may have function of N-acetylserotonin deacetylase. Furthermore, based on the phylogenetic, motif, and gene structure analysis, the same clusters orchid ASDAC or ASDAC-like genes generally contained similar introns and motifs, suggesting the distribution pattern of exons/introns and motifs were strongly related to phylogeny on an evolutionary basis. Interestingly, homologous genes of OsHDAC10 and AtHDAC14 in Gastrodia elata have low homology and not cluster together with rice and A.thaliana ASDACs, showing that ASDAC gene family may lost in the holomycoheterotrophic orchids. The Ka/Ks ratios of ASDAC gene pairs from lower plant to higher plant were less than one, suggested that ASDAC genes have undergone purifying selection during the evolution process. Cis-acting element analysis results showed that the promoter regions of orchid ASDAC genes contained plant growth and development, phytohormone, and stress-responsive elements. Moreover, most orchid ASDACs were expressed in the roots, stems, leaves, flowers, and seeds. Combined cis-acting element and tissue expression analysis, indicating orchid ASDAC genes are involved in melatonin regulation of growth and development, as well as melatonin responding to various stresses in orchids. These findings of orchid ASDAC genes may provide valuable information for further study of the role of melatonin in orchids.

bioinformatics↗
bioRxiv2024

Somatic gene delivery for flexible in vivo modeling of high-risk sarcoma

A particular challenge hampering therapeutic advancements for high-risk sarcoma patients is the broad spectrum of molecularly distinct sarcoma entities and the corresponding lack of suitable model systems to recapitulate and study these diseases. To overcome this predicament, we developed a novel genetically-controlled, yet versatile mouse modeling platform allowing delivery of different genetic lesions by electroporation (EPO) of the thigh muscle wildtype mice. This optimized sarcoma EPO-GEMM (EPO-based genetically engineered mouse model) platform allowed the generation of ten biologically distinct sarcoma entities, including Synovial Sarcoma (SS), fusion-positive and fusion-negative Rhabdomyosarcoma (RMS), Alveolar Soft Part Sarcoma (ASPS), Undifferentiated Pleomorphic Sarcoma (UPS) and Infantile Fibrosarcoma (IFS). Comprehensive molecular profiling and cross-species analyses confirmed faithful recapitulation of the human disease, including the expression of relevant immunotherapy targets. Syngeneic allografting enabled reliable preservation and scalability of Sarcoma-EPO-GEMMs for treatment trials, such as B7-H3-directed CAR-T cell therapy in an immunocompetent background.

cancer biology↗
bioRxiv2022

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics

Accurate cell typing is fundamental to analysis of spatial single-cell transcriptomics, but legacy scRNA-seq algorithms can underperform in this new type of data. We have developed a cell typing algorithm, Insitutype, designed for statistical and computational efficiency in spatial transcriptomics data. Insitutype is based on a likelihood model that weighs the evidence from every expression value, extracting all the information available in each cells expression profile. This likelihood model underlies a Bayes classifier for supervised cell typing, and an Expectation-Maximization algorithm for unsupervised and semi-supervised clustering. Insitutype also leverages alternative data types collected in spatial studies, such as cell images and spatial context, by using them to inform prior probabilities of cell type calls. We demonstrate rapid clustering of millions of cells and accurate fine-grained cell typing of kidney and non-small cell lung cancer samples.

bioinformatics↗
bioRxiv2022

Single-Cell RNA Sequencing Reveals the Effects of Chemotherapy on Human Pancreatic Adenocarcinoma and its Tumor Microenvironment

The tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) is a complex ecosystem that drives tumor progression; however, in-depth single cell characterization of the PDAC TME and its role in response to therapy is lacking. We performed single-cell RNA sequencing on freshly collected human PDAC samples either before or after chemotherapy. Overall, we found a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibited similar transcriptional responses to chemotherapy, and did not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observed decreased ligand-receptor interactions in treated samples, particularly TIGIT on CD8+ T cells and its receptor on cancer cells, and identified TIGIT as the major inhibitory checkpoint molecule of CD8+ T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may promote resistance to immunotherapy.

cancer biology↗
bioRxiv2022

Wireless closed-loop smart bandage for chronic wound management and accelerated tissue regeneration

Chronic non-healing wounds represent a major source of morbidity for patients and a significant economic burden. Current wound care treatments are generally passive and are unable to adapt to changes in the wound environment in real time. By integrating multimodal sensors and adding stimulators in a bandage, real-time physiological monitoring is possible and provides an opportunity for active intervention into the complex wound environment. Here, we develop a battery-free flexible bioelectronic system consisting of wirelessly powered, closed-loop sensing and stimulation circuits with tissue-interfacing tough conducting hydrogel electrodes for robust signal transduction, on-demand adhesion, and detachment. Using multiple pre-clinical models, we demonstrate the capability of our wound care system to continuously monitor skin impedance and temperature, to trigger directional electrical stimulation. The accelerated wound closure was confirmed to be due to the activation of pro-regenerative genes linked to accelerated wound closure, increased neovascularization, and enhanced dermal recovery.

