Author Correction: Inhibiting macrophage-derived lactate transport restores cGAS-STING signalling and enhances antitumour immunity in glioblastoma.
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Published Erratum↗
Biomedical subjects
Le Zhang
Publications and source records attributed to Le Zhang.
The pseudouridine epitranscriptomic landscape of advanced prostate cancer therapeutic resistance identifies TIMM17A as a key player.
BACKGROUND: Resistance to androgen receptor signaling inhibitors (ARSIs) remains a major barrier of advanced prostate cancer (PCa) treatment. While RNA epitranscriptomic modifications are increasingly recognized as key regulators of tumor biology, the role of pseudouridine (Ψ) in therapeutic resistance is largely unexplored. METHODS: A darolutamide-resistant PCa cell model was established and subjected to integrated multi-omics profiling using bulk RNA sequencing and photo-crosslinking-assisted Ψ sequencing (PA-Ψ-seq). Differential expression and pseudouridylation analyses were combined to identify Ψ-associated genes. Public datasets validated expression and prognosis. Functional assays including RNA knockdown, cell proliferation, colony formation, and xenograft models were conducted. Single-cell RNA sequencing investigated tumor microenvironment (TME) interactions. RESULTS: We identified extensive transcriptomic and pseudouridylation alterations associated with ARSI resistance, with a significant positive correlation between Ψ modification and mRNA expression. Integrated analysis highlighted a subset of "hyper-up" genes enriched in resistance-related pathways. Thus, TIMM17A was identified as a novel candidate. TIMM17A expression was significantly elevated in PCa and correlated with disease progression and poor prognosis. Experimental validations demonstrated that TIMM17A promoted tumor growth and resistance, while its knockdown restored sensitivity to darolutamide both in vitro and in vivo. Mechanistically, TIMM17A expression may be regulated by PUS1‑mediated pseudouridylation. Single-cell analysis further revealed that TIMM17A is enriched in malignant epithelial cells and associated with enhanced cell-cell communication within the TME. CONCLUSIONS: This study delineates the pseudouridine epitranscriptomic landscape in advanced PCa and identifies TIMM17A as a key mediator of therapeutic resistance. Targeting the Ψ-TIMM17A axis may offer a novel strategy to overcome ARSI resistance.
Advanced prostate cancer↗
Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type, and sex specificity of gene expression with novel genetic risk for MERTK in female.
BackgroundAlzheimer's disease (AD), the most common age-related neurodegenerative disease, is closely associated with both amyloid-β plaque and neuroinflammation. Two thirds of AD patients are female, and they have a higher disease risk; women with AD have more extensive brain histological changes than men along with more severe cognitive symptoms and neurodegeneration.ObjectiveThis study aimed to determine how sex difference induces structural brain changes and molecular cell vulnerabilities in AD, with a focus on identifying sex-specific transcriptional alterations and genetic risk factors.MethodsWe performed single nucleus RNA sequencing on postmortem brains from individuals with AD and age- and sex-matched controls, focusing on the middle temporal gyrus, a cortical brain region strongly affected by the disease, and integrated single nucleus RNA sequencing results with genome-wide association study (GWAS) data using cell type-specific enrichment and generalized gene-set analysis approaches. The analysis pipeline is provided with threshold information.ResultsWe identified a selectively vulnerable subpopulation of layer 2/3 excitatory neurons that were RORB-negative and CDH9-expressing in both males and females. Disease-associated, but sex-independent, reactive astrocyte signatures were also present. In clear contrast, the microglia signatures of AD brains differed between males and females. Integrating single cell transcriptomic data with results from GWAS, we identified MERTK genetic variation as a candidate novel risk factor for AD selectively in females.ConclusionsTaken together, our single cell atlas of middle temporal gyrus revealed a unique cellular-level view of sex-specific transcriptional changes in AD, illuminating GWAS identification of sex-specific AD genes. These data serve as a rich resource for interrogation of the molecular and cellular basis of AD.
