Transcript somatic hypermutation motifs in B cells

somatic hypermutation
motifs in B cells :
Data processing,
motif analysis and
graphical view
Hadas Shcolnik
Shira Hess
Project Advisors:
Prof. Ramit Mehr
Neta Zuckerman
Topics
•
•
•
•
•
Scientific background
Problem Overview
Project Goals
Solution Overview
Conclusions
Scientific
background
The Humoral Immune
Response
• Antigen (Ag): A molecule which elicits a specific immune response
when introduced into an animal.
• Immunoglobulin (Ig): Proteins that are used by the immune
system to identify and neutralize foreign objects.
Somatic Hypermutation (SHM)
• During the immune response, some B cells form clusters of rapidly
dividing cells called germinal centers (GC).
• During these divisions B cells hyper-mutate their variable DNA
regions that code for the Ig.
SHM Mechanism
Motifs
• Motifs are nucleotide patterns that are targets for the SHM
mechanism, and appear in the Ig variable region gene segment.
• Known motifs found in literature:
• The analysis of motifs in an Ig may aid in the understanding of
the SHM mechanism in normal individuals as well as in diseases of
GC B cells.
Lineage trees
• Describe the evolutionary history of B cells in the GC:
– Each son node differs from his parent node by one mutation.
– The root is the founder B cell, before mutations occur in its
variable regions.
Root
0
Split
Nodes
Trunk
1
2
4
Pass Through
Nodes
3
5
6
11
7
8
9
10
12
13
Leaves
IgTree©
• IgTree©, developed in the Mehr lab by Barak et al., constructs B cell
clonal lineage trees based on point mutations that the B cells
undergo during SHM in the GC.
IgTree© also counts the nucleotides upstream and downstream of
the mutation.
• The results are given in 12 output files: 6 of the GL nodes (as
control data) and 6 for the mutation nodes.
• Each file holds the counts of nucleotides around each base for each
flanking position (-3 to +3).
Problem
Overview
Automation
• The counts output files
of IgTree© are exported
to an excel sheet, for
further statistical
analysis,
that eventually reveals
the SHM motifs.
• This manual export is
inefficient and time
consuming.
Graphical view
• The final results, after they were processed in the excel sheet,
are presented in excel graphs.
• Disadvantages:
– Manual data processing to present the results.
– Inconvenient way to analyze the results in this view, e.g.
missing colors.
Additional Counts
• Additional data can be extracted from the sequence data of the
IgTree© program, and not only the motif finding.
• E.g. How many transition / transversion mutation occurred?
Project Goals
Project Goals
• Automate data analysis for finding motifs.
• Improve the graphical display of the motifs.
• Add new motif count features to IgTree© (existing program).
Solution
Overview
Pictogram - Automation
• The Pictogram program analyses the clonal sequences according
to the lineage tree as interpreted and constructed by IgTree©.
• It receives counts on the nucleotides flanking a mutation on both
sides (three positions on the left and right).
• It identifies mutational hotspot motifs, per tree.
Pictogram – Main Screen
Pictogram Graphs
• Positive - nucleotides that
are in excess around the
mutational spot.
• Negative - nucleotides that
are in paucity around the
mutational spot.
Significance Level
• Chi-square test is applied on the counts.
• The results are presented on the graph with asterisks:
* = p. value < 0.05
** = p. value < 0.005
*** = p. value < 0.0005
Significance Level – cont.
• The p. values of the graph are presented in a table.
• The significant values are marked in red.
Additional Counts
• Additional counts include:
– The number of mutations from each nucleotide in the sequence.
– The number of mutations of each type: transition, transversion.
– The percentage of mutations which were identified in reported
motif vs. the percentage of mutations which were not.
• The counts are presented in an additional text file.
Conclusions
Pictogram Motifs
• Pictogram© has become a part of the routine work in the lab since
it was first released.
• It is used to examine motifs in B-cell Lymphomas, autoimmune
diseases and normal responses.
• The Pictogram© graphs support the normal motif findings, as
reported in literature.
• Combined with proper experiments, new motifs (which are not
reported in literature) can be revealed by Pictogram© while known
motifs can be refuted.
• The Pictogram© may assist in discovering defects in the SHM
mechanism and aspects regarding disease development.
Additional Counts
• The Additional Counts where not yet used in the lab.
• The Counts will provide additional information regarding SHM in
disease versus normal responses.
Questions???