Biology subjects

Zhang, J.

Publications and source records attributed to Zhang, J..

At least 289 records · Page 16Linked to original sources

bioRxiv2026

iGS: A Zero-Code Dual-Engine Graphical Software for Polygenic Trait Prediction

Genomic selection (GS) has become the core driving force in modern plant and animal breeding. However, state-of-the-art comprehensive GS tools often rely on complex underlying environment configurations and command-line operations, posing significant technical barriers for breeders lacking programming expertise. To address this critical pain point, this study developed a fully "zero-code" graphical user interface (GUI) decision support system for genomic selection. The platform innovatively employs a "portable dual-engine architecture" (R-Portable and Python-Portable) to achieve completely dependency-free, "out-of-the-box" deployment, and integrates a standardized six-step end-to-end workflow from data quality control to result export. Furthermore, the platform comprehensively integrates 33 cutting-edge prediction models across four major paradigms, linear, Bayesian, machine learning, and deep learning, and features an original intelligent parameter configuration system that dynamically renders algorithm parameters to provide a minimalist UI interaction experience. Benchmark testing on the Wheat2000 dataset across six complex agronomic and quality traits, including thousand-kernel weight (TKW) and grain protein content (PROT), demonstrated that classic linear models remain highly robust for polygenic additive traits, while tree-based machine learning and hybrid deep learning architectures exhibit superior predictive potential and noise resilience when resolving complex epistatic effects and low-heritability traits. The successful deployment of this platform fundamentally liberates biologists from the constraints of computational science, providing robust digital infrastructure to accelerate the popularization and practical application of GS technologies in agricultural production.

bioinformatics↗

bioRxiv2026

Multiomic Spatial Imaging Assay (MSIA) -- A High-plex In Situ Detection Method for mRNAs, Proteins, and Protein-Protein Interactions using Manual And Semi-Automated Workflows

We report Multiomics Spatial Image Analysis (MSIA), a suite of technologies that include streamlined manual and semi-automated workflows to image 100s of RNAs from fresh frozen and formalin-fixed paraffin-embedded (FFPE) samples, contextualize RNAs with select protein markers including imaging synaptic protein interactions, a data analysis pipeline that includes corrections for chromatic aberration, algorithms for image stitching, deep learning models for image analysis (cell segmentation and RNA dot detection using hundreds of RNAscope images) from multiple stain-image cycles and proof of concept for a language model trained on extensive Parkinsons Disease (PD) literature. We used the language model to identify potential new biomarkers that were confirmed in MPTP-treated mouse model of PD. Furthermore, by incorporating more stain-image cycles alongside an additional fluorescent channel and advanced error-correcting decoding schemes, the MSIA workflow demonstrates the scalability to potentially quantify over 1,000 genes.

neuroscience↗

bioRxiv2026

Microhomology-Mediated Tandem Duplication Drives Tandem Repeat Formation Across Life

Tandem repeats (TR) are common genomic elements that are highly variable and with major functional consequences. Yet, the evolutionary trajectory for their formation remains poorly understood. One proposed mechanism is microhomology-mediated tandem duplication (MTD), in which single-copy DNA segments flanked by microhomology undergo tandem duplication (TD) and can further expand into TRs. Although MTD was first identified in the fission yeast Schizosaccharomyces pombe, its universal occurrence and postulated role in TR evolution have not been established. Using whole-genome deep sequencing and new analytical tools, we show that MTDs occur de novo universally across bacteria, archaea, fungi, and viruses. Further analysis of 2,245 reference genomes and millions of isolate genomes from 103 prokaryotic and eukaryotic microbial species, combined with human population TD data, somatic-germline mutations, and disease-associated variants, reveals that MTDs are consistently the dominant TD-forming mechanism across domains of life. Evidence suggests that MTDs have initiated the formation of the majority of existing TRs in genomes. Importantly, MTDs also prevail in human pathogenic TR mutations, including those linked to cancers. Mechanistically, deletion of the conserved mutator gene Rad27 specifically increased de novo MTD frequency in the budding yeast Saccharomyces cerevisiae, implicating Rad27-mediated Okazaki fragment maturation in MTD formation. These findings establish MTD as a universal and functionally significant mechanism for TR genesis. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/708716v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@db9ff1org.highwire.dtl.DTLVardef@e1a14corg.highwire.dtl.DTLVardef@124be79org.highwire.dtl.DTLVardef@15531ea_HPS_FORMAT_FIGEXP M_FIG C_FIG Significance StatementMicrohomology-mediated tandem duplications (MTDs) represent a universal mechanism driving tandem repeat formation across all domains of life--from viruses to humans. These duplications initiate genome evolution by expanding into functional tandem repeats and are the predominant form of pathogenic tandem duplications in human cancers and heritable disorders. Critically, disruption of the conserved Okazaki fragment processing pathway promotes MTD formation, establishing a fundamental link between DNA replication fidelity and genomic plasticity.

