Biology subjects

Gu, J.

Publications and source records attributed to Gu, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

NDR1/2 kinases regulate cell polarization and cell motility through Cdc42 GTPase and Pard3 signaling in mammalian cells

Controlling cell polarity and the directionality of cell motility is critical for effective cell migration during wound healing. NDR (nuclear dbf2-related) kinase pathways have roles in cell morphogenesis that are conserved from yeast to humans. Here, we reveal that knockdown of NDR1/2 kinases significantly alters cell size, shape, and the actin cytoskeleton, while reducing migration persistence and impairing cell polarization in wound healing assays. Mechanistically, we find that NDR1/2 kinases regulate the spatial and temporal dynamics of Cdc42 GTPase. Reduced NDR kinase levels increase Cdc42 GTPase activity and disrupt Pard3 subcellular location. NDR kinases phosphorylate Pard3 at Serine144, and overexpressing Pard3 can partially restore wound healing in NDR-depleted cells, an effect lost when Serine144 is mutated. Finally, we determine that NDR1 knockdown significantly impairs wound closure in human skin ex vivo wound healing assays, highlighting NDR kinase physiological importance. Collectively, this study demonstrates that NDR kinases modulate cell motility and polarization through the control of Pard3 and Cdc42 signaling in human fibroblasts.

cell biology↗

bioRxiv2025

A spatially-resolved human brain proteome atlas for understanding function and disease

While the brain performs specialized functions across distinct regions, the spatial organization of the human brain proteome remains largely uncharted. Here we present a comprehensive spatially-resolved proteome atlas of the human brain, analyzing over two thousand MRI- guided locations across four individuals. Proteome analysis integrated with transcriptomics reveals extensive post-transcriptional regulation, with cortical regions showing markedly higher protein diversity than transcript. Unsupervised molecular clustering defines distinct brain territories that transcend anatomical boundaries, instead reflecting metabolic demands and functional specialization patterns. Application to epilepsy brain tissue uncovered disrupted astrocyte metabolism, protein homeostasis and therapeutic targets including the seizure-associated purinergic receptor P2RX7. This resource bridges molecular and systems neuroscience to accelerate neurological drug discovery.

neuroscience↗

bioRxiv2025

Pharmacological Induction of Irreversible Senescence in Drug-Tolerant Persister Cells Prevents Tumor Relapse

Despite substantial advances in targeted therapies, most patients with advanced cancers ultimately relapse. This relapse is frequently seeded by drug-tolerant persister cells (DTPs), which persist as minimal residual disease (MRD) by adopting a reversible quiescent state and later reacquire proliferative capacity under ongoing treatment. To address this, we sought to convert quiescent DTPs into an irreversible, non-proliferative state by pharmacologically inducing senescence. Through large-scale chemical screening and combination optimization, we identified SAHA plus tilorone dihydrochloride (SIC) as a regimen that robustly induces senescence in melanoma DTPs and abolishes their proliferative potential in vitro. Mechanistically, SAHA relieves HDAC1-mediated repression of HMGA2, a key senescence driver, while tilorone synergistically inhibits autophagy to promote a fully senescent state. In a melanoma xenograft mouse model receiving MAPK-targeted therapy, SIC treatment significantly suppressed MRD progression. Importantly, SIC also induced robust senescence across diverse DTP models irrespective of tumor origin, mutational background, or prior treatment. Collectively, these findings provide proof-of-concept that converting DTPs from reversible quiescence to permanent senescence represents an effective strategy to prevent relapse under targeted therapy and position SIC as a broadly applicable pharmacological approach for durable control of residual disease in advanced cancers.

cancer biology↗

bioRxiv2025

Distilling Direct Effects via Conditional Differential Gene Expression Analysis

Differential gene expression (DGE) analysis is foundational for interpreting RNA sequencing data, but it conflates direct biological effects with correlations propagated through gene co-expression. Across three RNA sequencing datasets (including a genome-scale perturb-seq experiment), we find that only a small fraction of differentially expressed genes have direct effects on the trait of interest, while the majority are undirected or passengers whose associations are mediated through other genes. To distinguish direct effect genes, we introduce conditional differential gene expression (CDGE) analysis, a framework that tests for conditional rather than marginal association between each gene and the trait of interest. Implemented via the GhostKnockoff procedure with lasso regression, CDGE delivers false discovery rate control, operates on summary statistics from existing DGE pipelines, and accommodates batch effects. The genes identified by CDGE mediate the effects of most other differentially expressed genes and show stronger enrichment for known protein-protein interactions and biological pathways than DGE-identified genes. These results suggest that the field has been systematically over-interpreting DGE outputs, and that distinguishing direct from mediated effects is essential for prioritizing genes for functional follow-up and therapeutic development.

