Martin Clynes

Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis

Weighted gene coexpression network analysis (WGCNA) is a powerful guilt-by-association-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

Engineering CHO cell growth and recombinant protein productivity by overexpression of miR-7

The efficient production of recombinant proteins by Chinese Hamster Ovary (CHO) cells in modern bioprocesses is often augmented by the use of proliferation control strategies. The most common method is to shift the culture temperature from 37 °C to 28-33 °C though genetic approaches to achieving the same effect are also of interest. In this work we used qRT-PCR-based expression profiling using TLDA™ cards to identify miRNAs displaying differential expression 24h after temperature-shift (TS) from 37 °C to 31 °C. Six miRNAs were found to be significantly up-regulated (mir-219, mir-518d, mir-126, mir-30e, mir-489 and mir-345) and four down-regulated (mir-7, mir-320, mir-101 and mir-199). Furthermore, qRT-PCR analysis of miR-7 expression over a 6 day batch culture, with and without TS, demonstrated decreased expression over time in both cultures but to a significantly greater extent in cells shifted to a lower culture temperature. Unexpectedly, when miR-7 levels were increased transiently by transfection with miR-7 mimic in CHO-K1 cells, cell proliferation at 37 °C was effectively blocked over a 96 h culture period. On the other hand, transient inhibition of endogenous miR-7 levels using antagonists had no impact on cell growth. The exogenous overexpression of miR-7 also resulted in increased normalised (per cell) production at 37 °C, though the yield was lower than cells grown at reduced temperature. This is the first report demonstrating a functional impact of specific miRNA disregulation on CHO cell behavior in batch culture and provides some evidence of the potential which these molecules may have in terms of engineering targets in CHO production clones. Finally, we report the cloning and sequencing of the hamster-specific cgr-miR-7.

Microarray and proteomics expression profiling identifies several candidates, including the valosin-containing protein (VCP), involved in regulating high cellular growth rate in production CHO cell lines.

A high rate of cell growth (µ) leading to rapid accumulation of viable biomass is a desirable phenotype during scale up operations and the early stages of production cultures. In order to identify genes and proteins that contribute to higher growth rates in Chinese hamster ovary (CHO) cells, a combined approach using microarray and proteomic expression profiling analysis was carried out on two matched pairs of CHO production cell lines that displayed either fast or slow growth rates. Statistical analysis of the microarray and proteomic data separately resulted in the identification of 118 gene transcripts and 58 proteins that were differentially expressed between the fast‐ and slow‐growing cells. Overlap comparison of both datasets identified a priority list of 21 candidates associated with a high growth rate phenotype in CHO. Functional analysis (by siRNA) of five of these candidates identified the valosin‐containing protein (VCP) as having a substantial impact on CHO cell growth and viability. Knockdown of HSPB1 and ENO1 also had an effect on cell growth (negative and positive, respectively). Further functional validation in CHO using both gene knockdown (siRNA) and overexpression (cDNA) confirmed that altered VCP expression impacted CHO cell proliferation, indicating that VCP and other genes and proteins identified here may play an important role in the regulation of CHO cell growth during log phase culture and are potential candidates for CHO cell line engineering strategies. Biotechnol. Bioeng. 2010; 106: 42–56.

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

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.

Large scale microarray profiling and coexpression network analysis of CHO cells identifies transcriptional modules associated with growth and productivity

Weighted gene coexpression network analysis (WGCNA) was utilised to explore Chinese hamster ovary (CHO) cell transcriptome patterns associated with bioprocess relevant phenotypes. The dataset set used in this study consisted of 295 microarrays from 121 individual CHO cultures producing a range of biologics including monoclonal antibodies, fusion proteins and therapeutic factors; non-producing cell lines were also included. Samples were taken from a wide range of process scales and formats that varied in terms of seeding density, temperature, medium, feed medium, culture duration and product type. Cells were sampled for gene expression analysis at various stages of the culture and bioprocess-relevant characteristics including cell density, growth rate, viability, lactate, ammonium and cell specific productivity (Qp) were determined. WGCNA identified six distinct clusters of co-expressed genes, five of which were found to have associations with bioprocess variables. Two coexpression clusters were found to be associated with culture growth rate (1 positive and 1 negative). In addition, associations between a further three coexpression modules and Qp were observed (1 positive and 2 negative). Gene set enrichment analysis (GSEA) identified a number of significant biological processes within coexpressed gene clusters including cell cycle, protein secretion and vesicle transport. In summary, the approach presented in this study provides a novel perspective on the CHO cell transcriptome.

MicroRNAs: tiny targets for engineering CHO cell phenotypes?

