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Pang, M.

Publications and source records attributed to Pang, M..

At least 19 recordsLinked to original sources

Functional orthogonality of WRN inhibitor resistance enables alternating therapy and mutation-tolerant inhibitor design in MSI-H cancers

Werner syndrome helicase (WRN) is a synthetic-lethal vulnerability in microsatellite instability-high (MSI-H)/mismatch repair-deficient (dMMR) cancers1-5, and non-covalent and covalent WRN inhibitors are now entering clinical development6-9. A central unresolved question is whether resistance to one WRN inhibitor class inevitably compromises the target, or instead creates actionable vulnerabilities to an alternate modality. Here we generated ten stable acquired-resistance models across three MSI-H cell lines using the non-covalent inhibitor HRO761 and the covalent inhibitor VVD-214. Whole-exome sequencing, biochemical reconstitution and isogenic knock-in models identified recurrent on-target WRN missense mutations as dominant resistance drivers, but with sharply modality-specific spectra: HRO761 resistance clustered at G729/F730/I852, whereas VVD-214 resistance concentrated at E846. These mutations impaired inhibitor engagement and abolished the canonical WRN inhibitor (WRNi)-induced pharmacodynamic cascade, including WRN reduction, DNA damage response (DDR) activation and G2/M arrest. Crucially, most resistance mutations retained biochemical, cellular and in vivo sensitivity to the alternate inhibitor modality, revealing a functional orthogonality that enabled a 7-day cyclic alternating regimen to delay tumour regrowth in xenografts. We further identified F730L as an engineered cross-resistant bottleneck model and used structure-guided, artificial intelligence (AI)-enabled optimization to generate GBA-007, a proof-of-concept mutation-tolerant WRN inhibitor candidate with promising activity against F730L in vitro and in vivo. Thus, clinically relevant WRN inhibitor classes impose distinct on-target resistance trajectories that can be exploited through schedule design, while cross-resistant bottlenecks can be addressed by rapid mutation-aware inhibitor engineering.

Cancer Biology↗

Glucose selectively drives a rapid oxidative burst and immunometabolic reprogramming in human neutrophils during Mycobacterium tuberculosis infection

Neutrophil functions have been linked to tuberculosis (TB)-associated tissue damage; however, the mechanisms driving immunopathology in the human TB lung remain poorly understood, due partly to the scarcity of human tissue for study. Here, we examine the metabolic and bioenergetic reprogramming of human neutrophils in response to Mycobacterium tuberculosis (Mtb) infection. In human necrotic TB granulomas, levels of NETosis-associated proteins are increased and co-localize with GLUT3, linking nutrient uptake to tissue damage. In vitro, Mtb elicits an immediate, contact-dependent oxidative burst in human neutrophils, and the magnitude of this response is carbon source-dependent. Glucose enables the most robust responses, indicating that glucose metabolism is a key driver of neutrophil-mediated inflammatory damage during TB. Mtb-induced responses are distinct from those induced by PMA, non-tuberculous mycobacteria, or other pathogenic intracellular bacteria, and are mediated through multiple neutrophil surface receptors. Notably, our data show that while the oxidative burst is carbon source-dependent, cytokine production is not. Further, Mtb infection reprograms neutrophil metabolism from glycolysis to the pentose phosphate pathway (PPP), generating NADPH required for the oxidative burst. Inhibiting G6PD, NADPH oxidase, or PAD4 significantly reduces this response, highlighting the PPP as a promising host target for mitigating TB immunopathology.

