Integrated miRNA, mRNA and protein expression analysis reveals the role of post-transcriptional regulation in controlling CHO cell growth rate

Abstract

BackgroundTo study the role of microRNA (miRNA) in the regulation of Chinese hamster ovary (CHO) cell growth, qPCR, microarray and quantitative LC-MS/MS analysis were utilised for simultaneous expression profiling of miRNA, mRNA and protein. The sample set under investigation consisted of clones with variable cellular growth rates derived from the same population. In addition to providing a systems level perspective on cell growth, the integration of multiple profiling datasets can facilitate the identification of non-seed miRNA targets, complement computational prediction tools and reduce false positive and false negative rates.Results51 miRNAs were associated with increased growth rate (35 miRNAs upregulated and 16 miRNAs downregulated). Gene ontology (GO) analysis of genes (n=432) and proteins (n=285) found to be differentially expressed (DE) identified biological processes driving proliferation including mRNA processing and translation. To investigate the influence of miRNA on these processes we combined the proteomic and transcriptomic data into two groups. The first set contained candidates where evidence of translational repression was observed (n=158). The second group was a mixture of proteins and mRNAs where evidence of translational repression was less clear (n=515). The TargetScan algorithm was utilised to predict potential targets within these two groups for anti-correlated DE miRNAs.ConclusionsThe evidence presented in this study indicates that biological processes such as mRNA processing and protein synthesis are correlated with growth rate in CHO cells. Through the integration of expression data from multiple levels of the biological system a number of proteins central to these processes including several hnRNPs and components of the ribosome were found to be post-transcriptionally regulated. We utilised the expression data in conjunction with in-silico tools to identify potential miRNA-mediated regulation of mRNA/proteins involved in CHO cell growth rate. These data have allowed us to prioritise candidates for cell engineering and/or biomarkers relevant to industrial cell culture. We also expect the knowledge gained from this study to be applicable to other fields investigating the role of miRNAs in mammalian cell growth.

Proteomic differences in recombinant CHO cells producing two similar antibody fragments

“…Differential proteomic analysis using label‐free LC‐MS/MS was carried out using Progenesis QI for proteomics version 1.0 (NonLinear Dynamics Limited, Newcastle upon Tyne, UK), essentially as recommended by the manufacturer and as previously described (Clarke et al, 2012). The raw data obtained from each of the LC‐MS/MS runs per sample was processed using Progenesis QI for proteomics software.…”

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling

“…In order to address this issue, a number of proteomic, transcriptomic and metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (e.g. Clarke et al, 2011;Clarke et al, 2012;Doolan et al, 2013;Jacob et al, 2010;Mead et al, 2009;Mead et al, 2012;Meleady et al, 2011;Sanchez et al, 2014;Selvarasu et al, 2012), however these models are usually developed from cell line data at the end of the cell line development process.…”

“…In order to address this issue, a number of proteomic, transcriptomic and metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (e.g. Clarke et al, 2011;Clarke et al, 2012;Doolan et al, 2013;Jacob et al, 2010;Mead et al, 2009;Mead et al, 2012;Meleady et al, 2011;Sanchez et al, 2014;Selvarasu et al, 2012), however these models are usually developed from cell line data at the end of the cell line development process.The goal of this work was to develop a screening system that would allow the selection of highly productive cell lines for monoclonal antibody (mAb) production early in the cell line development process that would use substantially less resource to achieve the same or better success rate as current methods. The vision was to be able to select a small number of cell lines based upon the analysis of data generated in multi-well plates, and take these straight to a lab-scale bioreactorevaluation stage (10 L) with a high probability that the selected cell lines were highly productive.…”

The advancements of genomics and data integration in sarcoma research

“…Due to the complex nature of sarcomas and the relatively small sample sizes in sarcoma studies, unleashing the power of statistics and data integration is critical for the identification of prognostic biomarkers and therapeutic targets in cancer patients (Figure 2). Hence, novel algorithms have been developed for data integration [87][88][89][90][91][92][93][94][95] . Data integration can refer to many different research areas, such as integrating different omics data sources, integrating molecular data with phenotypic and clinical data (e.g.…”

“…Some of these algorithms select biomarkers based on biological relationships between data sources, e.g. miRNA regulation of mRNAs as well as the corresponding proteomic information [87,95] . Others incorporate intrinsic feature selection methods to build models, such as iBAG, a model-based Bayesian approach that uses Lasso regression to impart sparsity to remove non-significant variables and infer potential interactions between cross-platform features in order to build models with clinical relevance [93] .…”