Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Differential gene expression in CHO cells cultivated in shake flasks and bioreactors

ABSTRACT: Differential gene expression in CHO cells cultivated in shake flasks and bioreactors

PROVIDER: PRJNA413823 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Differential gene expression in CHO cells cultivated in shake flasks and bioreactors

Project description:Chinese Hamster Ovary cell lines are currently the primary host for production of therapeutic glycoproteins. Fast process development resulting in robust and scalable processes is a critical success factor in the highly competitive market for biosimilars. In process development screening of hundreds of clones and selection of process conditions are routinely performed in uncontrolled cultivation systems like shake flasks. A handful of potential candidate clones is nominated to be evaluated more intensively in well controlled small-scale bioreactors. Cell performance in the uncontrolled systems and to a lesser extent in the small-scale bioreactors may, however, be different from that in the final production reactor, which may result in failures during scale-up and thus extra development time. In this work, the focus is on better understanding the differences in cell performance between controlled and uncontrolled systems, which can be used to make process development faster and more robust in terms of scale-up. For this, we evaluated differences in gene expression profiles between shake flask and bioreactor cultures at three different time points during the exponential and stationary phase of a batch culture using commercially available Affymetrix GeneChip CHO Gene 2.1 ST arrays and multivariate data analysis on the outcomes. The outcomes were correlated with differences in glycosylation patterns and other culture parameters. Results showed large differences in gene expression over time and much smaller differences between the two cultivation systems. Furthermore, our study identified differentially expressed genes and corresponding metabolic and mechanistic pathways between the two systems, which were directly related to the degree of the control of the systems.

2023-11-04 | GSE104787 | GEO

Untargeted metabolomics analysis of DSS-treated murine intestinal microbiota cultivated in continuous bioreactors

Project description:We cultivated murine faecal microbiota in six replicate continuous bioreactors. Three bioreactors were supplemented with 1% DSS and compared to the untreated control bioreactors.

2022-11-28 | ST002393 | MetabolomicsWorkbench

Defining lncRNAs Associated with CHO Cell Growth and IgG Productivity by RNA-Seq II

Project description:Small-scale bioreactors used in Chinese hamster ovary (CHO) cell line development allow transcriptomic studies on multiple cell lines. Here we define the CHO cell long non-coding RNA (lncRNA) transcriptome from cells grown in controlled miniature bioreactors under fed-batch conditions using RNA-Seq to identify lncRNAs and how the expression of these changes throughout growth and between IgG producers. We identify lncRNAs associated with productivity and growth characteristics, finding that Adapt15, linked to ER stress, GAS5, linked to mTOR signalling/growth arrest, and PVT1, linked to Myc expression, are differentially regulated during fed-batch culture and whose expression relates to productivity and growth. Changes in (non)-coding RNA expression between the seed train and the equivalent day of fed-batch culture are also reported, showing large differences in gene expression between these, and compared with existing datasets. Collectively, we present a comprehensive lncRNA CHO cell profiling and identify targets for engineering growth and productivity characteristics of CHO cells.

2019-11-20 | GSE140669 | GEO

Defining lncRNAs Associated with CHO Cell Growth and IgG Productivity by RNA-Seq I

2019-11-20 | GSE140668 | GEO

Expression data from CHO-K1 cells in putrescine-deprived conditions

Project description:CHO cells grown in serum-free medium have an absolute requirement for putrescine supplementation due to their deficiency in activity of the enzyme arginase. Consequently, principally in this system, polyamines play a central but poorly-understood role in cell proliferation Cell growth arrest, mainly in the S- and G2/M phases was observed. We used differential gene expression data to investigate the impact of polyamine deprivation on the transcriptome of CHO cells

2019-12-12 | GSE140223 | GEO

Bioreactors

Project description:Bioreactors Metagenome

| PRJNA531487 | ENA

CHO-S transcriptome in batch culture

Project description:Transcriptomic data from CHO-S cells were gathered from multiple time points during batch culture to provide information about gene presence/absence for generation of a CHO-S cell line specific model using the GIMME algorithm.

2016-11-23 | GSE77800 | GEO

Short-chain fatty acid (SCFA) analysis of DSS-treated murine intestinal microbiota using continuous cultivation in bioreactors

2022-12-06 | ST002394 | MetabolomicsWorkbench

Gene expression in CD8 vs. CD8+Cho cells

Project description:Gene expression in CD8 vs. CD8+Cho cells

| PRJNA435623 | ENA

Expression profile of HER2-overexpressing CHO cells treated with trastuzumab and pertuzumab

Project description:CHO-K1 (wildtype) and CHO-K6 (stablely overexpressing human HER2) cells were terated with 10 ug/ml transtuzumab or pertuzumab and their combination for 24 h and then the whole gene expression was analyzed by cDNA array.

2018-08-29 | GSE110189 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data