Biology subjects

Yang, C.

Publications and source records attributed to Yang, C..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

PhysioMotion Artifact: A Task-Driven EEG Dataset with Channel-Level Motion Artifact Annotations

Physiological artifacts pose persistent challenges in electroencephalo-gram (EEG) data acquisition, often compromising interpretation and post-analysis of EEG signals across research and clinical applications. To address such limitations, including various artifact types, insufficient annotations, and low spatial resolutions, we present PhysioMotion Artifact, a large-scale, task-driven EEG dataset with channel-level artifact annotations. EEG data was acquired from 30 healthy participants performing 16 systematically designed single-type and multi-type movement tasks, inducing 14 distinct types of physiological artifacts. To demonstrate the utility of the dataset, we implemented a Convolutional Neural Networks-Transformer hybrid model for artifact detection and classification, achieving 98% accuracy in binary classification and 85% in 14-class classification tasks.

neuroscience↗

bioRxiv2025

Machine learning-based definition of cellular senescence reveals pro-senescence potential implication in lung adenocarcinoma

Despite growing evidence implicating cellular senescence in tumor progression, methodological challenges in objectively quantifying senescent cell burden across cancer types continue to limit mechanistic studies and clinical translation. To address this, we developed the Predictive Cellular Senescence Model (PreCSenM), a machine learning-based tool that assigns a CS score by integrating senescence-associated features across 888 samples. Benchmarking analyses confirmed that PreCSenM markedly outperforms existing methodologies in reliably predicting senescent states within both normal and cancerous transcriptomic datasets. In lung adenocarcinoma (LUAD), PreCSenM identified the CS score as a robust predictor of clinical outcomes. Multi-omics analyses further revealed that lower CS levels correlate with decreased genomic stability and enhanced immune responses, aligning with clinical observations. Notably, drug discovery using PreCSenM identified histone deacetylase inhibitors (HDACis) as potent inducers of CS in LUAD. Transcriptional and epigenetic profiling of HDACi-treated cells pinpointed FOSB as a core transcription factor driving CS, and FOSB knockdown blocked HDACi-induced senescence. Overall, our study establishes PreCSenM as a multidimensional senescence quantification tool, bridging computational prediction with clinical relevance and mechanistic validation to enable senescence-directed therapies in precision oncology.

bioinformatics↗

bioRxiv2025

Ethylene-induced host responses enhance resistance against the root-parasitic plant Phelipanche aegyptiaca

AbstractsThe root parasitic plant Phelipanche aegyptiaca and related species pose a grand challenge for agriculture. By directly attaching to host crops to acquire resources, they can severely decrease yields. At the same time, their underground location hides them from sight for much of their life and protects them from typical weed control methods. New strategies for parasitic weed control are needed, with host resistance among the most attractive, but the mechanisms by which hosts respond to parasitic plant attacks are still unclear. In plants, the phytohormone ethylene is a crucial modulator responding to various stresses such as flooding and pathogen attack, and our data suggest that ethylene signaling may be important in host response to P. aegyptiaca. Here, we demonstrated that ethylene plays a role against P. aegyptiaca using two host plants, Arabidopsis and tomato. Arabidopsis plants with the ethylene reporter construct (EBS::GUS) were analyzed and revealed that stress from excess water and P. aegyptiaca parasitism both induced ethylene responses in the host Arabidopsis roots. We also observed that applying an ethylene precursor (ACC) to host roots inhibits the attachment of P. aegyptiaca. Lines of Arabidopsis and tomato with mutations in Ethylene-Resistant 1 (ETR1) and Constitutive Triple-Response 1 (CTR1) have altered tolerance to P. aegyptiaca, suggesting that the ethylene signaling pathway is associated with enhanced resistance to parasitism. These results point to ethylene-mediated responses as a starting point to gain insight into host response to parasitism, with potential to increase host resistance to parasitic plants.

plant biology↗

bioRxiv2025

Are Different Populations Fairly Represented in Single-Cell Omic Atlases?