bioinformatics↗

bioRxiv2025

SpatialZoomer: multi-scale feature analysis of spatial transcriptomics

Single-cell resolution spatial transcriptomics (ST) provides a great opportunity to explore the complex cellular contents in different tissues. In this study, we introduce SpatialZoomer, a spectral graph-based method that applies a set of low-pass filters to efficiently extract spatial molecular features from ST data at multiple resolutions or scales, including the single cell scale, the niche scale with dozens of closely interacting cells, and the domain scale with spatially-organized cell contents. The corresponding "critical" scales can be automatically identified by partitioning a cross-scale similarity map. Results show that SpatialZoomer can identify disease-progression signals at specific scales in Alzheimers Disease, and spatial context dependent cell subtypes in tumor microenvironment. The extracted multi-scale features also uncovered spatially heterogeneous niches in cancer. SpatialZoomer also takes advantage of high computational efficiency and low hardware requirements.

bioinformatics↗

bioRxiv2025

High-Fat Diet-Induced Obesity Impairs Endothelium-Dependent Relaxation via MLCK Activation: Reversal by ML-7 in Rabbits

ObjectiveObesity is an independent risk factor for CVD(cardiovascular diseases) and elevated cardiovascular mortality, though the precise mechanisms remain incompletely elucidated. Impaired endothelium-dependent relaxation is an early manifestation of cardiovascular diseases. This study investigated the impact of HFD(high-fat diet)-induced obesity on endothelium-dependent relaxation function and explored the potential role of MLCK(myosin light chain kinase) signaling. MethodsForty-five New Zealand White rabbits were randomized into three groups: a control group (normal diet), HFD(high-fat diet) group, and an ML-7 group (HFD plus the MLCK inhibitor ML-7). After 8 weeks of feeding, vascular endothelial function was assessed in vivo by measuring FMD(flow-mediated dilation). Serum lipids and blood glucose levels were determined. Endothelium-dependent vasodilation was evaluated ex vivo using aortic ring assays. The protein expression levels of MLCK and eNOS(endothelial nitric oxide synthase), as well as the phosphorylation level of MLC(myosin light chain), in rabbit arteries were analyzed by Western blot. ResultsHFD-fed rabbits developed obesity, dyslipidemia (elevated TC[total cholesterol], LDL-C[low-density lipoprotein cholesterol], and ox-LDL[oxidized low-density lipoprotein]), perirenal adipose tissue deposition, and significant endothelial dysfunction, evidenced by impaired endothelium-dependent relaxation function and morphological endothelial damage. Western blot analysis revealed upregulated MLCK and eNOS expression, along with increased phosphorylation of MLC in the arterial walls of obese rabbits, despite no significant change in NO(nitric oxide) levels compared to controls. Treatment with ML-7 ameliorated endothelium-dependent relaxation function impairment. Furthermore, the inherent limitations of FMD as a non-invasive technique in terms of accuracy and sensitivity for evaluating endothelium-dependent dilation. ConclusionsObesity-induced endothelial dysfunction arise from visceral fat accumulation, dyslipidemia, oxidative stress, and MLCK overexpression. Notably, MLCK inhibition via ML-7 restores vascular function, highlighting its therapeutic potential in obesity-related vascular injury.

physiology↗

bioRxiv2025

Output-Contingent Working Memory and Decision-Making in Economic Choices

In daily life, economic decisions often unfold sequentially. The brain is thought to compute the subjective value of each option, and comparisons can occur in different reference frames, for example, based on the commodity or presentation order. While primate prefrontal recordings have identified various reference frames for economic choice, it remains unclear how distinct neural mechanisms support them even in similar tasks. To address this, we trained recurrent neural networks (RNNs) on two sequential economic decision-making tasks differing only in output contingencies. Analysis of RNN activity, combined with latent connectivity inference, revealed distinct regimes: commodity-based choices with attractor dynamics and order-based choices with rotational dynamics. Moreover, value and choice representations in the order-based tasks aligned with neural data from a novel experiment where reference frames were not explicitly constrained. Our results suggest that different reference frames emerge depending on task demands and engage distinct working memory and decision-making mechanisms.