microbiology↗

Query-driven generative AI synthesizes multi-modal spatial omics from histology

Spatial omics technologies offer unprecedented insights into the cellular organization of tissues; however, they are not yet scalable for routine clinical use. In contrast, images of histological staining remain the foundation of pathological diagnosis despite lacking molecular information. Bridging this gap requires computational methods that can accurately infer spatial molecular data from histology alone. Here, we introduce TissueCraftAI, a generative artificial intelligence framework that predicts multi-modal spatial omics maps directly from standard histology images using natural language prompts. To train and validate our model, we created PRISM-12M, a large-scale dataset comprising over twelve million spatially registered histology and spatial omics image patches across fourteen tissue types from humans and mice. TissueCraftAI significantly outperforms existing methods in generating realistic histology images and predicting spatial proteomics and transcriptomics data with high fidelity. We demonstrated its utility in various downstream applications, including improving cell type annotation and enhancing the accuracy of patient survival predictions across multiple cancer types. By enabling flexible, query-driven in silico spatial molecular analysis using routine histology images, TissueCraftAI opens up new research avenues in computational pathology.

genomics↗

Parkinsons disease modeling in regenerative spiny mice (Acomys dimidiatus) captures key disease-relevant behavioral, histological, and molecular signatures

Parkinsons disease (PD) is a multifactorial neurodegenerative disorder that has been modeled extensively in animals, primarily rodents, but also in non-human primates and non-mammalian organisms. However, no single animal model fully recapitulates the hallmarks of PD pathology. Here, we extend this work by modeling PD for the first time in the spiny mouse (Acomys dimidiatus), a mammal notable for its robust regeneration of multiple tissues. We show that the nigrostriatal pathway of A. dimidiatus is vulnerable to both acute 6-hydroxydopamine (6-OHDA) toxicity and chronic -synuclein (Syn) preformed fibril-induced aggregation. Mouse Syn PFFs produced widespread pS129-positive Syn inclusions across multiple brain regions, mirroring a key pathological hallmark of PD. Compared to C57BL/6J mice, A. dimidiatus exhibited more pronounced behavioral impairments, greater nigrostriatal degeneration, and higher pS129-Syn inclusion burden within substantia nigra pars compacta (SNpc) neurons. To probe the molecular underpinnings behind the vulnerability, we performed single-cell spatial proteomics, which revealed extensive proteomic alterations in dopaminergic neurons associated with Syn aggregation. Multiple proteins were dysregulated in A. dimidiatus, including those involved in proteasomal function, mitochondrial pathways, and oxidative stress regulation, which are processes commonly implicated in PD. Notably, proteomic analysis identified heightened astrocytic activation in the SNpc, which we validated histologically, suggesting a distinct glial response compared to mice. Together, these findings expand our understanding of PD-relevant pathophysiology across species and establish A. dimidiatus as a model for studying mechanisms of neurodegeneration.

neuroscience↗

Thermodynamic Modeling of mRNA with the Addition of Precipitants

Nucleic acid therapeutics (NATs) have recently emerged as an exciting therapeutic modality for a range of indications, most notably as vaccines for SARS-CoV-2. In many cases, the thermodynamics of a system containing nucleic acids (such as in the downstream purification of mRNA from solution or within the lipid nanoparticle) can significantly influence the properties and efficacy of that system. Consequently, an accurate thermodynamic description of the system is essential for understanding and optimizing that system. In this work, the SAFT-{gamma} Mie equation of state was used to predictively model mRNA solubility. Experimental measurements of the solubility of two different mRNA sequences in various conditions (namely choice of precipitant(s), precipitant concentration, and temperature) were obtained and used to validate the model. Not only was the thermodynamic model able to quantitatively predict the solubility of mRNA in solution under different conditions, it was also able to yield mechanistic insight into the factor driving precipitation, namely the disruption of water-mRNA hydrogen bonding. The developed model can be extended to other mRNA sequences in a range of conditions beyond the experimental data presented in this work.

biophysics↗

Molecularly-guided spatial proteomics captures single-cell identity and heterogeneity of the nervous system

