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Berberine and RNAi-Targeting Telomerase Reverse Transcriptase (TERT) and/or Telomerase RNA Component (TERC) Caused Oxidation in Colorectal Cancer Cell Line, HCT 116: An Integrative Approach using Molecular and Metabolomic Studies

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Abstract

Colorectal cancer (CRC) is the most common cancer in both men and women and is associated with increased telomerase levels and activity. The potential downstream effects of TERT and/or TERC downregulation by berberine (a telomerase inhibitor) or RNA interference (RNAi) on various target RNAs, proteins, relative telomerase activity (RTA), relative telomere length (RTL), hydrogen peroxide concentration [H2O2], percentage of cell cycle distribution, cell size and granularity as well as cellular metabolites were explored in HCT 116 cell line. Knockdown of TERT decreased TERC. The downregulation of TERT and/or TERC caused increment of [H2O2], G0/G1 phase arrest in addition to decreased S and G2/M phases, as well as diminished cell size. RTL was later reduced as a result of TERT, TERT and/or TERC downregulation which decreased RTA. It was discovered that xanthine oxidase (XO) was significantly and positively correlated at FDR-adjusted p value < 0.05 with RTA, TERT, TERT, TERC, and RTL. HCT 116 with decreased RTA was closely clustered in the Principal Component Analysis (PCA) indicating similarity of the metabolic profile. A total of 55 metabolites were putatively annotated in this study, potentially associated with RTA levels. The Debiased Sparse Partial Correlation (DSPC) Network revealed that RTA was directly correlated to TERT. There were 4 metabolic pathways significantly affected by low level of RTA which include (1) purine metabolism, (2) glycine, serine, and threonine metabolism, (3) glyoxylate and dicarboxylate metabolism, and (4) aminoacyl-tRNA biosynthesis. The Gene-Metabolite Interaction Network implied that reduced RTA level was related to the mechanism of oxidative stress. This study reveals the linkages between RTA to various selected RNAs, proteins, metabolites, oxidative stress mechanism and subsequently phenotypic changes in HCT 116 which is valuable to understand the intricate biological interactions and mechanism of telomerase in CRC.

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References

  1. Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., & Bray, F. (2021). Cancer statistics for the year 2020: an overview. International Journal of Cancer, 149, 778–789.

    Article  CAS  Google Scholar 

  2. Ferlay, J., Ervik, M., Lam, F., Colombet, M., Mery, L., Piñeros, M., Znaor, A., Soerjomataram, I., & Bray, F. (2020). Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. Lyon, France: International Agency for Research on Cancer.

    Google Scholar 

  3. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71, 209–249.

    PubMed  Google Scholar 

  4. Nikolouzakis, T. K., Vakonaki, E., Stivaktakis, P. D., Alegakis, A., Berdiaki, A., Razos, N., Souglakos, J., Tsatsakis, A., & Tsiaoussis, J. (2021). Novel prognostic biomarkers in metastatic and locally advanced colorectal cancer: Micronuclei frequency and telomerase activity in peripheral blood lymphocytes. Frontiers in Oncology, 11, 683605.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Peacock, S. D., Massey, T. E., Vanner, S. J., & King, W. D. (2018). Telomere length in the colon is related to colorectal adenoma prevalence. PloS One, 13, e0205697.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Jia, H., & Wang, Z. (2016). Telomere length as a prognostic factor for overall survival in colorectal cancer patients. Cellular Physiology and Biochemistry: International Journal of Experimental Cellular Physiology, Biochemistry, and Pharmacology, 38, 122–128.

    Article  CAS  PubMed  Google Scholar 

  7. Vishwakarma, K., Dey, R., Bhatt, H., 2023. Telomerase: A prominent oncological target for development of chemotherapeutic agents. European Journal of Medicinal Chemistry, 115121

  8. Chen, Y.-X., Gao, Q.-Y., Zou, T.-H., Wang, B.-M., Liu, S.-D., Sheng, J.-Q., Ren, J.-L., Zou, X.-P., Liu, Z.-J., & Song, Y.-Y. (2020). Berberine versus placebo for the prevention of recurrence of colorectal adenoma: a multicentre, double-blinded, randomised controlled study. The Lancet Gastroenterology & Hepatology, 5, 267–275.