neuroscience↗

bioRxiv2025

Single-cell map of the healthy human immune system across the lifespan reveals unique infant immune signatures

The human immune system undergoes continuous remodeling from infancy through old age, yet the timing and trajectory of these changes across the lifespan remain poorly defined. To address this, we profiled peripheral blood mononuclear cells from 95 healthy individuals (ages 2 months to 88 years), including infants (n=27), children (n=23), adults (n=18), and older adults (n=27) using scRNA-seq and snATAC-seq. MAIT and {gamma}{delta} T cells showed a "Rise and fall" pattern, which rise in childhood, peak in young adulthood, and decline with age. CD8+ T cells were the most affected by aging with decreasing naive T cells and increasing GzK+ CD8+ T cells and TEMRA cells. Infants had lower myeloid/lymphoid ratio, with a distinct composition marked by increased frequencies of CD16+ monocytes and plasmacytoid dendritic cells and reduced frequencies of CD14+ monocytes and conventional DCs. Their adaptive immune compartment also displayed unique features, including constitutive interferon-stimulated gene expression in T and B cells, and an expanded SOX4+ populations in naive CD4+, naive CD8+ and {gamma}{delta} T cells, comprising [~]30% of the naive T cell pool. SOX4+ naive CD4+ T cells displayed a Th2 epigenetic signature. This map provides critical insights into human immune system dynamics across the lifespan, emphasizing unique features of the infant immune system.

immunology↗

bioRxiv2025

Uncovering Heterogeneous Effects via Localized Feature Selection

Identifying features that interact to trigger disease, while accounting for heterogeneity across diverse populations, is essential for the development of precision and targeted medicine. Despite the availability of vast and complex health-related datasets, most existing works focus on identifying disease-associated features at the population level or within a few subpopulations, often overlooking individual-level heterogeneity within these groups. To address this limitation, we propose a novel framework that utilizes localized test statistics to identify disease-associated features tailored to individual profiles. Our method leverages the recently developed knockoffs methodology to control the noise level of the selection set so that the results are replicable. Moreover, it allows for the discovery of hidden heterogeneous effects within the data, as demonstrated in an application to single-cell RNA sequencing data for Alzheimers disease. By aggregating localized feature selection results, our framework also enables powerful population-level feature selection. Our framework provides a powerful tool for exploratory studies of precision medicine, offering the potential to generate novel hypotheses for confirmatory biological experiments.

bioinformatics↗

bioRxiv2025

Glis3 is a modifier of cyst progression in autosomal dominant polycystic kidney disease

BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is caused by mutations affecting polycystin-1 (PC1) or -2 (PC2). The existence of a cilia-dependent cyst activation (CDCA) pathway has been identified by demonstrating that structurally intact primary cilia are crucial for cyst growth following loss of polycystins. We previously used translating ribosome affinity purification (TRAP) RNA-Seq on pre- cystic mouse kidneys to determine the translatome that meet the criteria for CDCA and identified Glis2 as an early effector of polycystin signaling. Here, we investigate the potential role of Glis3 in ADPKD, which, while not transcriptionally altered, encodes a cilia-localized transcription factor belonging to the same family of genes as Glis2. MethodsWe used live cell confocal imaging to study the subcellular localization of Glis3 in the presence or absence of Pkd1. We used Glis3 conditional knockout mice to investigate the genetic interaction between Glis3 and Pkd1. Changes in gene expression and chromatin accessibility were assessed by RNA-Seq and ATAC-Seq on an allelic series of Glis3 and Pkd1 kidney selective inactivation models at 7-weeks age to evaluate the function of Glis3. ResultsThe ciliary localization of Glis3 is not affected by Pkd1 mutation status. Kidney selective Glis3 by itself does not affect kidney structure or function, but dual inactivation of Glis3 and Pkd1 significantly worsen polycystic kidney disease. Integration of transcriptomic profiling and chromatin accessibility assays suggested that kidney tubule specific Glis3 inactivation results in dysregulated fatty acid metabolism and circadian. ConclusionsGlis3 is a primary cilium localized transcription factor that genetically interacts with Pkd1 and modifies kidney epithelial cell metabolism and circadian function.