Single-cell spatial proteomics (scSP) holds substantial potential for profiling healthy and diseased tissues. The emerging method of molecularly-guided unbiased scSP has mostly been applied to peripheral somatic tissues. Here, we optimize and apply scSP to the healthy and diseased mammalian brain, using molecularly-guided laser capture microdissection and unbiased mass spectrometry. We systematically evaluate the effects of tissue fixation, marker staining, and sample input size on proteome coverage and quantitative accuracy. We benchmark this workflow by profiling region-specific neuronal proteomes and describing the response of non-neuronal cells to acute brain injury. Across these applications, we integrate complementary transcriptomic resources to evaluate cross-modality trends and refine neuronal proteomic results by filtering out protein signals likely arising from non-neuronal cells, an essential consideration in heterogeneous tissues such as the brain. Finally, we leverage this approach to resolve proteomic differences between dopaminergic neuron subpopulations with differential vulnerability to Parkinsons disease and to uncover disease-specific disruptions in -synuclein-aggregate-bearing single dopaminergic neurons. Together, these data demonstrate the utility of scSP in neuroscience research for understanding fundamental biology and the molecular drivers of neurological conditions.

neuroscience↗

Variational Autoencoder-based Model Improves Polygenic Prediction in Blood Cell Traits

Genetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic Risk Scores (PRS) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data. In this study, we seek to improve the predictive power of PRS through applying advanced deep learning techniques. We show that the Variational AutoEncoder-based model for PRS construction (VAE-PRS) outperforms currently state-of-the-art methods for biobank-level data in 14 out of 16 blood cell traits, while being computationally efficient. Through comprehensive experiments, we found that the VAE-PRS model offers the ability to capture interaction effects in high-dimensional data and shows robust performance across different pre-screened variant sets. Furthermore, VAE-PRS is easily interpretable via assessing the contribution of each individual marker to the final prediction score through the SHapley Additive exPlanations (SHAP) method, providing potential new insights in identifying trait-associated genetic variants. In summary, VAE-PRS presents a novel measure to genetic risk prediction by harnessing the power of deep learning methods, which could further facilitate the development of personalized medicine and genetic research.

bioinformatics↗

Convergent genomic and molecular features predict risk of metachronous metastasis in clear cell renal cell carcinoma

The molecular features determining the risk of metachronous metastases in clear cell renal cell carcinoma (ccRCC) are poorly defined. Using a systematic tumor transcriptome deconvolution approach, we investigated the genomic and transcriptomic profiles of 192 ccRCC primary tumors with extended clinical follow-up to identify cancer and stromal cell molecular features associated with metastatic risk. At the genomic level, we identified a significantly higher frequency of copy number loss at 1p31-36 in primary tumors that later progressed with metastases. Tumor transcriptome deconvolution identified significant down-regulation of epithelial cell polarity, including PATJ (1p31), and fatty acid metabolism, including CYP4A11 (1p33), in cancer cells of tumors that developed metastatic progression. We developed and benchmarked a compact 5-feature predictive model (5G) that demonstrated improved accuracy over existing ccRCC gene signatures in the prediction of metachronous metastasis risk. Overall, our study highlights convergent genomic and transcriptomic alterations in chromosome 1p, driving dysregulation of epithelial cell polarity and fatty acid metabolism, as putative novel risk factors of metachronous metastasis in ccRCC.

cancer biology↗

CelloType: A Unified Model for Segmentation and Classification of Tissue Images

Cell segmentation and classification are critical tasks in spatial omics data analysis. We introduce CelloType, an end-to-end model designed for cell segmentation and classification of biomedical microscopy images. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multi-task learning approach that connects the segmentation and classification tasks and simultaneously boost the performance of both tasks. CelloType leverages Transformer-based deep learning techniques for enhanced accuracy of object detection, segmentation, and classification. It outperforms existing segmentation methods using ground-truths from public databases. In terms of classification, CelloType outperforms a baseline model comprised of state-of-the-art methods for individual tasks. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multi-scale segmentation and classification of both cellular and non-cellular elements in a tissue. The enhanced accuracy and multi-task-learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.

