Browsing by Author "Li, Zihai"

Now showing 1 - 6 of 6
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    Asparagine restriction enhances CD8+ T cell metabolic fitness and antitumoral functionality through an NRF2-dependent stress response
    (Springer Nature, 2023) Gnanaprakasam, J. N. Rashida; Kushwaha, Bhavana; Liu, Lingling; Chen, Xuyong; Kang, Siwen; Wang, Tingting; Cassel, Teresa A.; Adams, Christopher M.; Higashi, Richard M.; Scott, David A.; Xin, Gang; Li, Zihai; Yang, Jun; Lane, Andrew N.; Fan, Teresa W. M.; Zhang, Ji; Wang, Ruoning; Pediatrics, School of Medicine
    Robust and effective T cell immune surveillance and cancer immunotherapy require proper allocation of metabolic resources to sustain energetically costly processes, including growth and cytokine production. Here, we show that asparagine (Asn) restriction on CD8+ T cells exerted opposing effects during activation (early phase) and differentiation (late phase) following T cell activation. Asn restriction suppressed activation and cell cycle entry in the early phase while rapidly engaging the nuclear factor erythroid 2-related factor 2 (NRF2)-dependent stress response, conferring robust proliferation and effector function on CD8+ T cells during differentiation. Mechanistically, NRF2 activation in CD8+ T cells conferred by Asn restriction rewired the metabolic program by reducing the overall glucose and glutamine consumption but increasing intracellular nucleotides to promote proliferation. Accordingly, Asn restriction or NRF2 activation potentiated the T cell-mediated antitumoral response in preclinical animal models, suggesting that Asn restriction is a promising and clinically relevant strategy to enhance cancer immunotherapy. Our study revealed Asn as a critical metabolic node in directing the stress signaling to shape T cell metabolic fitness and effector functions.
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    Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning
    (Elsevier, 2022-08-24) Chang, Yuzhou; He, Fei; Wang, Juexin; Chen, Shuo; Li, Jingyi; Liu, Jixin; Yu, Yang; Su, Li; Ma, Anjun; Allen, Carter; Lin, Yu; Sun, Shaoli; Liu, Bingqiang; Otero, José Javier; Chung, Dongjun; Fu, Hongjun; Li, Zihai; Xu, Dong; Ma, Qin; Medical and Molecular Genetics, School of Medicine
    Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.
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    IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis
    (Oxford University Press, 2021) Chang, Yuzhou; Allen, Carter; Wan, Changlin; Chung, Dongjun; Zhang, Chi; Li, Zihai; Ma, Qin; Medical and Molecular Genetics, School of Medicine
    Summary: Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline. Availability and implementation: IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM.
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    Platelet count correlates with stage and predicts survival in melanoma
    (Taylor & Francis, 2019) Rachidi, Saleh; Kaur, Maneet; Lautenschlaeger, Tim; Li, Zihai; Radiation Oncology, School of Medicine
    Cancer is a chronic inflammatory state which is often associated with increased platelet counts. Cancer cells induce thrombopoiesis and activate platelets, which in turn facilitate cancer invasion and metastasis. In this study, we investigate the correlation between platelet counts with each of stage and overall survival in melanoma. This is a retrospective cohort study of 642 melanoma patients diagnosed or treated at a tertiary medical center between 2000 and 2016. Multivariable analysis adjusted for age, sex, stage, and treatment modality. Using multivariable analysis, patients with thrombocytosis around time of diagnosis were more likely to present with distant metastasis (Prevalence Ratio 3.5, 95% CI 2.35–5.22). In patients with metastatic disease and in all stages combined, thrombocytosis predicted decreased 5-year overall survival in univariate and multivariable analysis, and this was most pronounced during the first year after diagnosis. Finally, we show that mice with thrombocytopenia due to the lack of heat shock protein gp96 in their megakaryocytes are protected from melanoma dissemination to the lungs. These findings are concordant with preclinical studies showing a role for platelets in cancer metastasis and suppression of antitumor immunity, further supporting targeting platelets as an adjuvant to immunotherapy in melanoma.
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    Preoperative platelet counts and postoperative outcomes in cancer surgery: a multicenter, retrospective cohort study
    (Taylor & Francis, 2019) Rachidi, Saleh; Li, Hong; Wallace, Kristin; Li, Zihai; Balch, Charles; Lautenschlaeger, Tim; Pediatrics, School of Medicine
    Platelets play roles in malignancy, wound healing, and immunity. Nevertheless, their significance in postoperative outcomes is not established. This is a retrospective cohort study of 100,795 patients undergoing cancer surgery in 2010 and 2014 in >500 hospitals. Patients were stratified into five groups based on preoperative platelet counts. Multivariable logistic regression was used to determine the risk of 30-day mortality, morbidities, readmission, and prolonged hospitalization using the mid-normal group as a reference. We adjusted for demographic variables, comorbidities, and operation complexity. In the 2014 cohort, multivariable analysis showed that mortality was higher in patients with thrombocytopenia (OR 1.49, 95% CI [1.23–1.81]), high-normal platelets (OR 1.29, [1.06–1.55]), and thrombocytosis (OR 1.78, [1.45–2.19]). Composite postoperative morbidity followed a similar trend with thrombocytopenia (OR 1.34, [1.25–1.43]), high-normal counts (OR 1.41, [1.33–1.49]), and thrombocytosis (OR 2.20, [2.05–2.36]). Concordantly, the risks of prolonged hospitalization and 30-day readmission followed the same pattern. These results were validated in a large colon cancer cohort from the 2010 database. In conclusion, platelet count is a prognostic indicator in cancer surgeries. This could be related to the role of platelets in wound healing and immunity on one hand, and propagating malignancy on the other.
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    Summary of the 2019 Blood and Marrow Transplant Clinical Trials Network Myeloma Intergroup Workshop on Minimal Residual Disease and Immune Profiling
    (Elsevier, 2020-10) Holstein, Sarah A.; Howard, Alan; Avigan, David; Bhutani, Manisha; Cohen, Adam D.; Costa, Luciano J.; Dhodapkar, Madhav V.; Gay, Francesca; Gormley, Nicole; Green, Damian J.; Hillengass, Jens; Korde, Neha; Li, Zihai; Mailankody, Sham; Neri, Paola; Parekh, Samir; Pasquini, Marcelo C.; Puig, Noemi; Roodman, G. David; Samur, Mehmet Kemal; Shah, Nina; Shah, Urvi A.; Shi, Qian; Spencer, Andrew; Suman, Vera J.; Usmani, Saad Z.; McCarthy, Philip L.; Medicine, School of Medicine
    The Blood and Marrow Transplant Clinical Trials Network (BMT CTN) Myeloma Intergroup has organized an annual workshop focused on minimal residual disease (MRD) testing and immune profiling (IP) in multiple myeloma since 2016. In 2019, the workshop took place as an American Society of Hematology (ASH) Friday Scientific Workshop entitled “Immune Profiling and Minimal Residual Disease Testing in Multiple Myeloma”. This workshop focused on four main topics: the molecular and immunological evolution of plasma cell disorders, the development of new laboratory- and imaging-based MRD assessment approaches, chimeric antigen receptor T-cell therapy research, and the statistical and regulatory issues associated with novel clinical endpoints. In this report, we provide a summary of the workshop and discuss future directions.