HEK-omics: The promise of omics to optimize HEK293 for recombinant adeno-associated virus (rAAV) gene therapy manufacturing
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Metabolic flux analysis has provided important insights into HEK293 metabolic limitations during viral vector production.
“Additionally, metabolic flux analysis has provided important insights into HEK293 metabolic limitations during viral vector production [20].”
From Mechanistic model for HEK293 viral vector processes and its application in a digital shadow framework (2026) · cites this paper as [20]
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Schneider, V. A., Graves-Lindsay, T., Howe, K., Bouk, N., Chen, H.-C., Kitts, P. A., Murphy, T. D., Pruitt, K. D., Thibaud-Nissen, F., Albracht, D., et al. (2017) · Genome Research · doi:10.1101/gr.213611.116 · Cited by 1,193 · see citing works
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
A community-driven global reconstruction of human metabolism
Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2488 · Cited by 1,019 · see citing works
Current clinical applications of in vivo gene therapy with AAVs
Mendell, J. R., Al-Zaidy, S. A., Rodino-Klapac, L. R., Goodspeed, K., Gray, S. J., Kay, C. N., Boye, S. L., Boye, S. E., George, L. A., Salabarria, S., et al. (2021) · Molecular Therapy · doi:10.1016/j.ymthe.2020.12.007 · Cited by 743 · see citing works
Manufacturing challenges and rational formulation development for AAV viral vectors
Srivastava, A., Mallela, K. M. G., Deorkar, N., & Brophy, G. (2021) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2021.03.024 · Cited by 269 · see citing works
A genomics approach reveals insights into the importance of gene losses for mammalian adaptations
Sharma, V., Hecker, N., Roscito, J. G., Foerster, L., Langer, B. E., & Hiller, M. (2018) · Nature Communications · doi:10.1038/s41467-018-03667-1 · Cited by 237 · see citing works
Pereira, S., Kildegaard, H. F., & Andersen, M. R. (2018) · Biotechnology Journal · doi:10.1002/biot.201700499 · Cited by 214 · see citing works
UCSC Genome Browser enters 20th year
Lee, C. M., Barber, G. P., Casper, J., Clawson, H., Diekhans, M., Gonzalez, J. N., Hinrichs, A. S., Lee, B. T., Nassar, L. R., Powell, C. C., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1012 · Cited by 191 · see citing works
The challenges of integrating multi-omic data sets
Palsson, B., & Zengler, K. (2010) · Nature Chemical Biology · doi:10.1038/nchembio.462 · Cited by 178 · see citing works
Long-Read DNA Sequencing: Recent Advances and Remaining Challenges
Warburton, P. E., & Sebra, R. P. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-101722-103045 · Cited by 163 · see citing works
Feichtinger, J., Hernández, I., Fischer, C., Hanscho, M., Auer, N., Hackl, M., Jadhav, V., Baumann, M., Krempl, P. M., Schmidl, C., et al. (2016) · Biotechnology and Bioengineering · doi:10.1002/bit.25990 · Cited by 148 · see citing works
Complex‐centric proteome profiling by SEC‐SWATH‐MS
Heusel, M., Bludau, I., Rosenberger, G., Hafen, R., Frank, M., Banaei‐Esfahani, A., van Drogen, A., Collins, B. C., Gstaiger, M., & Aebersold, R. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188438 · Cited by 141 · see citing works
Stolfa, G., Smonskey, M. T., Boniface, R., Hachmann, A., Gulde, P., Joshi, A. D., Pierce, A. P., Jacobia, S. J., & Campbell, A. (2018) · Biotechnology Journal · doi:10.1002/biot.201700227 · Cited by 97 · see citing works
Flagged first, then by citations
Research works printed but not linked: 173
- Abaandou, L., Sharma, A.K., Shiloach, J., 2021. Knockout of the caspase 8-associated protein 2 gene improves recombinant protein expression in HEK293 cells through up-regulation of the cyclin- dependent kinase inhibitor 2A gene. Biotechnol. Bioeng. 118, 186-198. https://doi.org/10.1002/bit.27561 · DOI
- Abbate, T., Dewasme, L., Bogaerts, P., Wouwer, A.V., 2019. Adaptive Flux Variability Analysis: A Tool To Deal With Uncertainties. IFAC-PapersOnLine 52, 70-75. https://doi.org/10.1016/j.ifacol.2019.06.039 · DOI
- Abbate, T., Dewasme, L., Vande Wouwer, A., Bogaerts, P., 2020. Adaptive flux variability analysis of HEK cell cultures. Comput. Chem. Eng. 133, 106633. https://doi.org/10.1016/j.compchemeng.2019.106633 · DOI
- Allis, C.D., Jenuwein, T., 2016. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487- 500. https://doi.org/10.1038/nrg.2016. · DOI · Paper page
- Anand, S., Samuel, M., Ang, CS., Keerthikumar, S., Mathivanan, S. 2017. Label-Based and Label-Free Strategies for Protein Quantitation. In: Keerthikumar, S., Mathivanan, S. (Eds.), Proteome Bioinformatics. Methods in Molecular Biology. 1549, pp. 31-43. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6740-7_4 · DOI
- Ankney, J.A., Muneer, A., Chen, X., 2018. Relative and absolute quantitation in mass spectrometry-based proteomics. Annu. Rev. Anal. Chem. 11, 49-77. https://doi.org/10.1146/annurev-anchem-061516- 045357 · DOI
- Antonakoudis, A., Strain, B., Barbosa, R., Jimenez del Val, I., Kontoravdi, C., 2021. Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells. Comput. Chem. Eng. 154, 107471. https://doi.org/10.1016/j.compchemeng.2021.107471 · DOI · Paper page
- Baysoy, A., Bai, Z., Satija, R., Fan, R., 2023. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695-713. https://doi.org/10.1038/s41580-023-00615-w · DOI · Paper page
- Beacon, T.H., Delcuve, G.P., López, C., Nardocci, G., Kovalchuk, I., van Wijnen, A.J., Davie, J.R., 2021. The dynamic broad epigenetic (H3K4me3, H3K27ac) domain as a mark of essential genes. Clin. Epigenetics 13, 138. https://doi.org/10.1186/s13148-021-01126-1 · DOI
- Berk, A.J., 2005. Recent lessons in gene expression, cell cycle control, and cell biology from adenovirus. Oncogene 24, 7673-7685. https://doi.org/10.1038/sj.onc.1209040 · DOI
- Bernstein, D.B., Sulheim, S., Almaas, E., Segrè, D., 2021. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol. 22, 64. https://doi.org/10.1186/s13059-021-02289-z · DOI
- Borsi, G., Motheramgari, K., Dhiman, H., Baumann, M., Sinkala, E., Sauerland, M., Riba, J., Borth, N., 2023. Single-cell RNA sequencing reveals homogeneous transcriptome patterns and low variance in a suspension CHO-K1 and an adherent HEK293FT cell line in culture conditions. J. Biotechnol. 364, 13-22. https://doi.org/10.1016/j.jbiotec.2023.01.006 · DOI
- Braatz, R., Srinivasan, P., Canova, C.T., Sha, S., Nguyen, T.N.T., Joseph, J., Sangerman, J., Maloney, A.J., Katsikis, G., Ou, R.W., Hong, M.S., Ng, J., Neufeld, C., Wolfrum, J., Barone, P., Sinskey, A., Springs, S., 2024. Multidose Transient Transfection of HEK293 Cells Modulates rAAV2/5 Rep Protein Expression and Influences the Enrichment Fraction of Filled Capsids. Authorea. March 16, 2024. https://doi.org/10.22541/au.171062191.10777478/v1 · DOI
