Biology subjects

Zhang, A.

Publications and source records attributed to Zhang, A..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Circulating tumor cells shed large extracellular vesicles in capillary-sized bifurcations

Circulating tumor cells (CTCs) and their clusters are the drivers of metastasis, but we have an incomplete understanding of how they interact with capillary beds. Using microfluidic models mimicking human capillary bifurcations, we observed cell size- and bifurcation-dependent shedding of nuclei-free fragments by patient CTCs, CTC-derived explant cells and numerous cancer cell lines. Shedding reduced cell sizes up to 61%, facilitating their transit through bifurcations. We demonstrated that shed fragments were a novel subclass of large extracellular vesicles (LEVs), "shearosomes", that require shear stress for their biogenesis and whose proteome was associated with immune-related pathways. Shearosomes exhibited functions characteristic of previously identified EVs including cell-directed internalization by endothelial and immune cells, and intercellular communication abilities such as disruption of endothelial barrier integrity, polarization of monocytes into M2 tumor-promoting macrophages and interactions between endothelial and immune cells. Cumulatively, these findings suggest that CTCs shed shearosomes in capillary beds that drive key processes involved in the formation of pre-metastatic niches.

cancer biology↗

bioRxiv2024

Viscoelastic Extracellular Matrix Enhances Epigenetic Remodeling and Cellular Plasticity

Extracellular matrices of living tissues exhibit viscoelastic properties, yet how these properties regulate chromatin and the epigenome remains unclear. Here, we show that viscoelastic substrates induce changes in nuclear architecture and epigenome, with more pronounced effects on softer surfaces. Fibroblasts on viscoelastic substrates display larger nuclei, lower chromatin compaction, and differential expression of distinct sets of genes related to the cytoskeleton and nuclear function compared to those on purely elastic surfaces. Slow-relaxing viscoelastic substrates reduce lamin A/C expression and enhance nuclear remodeling. These structural changes are accompanied by a global increase in euchromatin marks and local increase in chromatin accessibility at cis-regulatory elements associated with neuronal and pluripotent genes. Consequently, viscoelastic substrates improve the reprogramming efficiency from fibroblasts into neurons and induced pluripotent stem cells. Collectively, our findings unravel the roles of matrix viscoelasticity in epigenetic regulation and cell reprogramming, with implications for designing smart materials for cell fate engineering.

cell biology↗

bioRxiv2024

TLR2 Supports γδ T cell IL-17A Response to ocular surface commensals by Metabolic Reprogramming

The ocular surface is a mucosal barrier tissue colonized by commensal microbes, which tune local immunity by eliciting IL-17 from conjunctival {gamma}{delta} T cells to prevent pathogenic infection. The commensal Corynebacterium mastitidis (C. mast) elicits protective IL-17 responses from conjunctival V{gamma}4 T cells through a combination of {gamma}{delta} TCR ligation and IL-1 signaling. Here, we identify V{gamma}6 T cells as a major C. mast-responsive subset in the conjunctiva and uncover its unique activation requirements. We demonstrate that V{gamma}6 cells require not only extrinsic (via dendritic cells) but also intrinsic TLR2 stimulation for optimal IL-17A response. Mechanistically, intrinsic TLR2 signaling was associated with epigenetic changes and enhanced expression of genes responsible for metabolic shift to fatty acid oxidation to support Il17a transcription. We identify one key transcription factor, I{kappa}B{zeta}, which is upregulated by TLR2 stimulation and is essential for this program. Our study highlights the importance of intrinsic TLR2 signaling in driving metabolic reprogramming and production of IL-17A in microbiome-specific mucosal {gamma}{delta} T cells. SummaryOcular commensal Corynebacterium mastitidis (C. mast) induces IL-17 responses from {gamma}{delta} T cells by activating TLR2 signaling. {gamma}{delta} T cell-intrinsic TLR2 stimulation can increase Il17a transcription and promote fatty acid oxidation, favoring {gamma}{delta} T cell IL-17A responses. Highlights(1) V{gamma}6 T cells are a major C. mast-responsive subset in the conjunctiva (2) TLR2-deficient mice exhibit reduced {gamma}{delta} T cell responses to ocular commensal bacteria. (3) {gamma}{delta} T cell-intrinsic TLR2 deficiency causes defects of fatty acid oxidation and IL-17A production in a {gamma}{delta} subset-specific manner. (4) The transcription factor, I{kappa}B{zeta} is upregulated by TLR2 stimulation and supports {gamma}{delta} IL-17A production through fatty acid oxidation. O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/587519v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1d1ba87org.highwire.dtl.DTLVardef@2d17d9org.highwire.dtl.DTLVardef@78e846org.highwire.dtl.DTLVardef@1d96dc2_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Genetic Modulation of Protein Expression in Rat Brain

