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Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 271 records · Page 15Linked to original sources

Reactivation-coupled brain stimulation enables complete learning generalization

Generalization of learned knowledge to new contexts is essential for adaptive behavior. Despite extensive research on the brain plasticity mechanisms underlying learning specificity, the mechanisms that facilitate generalization remain poorly understood. Here, we investigate whether using brain stimulation to disrupt offline consolidation in visual cortex promotes learning generalization. Separate groups of participants (N = 144) were trained on visual detection tasks using either a reactivation-based protocol or conventional full-practice, combined with anodal or sham transcranial direct current stimulation (tDCS) over the visual cortex. Strikingly, only combination of reactivation-based learning with anodal tDCS produced complete generalization from trained to untrained stimuli, an effect consistently replicated across features (orientation, motion direction). In contrast, reactivation-based learning alone and conventional full-practice - whether with or without brain stimulation - yielded stimulus-specific learning. Importantly, reactivation-coupled brain stimulation achieved generalization with an 80% reduction in training trials while maintaining learning gains comparable to full-practice. These findings demonstrate that reactivation and neuromodulation interact to unlock learning generalization, revealing a key brain plasticity mechanism and offering a rapid, translatable strategy for sensory rehabilitation.

neuroscience↗

In vivo genome-wide CRISPR screens in human T cells to enhance T cell therapy for solid tumors

Large-scale CRISPR screening in human T cells holds significant promise for identifying genetic modifications that can enhance cellular immunotherapy. However, many genetic regulators of T cell performance in solid tumors may not be readily revealed in vitro. In vivo screening in tumor-bearing mice offers greater physiological relevance, but has historically been limited by low intratumoral T cell recovery. Here, we developed a new model system that achieves significantly higher human T cell recovery from tumors, enabling genome-wide in vivo screens with small numbers of mice. Tumor-infiltrating T cells in this model exhibit hallmarks of dysfunction compared to matched splenic T cells, creating an ideal context for screening for genetic modifiers of T cell activity in the tumor microenvironment. Using this platform, we performed two genome-wide CRISPR knockout screens to identify genes regulating T cell intratumoral abundance and effector function (e.g., IFN-{gamma} production). The intratumoral abundance screen uncovered the P2RY8-G13 GPCR signaling pathway as a negative regulator of human T cell infiltration into tumors. The effector function screen identified GNAS (Gs), a central signaling mediator downstream of multiple GPCRs that sense different suppressive ligands, as a key regulator of T cell dysfunction in tumors. Targeted GNAS knockout rendered T cells resistant to multiple suppressive cues and significantly improved therapeutic performance across diverse solid tumor models. Moreover, combinatorial knockout of P2RY8 (trafficking) and GNAS (effector function) further enhanced overall tumor control, demonstrating that genetic modifications targeting distinct T cell phenotypes can be combined to improve therapeutic potency. This flexible and scalable in vivo screening platform can be adapted to diverse tumor models and pooled CRISPR libraries, enabling future discovery of genetic strategies that equip T cell therapies to overcome barriers imposed by solid tumors.

immunology↗

Microcystin-LR aerosol exposure increases inflammatory drivers of asthma, Evidence of an NF-κB amplification mechanism

Microcystin-LR (MC-LR) is one of a large family of cyanotoxins which are naturally produced by cyanobacteria within harmful algal blooms occurring in bodies of water globally. Early findings of the toxicity of such blooms stemmed from fatalities of livestock drinking from affected water. Since then, various toxins have been identified such as the microcystins. Microcystin-LR has been studied as a representative congener due to its abundance and toxicity. While there have been extensive studies of microcystin-LR by oral route exposure, we have turned our attention to inhalation route exposure due to the recent findings of microcystin-containing lake and sea-spray aerosol. We have shown inflammatory outcomes in the airways of mice and in human cell culture models after microcystin aerosol exposure, and have found a consistent molecular patterns similar to those of Type 1/Type 17 driven neutrophilic asthma. Here we address the hypothesis that MC-LR will increase the inflammatory mediators of neutrophilic asthma leading to worsening symptoms. This is tested and characterized in both in vitro and in vivo models. We found that asthma symptoms and molecular signatures of inflammation are both worsened by MC-LR exposure in a mouse model of neutrophilic asthma. We found that 3D human airway cell culture models reconstructed from asthmatic donor cells are similarly affected, however healthy donor cells are nearly unaltered by comparison. Aggregating these findings with RNA sequencing data from all models, we developed a hypothetical molecular mechanism which relies on MC-LR mediated amplification of existing inflammatory signaling. We test this in a human reporter cell line of NF-{kappa}B activity and further demonstrate the mechanism by inhibitor testing. This study sheds light on the risk to asthmatic patients living near or recreating on affected bodies of water. Beyond asthma, we believe this study provides crucial insight into the findings over the last 40 years concerning disparate outcomes of MC-LR exposure as the result of exposure will be dependent on the signaling state of the tissue upon exposure.

