The need for new safe and efficacious therapies has led to an increased focus on biologics produced in mammalian cells. The human cell line HEK293 has bio-synthetic potential for human-like production attributes and is currently used for manufacturing of several therapeutic proteins and viral vectors. Despite the increased popularity of this strain we still have limited knowledge on the genetic composition of its derivatives. Here we present a genomic, transcriptomic and metabolic gene analysis of six of the most widely used HEK293 cell lines. Changes in gene copy and expression between industrial progeny cell lines and the original HEK293 were associated with cellular component organization, cell motility and cell adhesion. Changes in gene expression between adherent and suspension derivatives highlighted switching in cholesterol biosynthesis and expression of five key genes (RARG, ID1, ZIC1, LOX and DHRS3), a pattern validated in 63 human adherent or suspension cell lines of other origin.
1The need for new safe and efficacious therapies has led to an increased focus on biologics 2 produced in mammalian cells. The human cell line HEK293 has bio-synthetic potential 3 for human-like production and is today used for manufacturing of several therapeutic pro-4 teins and viral vectors. Despite this increasing popularity there is still limited knowledge 5 of the detailed genetic and composition of derivatives of this strain. Here we present a 6 genomic, transcriptomic and metabolic gene analysis of six of the most widely used 7 HEK293 cell lines. Changes in gene copy and expression between industrial progeny cell 8 lines and the original HEK293 were associated with cellular component organization, cell 9 motility and cell adhesion. Changes in gene expression between adherent and suspension 10 derivatives highlighted switching in cholesterol biosynthesis and expression of five key 11 genes (RARG, ID1, ZIC1, LOX and DHRS3), a pattern validated in 63 human adherent 12 or suspension cell lines of other origin.13 14
Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets.
SummaryHigher eukaryotic cell lines like HEK293 are the preferred hosts for production of therapeutic proteins requiring human post translational processing. However, recombinant protein production can result in severe stress on the cellular machinery, resulting in limited titre and product quality. To investigate the cellular and metabolic characteristics associated with these limitations, we compared erythropoietin (secretory) and GFP (non-secretory) protein producer HEK293 cell-lines using transcriptomics analysis. Despite the high demand for ATP in all protein producer clones, a significantly higher capacity for ATP production was observed with erythropoietin producers as evidenced by the enrichment of upregulated genes in the oxidative phosphorylation pathway. In addition, ribosomal genes exhibited specific patterns of expression depending on the recombinant protein and the production rate. In a clone displaying a dramatically increased erythropoietin secretion, we detected higher ER stress, including upregulation of the ATF6B gene. Our results are significant in recognizing key pathways for recombinant protein production and identifying potential target genes for further development of secretory power in mammalian cell factories.In BriefAlthough the protein secretion process has been widely studied, the complexity of it leaves many questions with regards to defining bottlenecks for successful protein secretion to be answered. By investigating the transcriptomic profiles of different HEK293 clones with varying translational rates producing either the secreted protein erythropoietin or the intracellular GFP, we reveal that high ATP production and improved capacity of specific post-translational pathways are key factors associated with boosting erythropoietin production.HighlightsTranscriptomics analysis of a panel of HEK293 stable cell lines expressing GFP or erythropoietin (EPO) at varying translational ratesExpression of mitochondrial ribosomal genes is positively correlated with EPO secretionExpression of different cytosolic ribosomal genes are correlated with productivity in a recombinant-protein specific mannerHigh EPO producing clones have significant upregulation of ATF6B, potentially enabling a beneficial ER stress response to cope with high protein secretionGraphical Abstract
Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets.Author SummaryThe secretory pathway is a series of intracellular compartments and enzymes that process and export proteins from the cell to the surrounding environment. Dysfunction of the secretory pathway is associated with many diseases, including cancer, and therefore constitutes a potential target for novel therapeutic strategies. The large number of interacting components that comprise the secretory pathway pose a challenge when attempting to identify where the dysfunction originates and/or how to restore healthy function. To improve our understanding of how the secretory pathway is changed within tumors, we used gene expression data from normal tissue and tumor samples from thousands of individuals which included many different types of cancers. The data was analyzed using various machine learning algorithms which we trained to predict sample characteristics, such as disease severity. This training quantified the relative degree to which each gene was associated with the tumor characteristic, allowing us to predict which secretory pathway components were important for processes such as tumor progression—both within specific cancer types and across many different cancer types. Our approach demonstrated excellent performance compared to traditional gene expression analysis methods and identified several secretory pathway components with strong evidence of involvement in tumor development.
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