1. Emily Pachunka ● Spring 2017
Gateway node analysis of gene expression in the diauxic shift of Saccharomyces cerevisiae
Emily Pachunka ● Spring 2017

2. Motivation
DNA
RNA
Protein
Function
RNA sequences
DNA sequences
gene expression
protein sequences
protein structure
PPI and metabolomics
Epigenetics
High-throughput assays result in large volumes of biological data
Many data remain unanalyzed:
Lack of user-friendly and efficient software
Lack of standard protocol for data modeling and pattern discovery
Network modeling is a popular approach

3. Network Modeling Network  graph of nodes and edges
Nodes represent some object/entity
Edges represent relationships
Correlation network
Nodes  genes
Edges  correlations or co-expressions
Incorporate principles of graph theory for analysis and pattern recognition A C E D B G F H

4. Pairwise Correlation NetworksID
Replicate 1
Replicate 2
Replicate 3 A
1.00
3.00
6.00 B
2.00
4.00
7.00 C B A C

5. Methods – data validation
Scale-free topology
Few nodes with high degree,
and many nodes with low degree
Random topology
Many nodes with average degree,
and few nodes with high/low degree
Power-law degree distribution
Bell-curved degree distribution

6. Research Aims The goal:
Gateway node analysis
Uses correlation networks
Prediction tool
Gateway node  gene predicted to be co-regulated in two distinct cellular states
Biomedical and research applications
Dempsey K., Ali H. (2014).
The goal:
Strengthen the validity of gateway node analysis as a prediction tool

7. Yeast Quiescence Saccharomyces cerevisiae  yeast Quiescent = dormancy
Induced by cell stress
Low-nutrient environments
Nonquiescent = active
Well-studied cellular state/process
Literature-supported genes involved in cell quiescence

8. Methodology Overview

9. Methods – data collection
Gene Expression Omnibus (
Example queries:
“quiescent”
“nonquiescent”
“G0”
“Diauxic shift”
“Stationary”
“Relaxed”
Criteria:
Saccharomyces cerevisiae
WT strains only
At least 3 replicates
GSE8559  S288C quiescent and nonquiescent
GSE8542  BY4742 quiescent and nonquiescent
GSE55508  Time series: t0, t1, t2, and t3

10. Methods – network creation and validation✓
Network creation:
Pairwise Pearson correlation (⍴ <= | |, p-val <0.05)
Visualized networks using Cytoscape
Duplicate edges and self loops removed
Network validation:
Examined degree distribution in R
Kept if degree distribution followed power law

11. Methods – data validation
Data sets:
GSE8559  S288C quiescent and nonquiescent (10 replicates each)
GSE8542  BY4742 quiescent and nonquiescent (10 replicates each)
GSE55508  Time series: t0, t1, t2, and t3 (3 replicates each)
Four networks:
S288C_quiescent
S288C_nonquiescent
BY4742_quiescent
BY4742_nonquiscent

12. Methods - clustering
Clustering  finding dense groups of genes within the network
MCODE (in Cytoscape)
Performed for each network (4 total)
Kept clusters with density above 80%
Kept network interactions within these clusters
WCGNA (in R) – Unable to extract clusters A C E D B G F H

13. Methods – gateway node analysis
Gateway node  gene that connects clusters of each cellular state
Create networks from MCODE clusters
Merge nonquiescent and quiescent networks
Identify those genes that connect clusters from nonquiescent and quiescent states
Key:
= nonquiescent
= gateway node
= quiescent

14. Methods – gateway node analysis BY4742
Key:
= nonquiescent
= gateway node
= quiescent

15. Methods – gateway node analysis S288C
Key:
= nonquiescent
= gateway node
= quiescent

16. Results
BY4742:
SNZ1  member of stationary phase- induced gene family
S288C:
HSP104  heat shock protein; responsive to stresses
FES1  heat shock protein exchange factor
HSP150  required for cell wall stability

17. Gateway node analysis could be used as a “first-step” modeling tool
Discussion
Predicted several genes involved in cellular shift from nonquiescent to quiescent state
Acquired a better understanding of GEO data sets, clustering algorithms, etc
Future Directions:
Perform gene ontology enrichment analysis
Assess differences in gateway nodes predicted by altering threholds
Apply the methodology to other model pathways/organisms
Gateway node analysis could be used as a “first-step” modeling tool

18. Bibliography
1. Dempsey K., Ali H. (2014). Identifying aging-related genes in mouse hippocampus using gateway nodes. BMC Systems Biology 8. Available at Accessed September 23,
2. Pavlopoulous G., Secrier M., Moschopoulos C., Soldatos T., Kossida S., Aerts J., Schneider R., Bagos P. (2011). “Using graph theory to analyze biological networks.” BioMed Central 4(10). Available at Accessed February 8, 2016
3. Horvath, S. (2011). Weighted network analysis: Applications in genomics and systems biology. New York: Springer.
4. Gray J., Petsko G., Johnston G., Ringe D., Singer R., Werner-Washburne M. (2004). “Sleeping beauty: Quiescence in Saccharomyces cerevisiae.” Microbiology and Molecular Biology Reviews 68:
5. Bader G., Hogue C. (2003). An automated method for finding molecular complexes in large protein interaction networks. BCM Bioinformatics 4. Available at Accessed September 24, 2015.
6. Langfelder P., Horvath S. (2008). “WGCNA: an R package for weighted correlation network analysis.” BMC Bioinformatics 9 (559). Available

19. Thank you! Any questions or comments?