genetics↗

bioRxiv2025

An Adipose mTORC1 Effector Links Nutrient-induced Ribosomal Protein Translation to Lifespan in Drosophila

Mechanistic target of rapamycin complex 1 (mTORC1) senses nutrient availability to orchestrate metabolic processes critical for physiological homeostasis and organismal ageing1. While mTORC1 preferentially regulates the translation of 5'-terminal oligopyrimidine (TOP) motif-containing mRNAs that predominantly encode ribosomal proteins (RPs) via the translational repressor 4E-BP 2, this mTORC1 function is resistant to rapamycin inhibition 3. TOP mRNAs are exceptionally abundant, thus imposing a major translational burden on cells; yet how their translation is physiologically tuned and linked to longevity remain unexplored. Here we identify Lsp2, originally known as a storage protein 4, as an adipose effector of mTORC1 that modulates lifespan in Drosophila. Lsp2 is induced by essential amino acids (EAAs) via mTORC1. Genetic ablation of Lsp2 to blunt organismal response to protein diets drives robust lifespan extension without compromising key life-history traits such as reproduction. Translatomic profiling reveals that loss of Lsp2 selectively reduces global TOP mRNA translation in a 4E-BP-dependent manner, thereby extending lifespan via a mechanism distinct from rapamycin inhibition that fails to reduce 4E-BP phosphorylation in vivo. Finally, Lsp2 adipokine promotes 4E-BP phosphorylation and acts systemically across tissues to shape the lifespan responses to dietary protein. Collectively, our findings establish Lsp2 as a novel translational regulator of TOP genes that mechanistically couples physiological ribosomal protein synthesis with organismal longevity.

physiology↗

bioRxiv2025

AURKA inhibition amplifies DNA replication stress to foster WEE1 kinase dependency and synergistic antitumor effects with WEE1 inhibition in cancers

Highly elevated expression of the oncogene Aurora kinase A (AURKA) occurs in numerous human cancers harboring defective p53, nominating AURKA as a potential vulnerability in TP53-mutated cancer. However, clinical trials have indicated modest monotherapy activity of AURKA inhibitors. Here, we demonstrate that AURKA inhibition promotes phosphorylation of Replication Protein A (RPA), resulting in stalled DNA replication fork progression and eliciting a replication stress response in multiple TP53-mutated models, creating a druggable dependence on the mitotic checkpoint kinase WEE1. Combined inhibition of AURKA and WEE1 synergistically enhanced replication stress, tumor-specific apoptotic cell death, and mitotic catastrophe, and lead to marked tumor regression in cell line- and patient-derived xenograft models of TP53-mutated cancer. Our findings define enhanced DNA replication stress as underlying the strong synergy between AURKA and WEE1 inhibitors and offer preclinical confirmation of efficacy, indicating high potential for clinical translation of this synthetic lethal strategy for TP53-mutated carcinomas. Statement of significanceWe demonstrate that a small molecule AURKA inhibitor amplifies DNA replication stress in TP53-mutated carcinomas. This amplification of DNA replication stress can be leveraged this for synthetic lethal therapy in a combination with WEE1 inhibition that enhances antitumor effects in in vitro, in xenografts and in patient-derived xenograft models, advancing a promising novel combination therapy.

cancer biology↗

bioRxiv2025

Diffusion Neuroimaging of Speech Acquisition in Infants

How multisystem white-matter maturation supports early speech acquisition in infancy remains unclear. Using longitudinal diffusion MRI from the Baby Con-nectome Project, we quantified neurite density maturation from birth to 24 months across 33 white-matter tracts spanning motor, auditory, visual, ventral-language, and dorsal-language systems. Maturation was coordinated across systems rather than confined to canonical language pathways: dorsal-language association tracts began relatively immature near birth but showed steep postnatal growth, pro-gressively rebalancing with motor and sensory systems. Tract-behavior coupling was likewise distributed across developmental domains, with broadly positive age-adjusted associations for motor outcomes, a sparser, partly motor-like profile for gesture-related phrase comprehension, and predominantly negative associa-tions for lexical speech-related outcomes. Exploratory follow-up analyses further suggested motor and social precursor capacities that may help mediate these in-direct developmental routes from structural maturation to later speech-related outcomes. These findings support a multisystem developmental account in which early speech acquisition emerges not from an isolated language pathway, but from coordinated maturation across multiple systems and the precursor skills that link them.