    Article  Google Scholar 

  9. Samad, M. A., Saiman, M. Z., Abdul Majid, N., Karsani, S. A., & Yaacob, J. S. (2021). Berberine inhibits telomerase activity and induces cell cycle arrest and telomere erosion in colorectal cancer cell line, HCT 116. Molecules (Basel, Switzerland), 26, 376.

    Article  CAS  PubMed  Google Scholar 

  10. Tillhon, M., Ortiz, L. M. G., Lombardi, P., & Scovassi, A. I. (2012). Berberine: new perspectives for old remedies. Biochemical Pharmacology, 84, 1260–1267.

    Article  CAS  PubMed  Google Scholar 

  11. Zou, K., Li, Z., Zhang, Y., Zhang, H.-Y., Li, B., Zhu, W.-L., Shi, J.-Y., Jia, Q., & Li, Y.-M. (2017). Advances in the study of berberine and its derivatives: a focus on anti-inflammatory and anti-tumor effects in the digestive system. Acta Pharmacologica Sinica, 38, 157–167.

    Article  CAS  PubMed  Google Scholar 

  12. Luck, K., Jailkhani, N., Cusick, M., Rolland, T., Calderwood, M., Charloteaux, B., Vidal, M., 2016. Interactomes-Scaffolds of Cellular Systems

  13. Ge, L., Shao, W., Zhang, Y., Qiu, Y., Cui, D., Huang, D., & Deng, Z. (2011). RNAi targeting of hTERT gene expression induces apoptosis and inhibits the proliferation of lung cancer cells. Oncology Letters, 2, 1121–1129.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Klein, E. A., & Assoian, R. K. (2008). Transcriptional regulation of the cyclin D1 gene at a glance. Journal of Cell Science, 121, 3853–3857.

    Article  CAS  PubMed  Google Scholar 

  15. Bardelčíková, A., Šoltys, J., & Mojžiš, J. (2023). Oxidative stress, inflammation and colorectal cancer: an overview. Antioxidants, 12, 901.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Vasilishina, A., Kropotov, A., Spivak, I., & Bernadotte, A. 2019. Relative Human Telomere Length Quantification by Real-Time PCR, in: Demaria, M. (Ed.), Cellular Senescence: Methods and Protocols. Springer New York, New York, pp. 39-44

  17. Junglee, S., Urban, L., Sallanon, H., & Lopez-Lauri, F. (2014). Optimized assay for hydrogen peroxide determination in plant tissue using potassium iodide. American Journal of Analytical Chemistry, 5, 730. https://doi.org/10.4236/ajac.2014.511081.

    Article  CAS  Google Scholar 

  18. Bi, H., Krausz, K. W., Manna, S. K., Li, F., Johnson, C. H., & Gonzalez, F. J. (2013). Optimization of harvesting, extraction, and analytical protocols for UPLC-ESI-MS-based metabolomic analysis of adherent mammalian cancer cells. Analytical and Bioanalytical Chemistry, 405, 5279–5289.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hoffmann, M. A., Nothias, L.-F., Ludwig, M., Fleischauer, M., Gentry, E. C., Witting, M., Dorrestein, P. C., Dührkop, K., & Böcker, S. (2022). High-confidence structural annotation of metabolites absent from spectral libraries. Nature Biotechnology, 40, 411–421. https://doi.org/10.1038/s41587-021-01045-9.

    Article  CAS  PubMed  Google Scholar 

  20. Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., Fan, T. W., Fiehn, O., Goodacre, R., Griffin, J. L., Hankemeier, T., Hardy, N., Harnly, J., Higashi, R., Kopka, J., Lane, A. N., Lindon, J. C., Marriott, P., Nicholls, A. W., Reily, M. D., Thaden, J. J., & Viant, M. R. (2007). Proposed minimum reporting standards for chemical analysis. Metabolomics, 3, 211–221. https://doi.org/10.1007/s11306-007-0082-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., & Egertson, J. (2012). A cross-platform toolkit for mass spectrometry and proteomics. Nature Biotechnology, 30, 918–920.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Pluskal, T., Castillo, S., Villar-Briones, A., & Orešič, M. (2010). MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics, 11, 1–11.

