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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???