Single-cell Omic atlases are transforming biology and medicine, yet their demographic representativeness has not been systematically evaluated. We analyzed >13,500 samples from the Human Cell Atlas (HCA), Human Tumor Atlas Network (HTAN), and PsychAD Consortium. Benchmarking against global and US general and disease-prevalence data, we found a striking, pervasive European over-representation and under-representation of Asian and Latino individuals. Nearly 70% of HCA samples lacked ancestry annotation, and among annotated samples, Europeans were over-represented sixfold. PsychAD was nearly two-thirds European. HTAN tumors were 69% European, with several cancer types showing sex skews beyond expected incidence. These disparities highlight that current single-cell resources risk embedding inequities into AI foundational models, biomarker discovery, and therapeutic development. We contextualize these findings within the structural, economic, and regulatory, survey the growing ecosystem of diversity-focused initiatives, and provide an actionable, field-specific checklist to help research teams design single-cell studies whose benefits extend equitably across populations.

genomics↗

bioRxiv2025

Tumor Organoids Modeling Reveals Timed Responses and Interplay of Radiotherapy and Chemotherapy in Pancreatic Cancer

PurposeModel the therapeutic effects of chemotherapy, radiotherapy, and chemoradiotherapy to study the heterogeneous responses of patient-derived tumor organoids (PDTOs). MethodsWe proposed novel mathematical forecasting frameworks based on logistic growth ordinary differential equations (ODEs) and the characteristics of tumor responses to model the therapeutic effects of chemo- and radiation-induced killing. To validate the models, we cultured PDTOs from different patients and treated them with radiation (4 Gy and 8 Gy), chemotherapy (FOLFIRINOX at a previously determined organoid-specific IC50 dose), and combined regimens, respectively. The diameters of 20-40 organoids per patient were tracked and measured using brightfield images up to 7 or 9 days following each treatment to capture organoid growth dynamics data, which was used for model fitting. The accuracy of the modeling was evaluated by the average normalized mean squared errors (NMSE) of data fitting. ResultsThe proposed mathematical modeling frameworks accurately captured the observed growth dynamics of three PDTO samples after chemotherapy, radiotherapy, and chemoradiotherapy, as reflected by the average NMSE of data fitting, which are all close to zero (less than 0.0045). The fitted parameters, including killing strength, effect window, and peak killing timing, revealed significant heterogeneities in treatment responses across different PDTOs. Chemotherapy shows pronounced effectiveness in the early stage, while radiotherapy exhibits the effect later in the first week, and a significantly stronger secondary response occurs one week after radiation. Chemoradiotherapy combines the strengths of both modalities, producing pronounced effects in both response phases, with modeling results suggesting that the radiation-induced killing effect may play a dominant role in the synergistic interaction. ConclusionsOur modeling frameworks demonstrated high accuracy in modeling the heterogeneous therapeutic responses of PDTOs and provided insights into the dynamic killing effects and interplay between chemotherapy and radiotherapy. The modeling of therapeutic responses of PDTOs provides a valuable tool for optimizing treatment regimens and informing clinical trial design, thereby improving the efficacy of personalized medicine.

cancer biology↗

bioRxiv2025

Endothelial CEPT1 Regulates Hepatic MTTP-Mediated Lipid Metabolism and Impacts Aortic Atherosclerosis

BackgroundThe regulation of hepatic lipid metabolism by vascular endothelial factors remains poorly characterized, despite its relevance to atherosclerosis and steatosis. Microsomal triglyceride transfer protein (MTTP) is essential for hepatic lipid metabolism, but its regulation by endothelial cells has not been previously investigated. ObjectiveThis study examined whether endothelial choline ethanolamine phosphotransferase 1 (CEPT1) modulates hepatic MTTP activity, impacting systemic lipid homeostasis and aortic plaque formation. Methods and ResultsHuman steatotic liver samples exhibited reduced CEPT1 and MTTP protein levels, correlating with diminished lipid exports. In mice, endothelial-specific Cept1 knockdown decreased hepatic MTTP expression, reduced serum triglyceride and cholesterol levels, and markedly attenuated aortic atherosclerosis without evidence of fat malabsorption. In vitro, endothelial CEPT1 silencing suppressed MTTP activity in co-cultured hepatocytes via a paracrine mechanism involving peroxisome proliferator-activated receptor (PPAR) signaling, which was rescued by fenofibrate treatment. Aortic histology confirmed reduced plaque burden and macrophage infiltration in CEPT1-deficient mice. ConclusionsEndothelial CEPT1 critically regulates hepatic MTTP through a paracrine axis, influencing lipid metabolism and atherogenesis. Targeting endothelial CEPT1 may represent a novel therapeutic approach to reduce steatosis and vascular atherosclerosis.