molecular biology↗

VesiclePy: A Machine Learning Vesicle Analysis Toolbox for Volume Electron Microscopy

Vesicles are critical components of neurons that package neurotransmitters and neuropeptides for their release, in order to communicate with other neurons and cells. However, due to their small size, the reconstruction of the full vesicle endowment across an entire neuronal morphology remains challenging. To achieve this, we have used, as a tool to identify and visualize vesicles, Volume Electron Microscopy (vEM), a method that has the nanoscale resolution to detect individual vesicle boundaries, content, and 3D locations. However, the large volume of vEM datasets poses a challenge in the segmentation, classification, and spatial analysis of tens of thousands of vesicles and their target cell in 3D. Here we report the development of VesiclePy, an integrated pipeline for automated segmentation, classification, proofreading, and spatial analysis of vesicles, relative to neuron masks in large-volume electron microscopy data. Our package integrates the efficiency of deep learning and the accuracy of human proofreading and provides a streamlined package in chunked processing and accurate indexing, localization, and visualization of single vesicle resolution in large vEM data. We demonstrate the viability of VesiclePy using high-pressure frozen serial EM data of Hydra vulgaris and quantify the performance of the package using ground truth manual annotations. We show that VesiclePy can process a multiterabyte serial EM dataset, efficiently annotate 53,851 vesicles from 20 complete neurons, and classify vesicles into 5 types. Each vesicle has a unique ID and 3D location for further spatial analysis in relation to neuron or non-neuronal targets nearby. Finally, by combining vesicle data and morphological information of each neuron, we can quantitatively cluster neurons into subtypes. VesiclePy is available at https://github.com/PytorchConnectomics/VesiclePy under an MIT license.

neuroscience↗

Brain Preparedness for Active Forgetting: Cortisol Awakening Response Proacts Prefrontal Control Over Hippocampal-Striatal Circuitry

Stress-related psychiatric pathologies (e.g., rumination in depression, intrusive memories in PTSD) involve impaired inhibitory control over emotional memories and disrupted cortisol circadian rhythms. However, how these mechanisms interact to support adaptive forgetting remains unclear. Here, by integrating multi-modal neuroendocrine profiling with fMRI-based memory suppression in 90 participants, we demonstrate that the cortisol awakening response (CAR)--a circadian surge priming the brain for daily demands--proactively and selectively enhances suppression of recently acquired but not overnight-consolidated emotional memories. This time-dependent facilitation manifests through dual neural mechanisms: Robust CAR broadly amplifies dorsolateral prefrontal cortex (dlPFC) engagement during suppression attempts and specifically strengthens top-down dlPFC control over hippocampal-striatal circuits exclusively for recent traces. Our findings bridge circadian biology, systems neuroscience, and clinical psychopathology to establish CAR as a neuroendocrine preparedness mechanism that proactively configures adaptive forgetting circuits across sleep-wake cycles, thereby informing circadian-based interventions for stress disorders.

neuroscience↗

A biotin ligation assay reveals a complex proxiome for HLA-A2 and implicates MIA3 in cell surface expression of MHC class I molecules

