Biology subjects

Huang, K.

Publications and source records attributed to Huang, K..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Inhibitory GABAergic neuron loss due to oxidative damage during ex vivo acute brain slice preparation influences genesis and dynamics of epileptiform activity

Ex vivo acute brain slice is a popular technique in neuroscience research. Since its inception five decades ago, many variations of the brain slice preparation method have emerged. While all variations are currently used by many labs throughout the world, no study has comprehensively examined the impact of these variation on the quality of the acute brain slice preparation. In this study, we comprehensively examined the effect of animal sacrifice methods (decapitation or transcardial perfusion) and cutting solution (normal or sucrose artificial cerebrospinal fluid) on brain slice preparation. Neuronal population was quantified by immunohistochemistry against various neuronal markers. Neuronal dynamics was evaluated by in vitro electrophysiology using two acute epilepsy models - zero-magnesium and 4-aminopyridine. To modulate the stress incurred in acute brain slice preparation, we administered antioxidants or HIF-1 inhibitor, chrysin, to the cutting solution. The method of brain slice preparation significantly affected the quality of the brain slice preparation. In general, the combination of transcardial perfusion and sucrose artificial cerebrospinal fluid produces the optimal brain slice preparation. This is evidenced by a preservation of inhibitory GABAergic neurons in slices prepared with this combination. We subsequently found that this loss of inhibitory GABAergic neurons significantly influenced the genesis and dynamics of induced acute epileptiform activity. The slices with preserved inhibition had less successful induction of acute epileptiform activity and a seizure-like event that is typical of those induced in brain with preserved inhibition. Finally, we found that loss of inhibitory GABAergic neurons during brain slice preparation is primarily due to oxidative damage. Limiting oxidative stress is an effective neuroprotection strategy to prevent loss of inhibition in brain slice preparation. In conclusion, consideration of brain slice preparation method is crucial in preservinginhibitory GABAergic neurons and the degree of inhibition in ex vivo acute brain slice preparation.

neuroscience↗

bioRxiv2025

Distinct myeloid precursors and their interaction with mesenchymal cells orchestrate the spreading of psoriatic disease from the skin to the joints

Psoriatic disease initially affects the skin, but later extends to the joints. Herein, we describe a two-step process that orchestrates spreading of inflammation from the skin to the joints. Induction of psoriatic skin disease in photoconvertible mice, followed by sequencing and computational characterization of skin-derived cells in the joints, identified a unique population of CD2+ MHC-II+ CCR2+ myeloid precursors that built up a skin-derived myeloid cell compartment in the joints. Single-cell cross-species reference mapping and mitochondrial variant tracing showed an orthologue human cell population. Interactome analyses in the joints showed that in a second step, resident regulatory CD200+ fibroblasts critically regulate the priming of the CD2+ MHC-II+ CCR2+ myeloid precursors, which subsequently regulate the IL-17 expression in T cells. Hence, spreading of inflammation requires a distinct migratory myeloid precursor population and a permissive local tissue environment, similar to tumour metastasis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/665302v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@38c440org.highwire.dtl.DTLVardef@dc574dorg.highwire.dtl.DTLVardef@1e79f72org.highwire.dtl.DTLVardef@19508cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Structure of Photosystem I with SOD reveals coupling between energy conversion and oxidative stress protection

In photosynthetic organisms, Photosystem I (PSI) not only converts energy into its chemical form but is also a major source of superoxide (O2-), which can damage cellular components. Its detoxification by superoxide dismutase (SOD) is essential for redox homeostasis. We report the structure of the PSI-SOD supercomplex from Chromera velia (C. velia), revealing a direct coupling between energy conversion and oxidative stress protection. The heterodimeric SOD comprises a catalytic SOD2 and a non-catalytic SOD1 anchoring it to the conserved stromal PSI surface near the ferredoxin-binding site with four contact areas. Structural comparison with plants and algae shows that SOD could occupy a similar position via interactions with PsaD. C. velia PSI displays several distinct features, including a split subunit PsaA linked by PsaV, a unique iso-fucoxanthin bound to PsaB, and an extended PsaF coordinating an extra chlorophyll in the membrane. The antenna includes five fucoxanthin-chlorophyll family proteins (FCPs) stabilized by adaptations unique to each antenna protein. These findings reveal the structural basis for the integration of a photoprotective mechanism with energy metabolism in photosynthetic membranes.