- Broche, J., Kungulovski, G., Bashtrykov, P., Rathert, P., Jeltsch, A., 2021. Genome-wide investigation of the dynamic changes of epigenome modifications after global DNA methylation editing. Nucleic Acids Res. 49, 158-176. https://doi.org/10.1093/nar/gkaa1169 · DOI
- Brunk, E., Sahoo, S., Zielinski, D.C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Preciat Gonzalez, G.A., Aurich, M.K., Prlić, A., Sastry, A., Danielsdottir, A.D., Heinken, A., Noronha, A., Rose, P.W., Burley, S.K., Fleming, R.M.T., Nielsen, J., Thiele, I., Palsson, B.O., 2018. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36, 272-281. https://doi.org/10.1038/nbt.4072 · DOI
- Burdett, T., Nuseibeh, S., 2023. Changing trends in the development of AAV-based gene therapies: a meta-analysis of past and present therapies. Gene Ther. 30, 323-335. https://doi.org/10.1038/s41434-022-00363-0 · DOI
- Canova, C.T., Inguva, P.K., Braatz, R.D., 2023. Mechanistic modeling of viral particle production. Biotechnol. Bioeng. 120, 629-641. https://doi.org/10.1002/bit.28296 · DOI
- Cao, T.M., Chen, D., Barnard, G.C., Shen, A., 2023. Recombinant adeno-associated virus production evaluation in Chinese hamster ovary cells. Biotechnol. Bioeng. https://doi.org/10.1002/bit.28578 · DOI
- Cavalli, G., Heard, E., 2019. Advances in epigenetics link genetics to the environment and disease. Nature 571, 489-499. https://doi.org/10.1038/s41586-019-1411-0 · DOI · Paper page
- Chancellor, D., Barrett, D., Nguyen-Jatkoe, L., Millington, S., Eckhardt, F., 2023. The state of cell and gene therapy in 2023. Mol. Ther. 31, 3376-3388. https://doi.org/10.1016/j.ymthe.2023.11.001 · DOI
- Chanda, D., Hensel, J.A., Higgs, J.T., Grover, R., Kaza, N., Ponnazhagan, S., 2017. Effects of Cellular Methylation on Transgene Expression and Site-Specific Integration of Adeno-Associated Virus. Genes. 8, 232. https://doi.org/10.3390/genes8090232 · DOI · Paper page
- Chaudhary, K., Poirion, O.B., Lu, L., Garmire, L.X., 2018. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin. Cancer Res. 24, 1248-1259. https://doi.org/10.1158/1078-0432.CCR-17-0853 · DOI · Paper page
- Chu, L., Robinson, D.K., 2001. Industrial choices for protein production by large-scale cell culture. Curr. Opin. Biotechnol. 12, 180-187. https://doi.org/10.1016/s0958-1669(00)00197-x · DOI
- Chung, C.-H., Murphy, C.M., Wingate, V.P., Pavlicek, J.W., Nakashima, R., Wei, W., McCarty, D., Rabinowitz, J., Barton, E., 2023. Production of rAAV by plasmid transfection induces antiviral and inflammatory responses in suspension HEK293 cells. Mol. Ther. Methods Clin. Dev. 28, 272-283. https://doi.org/10.1016/j.omtm.2023.01.002 · DOI
- Clish, C.B., 2015. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb. Mol. Case Stud. 1, a000588. https://doi.org/10.1101/mcs.a000588 · DOI
- Cortassa, S., Caceres, V., Bell, L.N., O'Rourke, B., Paolocci, N., Aon, M.A., 2015. From metabolomics to fluxomics: a computational procedure to translate metabolite profiles into metabolic fluxes. Biophys. J. 108, 163-172. https://doi.org/10.1016/j.bpj.2014.11.1857 · DOI
- Creyghton, M.P., Cheng, A.W., Welstead, G.G., Kooistra, T., Carey, B.W., Steine, E.J., Hanna, J., Lodato, M.A., Frampton, G.M., Sharp, P.A., Boyer, L.A., Young, R.A., Jaenisch, R., 2010. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. U. S. A. 107, 21931-21936. https://doi.org/10.1073/pnas.1016071107 · DOI · Paper page
- Dahodwala, H., Sharfstein, S.T., 2017. The 'omics revolution in CHO biology: roadmap to improved CHO productivity. In: Meleady, P. (Eds.), Heterologous Protein Production in CHO Cells. Methods Mol. Biol. 1603, pp. 153-168. Humana Press, New York, NY. https://doi: 10.1007/978-1-4939-6972-2_10 · DOI
- Dahodwala, H., Lee, K.H., 2019. The fickle CHO: a review of the causes, implications, and potential alleviation of the CHO cell line instability problem. Curr. Opin. Biotechnol. 60, 128-137. https://doi.org/10.1016/j.copbio.2019.01.011 · DOI
- Daskalaki, E., Pillon, N.J., Krook, A., Wheelock, C.E., Checa, A., 2018. The influence of culture media upon observed cell secretome metabolite profiles: The balance between cell viability and data interpretability. Anal. Chim. Acta 1037, 338-350. https://doi.org/10.1016/j.aca.2018.04.034 · DOI
- Denny, J.C., Collins, F.S., 2021. Precision medicine in 2030-seven ways to transform healthcare. Cell 184, 1415-1419. https://doi.org/10.1016/j.cell.2021.01.015 · DOI
- de Souza, N., Picotti, P., 2020. Mass spectrometry analysis of the structural proteome. Curr. Opin. Struct. Biol. 60, 57-65. https://doi.org/10.1016/j.sbi.2019.10.006 · DOI
- Destro, F., Joseph, J., Srinivasan, P., Kanter, J.M., Neufeld, C., Wolfrum, J.M., Barone, P.W., Springs, S.L., Sinskey, A.J., Cecchini, S., Kotin, R.M., Braatz, R.D., 2023. Mechanistic modeling explains the production dynamics of recombinant adeno-associated virus with the baculovirus expression vector system. Mol. Ther. Methods Clin. Dev. 30, 122-146. https://doi.org/10.1016/j.omtm.2023.05.019 · DOI
- Diallo, J.-S., De Jong, J., 2023. Overcoming the challenge of antiviral defense in viral vector manufacturing. Cell Gene Ther. Insights. 9, 1097-1104. https://doi.org/10.18609/cgti.2023.144 · DOI
- Dietmair, S., Hodson, M.P., Quek, L.-E., Timmins, N.E., Gray, P., Nielsen, L.K., 2012. A multi-omics analysis of recombinant protein production in Hek293 cells. PLoS One 7, e43394. https://doi.org/10.1371/journal.pone.0043394 · DOI
- Dobrowsky, T., Gianni, D., Pieracci, J., Suh, J., 2021. AAV manufacturing for clinical use: Insights on current challenges from the upstream process perspective. Curr. Opin. Biomed. Eng. 20, 100353. https://doi.org/10.1016/j.cobme.2021.100353 · DOI · Paper page
- Duarte, N.C., Becker, S.A., Jamshidi, N., Thiele, I., Mo, M.L., Vo, T.D., Srivas, R., Palsson, B.Ø., 2007. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. U. S. A. 104, 1777-1782. https://doi.org/10.1073/pnas.0610772104 · DOI · Paper page
- Dunn, M.J., 1987. Two-dimensional gel electrophoresis of proteins. J. Chromatogr. 418, 145-185. https://doi.org/10.1016/0378-4347(87)80008-7 · DOI
- Eisenhut, P., Marx, N., Borsi, G., Papež, M., Ruggeri, C., Baumann, M., Borth, N., 2024. Manipulating gene expression levels in mammalian cell factories: An outline of synthetic molecular toolboxes to achieve multiplexed control. N. Biotechnol. 79, 1-19. https://doi.org/10.1016/j.nbt.2023.11.003 · DOI
- Emwas, A.-H., Szczepski, K., Al-Younis, I., Lachowicz, J.I., Jaremko, M., 2022. Fluxomics -New Metabolomics Approaches to Monitor Metabolic Pathways. Front. Pharmacol. 13, 805782. https://doi.org/10.3389/fphar.2022.805782 · DOI