    Article  Google Scholar 

  23. R Core Team, 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing.

  24. Hoffmann, M. A., Nothias, L.-F., Ludwig, M., Fleischauer, M., Gentry, E. C., Witting, M., Dorrestein, P. C., Dührkop, K., Böcker, S., 2021. Assigning confidence to structural annotations from mass spectra with COSMIC. BioRxiv, 2021.2003. 2018.435634.

  25. Pang, Z., Zhou, G., Ewald, J., Chang, L., Hacariz, O., Basu, N., & Xia, J. (2022). Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nature Protocols, 17, 1735–1761.

    Article  CAS  PubMed  Google Scholar 

  26. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151.

    Article  Google Scholar 

  27. Suhr, D. D. (2005). Principal component analysis vs. exploratory factor analysis. SUGI 30 Proceedings, 203, 230.

    Google Scholar 

  28. Nijs, V., 2023. radiant: Business Analytics using R and Shiny. R package version 1.5.0

  29. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57, 289–300.

    MathSciNet  Google Scholar 

  30. Saccenti, E., Hendriks, M. H., & Smilde, A. K. (2020). Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models. Scientific Reports, 10, 438.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  31. Camacho, D., De La Fuente, A., & Mendes, P. (2005). The origin of correlations in metabolomics data. Metabolomics, 1, 53–63.

    Article  CAS  Google Scholar 

  32. Ramirez, J.-M., Bai, Q., Péquignot, M., Becker, F., Kassambara, A., Bouin, A., Kalatzis, V., Dijon-Grinand, M., & De Vos, J. (2013). Side scatter intensity is highly heterogeneous in undifferentiated pluripotent stem cells and predicts clonogenic self-renewal. Stem Cells and Development, 22, 1851–1860.

    Article  CAS  PubMed  Google Scholar 

  33. Triba, M. N., Le Moyec, L., Amathieu, R., Goossens, C., Bouchemal, N., Nahon, P., Rutledge, D. N., & Savarin, P. (2015). PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Molecular BioSystems, 11, 13–19.

    Article  CAS  PubMed  Google Scholar 

  34. Nahm, F. S. (2022). Receiver operating characteristic curve: overview and practical use for clinicians. Korean Journal of Anesthesiology, 75, 25–36.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Desi, N., & Tay, Y. (2019). The butterfly effect of RNA alterations on transcriptomic equilibrium. Cells, 8, 1634.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Mohd Zain, M. Z., Ismail, N. H., Ahmad, N., Sulong, S., Karsani, S. A., & Abdul Majid, N. (2020). Telomerase reverse transcriptase downregulation by RNA interference modulates endoplasmic reticulum stress and mitochondrial energy production. Molecular Biology Reports, 47, 7735–7743.

    Article  CAS  PubMed  Google Scholar 

  37. Fiszer-Kierzkowska, A., Vydra, N., Wysocka-Wycisk, A., Kronekova, Z., Jarząb, M., Lisowska, K. M., & Krawczyk, Z. (2011). Liposome-based DNA carriers may induce cellular stress response and change gene expression pattern in transfected cells. BMC Molecular Biology, 12, 1–9.

    Article  Google Scholar 

  38. Kleefeldt, J. M., Pozarska, A., Nardiello, C., Pfeffer, T., Vadász, I., Herold, S., Seeger, W., & Morty, R. E. (2020). Commercially available transfection reagents and negative control siRNA are not inert. Analytical Biochemistry, 606, 113828.

    Article  CAS  PubMed  Google Scholar 

  39. Yao, Z., Kim, Y. W., Amin, R., Volpe, E., Jogunoori, W., Mishra, L., & Mishra, B. (2008). Telomerase reverse transcriptase regulation by TGF-β signaling through adaptor ELF and Smad3 that is independent of c-Myc. Cancer Research, 68, 3442–3442.

