Browsing by Author "Jin, Lingtao"

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    Critical role of ASCT2-mediated amino acid metabolism in promoting leukaemia development and progression
    (Springer Nature, 2019-03) Ni, Fang; Yu, Wen-Mei; Li, Zhiguo; Graham, Douglas K.; Jin, Lingtao; Kang, Sumin; Rossi, Michael R.; Li, Shiyong; Broxmeyer, Hal E.; Qu, Cheng-Kui; Microbiology and Immunology, School of Medicine
    Amino acid (AA) metabolism is involved in diverse cellular functions, including cell survival and growth, however it remains unclear how it regulates normal hematopoiesis versus leukemogenesis. Here, we report that knockout of Slc1a5 (ASCT2), a transporter of neutral AAs, especially glutamine, results in mild to moderate defects in bone marrow and mature blood cell development under steady state conditions. In contrast, constitutive or induced deletion of Slc1a5 decreases leukemia initiation and maintenance driven by the oncogene MLL-AF9 or Pten deficiency. Survival of leukemic mice is prolonged following Slc1a5 deletion, and pharmacological inhibition of ASCT2 also decreases leukemia development and progression in xenograft models of human acute myeloid leukemia. Mechanistically, loss of ASCT2 generates a global effect on cellular metabolism, disrupts leucine influx and mTOR signaling, and induces apoptosis in leukemic cells. Given the substantial difference in reliance on ASCT2-mediated AA metabolism between normal and malignant blood cells, this in vivo study suggests ASCT2 as a promising therapeutic target for the treatment of leukemia.
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    SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data
    (Oxford University Press, 2022-07-27) Song, Qianqian; Zhu, Xuewei; Jin, Lingtao; Chen, Minghan; Zhang, Wei; Su, Jing; Biostatistics and Health Data Science, School of Medicine
    Unravelling the regulatory programs from single-cell multi-omics data has long been one of the major challenges in genomics, especially in the current emerging single-cell field. Currently there is a huge gap between fast-growing single-cell multi-omics data and effective methods for the integrative analysis of these inherent sparse and heterogeneous data. In this study, we have developed a novel method, Single-cell Multi-omics Gene co-Regulatory algorithm (SMGR), to detect coherent functional regulatory signals and target genes from the joint single-cell RNA-sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data obtained from different samples. Given that scRNA-seq and scATAC-seq data can be captured by zero-inflated Negative Binomial distribution, we utilize a generalized linear regression model to identify the latent representation of consistently expressed genes and peaks, thus enables the identification of co-regulatory programs and the elucidation of regulating mechanisms. Results from both simulation and experimental data demonstrate that SMGR outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of SMGR, we apply SMGR to mixed-phenotype acute leukemia (MPAL) and identify the MPAL-specific regulatory program with significant peak-gene links, which greatly enhance our understanding of the regulatory mechanisms and potential targets of this complex tumor.