biochemistry↗

bioRxiv2025

Role of Macrophage PARP1 in the Regulation of Crosstalk between Adipose Immune Cells and Adipocytes during Diet-induced Obesity

Adipose tissue consists of heterogeneous cell populations, including macrophages, which play a key role in maintaining adipose tissue homeostasis. We previously identified PARP1 as a critical regulator of proadipogenic gene expression in preadipocytes and proinflammatory gene expression in macrophages. To investigate the role of macrophage PARP1 in regulating adipose tissue homeostasis, we generated myeloid lineage-specific Parp1 knockout mice (Parp1 KOLysM). When subjected to a high fat diet for 12 weeks, the Parp1 KOLysM mice exhibited an obese phenotype accompanied by white adipose tissue (WAT) dysfunction, characterized by altered metabolite profile, pronounced adipocyte hypertrophy, and increased macrophage infiltration. Coculture of primary preadipocytes with the conditioned medium from bone marrow-derived macrophages (BMDMs) isolated from Parp1 KOLysM or control mice demonstrated that macrophage PARP1 depletion inhibited LPS-induced proinflammatory gene expression in BMDMs, but enhanced differentiation of preadipocytes into mature adipocytes. Single cell RNA-sequencing using CD45+-sorted WAT resident immune cells showed that macrophage PARP1 depletion increased the fraction of macrophages and NK cells, altered gene expression in both cell populations, and promoted intercellular communications. Taken together, our studies demonstrate a key role for macrophage PARP1 in the maintenance of adipose tissue inflammatory and metabolic homeostasis. Macrophage PARP1 depletion promotes cell-cell crosstalk among macrophages, fat cells, and other immune cell populations in adipose tissue, which cooperatively drives the development of obesity. SignificanceIn this study, we characterized a role for macrophage PARP1 in regulating adipose tissue homeostasis. Depletion of PARP1 in macrophages enhances cell-cell crosstalk among immune cells and fat cells, which exacerbates adipose dysfunction and drives obesity and adverse metabolic outcomes.

molecular biology↗

bioRxiv2025

Gene-embedding-based prediction and functional evaluation of perturbation expression responses with PRESAGE

Understanding the impact of genetic perturbations on cellular behavior is crucial for biological research, but comprehensive experimental mapping remains infeasible. We introduce PRESAGE (Perturbation Response EStimation with Aggregated Gene Embeddings), a simple, modular, and interpretable framework that predicts perturbation-induced expression changes by integrating diverse knowledge sources via gene embeddings. PRESAGE transforms gene embeddings through an attention-based model to predict perturbation expression outcomes. To assess model performance, we introduce a comprehensive evaluation suite with novel functional metrics that move beyond traditional regression tasks, including measures of accuracy in effect size prediction, in identifying perturbations with similar expression profiles (phenocopy), and in prediction of perturbations with the strongest impact on specific gene set scores. PRESAGE outperforms existing methods in both classical regression metrics and our novel functional evaluations. Through ablation studies, we demonstrate that knowledge source selection is more critical for predictive performance than architectural complexity, with cross-system Perturb-seq data providing particularly strong predictive power. We also find that performance saturates quickly with training set size, suggesting that experimental design strategies might benefit from collecting sparse perturbation data across multiple biological systems rather than exhaustive profiling of individual systems. Overall, PRESAGE establishes a robust framework for advancing perturbation response prediction and facilitating the design of targeted biological experiments, significantly improving our ability to predict cellular responses across diverse biological systems.