Antigen presentation via MHC class I molecules (MHC-Is) is a turning point in the establishment of immune responses to endogenous threats, such as viruses. From their synthesis to their cell surface display, MHC-Is travel to many compartments, including the ER, Golgi, and endosomes. They come close to a plethora of molecules, some of which regulate directly or indirectly their folding and trafficking. While many of these proteins are well characterized, such as those found in the peptide loading complex, others remain to be discovered. The proxiome can be studied using proximity labeling assays, such as BioID, which relies on a biotin ligase fused to a bait of interest that biotinylates lysine residues on nearby proteins. These modified targets can then be purified and identified by mass spectrometry. By fusing BioID to the HLA-A2 cytoplasmic tail, we have applied this technique to the MHC-I antigen and identified 209 potential specific interactors in HEK293 cells, including PDZD8 (LYVAC) and MIA3 (Tango1). We knocked out MIA3 in HEK293 cells and measured an increase in the expression of MHC-I molecules, suggesting a role for this vesicle budding protein in the regulation of MHC-I trafficking. Notably, MHC-I crosslinking identified targets that connect reverse signalling to diverse metabolic processes. Altogether, our results underscore the promise of HLA-coupled biotin ligases as a powerful approach to dissect antigen presentation pathways.

immunology↗

Class A PBPs reinforce the septal cell wall following initial synthesis by SEDS-bPBP pairs during bacterial cytokinesis

Bacterial cytokinesis requires the coordinated action of septal peptidoglycan (PG) synthases to ensure efficient and accurate septum formation. Two major types of synthases contribute: SEDS-bPBP and bifunctional class A PBPs (aPBPs). How their activities are organized in space and time has remained unclear. Using Deinococcus radiodurans, which often contains both constricting and closed septa in the same cell, we show that septal PG synthesis proceeds through distinct steps at different regions. Primary synthesis at the leading edge by SEDS-bPBP pairs drives constriction, whereas aPBPs act later at the lagging edge and in closed septa to thicken the wall, enhance cross-linking, and reinforce septal integrity. At the single-molecule level, both synthase types move directionally to perform processive PG synthesis at distinct sites. We also observed spatiotemporal separation of SEDS-bPBP and aPBP synthases in Staphylococcus aureus, suggesting that secondary septal PG synthesis is a conserved mechanism securing successful cytokinesis.

microbiology↗

A multimodal human-computer interaction dataset for neurocognitive user state evaluation

We introduce the Simulated Environment for Neurocognitive State Evaluation (SENSE-42), a multimodal dataset collected during user interactions with desktop computers. It is designed for studying spontaneous fluctuations in the neurocognitive state related to the tonic alertness of computer users, with recordings from 42 participants over 2-hour sessions. Within a simulated desktop environment, participants performed real-world routine tasks, including application switching, file management, typing, and web browsing. High-resolution data were recorded across physiological (electroencephalography, electrocardiography, respiration) and subjective modalities of alertness. At five-minute intervals, alertness state was reported using seven questions, addressing sleepiness (Karolinska Sleepiness Scale), mental and temporal demand, perceived performance, effort and frustration (NASA Task Load Index), as well as attentiveness. Behavioural data included keyboard, mouse and webcam inputs. Demographic information, experience metrics, habits, and preferences of computer usage were collected. In addition, individual differences in sleep quality were evaluated using the Pittsburgh Sleep Quality Index and the Epworth Sleepiness Scale. The SENSE-42 dataset can contribute to future research in user state monitoring, behavioural analysis and physiological computing.

neuroscience↗

Uncovering directionally and temporally variable genes with STAVAG

Spatially variable genes (SVGs) are crucial for understanding spatial heterogeneity in spatial transcriptomics. Yet, SVGs are challenging to align with established biological axes and lack straightforward mechanistic interpretation, thereby limiting their ability to inform downstream experiments or clinical applications. Here, we introduce the concept of directionally variable genes (DVGs) and temporally variable genes (TVGs). Also, we propose a unified framework, STAVAG, that models spatial or temporal information to identify DVGs and TVGs. STAVAG effectively identifies biologically meaningful DVGs for uncovering prognostically relevant gene modules that align with the malignant-immune-stroma structure in the tumor microenvironment, identifying marker genes in three-dimensional brain data, and detecting 12 polarization genes regulating planarian regeneration. Furthermore, by tracking the progression of myocardial infarction and tissue development, STAVAG reveals critical TVGs that can serve as diagnostic biomarkers for the ultra-acute phase of myocardial infarction. STAVAG also identifies both global and local gene modules involved in mouse placentation, as well as tissue-specific and interactive TVG modules that dynamically evolve during mouse embryonic development.