molecular biology↗

bioRxiv2025

Iron Metabolism and Antioxidant Defense: The Role of LI-rTMS in Cerebral Ischemia-Reperfusion

BACKGROUNDTreating cerebral ischemia/reperfusion injury (CIRI) is challenging, with iron metabolism imbalances and oxidative stress worsening the condition. While low-intensity repetitive transcranial magnetic stimulation (LI-rTMS) shows potential in brain protection, its effects on iron processing and antioxidant use are unclear. This study explored LI-rTMSs role in regulating iron homeostasis and antioxidant defense for potential therapeutic use in CIRI. METHODSA rat model of CIRI was established using a filament insertion technique, followed by neurological and infarct volume assessments. In PC12 cells, oxidative damage was induced with H2O2 or ferric ammonium citrate (FAC), then treated with LI-rTMS or deferoxamine (DFP). Parameters measured included oxidative stress markers, iron metabolism markers, and mitochondrial integrity. Gene knockdown and overexpression experiments explored mechanisms involving LI-rTMS and ACSL4. RESULTSLI-rTMS improved neurological deficits by boosting GPX4 activity and inhibiting the ACSL4-LPCAT3-LOX axis, reducing lipid peroxidation. It restored iron balance by lowering TFR and DMT1, decreasing ferritin and hepcidin, and increasing FPN for better iron efflux. LI-rTMS also prevented the Fenton reaction, reduced ROS production, and maintained mitochondrial structure and transmembrane potential. CONCLUSIONSLI-rTMS offers neuroprotection by regulating iron homeostasis through dual pathways: boosting export (via FPN) and reducing uptake/storage (via TFR/DMT1/FE). It also provides ACSL4-dependent antioxidant benefits, suggesting its potential to prevent ferroptosis in CIRI.

biophysics↗

bioRxiv2025

Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution

Formalin-fixed paraffin-embedded (FFPE) tissue-compatible spatial transcriptomics (ST) is transforming biology and histopathology, but achieving high sensitivity, genome-wide coverage, single-cell resolution and researcher-accessibility remains challenging. Here, we present a dendrimeric DNA coordinate-barcoding design for FFPE-adapted spatial RNA sequencing (Decoder-FFPE-seq), by integrating RNA-to-DNA probe conversion with blotting-assisted barcoding. Decoder-FFPE-seq provides single-cell resolution with a 2.54-4.25-fold sensitivity increase in mouse brain transcriptomic profiling over similar assays with an available protocol, successfully mapping rare genes and sparse cell types. It robustly resolves tumor microenvironments (TMEs) in clinical archives preserved 6-20 years. With expanded probe libraries, Decoder-FFPE-seq captures long non-coding and viral RNAs, revealing spatial co-expression networks with mRNAs in oral squamous cell carcinomas (OSCCs). In an OSCC immunotherapy cohort, it delineates TME heterogeneity across pathology response groups and tracks spatial cellular dynamics pre- and post-immuno-chemotherapy. Decoder-FFPE-seq establishes a robust, high-resolution framework for sensitive ST profiling in fundamental and translational studies of archival tissues.

genomics↗

bioRxiv2025

NicheScope: Identifying Multicellular Niches and Niche-Regulated Cell States in Spatial Transcriptomics

The functional state of a cell is intrinsically linked to its local microenvironment, or cell niche, a complex milieu formed by multiple interacting cell types. Deciphering how these multicellular niches regulate cell states is fundamental to understanding tissue biology and disease mechanisms, yet remains challenging with current computational approaches. Here, we present NicheScope, a computational framework for transcriptome-wide identification and characterization of Multicellular Niches (MCNs) and their corresponding Niche-Regulated Cell States (NRCSs) from spatial transcriptomics data. NicheScope operates on the principle that a cells transcriptional state is associated with its local multicellular composition. It employs a robust statistical approach to jointly model neighborhood composition and transcriptome-wide gene expression, enabling the simultaneous discovery of MCNs, defined by specific combinations of neighboring cell types, and their associated NRCSs, characterized by distinct gene programs. We demonstrate NicheScopes power and versatility across diverse tissues and platforms, including lymph node, lung adenocarcinoma, and head and neck cancer. NicheScope reproducibly dissected established tissue structures in lymph nodes across tissue regions and platforms, uncovered clinically relevant tumor cell-associated MCNs in lung adenocarcinoma, and revealed shared and condition-specific MCNs in primary and metastatic tumors. Our results establish NicheScope as a powerful, robust, scalable, and interpretable framework for dissecting the spatial and functional organization of complex tissues, providing new insights into multicellular coordination in health and disease.