bioengineering↗
bioRxiv2021

Large-scale miRNA-Target Data Analysis to Discover miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

Although growing evidence shows that microRNA (miRNA) regulates plant growth and development, miRNA regulatory networks in plants are not well understood. Current experimental studies cannot characterize miRNA regulatory networks on a large scale. This information gap provides a good opportunity to employ computational methods for global analysis and to generate useful models and hypotheses. To address this opportunity, we collected miRNA-target interactions (MTIs) and used MTIs from Arabidopsis thaliana and Medicago truncatula to predict homologous MTIs in soybeans, resulting in 80,235 soybean MTIs in total. A multi-level iterative bi-clustering method was developed to identify 483 soybean miRNA-target regulatory modules (MTRMs). Furthermore, we collected soybean miRNA expression data and corresponding gene expression data in response to abiotic stresses. By clustering these data, 37 MTRMs related to abiotic stresses were identified including stress-specific MTRMs and shared MTRMs. These MTRMs have gene ontology (GO) enrichment in resistance response, iron transport, positive growth regulation, etc. Our study predicts soybean miRNA-target regulatory modules with high confidence under different stresses, constructs miRNA-GO regulatory networks for MTRMs under different stresses and provides miRNA targeting hypotheses for experimental study. The method can be applied to other biological processes and other plants to elucidate miRNA co-regulation mechanisms.

plant biology↗
bioRxiv2020

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpaces improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data. In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

bioinformatics↗
bioRxiv2020

Evaluating rehabilitation progress using motion features identified by machine learning

Evaluating progress throughout a patients rehabilitation episode is critical for determining effectiveness of the selected treatments and contributing to the evidence-based practice. The evaluation process is complex due to the inherent large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress. However, selecting appropriate motion features for rehabilitation evaluation has always been challenging. This paper exploits unsupervised feature learning techniques to reduce the complexity of building the evaluation model of patients progress. A new feature learning technique is developed to select the most significant features from a large amount of kinematic features measured from robotics, providing clinically useful information to health practitioners with reduction of modeling complexity. A novel indicator that can reflect monotonicity and trendability is proposed to evaluate the suitability of kinematic features, which are derived from the collected data of a population of stroke patients participating in robot-aided rehabilitation. The selected kinematic features allow for human variations across a population of patients as well as over the sequence of rehabilitation sessions. The study is based on data records pertaining to 41 stroke patients using three different robot assisted exercises for upper limb rehabilitation. Consistent with the literature, the results indicate that features based on movement smoothness are the best measures among 17 kinematic features used to evaluate rehabilitation progress.

bioengineering↗
bioRxiv2020

Light-responsive monobodies for dynamic control of customizable protein binding

Customizable, high affinity protein-protein interactions, such as those mediated by antibodies and antibody-like molecules, are invaluable to basic and applied research and have become pillars for modern therapeutics. The ability to reversibly control the binding activity of these proteins to their targets on demand would significantly expand their applications in biotechnology, medicine, and research. Here we present, as proof-of-principle, a light-controlled monobody (OptoMB) that works in vitro and in vivo, whose affinity for its SH2-domain target exhibits a 300-fold shift in binding affinity upon illumination. We demonstrate that our SH2-OptoMB can be used to purify SH2-tagged proteins directly from crude E. coli extract, achieving 99.8% purity and over 40% yield in a single purification step. This OptoMB belongs to a new class of light-sensitive protein binders we call OptoBinders (OptoBNDRs) which, by virtue of their ability to be designed to bind any protein of interest, have the potential to find new powerful applications as light-switchable binders of untagged proteins with high affinity and selectivity, and with the temporal and spatial precision afforded by light.

biochemistry↗
bioRxiv2019

Reply to \"A discriminative learning approach to differential expression analysis for single-cell RNA-seq\"

Multivariate logistic regression (mLR) has been recently proposed by Ntranos et al. to perform gene differential expression analyses of single-cell RNA-sequencing (scRNAseq) data. Herein we reproduce and extend some of their findings. We notably show that while mLR performs better in simulated datasets, these simulations do not recapitulate important features of experimental datasets. Indeed, our results suggest that MAST followed by Sidak aggregation of the p-values perform better than mLR on experimental datasets. Overall, we highlight that most of the new results obtained by Ntranos et al is likely due to the quantification of scRNAseq data at the transcript or transcript compatibility classes level, rather than the use of mLR.

bioinformatics↗