bioinformatics↗

N6-Methyladenine DNA modification regulates pathogen virulence in nematodes

Understanding the global regulatory mechanisms that control pathogen virulence gene expression is essential for elucidating the molecular basis of pathogenicity. N6-methyladenine (6mA) plays a crucial role in regulating gene expression in response to various environmental stresses; however, its role in pathogen virulence remains largely unexplored. Here, we report the widespread occurrence of 6mA across 17 nematode isolates and map its genomic landscape in six notorious agriculturally important pathogen root-knot nematodes (RKNs). We demonstrated that 6mA is characterized by a conserved GAG motif across nematodes, but exhibits species-specific distribution patterns and distinct effects on gene expression. In particular, its enrichment in transposable elements differs between polyploid and diploid nematodes, suggesting lineage-specific epigenetic regulation potentially associated with polyploidy. We further identified two functional 6mA demethylases, MiNMAD-1 and MiNMAD-2, and confirmed their catalytic activity and active sites. Host-induced gene silencing of minmad-1 significantly increased plant resistance to three polyploid RKN species. A detailed functional analysis revealed that minmad-1 knockdown disrupted virulence gene expression during the parasitic stage, thereby reducing nematode infectivity. Together, our findings suggest 6mA demethylase as a key epigenetic regulator of RKNs virulence, providing new insights into nematode biology and offering promising targets for the development of sustainable control strategies. Significance Statement6mA methyltransferase regulates the expression of virulence genes in certain pathogenic bacteria and plays a critical role in their infectivity. However, the regulation of virulence gene expression in eukaryotic pathogens, particularly plant pathogens, remains poorly understood. Most studies on pathogen virulence have focused on individual effectors, with little insight into global regulatory mechanisms. Here, we demonstrate that 6mA broadly shapes virulence gene expression patterns in polyploid RKNs. Moreover, transgenic tobacco, tomato, and rice plants expressing dsRNA against nematode demethylases showed significantly enhanced resistance to RKNs. These findings establish 6mA demethylase as a promising epigenetic target for controlling plant-parasitic nematodes.

molecular biology↗

STADiffuser: high-fidelity simulation and full-view 3D modeling of spatial transcriptomics

Spatial transcriptomics technologies have provided invaluable insights by profiling gene expression alongside precise spatial information. However, they encounter high costs, data sparsity, and limited resolution, hindering their broader adoption and utility. Moreover, the lack of flexible simulators capable of generating high-fidelity simulated data has impeded the development of computational tools for spatial transcriptomic data analysis. To this end, we introduce STADiffuser, a versatile deep generative model that leverages diffusion modeling for accurate simulation of spatial transcriptomic data. STADiffuser employs a two-stage architecture: an autoencoder with a graph attention mechanism for learning spot embeddings, followed by a latent diffusion model integrated with a spatial denoising network for data generation. STADiffuser is the first simulator designed for spatial transcriptomic data, capable of handling multiple samples and 3D coordinates while supporting user-defined conditions. STADiffuser facilitates various downstream analyses, including accurate imputation, super-resolution, and full-slice generation. Furthermore, its generative scheme enables in silico experiments, thereby enhancing the statistical power in detecting differentially expressed genes and identifying cell-type-specific genes while effectively controlling confounding factors. Notably, STADiffuser scales to millions of spots and supports the full-view 3D modeling of a marmoset cerebellum atlas with over 20 million spots, enabling detailed investigations from arbitrary viewing angles.

bioinformatics↗

Genomic signatures of local adaptation across parasitised cricket populations

Novel species interactions provide an opportunity to assess the earliest stages of genetic adaptation in wild populations. We explored the genomic and geographic landscape of adaptation in small, fragmented Hawaiian cricket populations, which are parasitised by larvae of an introduced fly that targets singing males. Multiple protective male-silencing cricket morphs have recently spread despite songs role in mate attraction. Combining population genomics and field selection experiments, we found sharp declines in the crickets effective population size after the flys introduction. Nevertheless, geographical variation in selection influences the distribution of male-silencing alleles, consistent with local adaptation. Populations showing the strongest evidence of recovery retain large numbers of singing-capable males, suggesting that initially adaptive responses to the fly created an evolutionary trap via loss of sexual signalling.