Alzheimer's disease↗
MacroH2A2-Enriched Domains Are Largely Stable Across the Cell Cycle but Focally Displaced at Mitotic Regulatory Elements.
The macroH2A variants mH2A1 and mH2A2 are structurally similar but not identical. Our previous study demonstrated that mH2A1 is reloaded during cell-cycle progression, but whether mH2A2 follows similar dynamics has remained unclear. Here, we used native ChIP-seq in synchronized Huh-7 cells to profile both variants at G1/S and G2/M. Although mH2A1- and mH2A2-enriched domains overlapped extensively, mH2A2 domains were largely stable across the cell cycle, in sharp contrast to the dynamic reloading of mH2A1. Only a small subset of mH2A2 domains showed phase-specific deposition or displacement. Among these, G1/S-unique mH2A2 domains were preferentially located in the active A compartment and coincided with reduced chromatin accessibility at binding sites for cell-cycle regulators. These G1/S-unique domains co-localize with genes involved in mitotic progression within the same A compartment, suggesting potential regulatory roles in both chromatin organization and transcriptional regulation. These findings refine the classical view of macroH2A variants as static repressive marks: mH2A2 is not entirely static, but its cell-cycle dynamics are far more restricted than those of its paralog mH2A1, occurring only at a small subset of genomic loci, with a regulatory logic distinct from that of mH2A1.
Histones↗
AI proteomics: from protein identification to virtual cells.
Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.
Proteomics↗
Integrated metagenomic and metaproteomic insights into current-carrying-coil magnetic field enhanced synergistic methanogenic system and antibiotic resistance gene reduction in cow manure anaerobic digestion.
Anaerobic digestion (AD) is a sustainable strategy for valorizing cow manure (CM). However, the high ammonia (NH3) concentration and low biodegradability of CM limit hydrolysis and methane production. This study investigated the application of a current-carrying-coil-based magnetic field (CCC-MF) to AD of CM. The CCC-MF digesters showed higher soluble chemical oxygen demand and attained 16.59 % higher ammonium nitrogen reduction, contributing to a 37.50 % higher average methane yield than the control. Further, CCC-MF digesters showed higher enzyme activities (alkaline protease + 30 %, acetate kinase + 22 % and hydrazine dehydrogenase + 26 %) and increased microbial metabolic indices (dehydrogenase activity + 17 % and electron transport system activity + 10 %) than the control. Metagenomics analysis revealed that abundances of the bacterial genera Mesotoga, Aminobacterium, Xiashengella, unclassified Candidatus Cloacimonadota, Advenella, Pseudomonas, and Comamonas increased, whereas the acetoclastic methanogen Methanothrix decreased by 2.58 %, accompanied by 2.07- and 1.64-fold increases in hydrogenotrophic methanogens Methanospirillum and Methanobacterium, respectively, in CCC-MF digesters. The abundance of nitrogen dissimilation and assimilation genes NirK, NorB, NarB, NapA, nmo, and GLT1 were enhanced by 1.14, 1.04, 2.30, 1.32, 1.17, and 1.29-fold in CCC-MF digesters compared to the control. Moreover, metaproteomics revealed higher up-regulated differentially expressed proteins in NH3 reduction-related amino acid metabolism pathways in CCC-MF digester compared to control. Additionally, reduced abundances of bacitracin, polymyxin, sulfonamide, and multidrug antibiotic resistance (MAR) gene types were observed in the CCC-MF digesters. The findings suggest that applying CCC-MF may be associated with higher methane production and ammonium reduction, potentially linked to a more favorable synergistic methanogenic system and nitrogen transformation pathways.
Manure↗
Inhibiting macrophage-derived lactate transport restores cGAS-STING signalling and enhances antitumour immunity in glioblastoma.