bioinformatics↗

bioRxiv2025

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatial precision, and non-invasiveness1,2,3. Here, we utilize a programmable, ultrasound-responsive drug delivery platform based on hydrogen-bonded organic framework (HOF) nanoparticles, enabling on-demand anesthetic release with long-term and durable analgesic efficacy. A machine learning (ML)-guided screening pipeline was developed to evaluate about 250 FDA-approved drugs, spanning both hydrophilic and lipophilic agents, and identified bupivacaine (lipophilic) and lidocaine hydrochloride (hydrophilic) as optimal candidates. These agents were efficiently encapsulated into HOF nanoparticles via diffusion and double-solvent methods. Ultrasound-triggered drug release in vitro transiently suppressed calcium signaling in adeno-associated virus (AAV)-transfected neurons. In vivo, ultrasound-activated release of bupivacaine or lidocaine HCl at the sciatic nerve site significantly elevated mechanical nociceptive thresholds for up to seven days, reduced autotomy (self-mutilaion) behavior, and improved motor function in a rat model of chronic pain. Notably, the machine learning-identified top candidates not only exhibited high loading efficiency, but also demonstrated superior therapeutic outcomes in vivo, establishing a direct link between computational prediction and biological efficacy. This non-invasive, programmable HOF-based system provides a clinically translatable platform for on-demand, spatiotemporally precise pain management.

bioengineering↗

bioRxiv2025

Beyond Antagonism: IL-4 Exploits TNF signaling to Shape Its Gene Expression Signature in Monocytes and Macrophages.

Investigation of crosstalk between antagonistic pro- and anti-inflammatory cytokines has focused on mechanisms and functional consequences of cross-inhibition. We investigated cross-regulation between proinflammatory TNF and anti-inflammatory IL-4 in primary human monocytes and in a skin wound-healing model. Surprisingly, TNF functioned mainly as a costimulator of IL-4-induced gene expression, whereas IL-4 selectively inhibited the TNF-induced IFN response, leaving inflammatory gene expression mostly intact. TNF and IL-4 synergistically induced gene sets important for regulating inflammation and tissue repair, which were highly induced during the phase of wound healing when these cytokines are co-expressed. Crosstalk between TNF and IL-4 was mediated by epigenetic chromatin-mediated mechanisms associated with cooperation between NF-{kappa}B and STAT6 transcription factors, erasure of negative histone mark H3K27me3, and selective inhibition of IRF1. These results identify a long-sought mechanism for expansion of the IL-4 response, and highlight the complexity of crosstalk between antagonistic cytokines that includes cooperation for select gene responses important in immune response and tissue repair.

immunology↗

bioRxiv2025

Putative long-range mossy fiber sprouting and regional hypermetabolic capacity in the hippocampus of patients with mesial temporal lobe epilepsy

Mesial temporal lobe epilepsy (MTLE) is pathologically characterized by neuronal loss in the dentate hilus, CA3 and CA1 regions, and mossy fiber (MF) sprouting into the inner molecular layer (iML). The latter forms aberrant excitatory circuities that are considered to facilitate recurrent seizures, with the subiculum also being related to epileptogenic activation. We recently identified a distinct expression of -smooth muscle actin (SMA) at the MF terminals in human hippocampus. This prompted us to explore MF sprouting in resected hippocampi (n=20) from patients with MTLE relative to postmortem control (n=20) using SMA along with reference markers for pathological cross-validation. Compared to control, neuronal loss assessed with neuron-specific nuclear antigen and sortilin immunolabeling reached CA1 in all resected hippocampi. SMA, zinc transporter 3 and {beta}-secretase 1 immunolabeling in the iML tended to be increased. The MF-related markers also revealed a preserved fibrous band extending across CA1 to subiculum. Cytochrome c oxidase immunolabeling also increased in iML and subiculum in the MTLE group. Taking together, the current findings point to the existence of long-range MF sprouting and a regional hypermetabolic compacity in the hippocampal formation of patients with drug resistant MTLE.