- Escandell, J.M., Pais, D.A., Carvalho, S.B., Vincent, K., Gomes-Alves, P., Alves, P.M., 2022. Leveraging rAAV bioprocess understanding and next generation bioanalytics development. Curr. Opin. Biotechnol. 74, 271-277. https://doi.org/10.1016/j.copbio.2021.12.009 · DOI
- Farrell, A., McLoughlin, N., Milne, J.J., Marison, I.W., Bones, J., 2014. Application of Multi-Omics Techniques for Bioprocess Design and Optimization in Chinese Hamster Ovary Cells. J. Proteome Res. 13, 3144-3159. https://doi.org/10.1021/pr500219b · DOI
- Fernandes, S., Robitaille, J., Bastin, G., Jolicoeur, M., Wouwer, A.V., 2016. Application of Dynamic Metabolic Flux Convex Analysis to CHO-DXB11 Cell Fed-batch Cultures. IFAC-PapersOnLine 49, 466- 471. https://doi.org/10.1016/j.ifacol.2016.07.386 · DOI
- Frankish, A., Diekhans, M., Jungreis, I., Lagarde, J., Loveland, J.E., Mudge, J.M., Sisu, C., Wright, J.C., Armstrong, J., Barnes, I., Berry, A., Bignell, A., Boix, C., Carbonell Sala, S., Cunningham, F., Di Domenico, T., Donaldson, S., Fiddes, I.T., García Girón, C., Gonzalez, J.M., Grego, T., Hardy, M., Hourlier, T., Howe, K.L., Hunt, T., Izuogu, O.G., Johnson, R., Martin, F.J., Martínez, L., Mohanan, S., Muir, P., Navarro, F.C.P., Parker, A., Pei, B., Pozo, F., Riera, F.C., Ruffier, M., Schmitt, B.M., Stapleton, E., Suner, M.-M., Sycheva, I., Uszczynska-Ratajczak, B., Wolf, M.Y., Xu, J., Yang, Y.T., Yates, A., Zerbino, D., Zhang, Y., Choudhary, J.S., Gerstein, M., Guigó, R., Hubbard, T.J.P., Kellis, M., Paten, B., Tress, M.L., Flicek, P., 2021. GENCODE 2021. Nucleic Acids Res. 49, D916-D923. https://doi.org/10.1093/nar/gkaa1087 · DOI · Paper page
- Fritsch, S.D., Weichhart, T., 2016. Effects of Interferons and Viruses on Metabolism. Front. Immunol. 7, 630. https://doi.org/10.3389/fimmu.2016.00630 · DOI
- Giani, A.M., Gallo, G.R., Gianfranceschi, L., Formenti, G., 2020. Long walk to genomics: History and current approaches to genome sequencing and assembly. Comput. Struct. Biotechnol. J. 18, 9-19. https://doi.org/10.1016/j.csbj.2019.11.002 · DOI
- Green, E.D., Gunter, C., Biesecker, L.G., Di Francesco, V., Easter, C.L., Feingold, E.A., Felsenfeld, A.L., Kaufman, D.J., Ostrander, E.A., Pavan, W.J., Phillippy, A.M., Wise, A.L., Dayal, J.G., Kish, B.J., Mandich, A., Wellington, C.R., Wetterstrand, K.A., Bates, S.A., Leja, D., Vasquez, S., Gahl, W.A., Graham, B.J., Kastner, D.L., Liu, P., Rodriguez, L.L., Solomon, B.D., Bonham, V.L., Brody, L.C., Hutter, C.M., Manolio, T.A., 2020. Strategic vision for improving human health at The Forefront of Genomics. Nature 586, 683-692. https://doi.org/10.1038/s41586-020-2817-4 · DOI
- Gu, C., Kim, G.B., Kim, W.J., Kim, H.U., Lee, S.Y., 2019. Current status and applications of genome-scale metabolic models. Genome Biology. 20, 121. https://doi.org/10.1186/s13059-019-1730-3 · DOI
- Heffner, K.M., Wang, Q., Hizal, D.B., Can, Ö., Betenbaugh, M.J., 2021. Glycoengineering of Mammalian Expression Systems on a Cellular Level. Adv. Biochem. Eng. Biotechnol. 175, 37-69. https://doi.org/10.1007/10_2017_57 · DOI
- Henry, O., Durocher, Y., 2011. Enhanced glycoprotein production in HEK-293 cells expressing pyruvate carboxylase. Metab. Eng. 13, 499-507. https://doi.org/10.1016/j.ymben.2011.05.004 · DOI
- Henry, O., Perrier, M., Kamen, A., 2005. Metabolic flux analysis of HEK-293 cells in perfusion cultures for the production of adenoviral vectors. Metab. Eng. 7, 467-476. https://doi.org/10.1016/j.ymben.2005.08.002 · DOI
- Herzog, R.W., VandenDriessche, T., Ozelo, M.C., 2023. First hemophilia B gene therapy approved: More than two decades in the making. Mol. Ther. 31, 1-2. https://doi.org/10.1016/j.ymthe.2022.12.001 · DOI
- High-dose AAV gene therapy deaths, 2020. Nat. Biotechnol. 38, 910. https://doi.org/10.1038/s41587- 020-0642-9 · DOI · Paper page
- Hilliard, W., Lee, K.H., 2021. Systematic identification of safe harbor regions in the CHO genome through a comprehensive epigenome analysis. Biotechnol. Bioeng. 118, 659-675. https://doi.org/10.1002/bit.27599 · DOI
- Houldcroft, C.J., Beale, M.A., Breuer, J., 2017. Clinical and biological insights from viral genome sequencing. Nat. Rev. Microbiol. 15, 183-192. https://doi.org/10.1038/nrmicro.2016.182 · DOI
- Huang, Y.-F., Aoki, K., Akase, S., Ishihara, M., Liu, Y.-S., Yang, G., Kizuka, Y., Mizumoto, S., Tiemeyer, M., Gao, X.-D., Aoki-Kinoshita, K.F., Fujita, M., 2021. Global mapping of glycosylation pathways in human-derived cells. Dev. Cell 56, 1195-1209.e7. https://doi.org/10.1016/j.devcel.2021.02.023 · DOI
- Igolkina, A.A., Zinkevich, A., Karandasheva, K.O., Popov, A.A., Selifanova, M.V., Nikolaeva, D., Tkachev, V., Penzar, D., Nikitin, D.M., Buzdin, A., 2019. H3K4me3, H3K9ac, H3K27ac, H3K27me3 and H3K9me3 Histone Tags Suggest Distinct Regulatory Evolution of Open and Condensed Chromatin Landmarks. Cells 8, 1034. https://doi.org/10.3390/cells8091034 · DOI
- Iurlaro, R., Muñoz-Pinedo, C., 2016. Cell death induced by endoplasmic reticulum stress. FEBS J. 283, 2640-2652. https://doi.org/10.1111/febs.13598 · DOI
- Jeltsch, A., Broche, J., Bashtrykov, P., 2018. Molecular Processes Connecting DNA Methylation Patterns with DNA Methyltransferases and Histone Modifications in Mammalian Genomes. Genes 9, 566. https://doi.org/10.3390/genes9110566 · DOI
- Joshi, P.R.H., Venereo-Sanchez, A., Chahal, P.S., Kamen, A.A., 2021. Advancements in molecular design and bioprocessing of recombinant adeno-associated virus gene delivery vectors using the insect-cell baculovirus expression platform. Biotechnol. J. 16, e2000021. https://doi.org/10.1002/biot.202000021 · DOI
- Kang, M., Ko, E., Mersha, T.B., 2022. A roadmap for multi-omics data integration using deep learning. Brief. Bioinform. 23, bbab54. https://doi.org/10.1093/bib/bbab454 · DOI · Paper page
- Kaur, H., 2021. Characterization of glycosylation in monoclonal antibodies and its importance in therapeutic antibody development. Crit. Rev. Biotechnol. 41, 300-315. https://doi.org/10.1080/07388551.2020.1869684 · DOI
- Kildegaard, H.F., Baycin-Hizal D., Lewis N.E., Betenbaugh M.J. The emerging CHO systems biology era: harnessing the 'omics revolution for biotechnology. 2013. Curr. Opin. Biotechnol. 24,1102-7. https://doi: 10.1016/j.copbio.2013.02.007 · DOI · Paper page
- Kim, M., O'Callaghan, P.M., Droms, K.A., James, D.C., 2011. A mechanistic understanding of production instability in CHO cell lines expressing recombinant monoclonal antibodies. Biotechnol. Bioeng. 108, 2434-2446. https://doi.org/10.1002/bit.23189 · DOI