The ability of microRNAs to influence gene expression is now recognized as a fundamental layer of regulation within the cell. MicroRNAs have a major impact on most biological processes and have generated considerable interest as potential biomarkers as well as therapeutic or engineering targets. In this review we provide a brief overview of their biogenesis, genomic organization and mode of action, followed by a description of the methods and approaches to studying their expression. We go on to consider some of the approaches to utilizing them as tools and their potential application in the bioprocessing area, with particular emphasis on Chinese hamster ovary cell engineering.

Predicting cell-specific productivity from CHO gene expression.

Improving the rate of recombinant protein production in Chinese hamster ovary (CHO) cells is an important consideration in controlling the cost of biopharmaceuticals. We present the first predictive model of productivity in CHO bioprocess culture based on gene expression profiles. The dataset used to construct the model consisted of transcriptomic data from 70 stationary phase, temperature-shifted CHO production cell line samples, for which the cell-specific productivity had been determined. These samples were utilised to investigate gene expression over a range of high to low monoclonal antibody and fc-fusion-producing CHO cell lines. We utilised a supervised regression algorithm, partial least squares (PLS) incorporating jackknife gene selection, to produce a model of cell-specific productivity (Qp) capable of predicting Qp to within 4.44 pg/cell/day root mean squared error in cross model validation (RMSE(CMV)). The final model, consisting of 287 genes, was capable of accurately predicting Qp in a further panel of 10 additional samples which were incorporated as an independent validation. Several of the genes constituting the model are linked with biological processes relevant to protein metabolism.

MiR-7 Triggers Cell Cycle Arrest at the G1/S Transition by Targeting Multiple Genes Including Skp2 and Psme3

MiR-7 acts as a tumour suppressor in many cancers and abrogates proliferation of CHO cells in culture. In this study we demonstrate that miR-7 targets key regulators of the G1 to S phase transition, including Skp2 and Psme3, to promote increased levels of p27KIP and temporary growth arrest of CHO cells in the G1 phase. Simultaneously, the down-regulation of DNA repair-specific proteins via miR-7 including Rad54L, and pro-apoptotic regulators such as p53, combined with the up-regulation of anti-apoptotic factors like p-Akt, promoted cell survival while arrested in G1. Thus miR-7 can co-ordinate the levels of multiple genes and proteins to influence G1 to S phase transition and the apoptotic response in order to maintain cellular homeostasis. This work provides further mechanistic insight into the role of miR-7 as a regulator of cell growth in times of cellular stress.

CHO cell culture longevity and recombinant protein yield are enhanced by depletion of miR-7 activity via sponge decoy vectors.

Improving the efficiency of recombinant protein production by CHO cells is highly desirable as more complex proteins (MAbs, fusion proteins, blood/clotting factors, etc.) go into development and come onto the market. Previous reports have shown that microRNA (miRNA)-7 overexpression arrests the growth of CHO cells and that its depletion increases the proliferation of various cell types. In this study we generated stable CHO clones that overexpressed a miR-7-specific decoy transcript (sponge) downstream of a green fluorescent protein reporter gene. The miR-7 sponge efficiently diverted miR-7 away from its endogenous targets as exemplified by the increased expression of CDC7. Although the sponge effectively sequestered miR-7, it also appeared to protect the bound miRNA sequence from degradation in the cell, as exemplified by the apparent increase in mature miR-7 levels without any change in primary transcription. Phenotypically, CHO clones with sequestered miR-7 displayed improved maximum cell density (40%), significantly improved viability and an almost two-fold increase in yield of secreted protein in a fed-batch culture. These findings demonstrate that miRNA sponge transcripts could potentially be used in cell line development projects to generate producer clones that grow to higher densities and last longer in the bioreactor - thereby improving product yield.

Transcriptomic analysis of clonal growth rate variation during CHO cell line development.

The selection of clones displaying a high rate of cell growth is an essential component of Chinese hamster ovary (CHO) cell line development. In recent years various omics technologies have been utilised to understand the mechanisms underlying bioprocess phenotypes. In this study, gene expression analysis using a CHO-specific microarray was conducted for a panel of CHO-K1 MAb-secreting cell lines spanning a range of growth rates that were derived from a single cell line development project. In-silico functional analysis of the resulting transcriptomic data revealed the overrepresentation of biological processes such as cell cycle and translation within those genes upregulated during fast growth, while genes associated with cellular homeostasis were downregulated. Using differential expression and correlation analysis we identified a high priority group of 416 transcripts (190 upregulated; 226 downregulated) associated with growth rate. Expression changes of eight of these genes were independently confirmed by qPCR. Finally, we demonstrate the enrichment of predicted mRNA targets of miR17-92, a microRNA (miRNA) cluster known to be upregulated during rapid proliferation, within downregulated transcripts.