neuroscience↗

bioRxiv2025

Structural insights into Measles virus RNA synthesis: C protein-mediated regulation of replication-transcription switch and allosteric inhibition by ERDRP-0519

Nonsegmented negative-sense RNA viruses (nsNSVs) rely on a multifunctional RNA-dependent RNA polymerase (RdRP) complex for transcription and replication. In measles virus (MeV), the nonstructural protein C has long been implicated in regulating RNA synthesis, yet its precise role remains unclear. Here, we show that the MeV C protein directly associates with the RdRP complex. Using cryo-electron microscopy, we determined atomic-resolution structures of the MeV polymerase with and without C, revealing that C binding stabilizes the accessory domains of L and locks the complex into a replication-competent elongation state. Biochemical data further show that C promotes N protein recruitment, enhancing polymerase processivity through facilitating encapsidation during replication. Additionally, we also resolved high-resolution structures of MeV and Nipah virus (NiV) polymerases bound to ERDRP-0519, an orally available morbillivirus inhibitor. Unexpectedly, the compound occupies a previously unrecognized allosteric pocket within the RdRp domain, rather than the predicted PRNTase domain, overlapping conserved resistance sites. This binding induces conformational changes in palm subdomain, blocking RNA template and nucleotide engagement, thereby halting RNA synthesis. These findings uncover distinct regulatory and inhibitory mechanisms in paramyxovirus polymerases and provide a structural framework for the rational design of broad-spectrum antivirals targeting MeV, NiV, and potentially other clinically relevant nsNSVs.

microbiology↗

bioRxiv2025

Temporal foundation model unveils ancient HepatitisB virus evolution

Since ancient hepatitis B virus (HBV) sequencing data are scarce and incomplete, the evolutionary dynamics of HBV have long remained enigmatic. This data gap hinders a comprehensive understanding of HBV genetic variations, evolutionary trajectories, and host interactions in different historical periods, preventing insight into its long-standing co-evolutionary relationship with humans. In this study, we developed a sequence-based generative foundational model, ViraChron (Viral Chronology Foundation Model), that integrates multi-scale temporal information to compensate for the lack of ancient HBV data. We confirmed the robustness of our model through sequence identity, phylogenetic, and recombination analyses. ViraChron enabled the identification of fixed mutations specific to HBV genotype A, demonstrating that HBV acquires survival advantages through mutations that are ultimately fixed by natural selection. Our model outperforms the original baseline in terms of sequence completion, achieving significantly higher accuracy. The model significantly reduced data acquisition time from 80 hours to 5.4 minutes per HBV genome, providing reliable, high-quality data for 7 new HBV genomes from specific archeological sites. ViraChron overcomes previous limitations, obtaining high-quality genomic data from samples over ten thousand years old, thereby facilitating a systematic understanding of viral evolution and its biological characteristics.

paleontology↗

bioRxiv2025

Neural mechanisms of structural inference: an EEG investigation of linguistic phrase structure categorization

Language comprehension is a complex cognitive process of understanding spoken or written language, during which the brain derives meanings and interprets messages by constructing hierarchical structures from word sequences. A key component underlying this process is structural inference. This computation determines the syntactic category ("head") of phrases, such as noun phrases (NP) and verb phrases (VP). Categorization confers distinct semantic and distributional properties. However, isolating neural correlates of structural inference is challenging, as modulations of syntactic category usually alter lexical content. Here, we collected the electroencephalography (EEG) data while participants read Mandarin NPs and VPs, which differ in their syntactic "headedness" but with lexical-semantic, lexical-syntactic categories and word order strictly conserved. We found significant theta power increases at left central-parietal scalp sites in NPs relative to VPs around the presentation (0-210 ms) of the nouns, where only the nouns in NPs project a head. Both NPs and VPs relative to one-word phrases exhibited spatiotemporally consistent low-frequency power increases at the point of linguistic composition, consistent with previous studies. Overall, our findings offer novel constraints on neurocomputational accounts of linguistic structure-building through these previously undocumented signatures of syntactic inference.

neuroscience↗

bioRxiv2025

Genomic Touchstone: Benchmarking Genomic Language Models in the Context of the Central Dogma