Glioblastoma (GBM) is a malignancy with a complex tumour microenvironment (TME) dominated by GBM stem cells (GSCs) and infiltrated by tumour-associated macrophages (TAMs) and exhibits aberrant metabolic pathways. Lactate is a critical glycolytic metabolite that promotes tumour progression; however, the mechanisms of lactate transport and lactylation in the TME of GBM remain elusive. Here we show that lactate is transported from TAMs to GSCs via MCT4-MCT1. TAMs provide lactate to GSCs, promoting GSC proliferation and inducing lactylation of the non-homologous end joining protein KU70 at lysine 317 (K317), which inhibits cGAS-STING signalling and remodels the immunosuppressive TME. Inhibition of lactate transport or targeting the lactylation of KU70, in combination with the immune checkpoint blockade, demonstrates additive therapeutic benefits in immunocompetent xenograft models. This study unveils TAM-derived lactate and lactylation as critical regulators in GSCs to enforce an immunosuppressive microenvironment, opening avenues for developing combinatorial therapy for GBM.
Glioblastoma↗
Correction: Generative AI Models in Time-Varying Biomedical Data: Scoping Review.
[This corrects the article DOI: 10.2196/59792.].
Published Erratum↗
Genome mining based on transcriptional regulatory networks uncovers a novel locus involved in desferrioxamine biosynthesis.
Bacteria produce a plethora of natural products that are in clinical, agricultural and biotechnological use. Genome mining has uncovered millions of biosynthetic gene clusters (BGCs) that encode their biosynthesis, the vast majority of them lacking a clear product or function. Thus, a major challenge is to predict the bioactivities of the molecules these BGCs specify, and how to elicit their expression. Here, we present an innovative strategy whereby we harness the power of regulatory networks combined with global gene expression patterns to predict BGC functions. Bioinformatic analysis of all genes predicted to be controlled by the iron master regulator DmdR1 combined with co-expression data, led to identification of the novel operon desJGH that plays a key role in the biosynthesis of the iron overload drug desferrioxamine (DFO) B in Streptomyces coelicolor. Deletion of either desG or desH strongly reduces the biosynthesis of DFO B, while that of DFO E is enhanced. DesJGH most likely act by changing the balance between the DFO precursors. Our work shows the power of harnessing regulation-based genome mining to functionally prioritize BGCs, accelerating the discovery of novel bioactive molecules.
Deferoxamine↗
Generative AI Models in Time-Varying Biomedical Data: Scoping Review.
BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care. OBJECTIVE: While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science. METHODS: We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality. RESULTS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS: We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.
Artificial Intelligence↗
Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type and sex specificity of gene expression with novel genetic risk for MERTK in female.
Alzheimer's disease, the most common age-related neurodegenerative disease, is closely associated with both amyloid-ß plaque and neuroinflammation. Two thirds of Alzheimer's disease patients are females and they have a higher disease risk. Moreover, women with Alzheimer's disease have more extensive brain histological changes than men along with more severe cognitive symptoms and neurodegeneration. To identify how sex difference induces structural brain changes, we performed unbiased massively parallel single nucleus RNA sequencing on Alzheimer's disease and control brains focusing on the middle temporal gyrus, a brain region strongly affected by the disease but not previously studied with these methods. We identified a subpopulation of selectively vulnerable layer 2/3 excitatory neurons that that were RORB-negative and CDH9-expressing. This vulnerability differs from that reported for other brain regions, but there was no detectable difference between male and female patterns in middle temporal gyrus samples. Disease-associated, but sex-independent, reactive astrocyte signatures were also present. In clear contrast, the microglia signatures of diseased brains differed between males and females. Combining single cell transcriptomic data with results from genome-wide association studies (GWAS), we identified MERTK genetic variation as a risk factor for Alzheimer's disease selectively in females. Taken together, our single cell dataset revealed a unique cellular-level view of sex-specific transcriptional changes in Alzheimer's disease, illuminating GWAS identification of sex-specific Alzheimer's risk genes. These data serve as a rich resource for interrogation of the molecular and cellular basis of Alzheimer's disease.
Journal Article↗