bioinformatics↗

bioRxiv2025

Biomni: A General-Purpose Biomedical AI Agent

Biomedical research underpins progress in our understanding of human health and disease, drug discovery, and clinical care. However, with the growth of complex lab experiments, large datasets, many analytical tools, and expansive literature, biomedical research is increasingly constrained by repetitive and fragmented workflows that slow discovery and limit innovation, underscoring the need for a fundamentally new way to scale scientific expertise. Here, we introduce Biomni, a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across diverse biomedical subfields. To systematically map the biomedical action space, Biomni first employs an action discovery agent to create the first unified agentic environment - mining essential tools, databases, and protocols from tens of thousands of publications across 25 biomedical domains. Built on this foundation, Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling it to dynamically compose and carry out complex biomedical workflows - entirely without relying on predefined templates or rigid task flows. Systematic benchmarking demonstrates that Biomni achieves strong generalization across heterogeneous biomedical tasks - including causal gene prioritization, drug repurposing, rare disease diagnosis, micro-biome analysis, and molecular cloning - without any task-specific prompt tuning. Real-world case studies further showcase Biomnis ability to interpret complex, multi-modal biomedical datasets and autonomously generate experimentally testable protocols. Biomni envisions a future where virtual AI biologists operate alongside and augment human scientists to dramatically enhance research productivity, clinical insight, and healthcare. Biomni is ready to use at https://biomni.stanford.edu, and we invite scientists to explore its capabilities, stress-test its limits, and co-create the next era of biomedical discoveries.

bioinformatics↗

bioRxiv2025

Identify Alzheimer's disease subtypes and makers from multi-omic data of human brain and blood with a subspace merging algorithm

Identifying Alzheimers disease (AD) subtypes is essential for AD diagnosis and treatment. We integrated multiomics data from brain tissues of the ROSMAP and MSBB studies using a subspace merging algorithm and identified two AD patient clusters with notable cognitive and AD pathology differences. Analysis of differentially expressed genes (DEGs) in brain and blood samples pinpointed the LDLR gene as a potential blood biomarker linked to brain gene expression changes. Furthermore, we conducted PheWAS analysis on All of Us Projects EHR and WGS dataset for 105 eQTLs associated with the DEGs and revealed significant associations between these eQTLs and several phenotypes, shedding light on potential regulatory roles of these genes in diverse physiological processes. Our study successfully integrated multiomics data and proposes LDLR as a candidate blood biomarker for AD subtyping. The identified phenotypic signatures provide valuable insights on molecular mechanisms underlying AD heterogeneity, paving the way for personalized AD treatment.

bioinformatics↗

bioRxiv2025

Evolutionary Sequence and Structural Basis for the Epistatic Origins of Drug Resistance in HIV

The emergence of drug resistance in the human immunodeficiency virus (HIV) remains a formidable challenge to the long-term efficacy of antiretroviral therapy (ART). A growing body of evidence highlights the critical role of epistasis, the dependence of mutational effects on the sequence context, in shaping the fitness landscape of HIV under ART-induced selection pressure. However, the biophysical origins of the epistatic interactions involved in engendering drug-resistance mutations (DRMs) remain unclear. Are the mutational correlations "intrinsic" to the properties of the protein, or do they arise because of drug binding? We use a Potts sequence-covariation statistical energy model built on patient-derived HIV-1 protein sequences to construct computational double mutant cycles that probe pairwise epistasis for all observed mutations across the three major HIV drug-target enzymes. We find that the strongest epistatic effects occur between mutations at residue positions that frequently mutate during the course of ART, termed resistance-associated positions. To investigate the structural origins of the strongest epistatic interactions, we perform [~]100 free energy perturbation molecular dynamics simulations, revealing that the primary contribution to the pairwise epistasis between DRMs arises from cooperative effects on protein stability and folding as an intrinsic consequence of the protein mutational landscape. The results collectively reinforce a mechanism of resistance evolution whereby viruses escape drug pressure by selectively engendering mutations at "intrinsically" coupled sites, allowing them to cooperatively ameliorate fitness detriments incurred by individual DRMs. SignificanceEpistasis refers to the phenomenon where the effect of a mutation on protein structure and function is dependent on the genetic sequence background of the mutation, resulting in the combined effect of mutations being non-additive. Epistasis plays a significant role in the evolution of drug resistance in viruses such as HIV under therapeutic selection pressure. We combine a protein sequence coevolutionary model and molecular dynamics free energy simulations to identify and probe the mechanistic origins of the strongest epistatic interactions connecting HIV drug-resistance mutations. The work establishes a foundation to probe the molecular bases of epistasis and predict the evolution of resistance predicated on the knowledge of epistatic interaction networks.