neuroscience↗

bioRxiv2025

Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman Spectroscopy and AI

PURPOSEIdentifying reliable predictors of immunotherapeutic response in melanoma remains an outstanding challenge. Existing transcriptomic and proteomic profiling methods for the tumor-immune microenvironment (TIME) are costly and may not faithfully capture modifications actively impacting tumor behavior. Here, we present a non-destructive, single-cell approach combining Raman spectroscopy and machine learning (ML) that enables rapid cell profiling and therapeutic response prediction. METHODSWe analyzed single-cell Raman spectra of mouse and human melanoma cell lines alongside nine melanoma patient-derived samples with known resistance profiles to targeted and immunotherapeutic inhibitors bemcentinib, cabozantinib, dabrafenib, nivolumab, and a combination of nivolumab and relatlimab. We assessed cell phenotyping classification and treatment resistance using random forests and feature importance analysis. For patient samples, we constructed a two-stage evaluation workflow to determine clinical drug resistance through aggregated single-cell predictions and identified corresponding highly variant spectral signatures using computational methods adapted from single-cell RNA sequencing methods. RESULTSIn cell lines, our approach achieved >96% differentiation accuracy across tumor microenvironment cell types and induced functional phenotypes. Persistent (drug-resistant) cells formed subclusters based on genetic mutations rather than sample origin, with Raman signatures reflecting biochemical changes relevant to therapeutic pathways. For patient samples, our workflow correctly inferred resistance likelihoods for 30 of 33 clinically-relevant patient-drug combinations (91% accuracy). CONCLUSIONSingle-cell Raman spectroscopy combined with machine learning offers a scalable, prognostic platform to predict therapeutic resistance likelihood, with further potential to advance clinical, multi-omic biomarker efforts for melanoma. Our approach may improve first-and second-line therapy selection assessments for precision medicine by providing rapid, non-destructive prediction of therapeutic response based on cellular spectral profiles. Context SummaryO_ST_ABSKey objectiveC_ST_ABSCan label-free, single-cell Raman spectroscopy and machine learning approach accurately profile melanoma cell states and therapeutic resistance likelihood to targeted and immunotherapeutic agents? Knowledge generatedRaman spectroscopy with machine learning differentiated tumor microenvironment cell types and functional phenotypes with >96% accuracy in cell lines. When applied to patient-derived metastatic melanoma samples, the approach correctly inferred patient response to a panel of targeted and immunotherapeutic inhibitors with 91% accuracy (30 of 33 cases). High-likelihood persistent and sensitive cells across diverse patients exhibited recurrent spectral features. RelevanceSingle-cell Raman-based profiling supports functional-diagnostic assessment or resistance likelihood and may contribute to improved therapeutic selection and precision oncology strategies for melanoma patients.

cancer biology↗

bioRxiv2025

Scalable Inference and Identifiability of Kinetic Parameters for Transcriptional Bursting from Single Cell Data

MotivationStochastic gene expression and cell-to-cell heterogeneity have attracted increased interest in recent years, enabled by advances in single-cell measurement technologies. These studies are also increasingly complemented by quantitative biophysical modeling, often using the framework of stochastic biochemical kinetic models. However, inferring parameters for such models (that is, the kineticrates of biochemical reactions) remains a technical and computational challenge, particularly doing so in a manner that can leverage high-throughput single-cell sequencing data. ResultsIn this work, we develop a Chemical Master Equation reference library-based computational pipeline to infer kinetic parameters describing noisy mRNA distributions from single cell RNA sequencing data, using the commonly applied stochastic telegraph model. Our pipeline also serves as a tool for comprehensive analysis of parameter identifiability, in both a priori (studying model properties in the absence of data) and a posteriori (in the context of a particular dataset) use cases. The pipeline can perform both of these tasks, i.e. inference and identifiability analysis, in an efficient and scalable manner, and also serves to disentangle contributions to uncertainty in inferred parameters from experimental noise versus structural properties of the model. We found that for the telegraph model, the majority of the parameter space is not practically identifiable from single cell RNA sequencing data, and low experimental capture rates worsen the identifiability. Our methodological framework could be extended to other data types in the fitting of small biochemical network models. AvailabilityAll code relevant to this work is available at https://github.com/Read-Lab-UCI/TelegraphLikelihoodInfer.

