Transcriptomic changes in CHO cells after adaptation to suspension growth in protein-free medium analysed by a species-specific microarray.

Chinese Hamster Ovary (CHO) cells are the preferred cell line for production of biopharmaceuticals. These cells are capable to grow without serum supplementation, but drastic changes in their phenotype occur during adaptation to protein-free growth, which typically include the change to a suspension phenotype with reduced growth rate. A possible approach to understand this transformation, with the intention to counteract the reduction in growth by targeted supplementation of protein-free media, is gene expression profiling. The increasing availability of genome-scale data for CHO now facilitates quests for a better understanding of metabolic pathways and gene networks. So far, systematic large-scale expression profiling in CHO cells by microarray was limited due to lack of publicly available array designs and limitations of alternative approaches. Based on the recent release of CHO and Chinese Hamster genome sequences, including an annotated RefSeq genome, we have constructed a publicly available microarray design for effective genome-scale expression profiling. The design employed microarray probes optimized for uniformity, sensitivity, and specificity, with probe properties computed using the latest thermodynamic models. We validated the platform in an analysis of gene expression changes in response to serum-free adaptation. The observed effects on the lipid metabolism as well as on nucleotide synthesis were used to successfully select media supplements that were able to increase growth rate.

Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with increased production capacity.

Over the last three decades, product yields from CHO cells have increased dramatically, yet specific productivity (qP) remains a limiting factor. In a previous study, using repeated cell-sorting, we have established different host cell subclones that show superior transient qP over their respective parental cell lines (CHO-K1, CHO-S). The transcriptome of the resulting six cell lines in different biological states (untransfected, mock transfected, plasmid transfected) was first explored by hierarchical clustering and indicated that gene activity associated with increased qP did not stem from a certain cellular state but seemed to be inherent for a high qP host line. We then performed a novel gene regression analysis identifying drivers for an increase in qP. Genes significantly implicated were first systematically tested for enrichment of GO terms using a Bayesian approach incorporating the hierarchical structure of the GO term tree. Results indicated that specific cellular components such as nucleus, ER, and Golgi are relevant for cellular productivity. This was complemented by targeted GSA that tested functionally homogeneous, manually curated subsets of KEGG pathways known to be involved in transcription, translation, and protein processing. Significantly implicated pathways included mRNA surveillance, proteasome, protein processing in the ER and SNARE interactions in vesicular transport.

CHO microRNA engineering is growing up: recent successes and future challenges.

microRNAs with their ability to regulate complex pathways that control cellular behavior and phenotype have been proposed as potential targets for cell engineering in the context of optimization of biopharmaceutical production cell lines, specifically of Chinese Hamster Ovary cells. However, until recently, research was limited by a lack of genomic sequence information on this industrially important cell line. With the publication of the genomic sequence and other relevant data sets for CHO cells since 2011, the doors have been opened for an improved understanding of CHO cell physiology and for the development of the necessary tools for novel engineering strategies. In the present review we discuss both knowledge on the regulatory mechanisms of microRNAs obtained from other biological models and proof of concepts already performed on CHO cells, thus providing an outlook of potential applications of microRNA engineering in production cell lines.

PLecDom: a program for identification and analysis of plant lectin domains.

PLecDom is a program for detection of Plant Lectin Domains in a polypeptide or EST sequence, followed by a classification of the identified domains into known families. The web server is a collection of plant lectin domain families represented by alignments and profile Hidden Markov Models. PLecDom was developed after a rigorous analysis of evolutionary relationships between available sequences of lectin domains with known specificities. Users can test their sequences for potential lectin domains, catalog the identified domains into broad substrate classes, estimate the extent of divergence of new domains with existing homologs, extract domain boundaries and examine flanking sequences for further analysis. The high prediction accuracy of PLecDom combined with the ease with which it handles large scale input, enabled us to apply the program to protein and EST data from 48 plant genome-sequencing projects in various stages of completion. Our results represent a significant enrichment of the currently annotated plant lectins, and highlight potential targets for biochemical characterization. The search algorithm requires input in fasta format and is designed to process simultaneous connection requests from multiple users, such that huge sets of input sequences can be scanned in a matter of seconds. PLecDom is available at http://www.nipgr.res.in/plecdom.html.