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Nahálková J. On the interface of aging, cancer, and neurodegeneration with SIRT6 and L1 retrotransposon protein interaction network. Ageing Res Rev 2024:102496. [PMID: 39251041 DOI: 10.1016/j.arr.2024.102496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/15/2024] [Accepted: 09/02/2024] [Indexed: 09/11/2024]
Abstract
Roles of the sirtuins in aging and longevity appear related to their evolutionarily conserved functions as retroviral-restriction factors. Retrotransposons also promote the aging process, which can be reversed by the inhibition of their activity. SIRT6 can functionally limit the mutation activity of LINE-1 (L1), a retrotransposon causing cancerogenesis-linked mutations accumulating during aging. Here, an overview of the molecular mechanisms of the controlling effects was created by the pathway enrichment and gene function prediction analysis of a protein interaction network of SIRT6 and L1 retrotransposon proteins L1 ORF1p, and L1 ORF2p. The L1-SIRT6 interaction network is enriched in pathways and nodes associated with RNA quality control, DNA damage response, tumor-related and retrotransposon activity-suppressing functions. The analysis also highlighted sumoylation, which controls protein-protein interactions, subcellular localization, and other post-translational modifications; DNA IR Damage and Cellular Response via ATR, and Hallmark Myc Targets V1, which scores are a measure of tumor aggressiveness. The protein node prioritization analysis emphasized the functions of tumor suppressors p53, PARP1, BRCA1, and BRCA2 having L1 retrotransposon limiting activity; tumor promoters EIF4A3, HNRNPA1, HNRNPH1, DDX5; and antiviral innate immunity regulators DDX39A and DDX23. The outline of the regulatory mechanisms involved in L1 retrotransposition with a focus on the prioritized nodes is here demonstrated in detail. Furthermore, a model establishing functional links between HIV infection, L1 retrotransposition, SIRT6, and cancer development is also presented. Finally, L1-SIRT6 subnetwork SIRT6-PARP1-BRCA1/BRCA2-TRIM28-PIN1-p53 was constructed, where all nodes possess L1 retrotransposon activity-limiting activity and together represent candidates for multitarget control.
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Affiliation(s)
- Jarmila Nahálková
- Biochemistry, Molecular, and Cell Biology Unit, Biochemworld co.; Snickar-Anders väg 17, 74394, Skyttorp, Uppsala County, Sweden.
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Krauze AV, Sierk M, Nguyen T, Chen Q, Yan C, Hu Y, Jiang W, Tasci E, Zgela TC, Sproull M, Mackey M, Shankavaram U, Meerzaman D, Camphausen K. Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel. Front Oncol 2023; 13:1127645. [PMID: 37637066 PMCID: PMC10448824 DOI: 10.3389/fonc.2023.1127645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 06/20/2023] [Indexed: 08/29/2023] Open
Abstract
Background Glioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response. Purpose We analyzed differential proteomic expression pre vs. post completion of concurrent chemoirradiation (CRT) in patient serum samples to explore proteomic alterations and classify GBM by integrating clinical and proteomic parameters. Materials and methods 82 patients with GBM were clinically annotated and serum samples obtained pre- and post-CRT. Serum samples were then screened using the aptamer-based SOMAScan® proteomic assay. Significant traits from uni- and multivariate Cox models for overall survival (OS) were designated independent prognostic factors and principal component analysis (PCA) was carried out. Differential expression of protein signals was calculated using paired t-tests, with KOBAS used to identify associated KEGG pathways. GSEA pre-ranked analysis was employed on the overall list of differentially expressed proteins (DEPs) against the MSigDB Hallmark, GO Biological Process, and Reactome databases with weighted gene correlation network analysis (WGCNA) and Enrichr used to validate pathway hits internally. Results 3 clinical clusters of patients with differential survival were identified. 389 significantly DEPs pre vs. post-treatment were identified, including 284 upregulated and 105 downregulated, representing several pathways relevant to cancer metabolism and progression. The lowest survival group (median OS 13.2 months) was associated with DEPs affiliated with proliferative pathways and exhibiting distinct oppositional response including with respect to radiation therapy related pathways, as compared to better-performing groups (intermediate, median OS 22.4 months; highest, median OS 28.7 months). Opposite signaling patterns across multiple analyses in several pathways (notably fatty acid metabolism, NOTCH, TNFα via NF-κB, Myc target V1 signaling, UV response, unfolded protein response, peroxisome, and interferon response) were distinct between clinical survival groups and supported by WGCNA. 23 proteins were statistically signficant for OS with 5 (NETO2, CST7, SEMA6D, CBLN4, NPS) supported by KM. Conclusion Distinct proteomic alterations with hallmarks of cancer, including progression, resistance, stemness, and invasion, were identified in serum samples obtained from GBM patients pre vs. post CRT and corresponded with clinical survival. The proteome can potentially be employed for glioma classification and biological interrogation of cancer pathways.