microbiology↗

bioRxiv2026

Exploring a mathematical framework for quantifying cell size- dependent glucose uptake in adipocytes

Insulin-stimulated glucose uptake (ISGU) in adipocytes is central to maintain systemic glucose homeostasis. Understanding how ISGU relates to adipocyte traits, such as cell-size, is critical for elucidating pressing questions related to metabolic dysfunction connected to adipose hypertrophy and hyperplasia. Cell size is considered a central trait reflecting multiple aspects of adipocyte metabolism. However, a robust quantitative approach to estimate ISGU for a specific cell size is currently missing. Here, in an attempt to move towards such a method, we have formulated an approach using a mathematical framework. The framework consists of a linear equation: the product of the known number of cells (calculated using coulter counter-based cell-size distributions) and the unknown ISGU/cell, compared to the absolute ISGU (measured using 14C-glucose tracer assays). To solve this equation, we formulate a minimization problem which is optimized to find the unknown ISGU/cell for the best solution. Using different formulations of the equation we can investigate the need for either cell size-dependent or independent ISGU/cell, to describe varying glucose uptake in a cell sample of various cell sizes. While the framework needs further refinement, we demonstrate that cell size-dependent uptake slightly improved the agreement between model and experimental data for some groups. Together, with further validation this could serve as a useful tool to resolve long-standing questions regarding size-dependent characteristics like adipocyte size and cellular function. Key findingsHerein we explore a method to quantify cell size-dependent glucose uptake in adipocytes

cell biology↗

bioRxiv2026

MAP: A Knowledge-driven Framework for Predicting Single-cell Responses for Unprofiled Drugs

Predicting how cells respond to chemical perturbations is one of the goals for building virtual cells, yet experimentally profiled compounds cover only a small fraction of this space. Existing models struggle to generalize to unprofiled compounds, as they typically treat drugs as isolated identifiers without encoding their mechanistic relationships. We present MAP, a framework that integrates structured biological knowledge into cellular perturbation modeling and supports zero-shot prediction for small molecules with scarce or absent perturbation profiles. Specifically: (i) we construct MAP-KG, a large-scale knowledge graph tailored for cellular perturbation modeling that unifies 14 public resources, spanning 187k drugs, 23k genes, and 694k mechanistic relationships; (ii) we propose a knowledge-driven pre-training strategy that aligns molecular structures, protein sequence features, and textual mechanistic descriptions into a unified embedding space via contrastive learning, producing mechanism-aware and transferable gene and compound embeddings. The resulting knowledge-informed gene and drug representations are then coupled with a pretrained single-cell foundation model to condition perturbation response prediction; (iii) we evaluate MAP under two zero-shot generalization regimes: unseen cell type-drug combinations and the stricter setting of unprofiled drugs, where it improves top-50 DEG Pearson delta correlation by up to +13.3% and +12.2%, respectively, over the strongest baselines across three benchmarks. We further perform pathway-level functional analysis via GSEA for in-silico screening, where MAP predicts coherent, mechanism-consistent programs on unprofiled candidate drugs, and prioritizes 4 of 5 approved anti-cancer drugs in A-549 (non-small cell lung cancer).

bioinformatics↗

bioRxiv2026

Transforming Histology into Virtual Multiplex Immunofluorescence to Decode Prognostic Spatial Immunity in Hepatocellular Carcinoma