Genetic variations in protein expression are implicated in a broad spectrum of common diseases and complex traits. However, the fundamental genetic architecture and variation of protein expression have received comparatively less attention than either mRNA or classical phenotypes. In this study, we systematically quantified proteins in the brains of a large family of rats using tandem mass tag (TMT)-based quantitative mass-spectrometry (MS) technology. We identified and quantified a comprehensive proteome of 8,119 proteins from Spontaneously Hypertensive (SHR/Olalpcv), Brown Norway with polydactyly-luxate (BN-Lx/Cub), and 29 of their fully inbred HXB/BXH progeny. Differential expression (DE) analysis identified 597 proteins with significant differences in expression between the parental strains (fold change > 2 and FDR < 0.01). We characterized 95 variant peptides by proteogenomics approach and discovered 464 proteins linked to strong cis-acting quantitative trait loci (pQTLs, FDR < 0.05). We also explored the linkage of pQTLs with behavioral phenotypes in rats and examined the sex-specific pQTLs to reveal both distinct and shared cis-pQTLs between sexes. Furthermore, by creating a novel view of the rat pangenome, we improved the ability to pinpoint candidate genes underlying pQTL. Finally, we explored the connection between the pQTLs in rat and human disorders, underscoring the translational potential of our findings. Collectively, this work demonstrates the value of large and systematic proteo-genetic datasets in understanding protein modulation in the brain and its functional linkage to complex central nervous system (CNS) traits.

genetics↗

bioRxiv2024

Characterization of the SARS-CoV-2 BA.5 Variants in H11-K18-hACE2 Hamsters

This study aims to comprehensively characterize the SARS-CoV-2 BA.5 variants using K18 hACE2 transgenic mice and golden hamsters as model organisms. Previous research on SARS-CoV-2 has utilized both mouse and hamster models, leading to conflicting results concerning the viruss lethality. In our study, the finding suggests that H11-K18 hACE2 golden hamsters closely mimic the disease progression observed in human COVID-19 cases caused by BA.5 variants, demonstrating consistent severity and symptoms comparable to severe infections. Additionally, hamsters exhibit heightened respiratory viral replication, accurately reflecting the clinical viral kinetics observed in humans. The study emphasizes the critical importance of selecting an appropriate animal model for SARS-CoV-2 research, while also providing robust support for the hypothesis that BA.5 variants contribute to fatal outcomes in COVID-19 cases. These findings highlight the pivotal role of the golden hamster model in advancing our understanding of the pathogenic mechanisms underlying SARS-CoV-2 variants, as well as in the development of targeted therapeutic strategies. Significance StatementOur research work explores groundbreaking insights that could reshape our understanding of COVID-19 and pave the way for targeted therapies. We use golden hamster models to express the possibility of different animal models could contribute to human diseases. We hope this finding could clarify some conflicts existed, and help further development of medication for COVID.

immunology↗

bioRxiv2024

Architecture of the spinach plastid-encoded RNA polymerase

The plastid-encoded RNA polymerase (PEP) serves as the principal transcription machinery within chloroplasts and is responsible for transcribing over 80% of primary plastid transcripts. Plant PEP is composed of a prokaryotic-like multisubunit core enzyme known as the PEP core, supplemented by newly evolved PEP-associated proteins (PAPs). In this study, we present the cryo-electron microscopy (cryo-EM) structure of a PEP complex derived from spinach (Spinacia oleracea). Our structural analysis unveils the presence of 14 PAPs closely associated with the PEP core complex consisting of 5 subunits, forming an irregular-shaped supercomplex. We provide a detailed depiction of the PEP core subunits and the 14 PAPs, elucidating their precise spatial arrangement and interactions. This research offers a crucial structural basis for future investigations into the functions and regulatory mechanisms governing plastid transcription.

biochemistry↗

bioRxiv2024

Unraveling wheat endosperm development: epigenetic regulation and novel regulators for enhanced yield and quality