bioinformatics↗

Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging

The bone marrow is the organ responsible for blood production. Diverse non-hematopoietic cells contribute essentially to hematopoiesis. However, these cells and their spatial organization remain largely uncharacterized as they have been technically challenging to study in humans. Here, we used fresh femoral head samples and performed single-cell RNA sequencing (scRNA-Seq) to profile 29,325 enriched non-hematopoietic bone marrow cells and discover nine transcriptionally distinct subtypes. We next employed CO-detection by inDEXing (CODEX) multiplexed imaging of 18 individuals, including both healthy and acute myeloid leukemia (AML) samples, to spatially profile over one million single cells with a novel 53-antibody panel. We discovered a relatively hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, niche for early hematopoietic stem and progenitor cells. We used our atlas to predict cell type labels in new bone marrow images and used these predictions to uncover mesenchymal stromal cell (MSC) expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. Our work represents the first comprehensive, spatially-resolved multiomic atlas of human bone marrow and will serve as a reference for future investigation of cellular interactions that drive hematopoiesis.

cell biology↗

NSD2 is a requisite subunit of the AR/FOXA1 neo-enhanceosome in promoting prostate tumorigenesis

The androgen receptor (AR) is a ligand-responsive transcription factor that binds at enhancers to drive terminal differentiation of the prostatic luminal epithelia. By contrast, in tumors originating from these cells, AR chromatin occupancy is extensively reprogrammed to drive hyper-proliferative, metastatic, or therapy-resistant phenotypes, the molecular mechanisms of which remain poorly understood. Here, we show that the tumor-specific enhancer circuitry of AR is critically reliant on the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2), a histone 3 lysine 36 di-methyltransferase. NSD2 expression is abnormally gained in prostate cancer cells and its functional inhibition impairs AR trans-activation potential through partial off-loading from over 40,000 genomic sites, which is greater than 65% of the AR tumor cistrome. The NSD2-dependent AR sites distinctly harbor a chimeric AR-half motif juxtaposed to a FOXA1 element. Similar chimeric motifs of AR are absent at the NSD2-independent AR enhancers and instead contain the canonical palindromic motifs. Meta-analyses of AR cistromes from patient tumors uncovered chimeric AR motifs to exclusively participate in tumor-specific enhancer circuitries, with a minimal role in the physiological activity of AR. Accordingly, NSD2 inactivation attenuated hallmark cancer phenotypes that were fully reinstated upon exogenous NSD2 re-expression. Inactivation of NSD2 also engendered increased dependency on its paralog NSD1, which independently maintained AR and MYC hyper-transcriptional programs in cancer cells. Concordantly, a dual NSD1/2 PROTAC degrader, called LLC0150, was preferentially cytotoxic in AR-dependent prostate cancer as well as NSD2-altered hematologic malignancies. Altogether, we identify NSD2 as a novel subunit of the AR neo-enhanceosome that wires prostate cancer gene expression programs, positioning NSD1/2 as viable paralog co-targets in advanced prostate cancer.

cancer biology↗

ANKRD49 promotes the metastasis of NCI-H1299 and NCI-H1703 cells via activating JNK-ATF2/c-Jun-MMP-2/9 axis

Ankyrin repeat domain 49 (ANKRD49) has been found to highly expressed in multiple cancer including lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC). However, the function of ANKRD49 in the pathogenesis of NSCLC still remains elusive. Previously, ANKRD49 has been demonstrated to promote the invasion and metastasis of A549 cells, a LUAD cell line, via activating the p38-ATF-2-MMP2/MMP9 pathways. Considering the heterogeneity of tumor cells, the function and mechanism of ANKRD49 in NSCLC need more NSCLC-originated cells to clarify. We found that ANKRD49 promoted the migration and invasion of NCI-H1299 and NCI-H1703 cells via enhancing the levels of MMP-2 and MMP-9. Furthermore, ANKRD49 elevated phosphorylation of JNK and then activated c-Jun and ATF2 which interact in nucleus to promote the binding of ATF2:c-Jun with the promoter MMP-2 or MMP-9. In vivo assay showed that ANKRD49 promoted lung metastasis of injected- NSCLC cells and the high metastatic rate was positively correlated with the high expression of ANKRD49, MMP-2, MMP-9, p-JNK, p-c-Jun and p-ATF2. In conclusion, the present study indicated that ANKRD49 accelerated the invasion and metastasis of NSCLC cells via JNK-mediated transcription activation of c-Jun and ATF2 which regulated the expression of MMP-2/MMP-9.