- Konstantinidis, K.T., Tiedje, J.M., 2005. Genomic insights that advance the species definition for prokaryotes. Proc. Natl. Acad. Sci. U.S.A. 102, 2567-2572. https://doi.org/10.1073/pnas.0409727102 · DOI
- Kotidis, P., Kontoravdi, C., 2020. Harnessing the potential of artificial neural networks for predicting protein glycosylation. Metab. Eng. Commun. 10, e00131. https://doi.org/10.1016/j.mec.2020.e00131 · DOI
- Kurien, B.T., Scofield, R.H., 2015. Western blotting: an introduction. Methods Mol. Biol. 1312, 17-30. https://doi.org/10.1007/978-1-4939-2694-7_5 · DOI
- Kuzmin, D.A., Shutova, M.V., Johnston, N.R., Smith, O.P., Fedorin, V.V., Kukushkin, Y.S., van der Loo, J.C.M., Johnstone, E.C., 2021. The clinical landscape for AAV gene therapies. Nat. Rev. Drug Discov. 20, 173-174. https://doi.org/10.1038/d41573-021-00017-7 · DOI · Paper page
- Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D.J., Hicks, S.C., Robinson, M.D., Vallejos, C.A., Campbell, K.R., Beerenwinkel, N., Mahfouz, A., Pinello, L., Skums, P., Stamatakis, A., Attolini, C.S.-O., Aparicio, S., Baaijens, J., Balvert, M., Barbanson, B. de, Cappuccio, A., Corleone, G., Dutilh, B.E., Florescu, M., Guryev, V., Holmer, R., Jahn, K., Lobo, T.J., Keizer, E.M., Khatri, I., Kielbasa, S.M., Korbel, J.O., Kozlov, A.M., Kuo, T.-H., Lelieveldt, B.P.F., Mandoiu, I.I., Marioni, J.C., Marschall, T., Mölder, F., Niknejad, A., Raczkowski, L., Reinders, M., Ridder, J. de, Saliba, A.-E., Somarakis, A., Stegle, O., Theis, F.J., Yang, H., Zelikovsky, A., McHardy, A.C., Raphael, B.J., Shah, S.P., Schönhuth, A., 2020. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31. https://doi.org/10.1186/s13059-020-1926-6 · DOI · Paper page
- Lamichhane, S., Sen, P., Dickens, A.M., Hyötyläinen, T., Orešič, M., 2018. Chapter Fourteen -An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives, in: Jaumot, J., Bedia, C., Tauler, R. (Eds.), Comprehensive Analytical Chemistry. Elsevier, pp. 387-413. https://doi.org/10.1016/bs.coac.2018.07.001 · DOI
- Lavado-García, J., González-Domínguez, I., Cervera, L., Jorge, I., Vázquez, J., Gòdia, F., 2020a. Molecular Characterization of the Coproduced Extracellular Vesicles in HEK293 during Virus-Like Particle Production. J. Proteome Res. 19, 4516-4532. https://doi.org/10.1021/acs.jproteome.0c00581 · DOI
- Lavado-García, J., Jorge, I., Cervera, L., Vázquez, J., Gòdia, F., 2020b. Multiplexed Quantitative Proteomic Analysis of HEK293 Provides Insights into Molecular Changes Associated with the Cell Density Effect, Transient Transfection, and Virus-Like Particle Production. J. Proteome Res. 19, 1085-1099. https://doi.org/10.1021/acs.jproteome.9b00601 · DOI
- Lavado-García, J., Pérez-Rubio, P., Cervera, L., Gòdia, F., 2022. The cell density effect in animal cell-based bioprocessing: Questions, insights and perspectives. Biotechnol. Adv. 60, 108017. https://doi.org/10.1016/j.biotechadv.2022.108017 · DOI
- Lazarou, G., Chelliah, V., Small, B.G., Walker, M., van der Graaf, P.H., Kierzek, A.M., 2020. Integration of omics data sources to inform mechanistic modeling of immune-oncology therapies: A tutorial for clinical pharmacologists. Clin. Pharmacol. Ther. 107, 858-870. https://doi.org/10.1002/cpt.1786 · DOI
- Lequin, R.M., 2005. Enzyme immunoassay (EIA)/enzyme-linked immunosorbent assay (ELISA). Clin. Chem. 51, 2415-2418. https://doi.org/10.1373/clinchem.2005.051532 · DOI
- Li, F., Vijayasankaran, N., Shen, A.Y., Kiss, R., Amanullah, A., 2010. Cell culture processes for monoclonal antibody production. MAbs 2, 466-479. https://doi.org/10.4161/mabs.2.5.12720 · DOI · Paper page
- Lin, D., Yalamanchili, H.B., Zhang, X., Lewis, N.E., Alves, C.S., Groot, J., Arnsdorf, J., Bjørn, S.P., Wulff, T., Voldborg, B.G., Zhou, Y., Zhang, B., 2020. CHOmics: A web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines. PLoS Comput. Biol. 16, e1008498. https://doi.org/10.1371/journal.pcbi.1008498 · DOI
- Lin, Y.-C., Boone, M., Meuris, L., Lemmens, I., Van Roy, N., Soete, A., Reumers, J., Moisse, M., Plaisance, S., Drmanac, R., Chen, J., Speleman, F., Lambrechts, D., Van de Peer, Y., Tavernier, J., Callewaert, N., 2014. Genome dynamics of the human embryonic kidney 293 lineage in response to cell biology manipulations. Nat. Commun. 5, 4767. https://doi.org/10.1038/ncomms5767 · DOI
- Lin, Y.-C., Lu, M., Cai, W., Hu, W.-S., 2024. Comparative transcriptomic and proteomic kinetic analysis of adeno-associated virus production systems. Appl. Microbiol. Biotechnol. 108, 385. https://doi.org/10.1007/s00253-024-13203-5 · DOI
- Li, X., Ma, J., Leng, L., Han, M., Li, M., He, F., Zhu, Y., 2022. MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis. Front. Genet. 13, 806842. https://doi.org/10.3389/fgene.2022.806842 · DOI
- Lowe, R., Shirley, N., Bleackley, M., Dolan, S., Shafee, T., 2017. Transcriptomics technologies. PLoS Comput. Biol. 13, e1005457. https://doi.org/10.1371/journal.pcbi.1005457 · DOI
- Lu, M., Lee, Z., Hu, W.-S., 2024. Multi-omics kinetic analysis of recombinant adeno-associated virus production by plasmid transfection of HEK293 cells. Biotechnol. Prog. 40, e3428. https://doi.org/10.1002/btpr.3428 · DOI
- Malm, M., Kuo, C.-C., Barzadd, M.M., Mebrahtu, A., Wistbacka, N., Razavi, R., Volk, A.-L., Lundqvist, M., Kotol, D., Tegel, H., Hober, S., Edfors, F., Gräslund, T., Chotteau, V., Field, R., Varley, P.G., Roth, R.G., Lewis, N.E., Hatton, D., Rockberg, J., 2022. Harnessing secretory pathway differences between HEK293 and CHO to rescue production of difficult to express proteins. Metab. Eng. 72, 171-187. https://doi.org/10.1016/j.ymben.2022.03.009 · DOI
- Malm, M., Saghaleyni, R., Lundqvist, M., Giudici, M., Chotteau, V., Field, R., Varley, P.G., Hatton, D., Grassi, L., Svensson, T., Nielsen, J., Rockberg, J., 2020. Evolution from adherent to suspension: systems biology of HEK293 cell line development. Sci. Rep. 10, 18996. https://doi.org/10.1038/s41598-020-76137-8 · DOI
- Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., Walley, A.J., Froguel, P., Carlsson, L.M., Uhlen, M., Nielsen, J., 2013. Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol. Syst. Biol. 9, 649. https://doi.org/10.1038/msb.2013. · DOI
- Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Uhlen, M., Nielsen, J., 2014. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5, 3083. https://doi.org/10.1038/ncomms4083 · DOI
- Marouga, R., David, S., Hawkins, E., 2005. The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal. Bioanal. Chem. 382, 669-678. https://doi.org/10.1007/s00216-005-3126-3 · DOI
- Martínez-Monge, I., Albiol, J., Lecina, M., Liste-Calleja, L., Miret, J., Solà, C., Cairó, J.J., 2019. Metabolic flux balance analysis during lactate and glucose concomitant consumption in HEK293 cell cultures. Biotechnol. Bioeng. 116, 388-404. https://doi.org/10.1002/bit.26858 · DOI