Starch content and seed storage protein (SSP) composition are critical factors influencing wheat grain yield and quality. To uncover the molecular mechanisms governing their biosynthesis, we conducted transcriptome and epigenome profiling across key endosperm developmental stages, revealing that chromatin accessibility, H3K27ac, and H3K27me3 collectively regulate SSP and starch genes with varying impact. Population transcriptome and phenotype analyses highlighted the crucial role of accessible promoter regions as a genetic variation resource, influencing grain yield and quality in a core collection of wheat accessions. By integrating time-serial RNA-seq and ATAC-seq data, we constructed a hierarchical transcriptional regulatory network (TRN) governing starch and SSP biosynthesis, identifying 42 high-confidence novel candidates. These candidates exhibited overlap with genetic regions associated with grain size and quality traits, and their functional significance was validated through expression-phenotype association analysis among wheat accessions and TILLING mutants. In-depth functional analysis of wheat abscisic acid insensitive 3-A1 (TaABI3-A1) with genome editing knock-out lines demonstrated its role in promoting SSP accumulation while repressing starch biosynthesis through transcriptional regulation. An elite haplotype of TaABI3-A1 with higher grain weight was identified during the breeding process in China, and its superior trait was associated with altered TaABI3-A1 expression levels. Additionally, we identified the potential upstream regulator, wheat GAGA-binding transcription factor 1 (TaGBP1), influencing TaABI3-A1 expression. Our study provides novel and high-confidence regulators, presenting an effective strategy for understanding the regulation of SSP and starch biosynthesis and contributing to breeding enhancement.

plant biology↗

bioRxiv2024

Insights into Human Epileptogenesis with Proteomic Profiling

Epilepsy affects millions globally, and drug-resistant epilepsy remains a challenge. Molecular mechanisms underlying epilepsy remain elusive. Protein profiling through proteomics offers insight into biomarkers and therapeutic targets. Human brain tissue from epilepsy surgeries was analyzed using data-independent acquisition (DIA) proteomics. Samples were categorized into Core (epileptogenic focus), Border (marginal excision tissue), and Nonepileptic control groups. Differential expression proteins (DEPs) were identified and shared proteins were analyzed. 163 DEPs were identified which may has potential roles in the initiation of epileptic electrical firing, 412 DEPs which indicating the difference between epilepsy and Nonepilepsy patients and 10 DEPs consistently altered in Core which indicating potential roles in epileptogenesis. Notably, P35754/GLRX, O75335/PPFIA4, and Q96KP4/CNDP2 were consistently expressed differently in all group pairs. From validation experiments, the expression of Kv3.2 significant reduced in the Core group compare to border group by immunohistochemistry and knockdown of Kv3.2 increased seizure susceptibility and altered neuronal excitability through our cellular and animal experimentation.

neuroscience↗

bioRxiv2023

DARDN: Identifying transcription factor binding motifs from long DNA sequences using multi-CNNs and DeepLIFT

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the models output in terms of the contributions of its input features [1], for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDNs ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identified potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data.

bioinformatics↗

bioRxiv2023

DeepGSEA: Explainable Deep Gene Set Enrichment Analysis for Single-cell Transcriptomic Data

Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, due to the extreme heterogeneity of single-cell gene expression, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in specific applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, primarily due to interpretability challenges. In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the expressiveness of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained. The source code of DeepGSEA is available at https://github.com/Teddy-XiongGZ/DeepGSEA.

bioinformatics↗

bioRxiv2023

Streamlined DNA template preparation and co-transcriptional 5' capped RNA synthesis enabled by solid-phase catalysis

The success of SARS-CoV-2 mRNA vaccines demonstrated that rapid, large-scale manufacturing of synthetic mRNA is necessary for an effective and timely response to a pandemic. Innovations in areas such as template design and manufacturing processes are being implemented to facilitate more simple, cost-effective and scalable mRNA synthesis. In this study, for the first time, we demonstrate that the enzymatic steps in mRNA production (including DNA template linearization, RNA synthesis, 5' capping and methylation) can be carried out using enzymes immobilized to a solid support. Specifically, we demonstrate efficient IVT template DNA linearization using immobilized BspQI, where the linearized template DNA can be directly used in IVT without the need of purification. We also showed that immobilized T7 RNA polymerase, Faustovirus RNA capping enzyme (FCE), vaccinia cap 2'-O-methyltransfease (2'OMTase) and a novel FCE::T7RNAP fusion enable efficient enzymatic synthesis of Cap-1 RNA in a one-pot format. This solid-phase enzymatic platform may enable highly efficient, seamless and continuous mRNA synthesis workflows that minimizes sample loss and units of operation in biopharmaceutical manufacturing.