biophysics↗

bioRxiv2025

Changes in nuclear and actin mechanics from G1 to G2 affect nuclear integrity

The structural integrity of the nucleus is dependent on nuclear mechanical elements of chromatin and lamins to resist antagonistic actin cytoskeleton forces. Imbalance results in nuclear blebbing, rupture, and cellular dysfunction found in many human diseases. We used Fluorescent Ubiquitin Cell Cycle Indicator (FUCCI) cells to determine how cell cycle changes affect the nucleus and actin force balance. While nuclear blebs are present equally throughout interphase, nuclear blebs form predominantly in G1 and then persist into G2 due to increased actin-based nuclear confinement and focal adhesion density in G1 vs. G2 cells. Upon artificial confinement, G2 nuclei ruptured more than G1 nuclei. Single nucleus micromanipulation force measurements confirmed that G1 nuclei are stronger than G2 nuclei in both the chromatin-based and lamin-based nuclear stiffness regimes. Decreased nuclear stiffness can be explained by loss of peripheral H3K9me3 from G1 to G2, recapitulated by H3K9me3 inhibition via Chaetocin. Cell cycle-based changes in nuclear and actin mechanics impact nuclear integrity and shape.

cell biology↗

bioRxiv2025

Modular Engineering of Escherichia coli for Enhanced Nickel Uptake, Survival, Biomineralization, and Hydrogen-Supported Bioremediation

Nickel contamination poses increasing environmental and industrial challenges, demanding effective and sustainable remediation strategies. Here, we present a modular synthetic biology approach to engineer Escherichia coli for efficient nickel enrichment, resilience in toxic environments, intracellular biomineralization, and hydrogen-based co-culture stability. Our system comprises four functional modules: (1) a nickel enrichment module, in which the fusion protein NixA-F1v outperformed existing uptake systems, and a mutant repressor RcnRC35L enhanced retention by suppressing nickel efflux; (2) a survival module where heterologous expression of Helicobacter pylori Hpn conferred tolerance to high nickel concentrations, and YejM increased phage resistance; (3) a nickel microparticle module enabling intracellular bioconversion of nickel into detectable microparticles even without engineered uptake proteins; and (4) a hydrogen supply module supporting co-culture stability through E. coli-cyanobacteria adhesion for sustained hydrogen production. These modules were experimentally validated through comparative uptake assays, survival profiling, and microscopy-based particle detection. Our findings demonstrate a customizable and integrative microbial platform for metal bioremediation and biosensing, with potential applications in environmental engineering and industrial waste treatment.

synthetic biology↗

bioRxiv2025

SpatialAgent: An Autonomous AI Agent for Spatial Biology

Advances in AI are transforming scientific discovery, yet spatial biology, a field that deciphers the molecular organization within tissues, remains constrained by labor-intensive workflows. Here, we present SpatialAgent, an autonomous AI agent for spatial biology research. SpatialAgent couples large language models with a Plan-Act-Conclude architecture, dynamic tool and skill retrieval, multimodal interpretation, and verification modules that audit generated claims. It supports the full discovery loop, from gene-panel design and multimodal annotation to trajectory inference, cell-cell communication analysis, imputation, and hypothesis generation. Across human and mouse brain, heart, tonsil, colon, and prostate datasets, SpatialAgent outperformed established computational baselines and matched or surpassed expert scientists in key tasks. In open-ended case studies, it recovered known tissue organization and generated spatially grounded hypotheses. In a prospective mouse prostate cancer Xenium study, it designed a compact 100-gene add-on panel that profiled 4.2 million cells across 21 samples, improved cell-type and malignant-state prediction, and captured spatially structured tumor and microenvironment programs. SpatialAgent establishes a framework for autonomous and collaborative discovery in spatial biology.

bioinformatics↗

bioRxiv2025

Lipid metabolism of hepatocyte-like cells supports intestinal tumor growth by promoting tracheogenesis

Tumors reprogram lipid metabolism in distant tissues to support their growth. In adult Drosophila, gut tumors secrete the PDGF/VEGF-like factor Pvf1, which activates the TORC1-Hnf4 pathway in hepatocyte-like oenocytes. This drives production of very long-chain fatty acids and wax esters essential for tracheal growth around the tumor. Blocking Hnf4 or the elongase mElo in oenocytes strongly suppresses tracheogenesis, tumor progression, and cachexia-like organ wasting, while extending host lifespan. The same pathway also controls tracheal development in healthy flies. Lipoprotein receptor LpR2 depletion in oenocytes rescues the observed tumor induced tracheal tracheal remodeling. This tumor-host interaction is conserved: VEGF-A induces lipid metabolism genes in human hepatocytes, and lung tumor-bearing mice show elevated hepatic Hnf4 and Elovl7. The study reveals a non-autonomous role of the TORC1-Hnf4 axis in lipid-mediated tumor progression and identifies potential therapeutic targets for cancer-associated metabolic dysfunction.

