Nature as blueprint to design antibody factories Life Science Technologies Project course 2016 Aalto CHEM

Project background Antibodies are complex, tetrameric proteins that need a special environment for proper assembly Braakman and Bulleid, Protein folding and modification in the mammalian endoplasmic reticulum. Annu Rev Biochem. 80:71-99.

Annual sales of monoclonal antibody products Annual sales of the top six selling monoclonal antibodies compared to the non- antibody recombinant proteins Avonex and Rebif Great commercial interest drives the development of antibody expression platforms but product yields have been modest in many cases. Ecker, Jones, and Levine The therapeutic monoclonal antibody market. MAbs. 7(1):9-14

Native human plasma cells are optimized by evolution to secrete large amounts of fully functional antibodies. The target of this project is to find the enriched GO terms in plasma cells and the genes comprising those terms to study the effects of those genes on antibody secretion in S. cerevisiae What can we learn from professional secretory cells for reprogramming of baker’s yeast?

Obtaining quantitative date of gene expression Transcriptomics Proteomics Data sets available at

Alternative I: activated macrophages Cytokine production is up-regulated after stimulus with LPS TNF is secreted through a constitutive secretion pathway Starting point: Schott et al., 2014 Data sets: GSE52449

Alternative II: pancreatic beta- cell differentiation Insulin is secreted via regulated exocytosis Glucose up-take induces insulin release

Beta-cell differentiation DataSet Record: GSE61714

GO Enrichment Analysis One of the main uses of the gene ontology (GO) is to perform enrichment analysis on gene sets. For example, given a set of genes that are up- regulated under certain conditions, an enrichment analysis will find which GO terms are over- represented (or under-represented) using annotations for that gene set.

GO structure This means genes can be grouped according to user-defined levels Allows broad overview of gene set or genome

GO structure GO terms divided into three parts: cellular component molecular function biological process

GO for microarray analysis microarray 1000 genes microarray experiment 100 genes differentially regulated Biological processGenes on array# genes expected in 100 randomly sampled genes Detected Mitosis800/ Apoptosis400/ Cell cycle control100/ cell proliferation actually contains more differentially regulated genes than you would expect by chance statistical test needed to see if this overrepresentation or enrichment of a certain class is statistically significant

Project organization and deliverables Part I: Getting familiar with the experimental question Organization and distribution of work between subgroups A and B It is also feasible to conduct only Part I and Part II of the project without experimental work. Deliverable: A project plan Part II: Computational work, performed by subgroup A The processing and analyzing of data will be conducted in R software environment (R Core Team, 2014). All necessary data sets are available in the Gene Expression Omnibus (GEO) database maintained by the National Center for Biotechnology Information (NCBI) (Edgar et al., 2002). Subgroups A and B analyze the obtained data and decide together which genetic targets will be tested in vivo Deliverable: Report I should include the Methods used for the data analysis and the obtained results of the computational analysis. In addition, the report should point out the target genes for part III and the justification for selecting them in the context of the biological function. Part III: Laboratory experiments Four genes will be chosen. The selected genes will be either deleted from the genome and / or overexpressed. The effects of the modifications will be analyzed using an antibody secreting S. cerevisiae strain. Part III can be conducted also during summer. Deliverable: Report II should include the methods and results of the laboratory experiments. In addition, the report should contain the summary of the complete project.

Open points: 2 groups working on separate systems? How to organize coaching session, how often? where? Laboratory experiments Not a basic lab training course, largely independent working