biochemistry↗

bioRxiv2023

Spiral NeuroString: High-Density Soft Bioelectronic Fibers for Multimodal Sensing and Stimulation

Bioelectronic fibers hold promise for both research and clinical applications due to their compactness, ease of implantation, and ability to incorporate various functionalities such as sensing and stimulation. However, existing devices suffer from bulkiness, rigidity, limited functionality, and low density of active components. These limitations stem from the difficulty to incorporate many components on one-dimensional (1D) fiber devices due to the incompatibility of conventional microfabrication methods (e.g., photolithography) with curved, thin and long fiber structures. Herein, we introduce a fabrication approach, "spiral transformation'', to convert two-dimensional (2D) films containing microfabricated devices into 1D soft fibers. This approach allows for the creation of high density multimodal soft bioelectronic fibers, termed Spiral NeuroString (S-NeuroString), while enabling precise control over the longitudinal, angular, and radial positioning and distribution of the functional components. We show the utility of S-NeuroString for motility mapping, serotonin sensing, and tissue stimulation within the dynamic and soft gastrointestinal (GI) system, as well as for single-unit recordings in the brain. The described bioelectronic fibers hold great promises for next-generation multifunctional implantable electronics.

bioengineering↗

bioRxiv2023

Biochemically-functionalized probes for cell type-specific targeting and recording in the brain

Selective targeting and modulation of distinct cell types and neuron subtypes is central to understanding complex neural circuitry, and could enable electronic treatments that target specific circuits while minimizing off-target effects. However, current brain-implantable electronics have not yet achieved cell-type specificity. We address this challenge by functionalizing flexible mesh electronic probes, which elicit minimal immune response, with antibodies or peptides to target specific cell markers. Histology studies reveal selective association of targeted neurons, astrocytes and microglia with functionalized probe surfaces without accumulating off-target cells. In vivo chronic electrophysiology further yields recordings consistent with selective targeting of these cell types. Last, probes functionalized to target dopamine 2 receptor expressing neurons show the potential for neuron subtype specific targeting and electrophysiology.

bioengineering↗

bioRxiv2023

Adolescent oligodendrogenesis and myelination restrict experience-dependent neuronal plasticity in adult visual cortex

BACKGROUNDDevelopmental myelination is a protracted process in the mammalian brain. One theory for why oligodendrocytes mature so slowly posits that myelination may stabilize neuronal circuits and temper neuronal plasticity as animals age. We tested this hypothesis in the visual cortex, which has a well-defined critical period for experience-dependent neuronal plasticity. OBJECTIVES/METHODSTo prevent myelin progression, we conditionally deleted Myrf, a transcription factor necessary for oligodendrocyte maturation, from oligodendrocyte precursor cells (Myrf cKO) in adolescent mice. To induce experience-dependent plasticity, adult control and Myrf cKO mice were monocularly deprived by eyelid suture. Functional and structural neuronal plasticity in the visual cortex were assessed in vivo by intrinsic signal optical imaging and longitudinal two photon imaging of dendritic spines, respectively. RESULTSDuring adolescence, visual experience modulated the rate of oligodendrocyte maturation in visual cortex. Myrf deletion from oligodendrocyte precursors during adolescence led to inhibition of oligodendrocyte maturation and myelination that persisted into adulthood. Following monocular deprivation, visual cortex activity in response to visual stimulation of the deprived eye remained stable in adult control mice, as expected for post-critical period animals. By contrast, visual cortex responses to the deprived eye decreased significantly following monocular deprivation in adult Myrf cKO mice, reminiscent of the plasticity observed in adolescent mice. Furthermore, visual cortex neurons in adult Myrf cKO mice had fewer dendritic spines and a higher level of spine turnover. Finally, monocular deprivation induced spatially coordinated spine size decreases in adult Myrf cKO, but not control, mice. CONCLUSIONSThese results demonstrate a critical role for oligodendrocytes in shaping the maturation and stabilization of cortical circuits and support the concept of myelin acting as a brake on neuronal plasticity during development.

neuroscience↗

bioRxiv2023

A telomere-to-telomere genome assembly of Zhonghuang 13, a widely-grown soybean variety from the original center of Glycine max