cell biology↗

bioRxiv2025

Multimodal brain cell atlas across the adult macaque lifespan

High-throughput single-cell omics of non-human primate tissues present a remarkable opportunity to study primate brain aging. Here, we introduce a transcriptomic and chromatin accessibility landscape of 1,985,317 cells from eight brain regions of 13 cynomolgus female monkeys spanning adult lifespan including exceptionally old individuals up to 29-years old. This dataset uncovers dynamic molecular changes in critical brain functions such as synaptic communication and axon myelination, exhibiting a high degree of cell type and brain region specificity. We identify the multicellular networks of the pons and medulla as a previously unrecognized hotspot for aging. Furthermore, comparative analyses with human neurodegeneration datasets highlight both shared and distinct mechanisms contributing to aging and disease. In addition, we uncover transcription factors implicated in monkey brain aging and pinpoint aging-regulated loci linked to longevity and neurodegeneration. This spatiotemporal atlas will advance our understanding of primate brain aging and its broader implications for health and disease.

neuroscience↗

bioRxiv2025

A genetic signature of resistance to activity-based anorexia in striatal projecting cortical neurons

ObjectiveConverging evidence from neuroimaging studies and genome-wide association study (GWAS) suggests the involvement of prefrontal cortex (PFC) and striatum dysfunction in the pathophysiology of anorexia nervosa (AN). However, identifying the causal role of circuit-specific genes in the development of AN-like phenotype remains challenging and requires the combination of novel molecular tools and preclinical models. MethodsWe used the activity-based anorexia (ABA) rat model in combination with a novel viral-based translating ribosome affinity purification (TRAP) technique to identify transcriptional differences within a specific neural pathway that we have previously demonstrated to mediate pathological weight loss in ABA rats (i.e. medial prefrontal cortex neurons that project to the nucleus accumbens shell). We compared actively transcribed genes in rats susceptible to weight loss to the subpopulation of rats resistant to weight loss under the same experimental conditions. ResultsWe reveal 1424 differentially expressed genes between Susceptible and Resistant rats, highlighting important transcriptional changes associated with ABA within this pathway. The changes observed were independent of current calorie deficit and associated with metabolic, mitochondrial and neural functions. Further, we show that genes upregulated in Resistant rats were involved in mitochondrial function, while downregulated genes were associated with cytoskeletal, postsynaptic and axonal functions, supporting the hypothesis that hyperexcitability of cortico-striatal circuit function is a critical mediator of pathological weight loss in ABA. DiscussionThese findings represent an essential first step in understanding how circuit-specific gene expression patterns may contribute to susceptibility to ABA and provide potential molecular targets for manipulation in this animal model of AN. Public SignificanceThis study identifies specific brain gene activity patterns that may explain why some individuals are more vulnerable to extreme weight loss, as seen in anorexia nervosa. Using an advanced molecular technique in a well-established animal model, key differences in a neural pathway linked to cognitive control were observed. These findings pave the way for more targeted treatments that could prevent or reverse this dangerous condition. SummaryO_LITranscriptional differences within medial prefrontal cortex to nucleus accumbens shell (mPFC-AcbSh) neurons distinguish rats highly susceptible to activity-based anorexia (ABA) from those resistant to pathological weight loss. C_LIO_LIResistant rats showed upregulation of mitochondrial function genes, while Susceptible rats had upregulation of genes related to synaptic structure and signalling, implicating excitability of this circuit in driving maladaptive weight loss behaviour. C_LIO_LITranscriptomic changes align with human genome-wide association study (GWAS) findings and support links between anorexia nervosa and both metabolic and psychiatric comorbidities. C_LIO_LIThe study highlights potential molecular targets for future gene manipulation and therapeutic intervention. It also provides a foundation for creating refined genetic animal models that integrate multiple AN-associated variants to better reflect the polygenic nature of the disorder. C_LI

neuroscience↗

bioRxiv2025

Early cell cycle genes in cortical organoid progenitors predict interindividual variability in infant brain growth trajectories

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individuals genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

neuroscience↗

bioRxiv2025

Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using Deep Transfer Learning