evolutionary biology↗

Lineage-Specific Epigenomic Mapping of Proximal and Distal Fallopian Tube Epithelium Reveals Mesenchymal/Wnt-Driven Tumor Phenotype Diversification

High-grade serous ovarian cancer (HGSC) typically originates from the distal fallopian tube epithelium (disFTE), while the proximal epithelium (proFTE) remains less explored. Here, Using isogenic murine organoid models with common oncogenic drivers (Tp53-/-; Krasmut; Pten-/-), we show that disFTE and proFTE generate distinct tumor subtypes driven by region-specific transcriptional programs. Unlike disFTE, proFTE demonstrates continuous activation of epithelial-mesenchymal transition (EMT) across wild-type, mutant organoids, and derived tumors. Additionally, mutated proFTE organoids and their tumors exhibit persistent WNT signaling activation and grow independently of WNT signals. Chromatin profiling shows that mutant proFTE maintains a distinctive epigenetic profile with promoters associated with EMT and WNT pathways. Human proFTE with BRCAmut also shows epigenetic activation of EMT- and WNT-related genes. Tumors near the uterotubal junction (UTJ)--anatomically akin to proFTE--exhibit a pronounced EMT signature, increased Vimentin+E-cadherin+ and nuclear {beta}-catenin expression. These findings suggest proFTE-derived tumors may occupy a unique niche in gynecological malignancies, with regional identity shaping tumor phenotypes.

cancer biology↗

Hybrid In Vivo Breast Cancer Model Reveals Transcriptomic Insights into Cancer Progression with Age

Aging is a key risk factor for breast cancer, yet the independent role of the extracellular matrix (ECM) in tumor progression remains understudied. Recent studies have investigated healthy mammary tissue and aging to understand their relationship; however, the independent effects of the aged ECM remain understudied. Herein, we describe a hybrid in vivo model where MCF10A ductal carcinoma (DCIS.com) cells - with or without knockdown of select targets - were seeded onto decellularized ECM from aged murine mammary glands and implanted into the mammary fat pads of young Rag1-/- mice. Knockdown of selected targets, IL1B and LOX, reduced tumor growth on aged matrices in vivo and P4HA1 knockdown enhanced tumor growth. Additionally, analysis of LOX on aged ECM highlighted LOX as a driver of tumor progression where knockdown reduced transcriptomic programs related to invasion and cellular stress. To isolate the individual ECM influence on tumor growth, MCF10A cells were seeded atop young or aged matrices where it was found that tumors grown on the aged ECM after implantation exhibited significantly greater volume and a larger tumorigenic region when compared to those from the young ECM. Furthermore, single cell RNA-sequencing revealed transcriptional enrichment of inflammatory and invasive genes within the aged matrix. Together, these results identify LOX as a driver of tumor progression and potential therapeutic target, and demonstrate that the aged ECM alone can promote breast cancer progression

bioengineering↗

Leveraging Large Language Models for Redundancy-Aware Pathway Analysis and Deep Biological Interpretation

Extracting coherent, biologically meaningful insights from vast, complex multi-omics data remains challenging. Currently, pathway enrichment analysis serves as a cornerstone for the functional interpretation of such data. However, conventional approaches often suffer from extensive functional redundancy caused by shared molecular components and overlapping pathway definitions across databases. This redundancy can obscure key biological signals and compromise the interpretability of pathway enrichment results. Here, we present MAPA (Functional Module Identification and Annotation for Pathway Analysis Results Using Large Language Models [LLM]), an open-source computational framework that resolves redundancy and enhances pathway analysis result interpretation. MAPA computes functional similarity between pathways using LLM-based text embeddings, enabling comparison across different databases. It constructs pathway similarity networks and identifies functional modules via community detection algorithms. Crucially, MAPA employs LLMs for automated functional annotation, integrating Retrieval-Augmented Generation (RAG) to generate comprehensive and real-time biological summaries and reduce hallucinations. Benchmarking demonstrated MAPAs superior performance: the biotext embedding similarity showed a large effect size (Cliffs {delta} = 0.96) compared with the Jaccard index ({delta} = 0.73), and module identification achieved high accuracy (Adjusted Rand Index [ARI] = 0.95) versus existing methods (ARI = 0.23-0.33). Human expert evaluation confirmed that MAPAs annotations match expert-quality interpretations. Finally, a multi-omics aging case study illustrates that MAPA uncovers coherent functional modules and generates insights extending beyond conventional pathway analyses. Collectively, MAPA represents a significant advance in redundancy-aware pathway analysis, transforming pathway enrichment results from fragmented lists into biologically coherent narratives. By leveraging the capabilities of LLMs, MAPA offers researchers a robust, scalable tool for deriving deep mechanistic insights from complex and vast multi-omics datasets, marking a new direction for AI-driven bioinformatics.