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Affiliation(s)
- Andra Valentina Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Michael Sierk
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Trinh Nguyen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Qingrong Chen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Chunhua Yan
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Ying Hu
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - William Jiang
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Erdal Tasci
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Theresa Cooley Zgela
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Mary Sproull
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Megan Mackey
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Uma Shankavaram
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
| | - Daoud Meerzaman
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States
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González-Ortiz A, Pulido-Capiz A, Castañeda-Sánchez CY, Ibarra-López E, Galindo-Hernández O, Calderón-Fernández MA, López-Cossio LY, Díaz-Molina R, Chimal-Vega B, Serafín-Higuera N, Córdova-Guerrero I, García-González V. eIF4A/PDCD4 Pathway, a Factor for Doxorubicin Chemoresistance in a Triple-Negative Breast Cancer Cell Model. Cells 2022; 11:4069. [PMID: 36552834 PMCID: PMC9776898 DOI: 10.3390/cells11244069] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 11/28/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022] Open
Abstract
Cells employ several adaptive mechanisms under conditions of accelerated cell division, such as the unfolded protein response (UPR). The UPR is composed of a tripartite signaling system that involves ATF6, PERK, and IRE1, which maintain protein homeostasis (proteostasis). However, deregulation of protein translation initiation could be associated with breast cancer (BC) chemoresistance. Specifically, eukaryotic initiation factor-4A (eIF4A) is involved in the unfolding of the secondary structures of several mRNAs at the 5' untranslated region (5'-UTR), as well as in the regulation of targets involved in chemoresistance. Importantly, the tumor suppressor gene PDCD4 could modulate this process. This regulation might be disrupted in chemoresistant triple negative-BC (TNBC) cells. Therefore, we characterized the effect of doxorubicin (Dox), a commonly used anthracycline medication, on human breast carcinoma MDA-MB-231 cells. Here, we generated and characterized models of Dox chemoresistance, and chemoresistant cells exhibited lower Dox internalization levels followed by alteration of the IRE1 and PERK arms of the UPR and triggering of the antioxidant Nrf2 axis. Critically, chemoresistant cells exhibited PDCD4 downregulation, which coincided with a reduction in eIF4A interaction, suggesting a sophisticated regulation of protein translation. Likewise, Dox-induced chemoresistance was associated with alterations in cellular migration and invasion, which are key cancer hallmarks, coupled with changes in focal adhesion kinase (FAK) activation and secretion of matrix metalloproteinase-9 (MMP-9). Moreover, eIF4A knockdown via siRNA and its overexpression in chemoresistant cells suggested that eIF4A regulates FAK. Pro-atherogenic low-density lipoproteins (LDL) promoted cellular invasion in parental and chemoresistant cells in an MMP-9-dependent manner. Moreover, Dox only inhibited parental cell invasion. Significantly, chemoresistance was modulated by cryptotanshinone (Cry), a natural terpene purified from the roots of Salvia brandegeei. Cry and Dox co-exposure induced chemosensitization, connected with the Cry effect on eIF4A interaction. We further demonstrated the Cry binding capability on eIF4A and in silico assays suggest Cry inhibition on the RNA-processing domain. Therefore, strategic disruption of protein translation initiation is a druggable pathway by natural compounds during chemoresistance in TNBC. However, plasmatic LDL levels should be closely monitored throughout treatment.
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Affiliation(s)
- Alina González-Ortiz
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Angel Pulido-Capiz
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio de Biología Molecular, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - César Y. Castañeda-Sánchez
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Esmeralda Ibarra-López
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Octavio Galindo-Hernández
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Maritza Anahí Calderón-Fernández
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Leslie Y. López-Cossio
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Raul Díaz-Molina
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Brenda Chimal-Vega
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Nicolás Serafín-Higuera
- Facultad de Odontología Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
| | - Iván Córdova-Guerrero
- Facultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Tijuana 22424, Mexico
| | - Victor García-González
- Departamento de Bioquímica, Facultad de Medicina Mexicali, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
- Laboratorio Multidisciplinario de Estudios Metabólicos y Cáncer, Universidad Autónoma de Baja California, Mexicali 21000, Mexico
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Luo H, Ge H. Application of Proteomics in the Discovery of Radiosensitive Cancer Biomarkers. Front Oncol 2022; 12:852791. [PMID: 35280744 PMCID: PMC8904368 DOI: 10.3389/fonc.2022.852791] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/04/2022] [Indexed: 12/21/2022] Open
Abstract
Radiation therapy remains an important component of cancer treatment. Gene-encoded proteins were the actual executors of cellular functions. Proteomic was a novel technology that can systematically analysis protein composition and measure their levels of change, this was a high throughput method, and were the import tools in the post genomic era. In recent years, rapid progress of proteomic have been made in the study of cancer mechanism, diagnosis, and treatment. This article elaborates current advances and future directions of proteomics in the discovery of radiosensitive cancer biomarkers.
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Affiliation(s)
- Hui Luo
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
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