The spatial organization of the tumor immune microenvironment (TIME) drives hepatocellular carcinoma (HCC) prognosis but remains unquantifiable on routine H&E slides. Here, we present HCCExplorer, a deep learning framework that translates H&E into virtual multiplex immunofluorescence (mIF) and uses multi-modal graph learning to decode spatial survival signals. Trained on 30 H&E-mIF slide pairs, HCCExplorer evaluated a 1,813-slide multi-center cohort. It achieved superior overall survival stratification over clinical indices and state-of-the-art pathology and protein foundation models, yielding a concordance index of 0.71 and a Hazard Ratio (HR) of 15.46 (P < 0.001), maintaining stability across three external cohorts. Beyond risk stratification, interpretation of model features identified M1 macrophage infiltration as a protective determinant (HR=0.40, P < 0.05). Furthermore, it uncovered a protective "Containment Niche" at the invasion frontier (HR=0.02, P < 0.01), featuring macrophages co-localizing with Foxp3+ Tregs and CD4+ T cells. Ultimately, HCCExplorer provides actionable, spatially-resolved biomarkers from conventional histology for precision HCC management.

pathology↗

bioRxiv2026

A dual-color miniature endoscope for calcium imaging in behaving mice

Calcium imaging with miniature endoscopes has become an essential tool in neuroscience, but conventional miniscopes typically record signals from only a single calcium indicator. Here, we present a dual-color miniature endoscope (miniscope) that enables simultaneous calcium imaging from two neuronal populations using spectrally distinct genetically encoded indicators. In freely moving mice, we used this system to record activity from striatal neurons of the direct (dSPN) and indirect (iSPN) pathways. We showed that dSPNs were activated earlier than iSPNs during contraversive movements, with dSPNs preferentially active during acceleration and iSPNs during deceleration. During ipsiversive turns, however, this temporal relationship was reversed. These findings indicate that dSPNs and iSPNs are not concurrently active, but instead exhibit complementary, direction-dependent dynamics that govern movement velocity. Our dual-color miniscope provides a compact, cost-effective platform for simultaneous two-population imaging, offering new opportunities to dissect coordinated activity across neural circuits in freely behaving animals.

neuroscience↗

bioRxiv2026

A biofidelic Goat Model of Traumatic Optic Neuropathy with Optic Canal Fracture via Transnasal Endoscopy

Traumatic optic neuropathy (TON) is a major cause of irreversible vision loss following blunt cranial trauma, yet the absence of clinically relevant large animal models that faithfully recapitulate human TON has significantly impeded translational research. Current rodent models do not reproduce optic canal fracture, a key injury mechanism in many patients with TON. Here, we combined high-fidelity finite element analysis (FEA) with iterative engineering to establish a reproducible goat model of TON. We first built a high-resolution human-head finite element model to characterize force transmission to the optic nerve. Across clinically relevant periorbital impacts, stress preferentially concentrated in the intracanalicular segment, reaching a peak force density of approximately 500 N/m2 at 50.6 ms, about fivefold higher than in the intraorbital segment. Simulations further showed that direct optic canal impact reproduced comparable intracanalicular stress with a markedly lower input force: 195 N, compared with 3900 N for periorbital impact. Guided by these insights, we developed transnasal endoscopic impact systems capable of inducing optic canal fractures in goats. TON was confirmed within 24 hours by a characteristic relative afferent pupillary defect (RAPD), and at 1 month post-injury goats (n = 14) exhibited a 10%-20% reduction in ganglion cell complex (GCC) thickness and 40%-65% reductions in flash visual evoked potential (FVEP) and pattern electroretinogram (PERG) amplitude ratios (all P [≤] 0.001), with structural and functional preservation of the fellow eye. This study presents a robust, standardized, and clinically relevant large-animal platform for investigating TON pathophysiology.

neuroscience↗

bioRxiv2026

Saline-free preparation for chronic in vivo imaging in adult Drosophila

Longitudinal brain imaging is essential for understanding neural mechanisms. Here, we present a saline-free, chronic preparation for repeated neural recording in adult Drosophila over multiple days. We describe steps for mounting flies, performing manual surgery on the head cuticle without external saline, and resealing the opening to create a transparent optical window. We demonstrate the utility of this approach by tracking single-neuron spiking and neuronal calcium dynamics over 7-10 days. This protocol is potentially applicable to other insect species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/706199v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@abeb34org.highwire.dtl.DTLVardef@deaf93org.highwire.dtl.DTLVardef@1d8fc24org.highwire.dtl.DTLVardef@91a696_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Foundation Models Improve Perturbation Response Prediction