bioinformatics↗

Accelerated biomass loss in western US forestlands due to shifting fire regime

Wildfires influence the distribution of biomass across the Earths surface and drive losses of carbon from the land surface to the atmosphere. Although the global budget between terrestrial and atmospheric carbon pools is comparatively well understood (Jones et al., 2024; MacCarthy et al., 2024), the growing size and severity of wildfires present an increasing challenge to regional carbon accounting. To quantify vegetation biomass across arid systems in the western US, we used remote sensing data to estimate the aboveground live biomass density (Mg ha-1) at 30-meter resolution across the states of Utah and Nevada annually from 2000 to 2022. Time series biomass maps showed accelerated loss of terrestrial carbon to the atmosphere as a result of increasing wildfire, with annual biomass burnt increasing at 0.105 {+/-} 0.024 Mt yr-1 (mean {+/-} standard deviation) after 2015. Recent shifts in the wildfire regime show a transition from predominantly early-season fires in low-biomass grasses and shrubs to late-season fires in higher-biomass forestlands. The proportion of total biomass loss attributed to wildfires in forestland areas increased from an average of 76% per year before 2015 to 94% in the years that followed. Furthermore, we found that recent droughts contributed to increased biomass loss in forestland areas, whereas biomass in non-forestland areas appeared unaffected by drought conditions. These findings underscore the escalating impact of climate change on fire regimes, highlighting the urgent need for adaptive land management strategies to mitigate carbon loss and preserve ecosystem resilience in the face of ongoing aridification.

ecology↗

Interactive Visualization of Metric Distortion in Nonlinear Data Embeddings using the distortions Package

Nonlinear dimensionality reduction methods like UMAP and t-SNE can help to organize high-dimensional genomics data into manageable low-dimensional representations, like cell types or differentiation trajectories. Such reductions can be powerful, but inevitably introduce distortion. A growing body of work has demonstrated that this distortion can have serious consequences for downstream interpretation, for example, suggesting clusters that do not exist in the original data. Motivated by these developments, we implemented a software package, distortions, which builds on state-of-the-art methods for measuring local distortions and displays them in an intuitive and interactive way. Through case studies on simulated and real data, we find that the visualizations can help flag fragmented neighborhoods, support hyperparameter tuning, and enable method selection. We believe that this extra layer of information will help practitioners use nonlinear dimensionality reduction methods more confidently. The package documentation and notebooks reproducing all case studies are available online at https://krisrs1128.github.io/distortions/site/.

bioinformatics↗

CASP3-GSDME-mediated trophoblast pyroptosis contributes to systemic inflammation

Early-onset preeclampsia (EOPE) is associated with excessive apoptosis and inflammation, but the mechanistic link between these processes remains enigma. Here, we report elevated circulating pro-apoptotic proteins in EOPE patients at early pregnancy, along with concurrent CASP3 activation and GSDME cleavage in a subset of EOPE placentas. Using multiple trophoblast cell lines, we demonstrate that trophoblast cells, which highly express GSDME, undergo a shift from apoptosis to CASP3-dependent pyroptosis, driving inflammation. Notably, pyroptotic trophoblasts further induce pro-inflammatory macrophage polarization within placental villi organoids, establishing a feedback loop that amplifies both trophoblast pyroptosis and inflammatory responses in trophoblast organoids-macrophage assembloids. In vivo, CASP3-GSDME-mediated trophoblast pyroptosis contributes to systemic inflammation in wild-type pregnant mice but not in Gsdme-/- mice. Screening of EOPE prevention drugs reveals Vitamin D as a suppressor of GSDME activation and pyroptosis in trophoblast cells. These findings highlight the CASP3-GSDME axis as a promising therapeutic target for preeclampsia prevention.

cell biology↗