Predicting Favorable Landing Pads for Targeted Integrations in Chinese Hamster Ovary Cell Lines by Learning Stability Characteristics from Random Transgene Integrations

Overview

Journal Comput Struct Biotechnol J

Specialty Biotechnology

Date 2020 Dec 11

PMID 33304461

Citations 12

Authors

Heena Dhiman

Marguerite Campbell

Michael Melcher

Kevin D Smith

Nicole Borth

Affiliations

Soon will be listed here.

Abstract

Chinese Hamster Ovary (CHO) cell lines are considered to be the preferred platform for the production of biotherapeutics, but issues related to expression instability remain unresolved. In this study, we investigated potential causes for an unstable phenotype by comparing cell lines that express stably to such that undergo loss in titer across 10 passages. Factors related to transgene integrity and copy number as well as the genomic profile around the integration sites were analyzed. Horizon Discovery CHO-K1 (HD-BIOP3) derived production cell lines selected for phenotypes with low, medium or high copy number, each with stable and unstable transgene expression, were sequenced to capture changes at genomic and transcriptomic levels. The exact sites of the random integration events in each cell line were also identified, followed by profiling of the genomic, transcriptomic and epigenetic patterns around them. Based on the information deduced from these random integration events, genomic loci that potentially favor reliable and stable transgene expression were reported for use as targeted transgene integration sites. By comparing stable vs unstable phenotypes across these parameters, we could establish that expression stability may be controlled at three levels: 1) Good choice of integration site, 2) Ensuring integrity of transgene and observing concatemerization pattern after integration, and 3) Checking for potential stress related cellular processes. Genome wide favorable and unfavorable genomic loci for targeted transgene integration can be browsed at https://www.borthlabchoresources.boku.ac.at/.

Citing Articles

Selective Recruitment of a Synthetic Histone Acetyltransferase Can Boost CHO Cell Productivity.

Butterfield S, Sizer R, Saunders F, White R Biotechnol J. 2024; 19(12):e202400474.

PMID: 39655408 PMC: 11629143. DOI: 10.1002/biot.202400474.

Enhancing Cell Line Stability by CRISPR/Cas9-Mediated Site-Specific Integration Based on Histone Modifications.

Hertel O, Neuss A Methods Mol Biol. 2024; 2810:211-233.

PMID: 38926282 DOI: 10.1007/978-1-0716-3878-1_14.

Construction and application of a multifunctional CHO cell platform utilizing Cre/ and Dre/ site-specific recombination systems.

Zhang C, Chang F, Miao H, Fu Y, Tong X, Feng Y Front Bioeng Biotechnol. 2024; 11:1320841.

PMID: 38173869 PMC: 10761530. DOI: 10.3389/fbioe.2023.1320841.

Synthetic developmental biology: molecular tools to re-design plant shoots and roots.

Kocaoglan E, Radhakrishnan D, Nakayama N J Exp Bot. 2023; 74(13):3864-3876.

PMID: 37155965 PMC: 10826796. DOI: 10.1093/jxb/erad169.

High-efficiency and multilocus targeted integration in CHO cells using CRISPR-mediated donor nicking and DNA repair inhibitors.

Hamaker N, Lee K Biotechnol Bioeng. 2023; 120(9):2419-2440.

PMID: 37039773 PMC: 10524319. DOI: 10.1002/bit.28393.

References

Li H, Durbin R . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-60. PMC: 2705234. DOI: 10.1093/bioinformatics/btp324. View

Kotredes K, Gamero A . Interferons as inducers of apoptosis in malignant cells. J Interferon Cytokine Res. 2013; 33(4):162-70. PMC: 3624694. DOI: 10.1089/jir.2012.0110. View

Papapetrou E, Schambach A . Gene Insertion Into Genomic Safe Harbors for Human Gene Therapy. Mol Ther. 2016; 24(4):678-84. PMC: 4886940. DOI: 10.1038/mt.2016.38. View

Layer R, Chiang C, Quinlan A, Hall I . LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 2014; 15(6):R84. PMC: 4197822. DOI: 10.1186/gb-2014-15-6-r84. View

Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, Kallberg M . Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2015; 32(8):1220-2. DOI: 10.1093/bioinformatics/btv710. View

Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert B, Gillette M . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102(43):15545-50. PMC: 1239896. DOI: 10.1073/pnas.0506580102. View

Ramirez F, Dundar F, Diehl S, Gruning B, Manke T . deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 2014; 42(Web Server issue):W187-91. PMC: 4086134. DOI: 10.1093/nar/gku365. View

Strathdee D, Ibbotson H, Grant S . Expression of transgenes targeted to the Gt(ROSA)26Sor locus is orientation dependent. PLoS One. 2006; 1:e4. PMC: 1762389. DOI: 10.1371/journal.pone.0000004. View

Vcelar S, Jadhav V, Melcher M, Auer N, Hrdina A, Sagmeister R . Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting. Biotechnol Bioeng. 2017; 115(1):165-173. DOI: 10.1002/bit.26453. View

10.

Hetz C, Papa F . The Unfolded Protein Response and Cell Fate Control. Mol Cell. 2017; 69(2):169-181. DOI: 10.1016/j.molcel.2017.06.017. View

11.

Lim S, Kaldis P . Cdks, cyclins and CKIs: roles beyond cell cycle regulation. Development. 2013; 140(15):3079-93. DOI: 10.1242/dev.091744. View

12.

Anders S, Pyl P, Huber W . HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2014; 31(2):166-9. PMC: 4287950. DOI: 10.1093/bioinformatics/btu638. View

13.

Gaidukov L, Wroblewska L, Teague B, Nelson T, Zhang X, Liu Y . A multi-landing pad DNA integration platform for mammalian cell engineering. Nucleic Acids Res. 2018; 46(8):4072-4086. PMC: 5934685. DOI: 10.1093/nar/gky216. View

14.

Rausch T, Zichner T, Schlattl A, Stutz A, Benes V, Korbel J . DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics. 2012; 28(18):i333-i339. PMC: 3436805. DOI: 10.1093/bioinformatics/bts378. View

15.

OBrien S, Lee K, Fu H, Lee Z, Le T, Stach C . Single Copy Transgene Integration in a Transcriptionally Active Site for Recombinant Protein Synthesis. Biotechnol J. 2018; 13(10):e1800226. PMC: 7058118. DOI: 10.1002/biot.201800226. View

16.

Kong Q, Wu M, Huan Y, Zhang L, Liu H, Bou G . Transgene expression is associated with copy number and cytomegalovirus promoter methylation in transgenic pigs. PLoS One. 2009; 4(8):e6679. PMC: 2723931. DOI: 10.1371/journal.pone.0006679. View

17.

Zhao Y, Ye X, Shehata M, Dunker W, Xie Z, Karijolich J . The RNA quality control pathway nonsense-mediated mRNA decay targets cellular and viral RNAs to restrict KSHV. Nat Commun. 2020; 11(1):3345. PMC: 7334219. DOI: 10.1038/s41467-020-17151-2. View

18.

McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A . The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010; 20(9):1297-303. PMC: 2928508. DOI: 10.1101/gr.107524.110. View

19.

Bolger A, Lohse M, Usadel B . Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30(15):2114-20. PMC: 4103590. DOI: 10.1093/bioinformatics/btu170. View

20.

Love M, Huber W, Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15(12):550. PMC: 4302049. DOI: 10.1186/s13059-014-0550-8. View