Predicting cellular responses to genetic or chemical perturbations has been a long-standing goal in biology. Recent applications of foundation models to this task have yielded contradictory results regarding their superiority over simple baselines. We conducted an extensive analysis of over 600 different models across various prediction tasks and evaluation metrics, demonstrating that while some foundation models fail to outperform simple baselines, others significantly improve predictions for both genetic and chemical perturbations. Furthermore, we developed and evaluated methods for integrating multiple foundation models for perturbation prediction. Our results show that with sufficient data, these models approach fundamental performance limits, confirming that foundation models can improve cellular response simulations. Code and Data: https://github.com/genbio-ai/foundation-models-perturbation

bioinformatics↗

bioRxiv2026

Unlocking the Bile Acid Universe: Advanced Workflows and a Multidimensional Library of 280 Unique Species

Microbes and bile acids are tightly intertwined, especially in the gut. While the liver produces primary bile acids from cholesterol, gut bacteria transform these into diverse secondary forms which act as powerful signaling molecules, influencing host metabolism and immune function. Since bile acid changes are increasingly linked to health and disease, their accurate measurement in the gut and circulation is essential. Analytical evaluations, however, remain challenging as many bile acids co-elute in liquid chromatography (LC), share identical precursor masses in mass spectrometry (MS), and produce similar tandem mass spectrometry (MS/MS) spectra. As a result, conventional LC-MS/MS workflows struggle to differentiate bile acids, motivating the addition of orthogonal separations such as ion mobility spectrometry (IMS). Here, we assess optimal bile acid extraction parameters for stool, serum, and plasma; compare LC conditions; and assess electrospray ionization performance across polarities. Additionally, we created a multidimensional reference library containing LC retention times, IMS collision cross section values, and accurate precursor masses for 280 unique bile acids (264 endogenous and 16 deuterium-labeled species) including unconjugated, host-conjugated, and microbially conjugated bile acids. This multidimensional library empowers bile acid identification in complex samples and enables a more comprehensive exploration of their biological roles and disease associations.

microbiology↗

bioRxiv2026

Wildfire emitted particulate matter induces ovarian hyperandrogenism through aryl hydrocarbon receptor activation

Wildfires have become more frequent and intense worldwide. Wildfire emitted particulate matter (WFPM) can be more toxic than urban background PM due to its greater content of nanoscale size (WFPM0.1) and presence of more polar organic compounds, including polycyclic aromatic hydrocarbons (PAHs). While exposure to WFPM has been linked to cardiovascular and respiratory diseases, its impact on female reproduction remains elusive. Here, we used an in vivo mouse intratracheal exposure model and a 3D ovarian follicle culture system, together with molecular, transcriptomic, and computational approaches, to examine the female reproductive effects of lab-synthesized (LS-WFPM0.1) and real-world Canadian WFPM0.1 (C-WFPM0.1), collected from the New York City and New Jersey metropolitan area during the June 2023 wildfire events. Intratracheal exposure to environmentally relevant dose of LS-WFPM0.1 disrupted mouse estrous cycles and elevated serum concentrations of estradiol and testosterone. RT-qPCR and single-follicle RNA-sequencing (RNA-seq) analysis revealed altered steroidogenic genes, transcriptomic changes, and activation of aryl hydrocarbon receptor (AhR) in antral follicles from mice treated with LS-WFPM0.1. LS-WFPM0.1 consistently increased testosterone secretion and stimulated genes related to androgen synthesis and AhR in vitro. Single-follicle and single-oocyte RNA-seq analysis identified differentially expressed genes related to inflammation in somatic cells and mitochondrial respiratory chain in oocytes. Both C-WFPM0.1 and benzo[a]pyrene, a high-molecular-weight PAH, reproduced these ovarian defects. Mechanistically, AhR inhibition reversed hyperandrogenism induced by WFPM0.1. Together, our findings suggest that WFPM0.1, an increasingly pervasive environmental exposure, adversely impacts female reproductive functions by disrupting ovarian steroidogenesis and inducing hyperandrogenism through AhR activation, highlighting an urgent unmet need for further mechanistic studies and epidemiological investigations to define the reproductive risks of wildfire smoke exposure in human populations.

pharmacology and toxicology↗

bioRxiv2026

Decoding the Mechanism of Action of a Parasite TGFβAntagonist Inspires the Creation of Cell-type-specific TGFβ Modulators