- Marx, N., Eisenhut, P., Weinguny, M., Klanert, G., Borth, N., 2022. How to train your cell -Towards controlling phenotypes by harnessing the epigenome of Chinese hamster ovary production cell lines. Biotechnol. Adv. 56, 107924. https://doi.org/10.1016/j.biotechadv.2022.107924 · DOI
- Masson, H.O., Karottki, K.J. la C., Tat, J., Hefzi, H., Lewis, N.E., 2023. From observational to actionable: rethinking omics in biologics production. Trends Biotechnol. 41, 1127-1138. https://doi.org/10.1016/j.tibtech.2023.03.009 · DOI
- Maznyi, G., 2023. Development of a high-throughput screening approach to identify production enhancers of Adeno-associated virus. M.S. thesis, University of Ottawa, Canada. https://doi.org/10.20381/RUOR-29685 · DOI
- Mcallister, R., Schofield, C., Pettman, G., Mannix, C., 2002. Adaptation of Recombinant HEK-293 Cells to Growth in Serum Free Suspension, in: Bernard, A., Griffiths, B., Noé, W., Wurm, F. (Eds.), Animal Cell Technology: Products from Cells, Cells as Products: Proceedings of the 16th ESACT Meeting April 25-29, 1999, Lugano, Switzerland. Springer, Dordrecht, Netherlands, pp. 367-369. https://doi.org/10.1007/0-306-46875-1_79 · DOI
- Mehrmohamadi, M., Sepehri, M.H., Nazer, N., Norouzi, M.R., 2021. A Comparative Overview of Epigenomic Profiling Methods. Front. Cell Dev. Biol. 9, 714687. https://doi.org/10.3389/fcell.2021.714687 · DOI
- Miller, N.L., Clark, T., Raman, R., Sasisekharan, R., 2021. Glycans in Virus-Host Interactions: A Structural Perspective. Front. Mol. Biosci. 8, 666756. https://doi.org/10.3389/fmolb.2021.666756 · DOI
- Moritz, B., Becker, P.B., Göpfert, U., 2015. CMV promoter mutants with a reduced propensity to productivity loss in CHO cells. Sci. Rep. 5, 16952. https://doi.org/10.1038/srep16952 · DOI
- Moritz, B., Woltering, L., Becker, P.B., Göpfert, U., 2016. High levels of histone H3 acetylation at the CMV promoter are predictive of stable expression in Chinese hamster ovary cells. Biotechnol. Prog. 32, 776-786. https://doi.org/10.1002/btpr.2271 · DOI
- Morozova, O., Hirst, M., Marra, M.A., 2009. Applications of new sequencing technologies for transcriptome analysis. Annu. Rev. Genomics Hum. Genet. 10, 135-151. https://doi.org/10.1146/annurev-genom-082908-145957 · DOI
- Mullard, A., 2021. Gene therapy community grapples with toxicity issues, as pipeline matures. Nat. Rev. Drug Discov. 20, 804-805. https://doi.org/10.1038/d41573-021-00164-x · DOI · Paper page
- Nadeau, I., Gilbert, P.A., Jacob, D., Perrier, M., Kamen, A., 2002. Low-protein medium affects the 293SF central metabolism during growth and infection with adenovirus. Biotechnol. Bioeng. 77, 91-104. https://doi.org/10.1002/bit.10128 · DOI
- Nadeau, I., Jacob, D., Perrier, M., Kamen, A., 2000a. 293SF metabolic flux analysis during cell growth and infection with an adenoviral vector. Biotechnol. Prog. 16, 872-884. https://doi.org/10.1021/bp000098l · DOI
- Nadeau, I., Sabatié, J., Koehl, M., Perrier, M., Kamen, A., 2000b. Human 293 cell metabolism in low glutamine-supplied culture: interpretation of metabolic changes through metabolic flux analysis. Metab. Eng. 2, 277-292. https://doi.org/10.1006/mben.2000.0152 · DOI
- Nagy, A., Chakrabarti, L., Kurasawa, J., Mulagapati, S.H.R., Devine, P., Therres, J., Chen, Z., Schmelzer, A.E., 2023. Engineered CHO cells as a novel AAV production platform for gene therapy delivery. Sci. Rep. 13, 19210. https://doi.org/10.1038/s41598-023-46298-3 · DOI
- Narimatsu, Y., Joshi, H.J., Nason, R., Van Coillie, J., Karlsson, R., Sun, L., Ye, Z., Chen, Y.-H., Schjoldager, K.T., Steentoft, C., Furukawa, S., Bensing, B.A., Sullam, P.M., Thompson, A.J., Paulson, J.C., Büll, C., Adema, G.J., Mandel, U., Hansen, L., Bennett, E.P., Varki, A., Vakhrushev, S.Y., Yang, Z., Clausen, H., 2019. An Atlas of Human Glycosylation Pathways Enables Display of the Human Glycome by Gene Engineered Cells. Mol. Cell 75, 394-407.e5. https://doi.org/10.1016/j.molcel.2019.05.017 · DOI
- Naso, M.F., Tomkowicz, B., Perry, W.L., III, Strohl, W.R., 2017. Adeno-associated virus (AAV) as a vector for gene therapy. BioDrugs 31, 317-334. https://doi.org/10.1007/s40259-017-0234-5 · DOI · Paper page
- Nguyen, T.N.T., Sha, S., Hong, M.S., Maloney, A.J., Barone, P.W., Neufeld, C., Wolfrum, J., Springs, S.L., Sinskey, A.J., Braatz, R.D., 2021. Mechanistic model for production of recombinant adeno- associated virus via triple transfection of HEK293 cells. Mol. Ther. Methods Clin. Dev. 21, 642-655. https://doi.org/10.1016/j.omtm.2021.04.006 · DOI · Paper page
- Nicora, G., Vitali, F., Dagliati, A., Geifman, N., Bellazzi, R., 2020. Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools. Frontiers in Oncology.10, 1030. https://doi.org/10.3389/fonc.2020.01030 · DOI · Paper page
- Nurk, S., Koren, S., Rhie, A., Rautiainen, M., Bzikadze, A.V., Mikheenko, A., Vollger, M.R., Altemose, N., Uralsky, L., Gershman, A., Aganezov, S., Hoyt, S.J., Diekhans, M., Logsdon, G.A., Alonge, M., Antonarakis, S.E., Borchers, M., Bouffard, G.G., Brooks, S.Y., Caldas, G.V., Chen, N.-C., Cheng, H., Chin, C.-S., Chow, W., de Lima, L.G., Dishuck, P.C., Durbin, R., Dvorkina, T., Fiddes, I.T., Formenti, G., Fulton, R.S., Fungtammasan, A., Garrison, E., Grady, P.G.S., Graves-Lindsay, T.A., Hall, I.M., Hansen, N.F., Hartley, G.A., Haukness, M., Howe, K., Hunkapiller, M.W., Jain, C., Jain, M., Jarvis, E.D., Kerpedjiev, P., Kirsche, M., Kolmogorov, M., Korlach, J., Kremitzki, M., Li, H., Maduro, V.V., Marschall, T., McCartney, A.M., McDaniel, J., Miller, D.E., Mullikin, J.C., Myers, E.W., Olson, N.D., Paten, B., Peluso, P., Pevzner, P.A., Porubsky, D., Potapova, T., Rogaev, E.I., Rosenfeld, J.A., Salzberg, S.L., Schneider, V.A., Sedlazeck, F.J., Shafin, K., Shew, C.J., Shumate, A., Sims, Y., Smit, A.F.A., Soto, D.C., Sović, I., Storer, J.M., Streets, A., Sullivan, B.A., Thibaud-Nissen, F., Torrance, J., Wagner, J., Walenz, B.P., Wenger, A., Wood, J.M.D., Xiao, C., Yan, S.M., Young, A.C., Zarate, S., Surti, U., McCoy, R.C., Dennis, M.Y., Alexandrov, I.A., Gerton, J.L., O'Neill, R.J., Timp, W., Zook, J.M., Schatz, M.C., Eichler, E.E., Miga, K.H., Phillippy, A.M., 2022. The complete sequence of a human genome. Science 376, 44-53. https://doi.org/10.1126/science.abj6987 · DOI · Paper page
- Ong, S.-E., Mann, M., 2007. Stable isotope labeling by amino acids in cell culture for quantitative proteomics. Methods Mol. Biol. 359, 37-52. https://doi.org/10.1007/978-1-59745-255-7_3 · DOI
- Opdam, S., Richelle, A., Kellman, B., Li, S., Zielinski, D.C., Lewis, N.E., 2017. A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models. Cell Syst. 4, 318-329.e6. https://doi.org/10.1016/j.cels.2017.01.010 · DOI
