Post genomics techniques for improved understanding of bioprocesses Caroline Evans, University of Sheffield www.sheffield.ac.uk/chelsi
Life Science Interface OMIC focussed research
Talk Overview Post genomic view applications of Proteomics Engineering strategies - Use of E coli as a platform for bioprocessing 1. Improving glycosylation efficiency 2. Mass spectrometry methods for glycosylation analysis New Advanced Biomanufacturing Centre University of Sheffield
Post genomics deals with complexity beyond the genome Genome Gene Transcription Transcriptome mrna (s) Splice variants Translation Proteome Protein (s) Multiple isoforms possible Proteome - Functional regulation Dynamic Metabolites post-translational modification, proteoforms altered protein-protein interactions protein turnover subcellular localisation Complexity
Proteomics Provides a Toolkit for bioprocess optimisation Proteins and peptides can be characterised using a combination of separation and characterisation High Performance Liquid Chromatography (HPLC) Typically peptide separation (shot gun, bottom up analysis) Mass spectrometry (MS) Measures molecular weight (as mass/charge)
Applications of Mass Spectrometry Protein identification based on mass measurements of peptides, peptide fragments (infer sequence) Perform quantification: Relative and Absolute Protein characterisation -Analysis of co and posttranslational modifications (PTMs), status and site localisation
Glycoprotein characterisation Dr Jags Pandhal, Dr Caroline Evans, Prof Phil Wright Glycoprotein and glycopeptides can be characterised using a combination of High Performance Liquid Chromatography (HPLC) separation of glycosylated and non glycosylated isoforms Mass spectrometry (MS) identification of glycopeptides, quantification
Why focus on Glycosylation? An enzymatic process that attaches glycans to proteins Biological Significance?: Improve pharmacokinetic properties Half life Solubility Stability 70% industrially relevant proteins are glycosylated
Types of Glycosylation N + O-Linked, CHO is the industrial platform for eukaryotic glycoprotein production Bacterial systems? Discovery of C. jejuni N glycosylation pathway. Functional on transfer into E. coli Industrial biotech application CHO alternative Benefits include cost reduction, faster time scale and reduction variable glycosylation specificity observed with CHO cells.
Engineering Approaches use of model system N-Glycosylation machinery successfully transferred into E. coli from C. jejuni Opens up the possibility to produce recombinant glycoproteins in the bacterial workhorse AcrA model protein for initial work
What can we do now? Prokaryotic Eukaryotic Eukaryotic type glycan desirable as reduce immunogenic response.
E. coli can glycosylate proteins The efficiency of the process is poor low yields, high metabolic burden Diversify the functionality of E. coli as a protein production host How? Improve glycosylation process efficiency By? Developing and implementing omics tools
Forward Engineering I Approach: Mass spectrometry (itraq) based protein profiling of E.coli cells expressing and glycosylating recombinant AcrA protein combined with probability based analysis of metabolic changes. Isocitrate lyase (ICL) key regulatory enzyme of glyoxylate cycle E. coli was forward engineered to increase expression of ICL enhance the flux through the glyoxylate cycle, increasing the amount of glycosylated AcrA by 300%. Pandhal, J, Ow SY, Noirel, J and Wright PC (2011) Biotechnology and Bioengineering, 108:902 912,
Forward Engineering II Using western blots and pseudo selective reactive monitoring to analyse glycosylation, codon optimisation of the bacterial oligosaccharyltransferase, PglB, when expressed in E. coli, improved AcrA glycosylation efficiency by approx 2 fold** Pandhal J, Desai P, Walpole C, Doroudi L, Malyshev D, Wright PC. 2012. Biochemical and Biophysical Research Communication 419:472-6.
** Improving the tool kit - Quantification Schiff s reagent Qualitative Western blots Semiquantitative Selective reaction monitoring (mass spectrometry) Quantitative Pandhal J, Desai P, Walpole C, Doroudi L, Malyshev D, Wright PC. 2012. Biochemical and Biophysical Research Communication 419:472-6.
yield coefficient (mg/g) Inverse metabolic engineering (codon optimisation) Increase base line level protein production Different E.coli strains: CLM24 Remove lipid anchor (PelB signal sequence retained) = soluble periplasmic glycoprotein Changed expression vector: arabinose inducible (dose dependent) 0.0080 0.0070 6.5 fold increase 0.0060 0.0050 0.0040 0.0030 0.0020 0.0010 0.0000 E.coli BL21 pet21a+ Soluble AcrA E.coli CLM24, pecacra Base line level: ca. 1.5 mg/l gacra Pandhal J, Woodruff LB, Jaffe S, Desai P, Ow SY, Noirel J, Gill RT, Wright PC (2013) Biotechnol Bioeng 110:2482-93
Novel MS Methodology for Glycopeptide Analysis Key requirements: Screen for glycopeptides Makes no assumption about precursor m/z Provides information for targeted analysis complement SRM/MRM Key Features: Monitor glycosylation diagnostic ions in relation to precursor MS High resolution MS for confidence in diagnostic ion assignment 17
Intensity Intensity Collision Induced Release of Glyco Diagnostic m/z 204.088, m/z 366.137 diagnostic ions Precursor ions HexNAc and Hex-HexNAc diagnostic Alignment Precursor and Fragment ion MS survey scan (10 ev) m/z Alternating bbcid MS/MS scan (25-75 ev) m/z Caroline Evans, Narciso Couto, Liliya Davlyatova, Phil Wright (manuscript in preparation) 18
Summary - Metabolic engineering using proteomics tools
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Acknowledgements Professor Phillip Wright Professor David James Dr Jags Pandhal Dr. S. Ow Dr. J. Noirel Dr Narciso Couto