Browsing by Author "Lu, Alex"

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    ATG14 plays a critical role in hepatic lipid droplet homeostasis
    (Elsevier, 2023) Huang, Menghao; Zhang, Yang; Park, Jimin; Chowdhury, Kushan; Xu, Jiazhi; Lu, Alex; Wang, Lu; Zhang, Wenjun; Ekser, Burcin; Yu, Liqing; Dong, X. Charlie; Biochemistry and Molecular Biology, School of Medicine
    Background & aims: Autophagy-related 14 (ATG14) is a key regulator of autophagy. ATG14 is also localized to lipid droplet; however, the function of ATG14 on lipid droplet remains unclear. In this study, we aimed to elucidate the role of ATG14 in lipid droplet homeostasis. Methods: ATG14 loss-of-function and gain-of-function in lipid droplet metabolism were analyzed by fluorescence imaging in ATG14 knockdown or overexpression hepatocytes. Specific domains involved in the ATG14 targeting to lipid droplets were analyzed by deletion or site-specific mutagenesis. ATG14-interacting proteins were analyzed by co-immunoprecipitation. The effect of ATG14 on lipolysis was analyzed in human hepatocytes and mouse livers that were deficient in ATG14, comparative gene identification-58 (CGI-58), or both. Results: Our data show that ATG14 is enriched on lipid droplets in hepatocytes. Mutagenesis analysis reveals that the Barkor/ATG14 autophagosome targeting sequence (BATS) domain of ATG14 is responsible for the ATG14 localization to lipid droplets. Co-immunoprecipitation analysis illustrates that ATG14 interacts with adipose triglyceride lipase (ATGL) and CGI-58. Moreover, ATG14 also enhances the interaction between ATGL and CGI-58. In vitro lipolysis analysis demonstrates that ATG14 deficiency remarkably decreases triglyceride hydrolysis. Conclusions: Our data suggest that ATG14 can directly enhance lipid droplet breakdown through interactions with ATGL and CGI-58.
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    Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer
    (Frontiers Media, 2023-06-12) Yang, Grace; Huang, Shaoyang; Hu, Kevin; Lu, Alex; Yang, Jonathan; Meroueh, Noah; Dang, Pengtao; Wang, Yijie; Zhu, Haiqi; Cao, Sha; Zhang, Chi; Electrical and Computer Engineering, School of Engineering and Technology
    Introduction: Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue. Method: In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data. Result: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. Discussion: This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.
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    FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data
    (Oxford University Press, 2023) Zhang, Zixuan; Zhu, Haiqi; Dang, Pengtao; Wang, Jia; Chang, Wennan; Wang, Xiao; Alghamdi, Norah; Lu, Alex; Zang, Yong; Wu, Wenzhuo; Wang, Yijie; Zhang, Yu; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.