SummaryThe examination of high-risk cells and regions in tissue samples from spatially resolved transcriptomics platforms offers meaningful insights into specific disease processes. For existing methods, while cell types or clusters can be identified and associated with disease attributes, individual cells are unable to be associated in the same manner. MethodDiag-nostic Evidence GAuge of Single-cells and spatial transcriptomics (DEGAS), solves the above problem by employing latent representations of gene expression data and domain adaptation to transfer disease attributes from patients to individual cells from single-cell RNA sequencing datasets. In this research, we present and evaluate DEGASs versatility in adapting to data arising from various single-cell spatially resolved transcriptomics (scSRT) platforms. DEGAS successfully identified high-risk cells and regions in liver hepatocellular carcinoma and skin cutaneous melanoma, which were validated through known markers. Additionally, DEGAS was applied to our newly generated Type II Diabetes Xenium dataset revealing high-risk cells within the tissue samples. Availability and ImplementationThe DEGAS software can be accessed at https://github.com/tsteelejohnson91/DEGAS. For the updated smoothing functions and associated codes, visit https://github.com/dchatter04/DEGAS-Spatial-Smoothing. Sources for the datasets reviewed are detailed in their respective sections. A description of some datasets, along with extra tables and figures, is provided in the Supplementary Materials file. Our newly generated Xenium data for Type II Diabetes can be found at https://doi.org/10.7303/syn68699752.

bioinformatics↗

bioRxiv2025

A novel deep optimal transport framework reveals prostate cancer risk heterogeneity, Alzheimers disease risk heterogeneity, and myeloma cells associated with a short-term sham bortezomib response and progressive disease

Leveraging single-cell gene expression profiles can significantly enhance our understanding of diseases by associating single cells with traits such as disease subtypes, prognosis, and drug response. Although previous efforts have linked single cell clusters and groups with these attributes, they have primarily focused on changes in cell proportions while overlooking transcriptional changes at the single cell level. To further unravel cell heterogeneity with clusters and reveal the nuanced behaviors of cellular subtypes, it is essential to assess the disease associations of individual cells. Previous methods often fail to capture complex patterns that are only discernible through summarizing non-linear relationships across multiple genes. The Diagnostic Evidence GAuge of Single-cells/Spatial-transcriptomics (DEGAS) framework advances these efforts by aligning single cells and/or spatial transcriptomics regions with patients through a unified latent space using non-linear transformations learned from deep neural networks (DNNs). DEGAS achieves superior performance in analyzing single cell and spatial transcriptomics datasets, including Alzheimers disease (AD), multiple myeloma (MM), and prostate cancer (PDAC). Here we present DEGAS version 2, which has been updated with optimial transport based transfer learning and improved time-to-event loss functions, more advanced model architecture, and improved model baseline evaluations. DEGASv2 outperformed other methods in both single cell and spatial transcriptomic baseline comparisons. On the multiple myeloma discovery dataset, DEGASv2 enabled us to discover cell types that exhibited distinct drug response patterns over various time frames and were validated with time series single cell multiomic data that we generated, demonstrating a dangerous subtype of cell and novel therapeutic target.

bioinformatics↗

bioRxiv2025

Elevated SUN1 promotes aging-related polarity defects through mechanical coupling microtubules to the nuclear lamina

In migratory fibroblasts, the front-rear polarity required for cell migration is defined by an anterior centrosome relative to the nucleus. To achieve this polarity, actin cables drive the nucleus backward by coupling to nuclear membrane proteins nesprin-2G and SUN2. Aging disrupts this cell polarity by increasing the protein levels of SUN1, a SUN2 homolog. Here, we investigated the molecular mechanisms behind this disruption and found that the dominant negative effect of SUN1 and progerin, an aging-related lamin A variant, required direct SUN1-lamin A interaction. Microtubule interaction and force transmission through a nesprin, identified as nesprin-2, are crucial for SUN1s effect. We further discovered that stable microtubules are both necessary and sufficient to inhibit cell polarity. Using SUN1-SUN2 chimeric proteins, we demonstrated that the SUN domains determine their roles in cell polarization. Our findings reveal how elevated SUN1 disrupts cell polarity through coupling microtubule and nuclear lamina, emphasizing the impact of altered microtubule stability and nuclear mechanotransduction in aging.

cell biology↗