cancer biology↗

COMT inhibitor tolcapone represses the migration and invasion of trophoblast and results in preeclampsia-like phenotypes in mice

Pregnancy in young-onset Parkinsons disease (YOPD) is rare; however, medication for this condition is critical for maternal and fetal health. Tolcapone is an effective antiparkinsonian drug. However, its safety studies on mothers and fetuses are limited. In this study, we aimed to investigate the effects of tolcapone on the mother and the developing fetus during different stages of gestation. Tolcapone was administered to pregnant mice at a dose of 60 (H-Tol) or 30 mg/kg/day (L-Tol) during early or mid-gestation. We observed that tolcapone administration during early gestation causes abortion and delays fetal development in a dose-dependent manner. During mid-gestation tolcapone barely caused embryo lethality; however, the mice developed preeclampsia-like phenotypes, including maternal hypertension, proteinuria, and fetal growth restriction. Histomorphological analysis of placentas from tolcapone treated mice revealed abnormalities in the trophoblast layer and the impaired trophoblast invasion in the decidua. Mechanistically, we revealed that tolcapone inhibits the invasion and migration of trophoblasts in vitro, with changes in the protein expression of Snail, Twist, and E-cadherin. In conclusion, tolcapone caused embryo lethality and growth restriction during early gestation, whereas it caused preeclampsia-like phenotypes in mice with defective trophoblast invasion in mid-gestation. Collectively, our study provides novel insights into the effects of tolcapone on pregnancy.

pharmacology and toxicology↗

Development of a ferritin-based nanoparticle vaccine against the SARS-CoV-2 Omicron variant

A new SARS-CoV-2 variant named Omicron (B.1.1.529) discovered initially in South Africa has recently been proposed as a variant of concern (VOC) by the World Health Organization, because of its high transmissibility and resistance to current vaccines and therapeutic antibodies. Therefore, rapid development of vaccines against prevalent variants including Omicron is urgently needed for COVID-19 prevention. Here, we designed a self-assembling ferritin-based nanoparticle (FNP) vaccine against the SARS-CoV-2 Omicron variant. The purified Fc-RBDOmicron automatically formed a dimer depending on the nature of the Fc tag, thus assembling onto the nanoparticles by the Fc-protein A tag interaction (FNP-Fc-RBDOmicron). The results of hACE2-transgenic mice immunization showed that SARS-CoV-2 Omicron RBD-specific IgG titer induced by FNP-Fc-RBDOmicron was much higher than that by Fc-RBDOmicron. Consistently, the sera showed a higher neutralizing activity against SARS-CoV-2 Omicron BA.1 and BA.2 in the FNP-Fc-RBDOmicron immunized mice, indicating that immunization of a self-assembling ferritin-based nanoparticle vaccine offers a robust humoral immune response against Omicron variants. This study offers a great potential for the quick response of the emerging SARS-CoV-2 variants and affords versatility to develop universal vaccines against other emerging and reemerging coronaviruses in the future.

