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Henry O.,Ecole Polytechnique de Montreal | Durocher Y.,NRC Biotechnology Research Institute
Metabolic Engineering | Year: 2011

There is an imperative need for expression systems allowing the efficient and robust manufacturing of high quality glycoproteins. In the present work, HEK-293 cells stably expressing interferon-α2b were further engineered with the insertion of the yeast pyruvate carboxylase 2 gene. In batch cultures, marked reductions in lactate and ammonia production were observed compared to the parental cell clone. Although the maximum specific growth rate remained unchanged, the altered metabolism led to a 2-fold increase in maximum cell density and 33% increase in the integral of viable cell concentration and interferon production yield. The underlying metabolic changes were further investigated using various 13C-labeled substrates and measuring the resulting lactate mass isotopomer distributions. Simultaneous metabolite and isotopomer balancing allowed the accurate determination of key intracellular fluxes. Such detailed and quantitative knowledge about the central carbon metabolism of the cells is instrumental to further support the development of high-yield fed-batch processes. © 2011 Elsevier Inc.

Masson L.,NRC Biotechnology Research Institute
Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology | Year: 2010

This perspective discusses current DNA technologies used in basic and applied microbiology research and speculates on possible new future technologies. DNA remains one of the most fascinating molecules known to humans and will continue to revolutionize many areas ranging from medicine, food and forensics to robotics and new industrial bioproducts/biofuel from waste materials. What's next with DNA is not always obvious, but history shows the international microbiology research community will readily adopt it. © 2010 Springer Science+Business Media B.V.

Dormond E.,NRC Biotechnology Research Institute
Methods in molecular biology (Clifton, N.J.) | Year: 2011

Adenoviral vector (AdV) of the third generation also known as helper-dependent adenoviral vector (HDV) is an attractive delivery system for gene therapy applications. However, obtaining high quality-grade HDV in sufficient amount remains a challenge that hampers the extensive use of this vector in preclinical and clinical studies. Here we review recent progress in the large-scale manufacturing of HDV. The production of HDV is now amenable to large-scale volume with reduced process duration under optimized rescue and co-infection conditions. Also, efficient downstream processing of HDV with acceptable recovery of HDV and minimal contamination by the helper virus is described.

Agrawal V.,NRC Biotechnology Research Institute
Methods in molecular biology (Clifton, N.J.) | Year: 2012

Camelid single domain antibodies fused to noncamelid Fc regions, also called chimeric heavy chain antibodies (cHCAb), offer great potential as therapeutic and diagnostic candidates due to their relatively small size (80 kDa) and intact Fc. In this chapter, we describe two approaches, limiting dilution and minipools, for generating nonamplified Chinese hamster ovary cell lines stably expressing cHCAb in suspension and serum-free cultures using a stringent antibiotic selection. Neither of the protocols necessitates the acquisition or implementation of expensive automated infrastructures and thus could be applied in any lab with minimal cell culture setup. The given protocol allows the isolation of stable clones capable of generating up to 100 mg/L of antibody in batch mode performed in shaker flasks.

Li J.,NRC Biotechnology Research Institute
Nature communications | Year: 2010

Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. Thus far, no algorithm has been able to successfully generate cancer prognostic gene signatures with high accuracy and robustness in order to identify these patients. In this paper, we developed an algorithm that identifies prognostic markers using tumour gene microarrays focusing on metastasis-driving gene expression signals. Application of the algorithm to breast cancer samples identified prognostic gene signature sets for both estrogen receptor (ER) negative (-) and positive (+) subtypes. A combinatorial use of the signatures allowed the stratification of patients into low-, intermediate- and high-risk groups in both the training set and in eight independent testing sets containing 1,375 samples. The predictive accuracy for the low-risk group reached 87-100%. Integrative network analysis identified modules in which each module contained the genes of a signature and their direct interacting partners that are cancer driver-mutating genes. These modules are recurrent in many breast tumours and contribute to metastasis.

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