Soybean (Glycine max) stands as a globally significant agricultural crop, and the comprehensive assembly of its genome is of paramount importance for unraveling its biological characteristics and evolutionary history. Nevertheless, previous soybean genome assemblies have harbored gaps and incompleteness, which have constrained in-depth investigations into soybean. Here, we present the first Telomere-to-Telomere (T2T) assembly of the Chinese soybean cultivar "Zhonghuang 13" (ZH13) genome, termed ZH13-T2T, utilizing PacBio Hifi and ONT ultralong reads. We employed a multi-assembler approach, integrating Hifiasm, NextDenovo, and Canu, to minimize biases and enhance assembly accuracy. The assembly spans 1,015,024,879 bp, effectively resolving all 393 gaps that previously plagued the reference genome. Our annotation efforts identified 50,564 high-confidence protein-coding genes, 707 of which are novel. ZH13-T2T revealed longer chromosomes, 421 not-aligned regions (NARs), 112 structure variations (SVs), and a substantial expansion of repetitive element compared to earlier assemblies. Specifically, we identified 25.67 Mb of tandem repeats, an enrichment of 5S and 48S rDNAs, and characterized their genotypic diversity. In summary, we deliver the first complete Chinese soybean cultivar T2T genome. The comprehensive annotation, along with precise centromere and telomere characterization, as well as insights into structural variations, further enhance our understanding of soybean genetics and evolution.

plant biology↗

bioRxiv2023

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences

The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate of designed single mutants is [~]50% to improve either luciferase activity or stability. These finding highlights natures ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in Renilla luciferase towards emitting blue light that holds advantages in terms of water penetration compared to other light spectrum. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

bioinformatics↗

bioRxiv2023

Methods for evaluating unsupervised vector representations of genomic regions

Representation learning models have become a mainstay of modern genomics. These models are trained to yield vector representations, or embeddings, of various biological entities, such as cells, genes, individuals, or genomic regions. Recent applications of unsupervised embedding approaches have been shown to learn relationships among genomic regions that define functional elements in a genome. Unsupervised representation learning of genomic regions is free of the supervision from curated metadata and can condense rich biological knowledge from publicly available data to region embeddings. However, there exists no method for evaluating the quality of these embeddings in the absence of metadata, making it difficult to assess the reliability of analyses based on the embeddings, and to tune model training to yield optimal results. To bridge this gap, we propose four evaluation metrics: the cluster tendency score (CTS), the reconstruction score (RCS), the genome distance scaling score (GDSS), and the neighborhood preserving score (NPS). The CTS and RCS statistically quantify how well region embeddings can be clustered and how well the embeddings preserve information in training data. The GDSS and NPS exploit the biological tendency of regions close in genomic space to have similar biological functions; they measure how much such information is captured by individual region embeddings in a set. We demonstrate the utility of these statistical and biological scores for evaluating unsupervised genomic region embeddings and provide guidelines for learning reliable embeddings. AvailabilityCode is available at https://github.com/databio/geniml

bioinformatics↗

bioRxiv2023

Glycolytic Metabolon Assembly on Mitochondria via Hexokinase O-GlcNAcylation Promotes Metabolic Efficiency

Glucose, the primary cellular energy source, is metabolized through glycolysis initiated by the rate-limiting enzyme Hexokinase (HK). In energy-demanding tissues like the brain, HK1 is the dominant isoform, primarily localized on mitochondria, crucial for efficient glycolysis-oxidative phosphorylation coupling and optimal energy generation. This study unveils a unique mechanism regulating HK1 activity, glycolysis, and the dynamics of mitochondrial coupling, mediated by the metabolic sensor enzyme O-GlcNAc transferase (OGT). OGT catalyzes reversible O-GlcNAcylation, a post-translational modification, influenced by glucose flux. Elevated OGT activity induces dynamic O-GlcNAcylation of HK1s regulatory domain, subsequently promoting the assembly of the glycolytic metabolon on the outer mitochondrial membrane. This modification enhances HK1s mitochondrial association, orchestrating glycolytic and mitochondrial ATP production. Mutations in HK1s O-GlcNAcylation site reduce ATP generation, affecting synaptic functions in neurons. The study uncovers a novel pathway that bridges neuronal metabolism and mitochondrial function via OGT and the formation of the glycolytic metabolon, offering new prospects for tackling metabolic and neurological disorders.

cell biology↗