microbiology↗

Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors

Recent developments in spatial transcriptomics (ST) technologies have enabled the profiling of transcriptome-wide gene expression while retaining the location information of measured genes within tissues. Moreover, the corresponding high-resolution hematoxylin and eosin-stained histology images are readily available for the ST tissue sections. Since histology images are easy to obtain, it is desirable to leverage information learned from ST to predict gene expression for tissue sections where only histology images are available. Here we present HisToGene, a deep learning model for gene expression prediction from histology images. To account for the spatial dependency of measured spots, HisToGene adopts Vision Transformer, a state-of-the-art method for image recognition. The well-trained HisToGene model can also predict super-resolution gene expression. Through evaluations on 32 HER2+ breast cancer samples with 9,612 spots and 785 genes, we show that HisToGene accurately predicts gene expression and outperforms ST-Net both in gene expression prediction and clustering tissue regions using the predicted expression. We further show that the predicted super-resolution gene expression also leads to higher clustering accuracy than observed gene expression. Gene expression predicted from HisToGene enables researchers to generate virtual transcriptomics data at scale and can help elucidate the molecular signatures of tissues.

bioinformatics↗

A positively Tuned Voltage Indicator Reveals Electrical Correlates of Calcium Activity in the Brain

Neuronal spiking activity is routinely recorded using genetically encoded calcium indicators (GECIs), but calcium imaging is limited in temporal resolution and does not report subthreshold voltage changes. Genetically encoded voltage indicators (GEVIs) offer better temporal resolution and subthreshold sensitivity, but spike detection with fast GEVIs has required specialized imaging equipment. Here, we report the ASAP4 subfamily of genetically encoded voltage indicators (GEVIs) that brighten in response to membrane depolarization, inverting the fluorescence-voltage relationship of previous ASAP GEVIs. Two variants, ASAP4b and ASAP4e, feature 128% and 178% fluorescence increases over 100-mV of depolarization, respectively, facilitating spike detection in single trials in vivo with standard 1 and 2-photon imaging systems. Simultaneous voltage and calcium imaging confirms improved temporal resolution and spike discernment by ASAP4 GEVIs. Thus, positively tuned ASAP4 voltage indicators enable recording of neuronal spiking activity using similar equipment as calcium imaging, while providing higher temporal resolution. One Sentence SummaryUpward ASAPs increase detection capability of GEVIs in vivo.

neuroscience↗

Light-Sheet Fluorescence Imaging Charts the Gastrula Origin of Vascular Endothelial Cells in Early Zebrafish Embryos

It remains challenging to construct a complete cell lineage map of the origin of vascular endothelial cells in any vertebrate embryo. Here, we report the application of in toto light-sheet fluorescence imaging of embryos to tracing the origin of vascular endothelial cells (ECs) at single-cell resolution in zebrafish. We first adapted a previously-reported method to mount embryos and light-sheet imaging, created an alignment, fusion, and extraction all-in-one software (AFEIO) for processing big data, and performed quantitative analysis of cell lineage relationships using commercially-available Imaris software. Our data revealed that vascular ECs originated from broad regions of the gastrula along the dorsal-ventral and anterior-posterior axes, of which the dorsal-anterior cells contributed to cerebral ECs, the dorsal-lateral cells to anterior trunk ECs, and the ventral-lateral cells to posterior trunk and tail ECs. Therefore, this work, to our knowledge, charts the first comprehensive map of the gastrula origin of vascular ECs in zebrafish, and has potential applications for studying the origin of any embryonic organs in zebrafish and other model organisms.

developmental biology↗

Multitask learning for Transformers with application to large-scale single-cell transcriptomes

AO_SCPLOWBSTRACTC_SCPLOWRecent progress in machine learning provides competitive methods for bioinformatics in many traditional topics, such as transcriptomes sequence and single-cell analysis. However, discovering biomedical correlation of cells that are present across large-scale data sets remains challenging. Our attention-based neural network module with 300 million parameters is able to capture biological knowledge in a data-driven way. The module contains high-quality embedding, taxonomy analysis and similarity measurement. We tested the model on Mouse Brain Atlas, which consists of 160,000 cells and 25,000 genes. Our module obtained some interesting findings that have been verified by biologists and got better performance when benchmarked against autoencoder and principal components analysis.

genomics↗