- Osterlehner, A., Simmeth, S., Göpfert, U., 2011. Promoter methylation and transgene copy numbers predict unstable protein production in recombinant Chinese hamster ovary cell lines. Biotechnol. Bioeng. 108, 2670-2681. https://doi.org/10.1002/bit.23216 · DOI · Paper page
- Paredes, V., Park, J.S., Jeong, Y., Yoon, J., Baek, K., 2013. Unstable expression of recombinant antibody during long-term culture of CHO cells is accompanied by histone H3 hypoacetylation. Biotechnol. Lett. 35, 987-993. https://doi.org/10.1007/s10529-013-1168-8 · DOI
- Passi, A., Tibocha-Bonilla, J.D., Kumar, M., Tec-Campos, D., Zengler, K., Zuniga, C., 2021. Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. Metabolites. 12, 14. https://doi.org/10.3390/metabo12010014 · DOI
- Patel, N.A., Anderson, C.R., Terkildsen, S.E., Davis, R.C., Pack, L.D., Bhargava, S., Clarke, H.R.G., 2018. Antibody expression stability in CHO clonally derived cell lines and their subclones: Role of methylation in phenotypic and epigenetic heterogeneity. Biotechnol. Prog. 34, 635-649. https://doi.org/10.1002/btpr.2655 · DOI
- Petronis, A., 2010. Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature 465, 721-727. https://doi.org/10.1038/nature09230 · DOI
- Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., Droit, A., 2021. Integration strategies of multi-omics data for machine learning analysis. Comput. Struct. Biotechnol. J. 19, P3735-P3746. https://doi.org/10.1016/j.csbj.2021.06.030 · DOI · Paper page
- Pistek, M., Kahlig, C.-I., Hackl, M., Unterthurner, S., Kraus, B., Grabherr, R., Grillari, J., Hernandez Bort, J.A., 2023. Comprehensive mRNA-sequencing-based characterization of three HEK-293 cell lines during an rAAV production process for gene therapy applications. Biotechnol. J. 18, e2200513. https://doi.org/10.1002/biot.202200513 · DOI
- Poirion, O.B., Jing, Z., Chaudhary, K., Huang, S., Garmire, L.X., 2021. DeepProg: An ensemble of deep- learning and machine-learning models for prognosis prediction using multi-omics data. Genome Med. 13, 112. https://doi.org/10.1186/s13073-021-00930-x · DOI
- Pulix, M., Lukashchuk, V., Smith, D.C., Dickson, A.J., 2021. Molecular characterization of HEK293 cells as emerging versatile cell factories. Curr. Opin. Biotechnol. 71, 18-24. https://doi.org/10.1016/j.copbio.2021.05.001 · DOI
- Qiao, C., Li, J., Skold, A., Zhang, X., Xiao, X., 2002. Feasibility of generating adeno-associated virus packaging cell lines containing inducible adenovirus helper genes. J. Virol. 76, 1904-1913. https://doi.org/10.1128/jvi.76.4.1904-1913.2002 · DOI
- Quek, L.-E., Dietmair, S., Hanscho, M., Martínez, V.S., Borth, N., Nielsen, L.K., 2014. Reducing Recon 2 for steady-state flux analysis of HEK cell culture. J. Biotechnol. 184, 172-178. https://doi.org/10.1016/j.jbiotec.2014.05.021 · DOI
- Reel, P.S., Reel, S., Pearson, E., Trucco, E., Jefferson, E., 2021. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol. Adv. 49, 107739. https://doi.org/10.1016/j.biotechadv.2021.107739 · DOI · Paper page
- Rivera, C.M., Ren, B., 2013. Mapping human epigenomes. Cell 155, 39-55. https://doi.org/10.1016/j.cell.2013.09.011 · DOI · Paper page
- Rodrigues, A.F., Formas-Oliveira, A.S., Bandeira, V.S., Alves, P.M., Hu, W.S., Coroadinha, A.S., 2013. Metabolic pathways recruited in the production of a recombinant enveloped virus: Mining targets for process and cell engineering. Metab. Eng. 20, 131-145. https://doi.org/10.1016/j.ymben.2013.10.001 · DOI
- Rose, D.R., 2012. Structure, mechanism and inhibition of Golgi α-mannosidase II. Curr. Opin. Struct. Biol. 22, 558-562. https://doi.org/10.1016/j.sbi.2012.06.005 · DOI
- Rumachik, N.G., Malaker, S.A., Poweleit, N., Maynard, L.H., Adams, C.M., Leib, R.D., Cirolia, G., Thomas, D., Stamnes, S., Holt, K., Sinn, P., May, A.P., Paulk, N.K., 2020. Methods Matter: Standard Production Platforms for Recombinant AAV Produce Chemically and Functionally Distinct Vectors. Mol. Ther. Methods Clin. Dev. 18, 98-118. https://doi.org/10.1016/j.omtm.2020.05.018 · DOI
- Ryu, J.Y., Kim, H.U., Lee, S.Y., 2015. Reconstruction of genome-scale human metabolic models using omics data. Integr. Biol. 7, 859-868. https://doi.org/10.1039/c5ib00002e · DOI
- Saghaleyni, R., Malm, M., Moruzzi, N., Zrimec, J., Razavi, R., Wistbacka, N., Thorell, H., Pintar, A., Hober, A., Edfors, F., Chotteau, V., Berggren, P.-O., Grassi, L., Zelezniak, A., Svensson, T., Hatton, D., Nielsen, J., Robinson, J.L., Rockberg, J., 2022. Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells. Cell Rep. 39, 110936. https://doi.org/10.1016/j.celrep.2022.110936 · DOI
- Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., Thakare, R.P., Banday, S., Mishra, A.K., Das, G., Malonia, S.K., 2023. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology 12, 997. https://doi.org/10.3390/biology12070997 · DOI · Paper page
- Scarrott, J.M., Johari, Y.B., Pohle, T.H., Liu, P., Mayer, A., James, D.C., 2023. Increased recombinant adeno-associated virus production by HEK293 cells using small molecule chemical additives. Biotechnol. J. 18, e2200450. https://doi.org/10.1002/biot.202200450 · DOI
- Schinn, S.-M., Morrison, C., Wei, W., Zhang, L., Lewis, N.E., 2021. A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures. Biotechnol. Bioeng. 118, 2118-2123. https://doi.org/10.1002/bit.27714 · DOI
- Sha, S., Huang, Z., Wang, Z., Yoon, S., 2018. Mechanistic modeling and applications for CHO cell culture development and production. Curr. Opin. Chem. Eng. 22, 54-61. https://doi.org/10.1016/j.coche.2018.08.010 · DOI
- Sha, S., Maloney, A.J., Katsikis, G., Nguyen, T.N.T., Neufeld, C., Wolfrum, J., Barone, P.W., Springs, S.L., Manalis, S.R., Sinskey, A.J., Braatz, R.D., 2021. Cellular pathways of recombinant adeno-associated virus production for gene therapy. Biotechnol. Adv. 49, 107764. https://doi.org/10.1016/j.biotechadv.2021.107764 · DOI · Paper page
- Shin, S., Kim, S.H., Shin, S.W., Grav, L.M., Pedersen, L.E., Lee, J.S., Lee, G.M., 2020. Comprehensive Analysis of Genomic Safe Harbors as Target Sites for Stable Expression of the Heterologous Gene in HEK293 Cells. ACS Synth. Biol. 9, 1263-1269. https://doi.org/10.1021/acssynbio.0c00097 · DOI
- Shupe, J., Zhang, A., Odenwelder, D.C., Dobrowsky, T., 2022. Gene therapy: challenges in cell culture scale-up. Curr. Opin. Biotechnol. 75, 102721. https://doi.org/10.1016/j.copbio.2022.102721 · DOI
- Singh, V., Pandey, S., Bhardwaj, A., 2022. From the reference human genome to human pangenome: Premise, promise and challenge. Front. Genet. 13, 1042550. https://doi.org/10.3389/fgene.2022.1042550 · DOI