The emergence of genomic language models (gLMs) has revolutionized the analysis of genomic sequences, enabling robust capture of biologically meaningful patterns from DNA sequences for an improved understanding of human genome-wide regulatory programs, variant pathogenicity and therapeutic discovery. Given that DNA serves as the foundational blueprint within the central dogma, the ultimate evaluation of a gLM is its ability to generalize across this entire biological cascade. However, existing evaluations lack this holistic and consistent framework, leaving researchers uncertain about which model best translates sequence understanding into downstream biological prediction. Here we present Genomic Touchstone, a comprehensive benchmark designed to evaluate gLMs across 36 diverse tasks and 88 datasets structured along the central dogmas modalities of DNA, RNA, and protein, encompassing 5.34 billion base pairs of genomic sequences. We evaluate 34 representative models encompassing transformers and convolutional neural networks, as well as emerging efficient architectures such as Hyena and Mamba. Our analysis yield four key insights. First, gLMs achieve comparable or superior performance on RNA and protein tasks compared to models pretrained on these molecules. Second, transformer-based models continue to lead in overall performance, yet efficient sequence models show promising task-specific capabilities and deserve further exploration. Third, the scaling behavior of gLMs remains incompletely understood. While longer input sequences and more diverse pretraining data consistently improve performance, increases in model size do not always translate into better results. Fourth, pretraining strategies, including the choice of training objectives and the composition of pretraining corpora, exert substantial influence on downstream generalization across different genomic contexts. Genomic Touchstone establishes a unified evaluation framework tailored to human genomics. By spanning multiple molecular modalities and biological tasks, it offers a valuable foundation for guiding future gLM design and understanding model generalizability in complex biological contexts.

bioinformatics↗

bioRxiv2025

Evolution of hunt, kill, devour in a Vibrio ecospecies

Understanding the molecular and evolutionary basis of adaptation is a central challenge in biology. We have recently identified bacterial "ecospecies": strains that share a well-mixed gene pool with the rest of the species in most of the genome, but carry nearly fixed differences in [~]100 coadapted genes. We hypothesized this structure reflects ecological selection, but ecospecies have so far been characterized only genomically. Here, using "Molassodon" of the marine bacterium Vibrio parahaemolyticus, we provide first experimental evidence linking a multilocus ecospecies genotype to a laboratory phenotype. We find that Molassodon swimming in viscous environments is enhanced by differentiated lateral flagella that no longer drive surface swarming. Using Tn-seq, RNA-seq, and molecular genetics, we link differentiated Type VI secretion and nutrient uptake genes to the swimming phenotype, potentially invoking a natural adaptation related to "hunting". We also identify a convergent genotype also linking lateral flagella and nutrient uptake, suggesting ecological differentiation in V. parahaemolyticus occurs along recurring phenotypic axes and involves borrowing traits from a broader Vibrio gene pool. Together, our results provide first experimental evidence that ecospecies are functionally coherent units and illustrate how they can provide a window into the complex adaptations that shape bacterial ecology.

microbiology↗

bioRxiv2025

AF9/KLF2 gene regulatory circuit links histone lactylation to breast cancer metastasis

Histone lysine L-lactylation (hereafter referred to as histone Kla) is a novel epigenetic mark induced by glycolytic metabolism, serving as a link between metabolic reprogramming and epigenetic regulation. In this study, we uncover an epigenetic-genetic transcriptional regulatory circuit involving AF9 and KLF2 that drives luminal breast cancer progression. AF9, identified as a reader of histone H3 lysine 9 lactylation (H3K9la), promotes KLF2 expression, while KLF2, functioning as a transcription factor for AF9, forms a positive feedback loop that amplifies lactylation-dependent effects. This circuit activates tumor-associated pathways, including TGF{beta}1, glucose and lactate transporters, and metabolic enzymes essential for glycolysis and serine biosynthesis, driving tumorigenesis and metastasis. Spatial and single-cell transcriptomics show AF9-positive tumor cells enriched in regions of active lactylation, correlated with immune evasion through interactions with M2 macrophages. Together, AF9, H3K9la, and KLF2 integrate metabolism, epigenetics, and signaling to promote tumor growth and metastasis, highlighting AF9s central role as a histone lactylation reader and a potential therapeutic target in breast cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/660498v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@56b216org.highwire.dtl.DTLVardef@1c5cbc1org.highwire.dtl.DTLVardef@c42a5corg.highwire.dtl.DTLVardef@b912ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