    Google Scholar 

  40. Zhang, K., Zhang, M., Luo, Z., Wen, Z., & Yan, X. (2020). The dichotomous role of TGF-β in controlling liver cancer cell survival and proliferation. Journal of Genetics and Genomics, 47, 497–512.

    Article  CAS  PubMed  Google Scholar 

  41. Cassar, L., Li, H., Jiang, F.-X., & Liu, J.-P. (2010). TGF-β induces telomerase-dependent pancreatic tumor cell cycle arrest. Molecular and Cellular Endocrinology, 320, 97–105.

    Article  CAS  PubMed  Google Scholar 

  42. Farnung, B. O., Brun, C. M., Arora, R., Lorenzi, L. E., & Azzalin, C. M. (2012). Telomerase efficiently elongates highly transcribing telomeres in human cancer cells. PloS One, 7, e35714.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  43. Shen, Y., Zhang, Y.-W., Zhang, Z.-X., Miao, Z.-H., & Ding, J. (2008). hTERT-targeted RNA interference inhibits tumorigenicity and motility of HCT116 cells. Cancer Biology & Therapy, 7, 228–236.

    Article  CAS  Google Scholar 

  44. Bakr, M., Abd-Elmawla, M. A., Elimam, H., El-Din, H. G., Fawzy, A., Abulsoud, A. I., & Rizk, S. M. (2023). Telomerase RNA component lncRNA as potential diagnostic biomarker promotes CRC cellular migration and apoptosis evasion via modulation of β-catenin protein level. Non-coding RNA Research, 8, 302–314.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Jirawatnotai, S., Hu, Y., Livingston, D. M., & Sicinski, P. (2012). Proteomic identification of a direct role for cyclin d1 in DNA damage repair. Cancer Research, 72, 4289–4293.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Davies, O., Mendes, P., Smallbone, K., & Malys, N. (2012). Characterisation of multiple substrate-specific (d) ITP/(d) XTPase and modelling of deaminated purine nucleotide metabolism. BMB Reports, 45, 259–264.

    Article  CAS  PubMed  Google Scholar 

  47. Förstermann, U., 2010. Chapter 5 - Uncoupling of Endothelial Nitric Oxide Synthase in Cardiovascular Disease and its Pharmacological Reversal, in: Ignarro, L. J. (Ed.), Nitric Oxide (Second Edition). Academic Press, San Diego, pp. 139–167

  48. Salway, J. G., 2016. Metabolism at a Glance. John Wiley & Sons

  49. Butterworth, P. J. (2005). Lehninger: principles of biochemistry (4th edn) D. L. Nelson and M. C. Cox, W. H. Freeman & Co., New York, 1119 pp (plus 17 pp glossary), ISBN 0-7167-4339-6 (2004). Cell Biochemistry and Function, 23, 293–294.

    Article  CAS  Google Scholar 

  50. Kelley, E. E., Khoo, N. K., Hundley, N. J., Malik, U. Z., Freeman, B. A., & Tarpey, M. M. (2010). Hydrogen peroxide is the major oxidant product of xanthine oxidase. Free Radical Biology and Medicine, 48, 493–498.

    Article  CAS  PubMed  Google Scholar 

  51. Ko, E., Seo, H. W., & Jung, G. (2018). Telomere length and reactive oxygen species levels are positively associated with a high risk of mortality and recurrence in hepatocellular carcinoma. Hepatology, 67, 1378–1391.

    Article  CAS  PubMed  Google Scholar 

  52. Li, P., Wu, M., Wang, J., Sui, Y., Liu, S., & Shi, D. (2016). NAC selectively inhibit cancer telomerase activity: a higher redox homeostasis threshold exists in cancer cells. Redox Biology, 8, 91–97.

    Article  CAS  PubMed  Google Scholar 

  53. Trachana, V., Petrakis, S., Fotiadis, Z., Siska, E. K., Balis, V., Gonos, E. S., Kaloyianni, M., & Koliakos, G. (2017). Human mesenchymal stem cells with enhanced telomerase activity acquire resistance against oxidative stress-induced genomic damage. Cytotherapy, 19, 808–820.