- Sorourian, S., Behzad Behbahani, A., Rafiei Dehbidi, G., Zare, F., Farajnia, S., Najafi, H., Safari, F., 2024. Omics and CRISPR in CHO bioproduction: a state-of-the-art review. Biotechnol. Bioprocess Eng. 29, 465-482. https://doi.org/10.1007/s12257-024-00026-4 · DOI
- Strasser, L., Boi, S., Guapo, F., Donohue, N., Barron, N., Rainbow-Fletcher, A., Bones, J., 2021. Proteomic landscape of adeno-associated virus (AAV)-producing HEK293 cells. Int. J. Mol. Sci. 22, 11499. https://doi.org/10.3390/ijms222111499 · DOI
- Sumit, M., Dolatshahi, S., Chu, A.-H.A., Cote, K., Scarcelli, J.J., Marshall, J.K., Cornell, R.J., Weiss, R., Lauffenburger, D.A., Mulukutla, B.C., Figueroa, B., Jr, 2019. Dissecting N-Glycosylation Dynamics in Chinese Hamster Ovary Cells Fed-batch Cultures using Time Course Omics Analyses. iScience 12, 102-120. https://doi.org/10.1016/j.isci.2019.01.006 · DOI
- Tan, E., Chin, C.S.H., Lim, Z.F.S., Ng, S.K., 2021. HEK293 Cell Line as a Platform to Produce Recombinant Proteins and Viral Vectors. Front. Bioeng. Biotechnol. 9, 796991. https://doi.org/10.3389/fbioe.2021.796991 · DOI
- Tan, S.Z., Begley, P., Mullard, G., Hollywood, K.A., Bishop, P.N., 2016. Introduction to metabolomics and its applications in ophthalmology. Eye 30, 773-783. https://doi.org/10.1038/eye.2016. · DOI
- Thuerauf, D.J., Morrison, L., Glembotski, C.C., 2004. Opposing Roles for ATF6α and ATF6β in Endoplasmic Reticulum Stress Response Gene Induction *. J. Biol. Chem. 279, 21078-21084. https://doi.org/10.1074/jbc.M400713200 · DOI
- Tsopanoglou, A., Jiménez del Val, I., 2021. Moving towards an era of hybrid modelling: advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses. Curr. Opin. Chem. Eng. 32, 100691. https://doi.org/10.1016/j.coche.2021.100691 · DOI
- Turunen, T.A., Väänänen, M.-A., Ylä-Herttuala, S., 2018. Epigenomics, in: Vasan, R.S., Sawyer, D.B. (Eds.), Encyclopedia of Cardiovascular Research and Medicine. Elsevier, Oxford, pp. 258-265. https://doi.org/10.1016/B978-0-12-809657-4.99575-9 · DOI
- Tworig, J., Grafton, F., Hörer, M., Reid, C.A., Mandegar, M.A., 2024. Transcriptomics-informed pharmacology identifies epigenetic and cell cycle regulators as enhancers of AAV production. bioRxiv. https://doi.org/10.1101/2024.06.14.599118 · DOI
- Tyers, M., Mann, M., 2003. From genomics to proteomics. Nature 422, 193-197. https://doi.org/10.1038/nature01510 · DOI
- van Meer, G., Voelker, D.R., Feigenson, G.W., 2008. Membrane lipids: where they are and how they behave. Nat. Rev. Mol. Cell Biol. 9, 112-124. https://doi.org/10.1038/nrm2330 · DOI · Paper page
- Väremo, L., Scheele, C., Broholm, C., Mardinoglu, A., Kampf, C., Asplund, A., Nookaew, I., Uhlén, M., Pedersen, B.K., Nielsen, J., 2016. Proteome-and Transcriptome-Driven Reconstruction of the Human Myocyte Metabolic Network and Its Use for Identification of Markers for Diabetes. Cell Rep. 14, 1567. https://doi.org/10.1016/j.celrep.2016.01.054 · DOI
- Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., Gocayne, J.D., Amanatides, P., Ballew, R.M., Huson, D.H., Wortman, J.R., Zhang, Q., Kodira, C.D., Zheng, X.H., Chen, L., Skupski, M., Subramanian, G., Thomas, P.D., Zhang, J., Gabor Miklos, G.L., Nelson, C., Broder, S., Clark, A.G., Nadeau, J., McKusick, V.A., Zinder, N., Levine, A.J., Roberts, R.J., Simon, M., Slayman, C., Hunkapiller, M., Bolanos, R., Delcher, A., Dew, I., Fasulo, D., Flanigan, M., Florea, L., Halpern, A., Hannenhalli, S., Kravitz, S., Levy, S., Mobarry, C., Reinert, K., Remington, K., Abu-Threideh, J., Beasley, E., Biddick, K., Bonazzi, V., Brandon, R., Cargill, M., Chandramouliswaran, I., Charlab, R., Chaturvedi, K., Deng, Z., Di Francesco, V., Dunn, P., Eilbeck, K., Evangelista, C., Gabrielian, A.E., Gan, W., Ge, W., Gong, F., Gu, Z., Guan, P., Heiman, T.J., Higgins, M.E., Ji, R.R., Ke, Z., Ketchum, K.A., Lai, Z., Lei, Y., Li, Z., Li, J., Liang, Y., Lin, X., Lu, F., Merkulov, G.V., Milshina, N., Moore, H.M., Naik, A.K., Narayan, V.A., Neelam, B., Nusskern, D., Rusch, D.B., Salzberg, S., Shao, W., Shue, B., Sun, J., Wang, Z., Wang, A., Wang, X., Wang, J., Wei, M., Wides, R., Xiao, C., Yan, C., Yao, A., Ye, J., Zhan, M., Zhang, W., Zhang, H., Zhao, Q., Zheng, L., Zhong, F., Zhong, W., Zhu, S., Zhao, S., Gilbert, D., Baumhueter, S., Spier, G., Carter, C., Cravchik, A., Woodage, T., Ali, F., An, H., Awe, A., Baldwin, D., Baden, H., Barnstead, M., Barrow, I., Beeson, K., Busam, D., Carver, A., Center, A., Cheng, M.L., Curry, L., Danaher, S., Davenport, L., Desilets, R., Dietz, S., Dodson, K., Doup, L., Ferriera, S., Garg, N., Gluecksmann, A., Hart, B., Haynes, J., Haynes, C., Heiner, C., Hladun, S., Hostin, D., Houck, J., Howland, T., Ibegwam, C., Johnson, J., Kalush, F., Kline, L., Koduru, S., Love, A., Mann, F., May, D., McCawley, S., McIntosh, T., McMullen, I., Moy, M., Moy, L., Murphy, B., Nelson, K., Pfannkoch, C., Pratts, E., Puri, V., Qureshi, H., Reardon, M., Rodriguez, R., Rogers, Y.H., Romblad, D., Ruhfel, B., Scott, R., Sitter, C., Smallwood, M., Stewart, E., Strong, R., Suh, E., Thomas, R., Tint, N.N., Tse, S., Vech, C., Wang, G., Wetter, J., Williams, S., Williams, M., Windsor, S., Winn- Deen, E., Wolfe, K., Zaveri, J., Zaveri, K., Abril, J.F., Guigó, R., Campbell, M.J., Sjolander, K.V., Karlak, B., Kejariwal, A., Mi, H., Lazareva, B., Hatton, T., Narechania, A., Diemer, K., Muruganujan, A., Guo, N., Sato, S., Bafna, V., Istrail, S., Lippert, R., Schwartz, R., Walenz, B., Yooseph, S., Allen, D., Basu, A., Baxendale, J., Blick, L., Caminha, M., Carnes-Stine, J., Caulk, P., Chiang, Y.H., Coyne, M., Dahlke, C., Deslattes Mays, A., Dombroski, M., Donnelly, M., Ely, D., Esparham, S., Fosler, C., Gire, H., Glanowski, S., Glasser, K., Glodek, A., Gorokhov, M., Graham, K., Gropman, B., Harris, M., Heil, J., Henderson, S., Hoover, J., Jennings, D., Jordan, C., Jordan, J., Kasha, J., Kagan, L., Kraft, C., Levitsky, A., Lewis, M., Liu, X., Lopez, J., Ma, D., Majoros, W., McDaniel, J., Murphy, S., Newman, M., Nguyen, T., Nguyen, N., Nodell, M., Pan, S., Peck, J., Peterson, M., Rowe, W., Sanders, R., Scott, J., Simpson, M., Smith, T., Sprague, A., Stockwell, T., Turner, R., Venter, E., Wang, M., Wen, M., Wu, D., Wu, M., Xia, A., Zandieh, A., Zhu, X., 2001. The sequence of the human genome. Science 291, 1304-1351. https://doi.org/10.1126/science.1058040 · DOI · Paper page
- Virgolini, N., Hagan, R., Correia, R., Silvano, M., Fernandes, S., Alves, P.M., Clarke, C., Roldão, A., Isidro, I.A., 2023a. Transcriptome analysis of Sf9 insect cells during production of recombinant Adeno- associated virus. Biotechnol. J. 18, e2200466. https://doi.org/10.1002/biot.202200466 · DOI
