Transcript Projectpresentation_LifeTech_finalx

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, 2011. 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 nonantibody recombinant
proteins Avonex and
Rebif
Ecker, Jones, and Levine. 2015. The therapeutic
monoclonal antibody market. MAbs. 7(1):9-14
• Great commercial interest drives the development of
antibody expression platforms but product yields have
been modest in many cases.
• Native human plasma cells are optimized by evolution
to secrete large amounts of fully functional antibodies.
What can we learn
from professional
secretory cells for
reprogramming of
baker’s yeast?
• 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
• Obtaining quantitative date of gene expression
• Transcriptomics
• Proteomics
• Data sets available at http://www.ncbi.nlm.nih.gov/gds
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 upregulated under certain conditions, an enrichment
analysis will find which GO terms are overrepresented (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
microarray
1000 genes
experiment
100 genes
differentially
regulated
Biological process
Genes on array
# genes expected
in 100 randomly
sampled genes
Detected
Mitosis
800/1000
80
80
Apoptosis
400/1000
40
40
Cell cycle control
100/1000
10
30
• 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