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Gibb M, Liu JY, Sayes CM. The transcriptomic signature of respiratory sensitizers using an alveolar model. Cell Biol Toxicol 2024; 40:21. [PMID: 38584208 PMCID: PMC10999393 DOI: 10.1007/s10565-024-09860-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 03/25/2024] [Indexed: 04/09/2024]
Abstract
Environmental contaminants are ubiquitous in the air we breathe and can potentially cause adverse immunological outcomes such as respiratory sensitization, a type of immune-driven allergic response in the lungs. Wood dust, latex, pet dander, oils, fragrances, paints, and glues have all been implicated as possible respiratory sensitizers. With the increased incidence of exposure to chemical mixtures and the rapid production of novel materials, it is paramount that testing regimes accounting for sensitization are incorporated into development cycles. However, no validated assay exists that is universally accepted to measure a substance's respiratory sensitizing potential. The lungs comprise various cell types and regions where sensitization can occur, with the gas-exchange interface being especially important due to implications for overall lung function. As such, an assay that can mimic the alveolar compartment and assess sensitization would be an important advance for inhalation toxicology. Some such models are under development, but in-depth transcriptomic analyses have yet to be reported. Understanding the transcriptome after sensitizer exposure would greatly advance hazard assessment and sustainability. We tested two known sensitizers (i.e., isophorone diisocyanate and ethylenediamine) and two known non-sensitizers (i.e., chlorobenzene and dimethylformamide). RNA sequencing was performed in our in vitro alveolar model, consisting of a 3D co-culture of epithelial, macrophage, and dendritic cells. Sensitizers were readily distinguishable from non-sensitizers by principal component analysis. However, few differentially regulated genes were common across all pair-wise comparisons (i.e., upregulation of genes SOX9, UACA, CCDC88A, FOSL1, KIF20B). While the model utilized in this study can differentiate the sensitizers from the non-sensitizers tested, further studies will be required to robustly identify critical pathways inducing respiratory sensitization.
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Affiliation(s)
- Matthew Gibb
- Institute of Biomedical Studies (BMS), Baylor University, Waco, TX, 76798-7266, USA
| | - James Y Liu
- Department of Environmental Science (ENV), Baylor University, One Bear Place #97266, Waco, TX, 76798-7266, USA
| | - Christie M Sayes
- Institute of Biomedical Studies (BMS), Baylor University, Waco, TX, 76798-7266, USA.
- Department of Environmental Science (ENV), Baylor University, One Bear Place #97266, Waco, TX, 76798-7266, USA.
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Gao J, Li Z, Lu Q, Zhong J, Pan L, Feng C, Tang S, Wang X, Tao Y, Lin J, Wang Q. Single-cell RNA sequencing reveals cell subpopulations in the tumor microenvironment contributing to hepatocellular carcinoma. Front Cell Dev Biol 2023; 11:1194199. [PMID: 37333982 PMCID: PMC10272598 DOI: 10.3389/fcell.2023.1194199] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/25/2023] [Indexed: 06/20/2023] Open
Abstract
Background: Hepatocellular carcinoma (HCC) is among the deadliest cancers worldwide, and advanced HCC is difficult to treat. Identifying specific cell subpopulations in the tumor microenvironment and exploring interactions between the cells and their environment are crucial for understanding the development, prognosis, and treatment of tumors. Methods: In this study, we constructed a tumor ecological landscape of 14 patients with HCC from 43 tumor tissue samples and 14 adjacent control samples. We used bioinformatics analysis to reveal cell subpopulations with potentially specific functions in the tumor microenvironment and to explore the interactions between tumor cells and the tumor microenvironment. Results: Immune cell infiltration was evident in the tumor tissues, and BTG1 + RGS1 + central memory T cells (Tcms) interact with tumor cells through CCL5-SDC4/1 axis. HSPA1B may be associated with remodeling of the tumor ecological niche in HCC. Cancer-associated fibroblasts (CAFs) and macrophages (TAMs) were closely associated with tumor cells. APOC1 + SPP1 + TAM secretes SPP1, which binds to ITGF1 secreted by CAFs to remodel the tumor microenvironment. More interestingly, FAP + CAF interacts with naïve T cells via the CXCL12-CXCR4 axis, which may lead to resistance to immune checkpoint inhibitor therapy. Conclusion: Our study suggests the presence of tumor cells with drug-resistant potential in the HCC microenvironment. Among non-tumor cells, high NDUFA4L2 expression in fibroblasts may promote tumor progression, while high HSPA1B expression in central memory T cells may exert anti-tumor effects. In addition, the CCL5-SDC4/1 interaction between BTG1 + RGS1 + Tcms and tumor cells may promote tumor progression. Focusing on the roles of CAFs and TAMs, which are closely related to tumor cells, in tumors would be beneficial to the progress of systemic therapy research.
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Affiliation(s)
- Jiamin Gao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Laboratory of Infectious Disease, The Fourth People’s Hospital of Nanning, Nanning, China
| | - Zhijian Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qinchen Lu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, China
| | - Jialing Zhong
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, China
| | - Lixin Pan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, China
| | - Chao Feng
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, China
| | - Shaomei Tang
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, China
| | - Xi Wang
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, China
| | - Yuting Tao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, China
| | - Jianyan Lin
- Administrative Office, The First People’s Hospital of Nanning, Nanning, China
| | - Qiuyan Wang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, China
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