Heligmosomoides polygyrus, a mouse parasite, modulates host immunity by secreting modular transforming growth factor-{beta} (TGF{beta}) mimics (TGMs). The agonist TGM1 interacts with TGFBR1, TGFBR2, and the co-receptor CD44 through domains D1/2, D3, and D4/5, respectively. In contrast, the antagonist TGM6, which lacks D1/2, but retains TGFBR2 binding through D3, targets different subsets of cells compared to TGM1. The TGM6 co-receptor is unknown. Using X-ray crystallography and binding studies, we show that TGM6 preferentially binds mouse TGFBR2 over human TGFBR2, and that this is essential for its antagonistic function. We identified low-density lipoprotein receptor-related protein 1 (LRP1) and betaglycan (TGFBR3) as co-receptors for TGM6. LRP1 enhances TGM6 efficacy and is vital for its specific antagonistic effects by promoting TGFBR2 degradation, while betaglycan counteracts TGM6 in a TGFBR2-dependent manner. The modular organization of TGMs enabled us to rationally design TGM1/6 chimeras or TGM-D3 fusion with an affibody that recognizes a specific cell-surface receptor, thereby altering cell-type specificity and functionality. Furthermore, we developed a TGFBR2 nanobody that, on its own, has no inhibitory effect but, when fused to a receptor antibody, antagonizes TGF{beta} signaling in a cell-selective manner. Thus, we designed programmable agents that modulate TGF{beta} signaling only in target co-receptor-expressing cells.

biochemistry↗

bioRxiv2026

Oxidation-Driven mtDNA B-Z Transition Activates ZBP1 to Mediate Acetaminophen Hepatotoxicity

Acetaminophen (APAP) overdose induces mitochondrial damage in hepatocytes, leading to secondary toxicity that resists to N-acetylcysteine (NAC) treatment and culminates in hepatocyte death and acute liver failure (ALF)1. The underlying mechanisms remain poorly understood, often necessitating liver transplantation2. Here, we identify oxidative modification-driven B-to-Z transitions in mtDNA as the central pathological driver of APAP-induced ALF. Upon APAP exposure, oxidized mtDNA fragments leak into the cytosol, activating ZBP1 signaling via its Z domain. Genetic inhibition of ZBP1 mitigates liver damage and improves survival. Using synthetic 12-bp dGdC duplexes, we demonstrate that 8-oxoG substitution, even under physiological salt concentrations, is sufficient to induce Z-DNA formation, enabling specific ZBP1 binding through its Z domain. The 8-oxoG repair enzyme OGG13, activated by TH107854, removes 8-oxoG modifications and reverses Z-DNA to B-DNA conformation. In mice with lethal APAP toxicity, delayed NAC treatment results in 50% mortality. In contrast, TH10785 monotherapy increases survival to 90%, while its combination with NAC achieves 100% survival. These results define the oxidized mt Z-DNA-ZBP1 axis as a critical driver of APAP hepatotoxicity, providing fundamental insights into DNA conformational dynamics and therapeutic opportunities in drug-induced liver failure.

cell biology↗

bioRxiv2026

Unwinding of RNA G-quadruplexes induces mouse and human totipotency

Totipotent stem cells (TotiSCs) have significant application prospects in regenerative medicine and assisted reproductive technologies. The mRNAs of totipotency-related genes are rich in G-quadruplex (G4) structures, while the G4 density of pluripotency-related genes mRNAs is relatively low, suggesting a potential functional link between RNA G4s and the totipotent state. Nevertheless, how G4s are involved in the establishment or maintenance of mouse and human totipotency remains poorly understood. Here, we demonstrate that FC3, a small-molecule RNA G4 unwinder, enables efficient induction and maintenance of mouse and human TotiSCs (mG4TotiSCs and hG4TotiSCs). These FC3-induced TotiSCs closely recapitulate the molecular features of 2-cell-stage blastomeres via transcriptomic and epigenomic profiling. Functionally, both mG4TotiSCs and hG4TotiSCs exhibit totipotent capacity: they differentiate into embryonic and extraembryonic lineages in vitro and in chimeric embryos; moreover, they autonomously self-organize into blastocyst-like structures without exogenous signalling. Mechanistically, we identify that Gata2 mRNA, a G4-containing transcript, is essential for FC3-mediated reprogramming. Genetic disruption of Gata2 would abolish totipotency induction. Collectively, RNA G4 unwinding is a new regulatory axis governing totipotency acquisition and maintenance, providing insight into the functional interplay between nucleic acid secondary structures and cell fate plasticity.

