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Genome-scale Process Control Mixtures

ABSTRACT: Using Mixtures of Biological Samples as Genome-Scale Process Controls

PROVIDER: PRJEB8231 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR732562.fastq.gz	Fastqsanger.gz
	ERR732562_1.fastq.gz	Fastqsanger.gz
	ERR732562_2.fastq.gz	Fastqsanger.gz
	ERR732563_1.fastq.gz	Fastqsanger.gz
	ERR732563_2.fastq.gz	Fastqsanger.gz

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Project description:Biotechnology-derived therapeutics manufacturing is highly regulated to assure product quality, safety and efficacy. Process conditions are closely monitored as they can influence product characteristics. Culture of mammalian cells at different scales is a vital part of biomanufacturing. Currently, scale up of bioreactors is largely done based on engineering parameters such as oxygen transfer and mixing characteristics. There is a lack of genomic translational studies in mammalian cell culture scale-up that can help delineate measurable cellular attributes for improved process understanding. These could be used for quantifying physiological response of cells due to changes in bioreactor environment. In this study, we identified 18 process-sensitive sentinel genes using microarray statistical analysis, which we further verified by qRT-PCR. These were differentially expressed transcripts between a typical 5 liter bench-scale bubble-aerated and impeller-agitated bioreactor and novel 35 mL high-throughput minibioreactors. Using expression changes and biochemical-pathway guidance of these sentinel genes, we were able to tune engineering parameters such that the improved scale-down resulted in both process parameter and sentinel gene profile convergence between the two systems, further solidifying the functionality evidence of these sentinel genes. This study serves as a starting point for developing qRT-PCR assays as comparability metrics that could be performed near-at-line during the cell culture process itself leading to enhanced process control. Additional broad applications of sentinel genes could be to validate different manufacturing facilities for the same product, and allow better process definition by fingerprinting the manufacturing process for more rapid biogenerics and vaccine approvals. We ran consecutive bioreactor (5L and HTCB) runs, each with an independent vial thaw, to achieve multiple biological replicates per time-point. Bioreactors were sampled approximately every 12 hours for RNA extraction. For the 5L bioreactors, microarray samples were run for day 1 (n=2), day 2 (n=2), day 3 (n=3), and day 3.5 (n=3). Here 2 or 3 of the three biological replicates run for each time-point were included in the analysis, based on >70% genes found. For the High-Throughput Controlled Minibioreactors (HTCB), microarray samples were run for day 1 (n=3), day 2 (n=3), day 3 (n=3), day 4 (n=2), and day 4.5 (n=2). Here 2 to 4 of the 4 biological replicate runs were included in the analysis, based on >70% genes found. We define early exponential as day 1, peak exponential as day 2 and day 3 and late stationary as day 3.5.

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Genome-scale Process Control Mixtures

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