- Virgolini, N., Silvano, M., Hagan, R., Correia, R., Alves, P.M., Clarke, C., Roldão, A., Isidro, I.A., 2023b. Impact of dual-baculovirus infection on the Sf9 insect cell transcriptome during rAAV production using single-cell RNA-seq. Biotechnol. Bioeng. 120, 2588-2600. https://doi.org/10.1002/bit.28377 · DOI
- Wang, D., Tai, P.W.L., Gao, G., 2019. Adeno-associated virus vector as a platform for gene therapy delivery. Nat. Rev. Drug Discov. 18, 358-378. https://doi.org/10.1038/s41573-019-0012-9 · DOI · Paper page
- Wang, J.-H., Gessler, D.J., Zhan, W., Gallagher, T.L., Gao, G., 2024. Adeno-associated virus as a delivery vector for gene therapy of human diseases. Signal Transduct. Target Ther. 9, 78. https://doi.org/10.1038/s41392-024-01780-w · DOI
- Wang, K.C., Chang, H.Y., 2018. Epigenomics: Technologies and Applications. Circ. Res. 122, 1191-1199. https://doi.org/10.1161/CIRCRESAHA.118.310998 · DOI
- Wang, T., Shao, W., Huang, Z., Tang, H., Zhang, J., Ding, Z., Huang, K., 2021. MOGONET integrates multi- omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat. Commun. 12, 3445. https://doi.org/10.1038/s41467-021-23774-w · DOI · Paper page
- Wang, Y., Fu, Q., Lee, Y.S., Sha, S., Yoon, S., 2023. Transcriptomic features reveal molecular signatures associated with recombinant adeno-associated virus production in HEK293 cells. Biotechnol. Prog. 39, e3346. https://doi.org/10.1002/btpr.3346 · DOI
- Wang, Z., Gerstein, M., Snyder, M., 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57-63. https://doi.org/10.1038/nrg2484 · DOI · Paper page
- Wiese, S., Reidegeld, K.A., Meyer, H.E., Warscheid, B., 2007. Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics 7, 340-350. https://doi.org/10.1002/pmic.200600422 · DOI
- Wilkins, M.R., Pasquali, C., Appel, R.D., Ou, K., Golaz, O., Sanchez, J.-C., Yan, J.X., Gooley, A.A., Hughes, G., Humphery-Smith, I., Williams, K.L., Hochstrasser, D.F., 1996. From Proteins to Proteomes: Large Scale Protein Identification by Two-Dimensional Electrophoresis and Amino Acid Analysis. Biotechnology 14, 61-65. https://doi.org/10.1038/nbt0196-61 · DOI
- Wippermann, A., Noll, T., 2017. DNA methylation in CHO cells. J. Biotechnol. 258, 206-210. https://doi.org/10.1016/j.jbiotec.2017.08.005 · DOI
- Wolf, J.B.W., 2013. Principles of transcriptome analysis and gene expression quantification: an RNA-seq tutorial. Mol. Ecol. Resour. 13, 559-572. https://doi.org/10.1111/1755-0998.12109 · DOI
- Wright, J.F., 2023. AAV vector production: Troublesome host innate responses in another setting. Mol. Ther. Methods Clin. Dev. 28, 412-413. https://doi.org/10.1016/j.omtm.2023.02.008 · DOI
- Wuest, D.M., Harcum, S.W., Lee, K.H., 2012. Genomics in mammalian cell culture bioprocessing. Biotechnol. Adv. 30, 629-638. https://doi.org/10.1016/j.biotechadv.2011.10.010 · DOI
- Wurm, F.M., 2004. Production of recombinant protein therapeutics in cultivated mammalian cells. Nat. Biotechnol. 22, 1393-1398. https://doi.org/10.1038/nbt1026 · DOI · Paper page
- Wurm, F.M., Wurm, M.J., 2017. Cloning of CHO Cells, Productivity and Genetic Stability-A Discussion. Processes 5, 20. https://doi.org/10.3390/pr5020020 · DOI
- Xiao, S., Chen, Y.C., Buehler, E., Mandal, S., Mandal, A., Betenbaugh, M., Park, M.H., Martin, S., Shiloach, J., 2016. Genome-scale RNA interference screen identifies antizyme 1 (OAZ1) as a target for improvement of recombinant protein production in mammalian cells. Biotechnol. Bioeng. 113, 2403-2415. https://doi.org/10.1002/bit.26017 · DOI
- Xie, Y., Butler, M., 2023. N-glycomic profiling of capsid proteins from adeno-associated virus serotypes. Glycobiology. 34, cwad074. https://doi.org/10.1093/glycob/cwad074 · DOI
- Yates, A.D., Achuthan, P., Akanni, W., Allen, J., Allen, J., Alvarez-Jarreta, J., Amode, M.R., Armean, I.M., Azov, A.G., Bennett, R., Bhai, J., Billis, K., Boddu, S., Marugán, J.C., Cummins, C., Davidson, C., Dodiya, K., Fatima, R., Gall, A., Giron, C.G., Gil, L., Grego, T., Haggerty, L., Haskell, E., Hourlier, T., Izuogu, O.G., Janacek, S.H., Juettemann, T., Kay, M., Lavidas, I., Le, T., Lemos, D., Martinez, J.G., Maurel, T., McDowall, M., McMahon, A., Mohanan, S., Moore, B., Nuhn, M., Oheh, D.N., Parker, A., Parton, A., Patricio, M., Sakthivel, M.P., Abdul Salam, A.I., Schmitt, B.M., Schuilenburg, H., Sheppard, D., Sycheva, M., Szuba, M., Taylor, K., Thormann, A., Threadgold, G., Vullo, A., Walts, B., Winterbottom, A., Zadissa, A., Chakiachvili, M., Flint, B., Frankish, A., Hunt, S.E., IIsley, G., Kostadima, M., Langridge, N., Loveland, J.E., Martin, F.J., Morales, J., Mudge, J.M., Muffato, M., Perry, E., Ruffier, M., Trevanion, S.J., Cunningham, F., Howe, K.L., Zerbino, D.R., Flicek, P., 2020. Ensembl 2020. Nucleic Acids Res. 48, D682-D688. https://doi.org/10.1093/nar/gkz966 · DOI
- Yates, J.R., 2011. A century of mass spectrometry: from atoms to proteomes. Nat. Methods. 8, 633-637. https://doi.org/10.1038/nmeth.1659 · DOI
- Zampieri, G., Vijayakumar, S., Yaneske, E., Angione, C., 2019. Machine and deep learning meet genome- scale metabolic modeling. PLoS Comput. Biol. 15, e1007084. https://doi.org/10.1371/journal.pcbi.1007084 · DOI
- Zhang, L., Gao, J., Zhang, X., Wang, X., Wang, T., Zhang, J., 2024. Current strategies for the development of high-yield HEK293 cell lines. Biochem. Eng. J. 205, 109279. https://doi.org/10.1016/j.bej.2024.109279 · DOI
- Zhang, L., Lu, Q., Chang, C., 2020. Epigenetics in Health and Disease, in: Chang, C., Lu, Q. (Eds.), Epigenetics in Allergy and Autoimmunity. Springer, Singapore, pp. 3-55. https://doi.org/10.1007/978-981-15-3449-2_1 · DOI · Paper page
- Zhang, Y., Baycin-Hizal, D., Kumar, A., Priola, J., Bahri, M., Heffner, K.M., Wang, M., Han, X., Bowen, M.A., Betenbaugh, M.J., 2017. High-Throughput Lipidomic and Transcriptomic Analysis To Compare SP2/0, CHO, and HEK-293 Mammalian Cell Lines. Anal. Chem. 89, 1477-1485. https://doi.org/10.1021/acs.analchem.6b02984 · DOI
- Zhou, Y., Priya, S., Ong, J.Y., 2024. Characterizing Glycosylation of Adeno-Associated Virus Serotype 9 Capsid Proteins Generated from HEK293 Cells through Glycopeptide Mapping and Released Glycan Analysis. Microorganisms. 12, 946. https://doi.org/10.3390/ · DOI
Cited by
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Kuchemüller, K. B., Austerjost, J., Machleid, R., Roßkamp, A., Niemann, J., & McCready, C. (2026) · Bioprocess and Biosystems Engineering · doi:10.1007/s00449-026-03362-2
- Authors
- Gurazada, S. G. R., Kennedy, H. M., Braatz, R. D., Mehrman, S. J., Polson, S. W., & Rombel, I. T.
- Venue
- Biotechnology Advances
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 79
- Pages
- 108506
- ISSN
- 0734-9750
- Cited by
- 8
- References
- 13
- Language
- en
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