cell biology↗

bioRxiv2026

Physically Grounded Generative Modeling of All-Atom Biomolecular Dynamics

Predicting the kinetic pathways of biomolecular systems at all-atom resolution is crucial for understanding protein function and drug efficacy, yet this task is hindered by the immense computational cost of conventional molecular dynamics (MD) simulations. While deep learning has revolutionized static structure prediction and equilibrium ensemble sampling, simulating the kinetics of conformational transitions remains a critical challenge. We introduce BioKinema, a physically grounded generative model that predicts continuous-time, all-atom biomolecular trajectories at a fraction of the cost of traditional simulations. In particular, BioKinema utilizes a spatial-temporal diffusion architecture motivated by the exponential decay of correlations characteristic of Langevin dynamics, and is explicitly trained to generate trajectories with the correct kinetics and thermodynamics. It employs a hierarchical forecasting-and-interpolation strategy to overcome the error accumulation that often plagues long-horizon generation. Through extensive validation, we demonstrate that BioKinema generates physically stable and dynamically accurate trajectories suitable for rigorous downstream analysis. For protein systems, it reproduces the equilibrium thermodynamics of the conformational ensemble and the underlying kinetics. For protein-ligand complexes, it successfully elucidates mechanisms such as ligand-driven conformational changes and allosteric interactions. Furthermore, BioKinema leverages enhanced sampling data to predict rare kinetic events, emerging as a powerful tool for estimating ligand unbinding pathways. Collectively, these results establish BioKinema as a computationally efficient complement to MD simulations that bridges the gap between static structure and dynamic function, enabling high-throughput exploration of the kinetic landscape for structural biology and drug discovery.

bioinformatics↗

bioRxiv2026

Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC

Batch effect correction is a common and often necessary step in data analysis to reduce bias due to technical and experimental factors when combining multiple batches of data. The severity of the batch effects dictates the correction strategy; therefore, a careful assessment of each datasets batch effects is necessary. BatchQC is an R package that provides reproducible tools and visualizations for quantitatively and qualitatively addressing batch effects across a broad range of data types. BatchQC integrates with standardized Bioconductor data structures and features an object-oriented design, enabling the application of workflows that can freely evaluate and process data within and outside the package tools. Common batch evaluation methods, along with novel quantitative metrics, help determine the benefits of batch correction for each dataset and enable direct comparisons between methods. Here, we present BatchQC as the first comprehensive batch-correction R package, with independent tools, reproducible workflows, visualization, and novel statistics.

bioinformatics↗

bioRxiv2026

Nerve Injury-Induced Protein 2 Is Necessary for the Lysosome Membrane Integrity and Protects Cells from Ferroptosis

Nerve injury-induced protein 1 (NINJ1), a cell adhesion molecule, is oligomerized during lytic cell death and mediates plasma membrane rupture to release large intracellular molecules that propagate the inflammatory response. We and others previously showed that NINJ2, a close relative of NINJ1, does not promote plasma membrane rupture to spread inflammation. Here, we identify that NINJ2 is necessary for the lysosome membrane integrity to protect cells from ferroptosis. Specifically, we found that NINJ2 localizes to lysosomes and interacts with LAMP1, an anchor glycoprotein of the lysosome membranes and a sensor of stressed lysosomes. We also found that loss of NINJ2 exacerbates lysosomal membrane permeabilization (LMP), which allows for selective leakage of lysosomal contents, such as labile iron, into the cytosol. Accordingly, loss of NINJ2 elevates cellular labile iron accumulation and decreases expression of ferritins, the primary intracellular iron storage protein complexes. Mechanistically, we found that loss of NINJ2 promotes ferritin FTH degradation in lysosomes, which can be reversed by knockdown of LAMP1. Moreover, we found that loss of NINJ2 sensitizes cells to ferroptosis induced by RSL3 and Erastin, consistent with a recent study that loss of Ninj2 predisposes mice to chronic inflammation. Together, these findings uncover a previously unrecognized activity of NINJ2 from lysosome homeostasis to ferroptosis, which can be explored as a cancer therapeutic strategy especially considering that NINJ2 and ferritins are found to be overexpressed and positively associated with iron-addicted cancers.

cancer biology↗