biophysics↗

bioRxiv2025

Locus Coeruleus to Medial Prefrontal Cortex Noradrenergic Neural Circuit Modulates States of Consciousness during Sevoflurane Anesthesia in Mice

How exactly does general anesthesia achieve its effects? It is so widely used in surgeries and medical treatments to enhance comfort for nearly one hundred years, yet its precise mechanisms underlying the loss and recovery of consciousness still remain unclear. Utilizing a variety of research methods-pharmacological, optogenetic, chemogenetic, fiber photometry, and gene knockdown approaches-our study has shed light on the significant modulatory function of locus coeruleus noradrenergic neurons during the transition from sevoflurane anesthesia to awakening. Furthermore, the activation of the LC-mPFC circuit has been found to have a substantial arousal effect, with 1-AR playing a crucial role in this process. Additionally, GABAA-R has been identified as the key binding site for sevoflurane within the locus coeruleus. These findings collectively offer novel insights into the neural network mechanisms underlying general anesthesia, advancing our understanding of this complex medical phenomenon.

neuroscience↗

bioRxiv2025

Anks3 mediates cilia dependent polycystin signaling and is essential for adult kidney homeostasis

The existence of a cilia-dependent cyst activation (CDCA) pathway underlying autosomal dominant polycystic kidney disease was identified by showing that cyst progression following loss of polycystins is significantly suppressed by removal of structurally intact cilia. We applied translating ribosome affinity purification RNASeq on pre-cystic mouse kidneys to determine a cell-autonomous in vivo translatome associated with CDCA and identified Glis2 as an early effector of polycystin signaling. Here, to discover additional components of CDCA, we used polycystin-dependent Glis2 expression as a functional readout and identified Anks3, from the CDCA pattern translatome, as a candidate cytosolic regulator of polycystin signaling. Anks3 regulates polycystin-dependent Glis2 expression both in vitro and in vivo. Anks3 also undergoes polycystin-dependent changes in phosphorylation state. Inactivation of Anks3 in Pkd1 mouse models suppresses cyst progression, but also results in rapidly progressive kidney injury independent of polycystins. Anks3 inactivation also normalizes a broader spectrum of polycystin-dependent CDCA translatome changes. These findings define Anks3 as a central regulator of cilia dependent polycystin signaling, functioning downstream of cilia and polycystins and upstream of Glis2, and show that Anks3 has broader functions in maintaining renal structural and functional homeostasis.

genetics↗

bioRxiv2025

GeST: Towards Building A Generative Pretrained Transformer for Learning Cellular Spatial Context

Learning spatial context of cells through pretraining on spatial transcriptomics (ST) data may empower us to systematically decipher tissue organization and cellular interactions. Yet, transformer-based generative models often focus on modeling individual cells, neglecting the intricate spatial relationships within them. We develop GeST, a deep transformer model that is pretrained by a novel spatially informed generation task: Predict cellular expression profile of a given location based on the information from its neighboring cells. We propose a spatial attention mechanism for efficient pretraining, a flexible serialization strategy for converting ST data into sequences, and a cell tokenization method for quantizing gene expression profiles. We pretrained GeST on large-scale ST datasets of different ST technologies and demonstrated its superior performance in generating unseen spatial cells. Our results also show that GeST can extract spatial niche embeddings in a zero-shot way and can be further fine-tuned for spatial annotation tasks. Furthermore, GeST can simulate gene expression changes in response to perturbations of cells within spatial context, closely matching existing experimental results. Overall, GeST offers a powerful generative pre-training framework for learning spatial contexts in spatial transcriptomics.

bioinformatics↗