    Article  CAS  PubMed  Google Scholar 

  54. Pérez-Rivero, G., Ruiz-Torres, M. P., Díez-Marqués, M. L., Canela, A., López-Novoa, J. M., Rodríguez-Puyol, M., Blasco, M. A., & Rodríguez-Puyol, D. (2008). Telomerase deficiency promotes oxidative stress by reducing catalase activity. Free Radical Biology and Medicine, 45, 1243–1251.

    Article  PubMed  Google Scholar 

  55. Mouilleron, H., Delcourt, V., & Roucou, X. (2015). Death of a dogma: eukaryotic mRNAs can code for more than one protein. Nucleic Acids Res, 44, 14–23.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011). Global quantification of mammalian gene expression control. Nature, 473, 337–342.

    Article  PubMed  ADS  Google Scholar 

  57. Li, J., Huang, X., Xie, X., Wang, J., & Duan, M. (2011). Human telomerase reverse transcriptase regulates cyclin D1 and G1/S phase transition in laryngeal squamous carcinoma. Acta Oto-laryngologica, 131, 546–551.

    Article  CAS  PubMed  Google Scholar 

  58. Liu, A.-Q., Ge, L.-Y., Lu, X.-L., Luo, X.-L., Cai, Y.-L., Ye, X.-Q., & Geng, F.-F. (2014). Silencing of the hTERT gene by shRNA inhibits colon cancer SW480 cell growth in vitro and in vivo. PloS One, 9, e107019–e107019.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  59. Shi, Y.-A., Zhao, Q., Zhang, L.-H., Du, W., Wang, X.-Y., He, X., Wu, S., & Li, Y.-L. (2014). Knockdown of hTERT by siRNA inhibits cervical cancer cell growth in vitro and in vivo. International Journal of Oncology, 45, 1216–1224.

    Article  CAS  PubMed  Google Scholar 

  60. Neurohr, G. E., Terry, R. L., Lengefeld, J., Bonney, M., Brittingham, G. P., Moretto, F., Miettinen, T. P., Vaites, L. P., Soares, L. M., & Paulo, J. A. (2019). Excessive cell growth causes cytoplasm dilution and contributes to senescence. Cell, 176, 1083–1097.e1018.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Gavia-García, G., Rosado-Pérez, J., Arista-Ugalde, T. L., Aguiñiga-Sánchez, I., Santiago-Osorio, E., & Mendoza-Núñez, V. M. (2021). Telomere length and oxidative stress and its relation with metabolic syndrome components in the aging. Biology, 10, 253.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Von Zglinicki, T. (2002). Oxidative stress shortens telomeres. Trends in Biochemical Sciences, 27, 339–344.

    Article  Google Scholar 

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Acknowledgements

The authors thank Universiti Malaya, Malaysia for the facilities and financial support (RP030C-15AFR) provided.

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This research was funded by Universiti Malaya (RP030C-15AFR).

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  1. Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Muhammad Azizan Samad, Mohd Zuwairi Saiman, Nazia Abdul Majid, Saiful Anuar Karsani & Jamilah Syafawati Yaacob

  2. INFRA High Impact Research (HIR), HIR Building, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Muhammad Azizan Samad

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Contributions

J.S.Y., N.A.M. and M.A.S. conceived and designed the experiment(s). M.A.S. conducted the experiments, collected the data and performed the statistical analysis. J.S.Y., M.Z.S., N.A.M. and S.A.K. advised on the preparation of materials. J.S.Y., M.Z.S. and M.A.S. contributed reagents/materials. J.S.Y. and M.Z.S. advised on the metabolomics section. J.S.Y., M.Z.S. and S.A.K. provided the facilities for analysis, M.A.S. wrote the manuscript. J.S.Y., M.Z.S. and N.A.M. read and edited the manuscript. All authors approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

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Samad, M.A., Saiman, M.Z., Abdul Majid, N. et al. Berberine and RNAi-Targeting Telomerase Reverse Transcriptase (TERT) and/or Telomerase RNA Component (TERC) Caused Oxidation in Colorectal Cancer Cell Line, HCT 116: An Integrative Approach using Molecular and Metabolomic Studies. Cell Biochem Biophys 82, 153–173 (2024). https://doi.org/10.1007/s12013-023-01210-8

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