Mel Reichman on Pool Shark’s Cues for More Efficient Drug Discovery
Epigenetics_Poster_Nafisah.v1
1. An Integrative Approach to Reveal Signaling Information from HIV cell-to-cell contact
Nafisah Islam1,2,3, Eunju Park3,4,5, Mark Horswill3,4,5, James Bruce3,4,5, Alicia Richards6, Gregory Potts6, Alexander Hebert6, Thevaa
Chandereng2,3, Joshua Coon6,7, Nathan Sherer4,5, Paul Ahlquist3,4,5,8, Anthony Gitter1,2,3
1Department of Computer Sciences and 2Department of Biostatistics and Medical Informatics, 3Morgridge Institute for Research, 4McArdle Laboratory for Cancer Research, 5Institute
for Molecular Virology, 6Depts. of Chemistry, 7Biomolecular Chemistry, 8Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, WI
Systems Biology Integrative Approach
Protein Phosphorylation (PP)
• Rapid activation, deactivation and modification of proteins
Gene Expression (GE)
• Genetic instructions synthesize gene products
Protein abundance (PA)
• Relative abundance under different cellular conditions
Background
• Cell-cell interaction transmits HIV-infection
• Infected/donor cell (Jurkat)
• Uninfected/target cell (SupT1)
• Collaboration with Ahlquist lab
Computational Approach
Statistical Analysis
• Extracting the most
significantly changing
genes from each of the
datasets based on
hypothesis testing
Network Modeling
• Integration of significant
genes found from
statistical analyses with a
background network to
reveal signaling pathways
Interpreting Results
• Validating and
interpreting resulting
signaling molecules and
pathways
• Experimental testing in
the Ahlquist lab
Expected Results
• Ensemble of networks where significant proteins are either connected directly or
via intermediate nodes
• Intermediate Steiner nodes are not experimental hits, but predicted to connect
other important proteins
• Networks will reveal novel components in signaling pathways
• Host signaling information could help inform drug discovery
References
1. Alvarez, RA., et al.
"Unique features of
HIV-1 spread through T
cell virological
synapses." PLoS
pathogens 10.12
(2014).
2. Tuncbag, Nurcan, et al.
"Network-Based
Interpretation of
Diverse High-
Throughput Datasets
through the Omics
Integrator Software
Package." PLoS
Comput Biol 12.4
(2016): e1004879.
Integration of Information and Network Construction
PP
GE
PA
From [1]
Acknowledgement
We acknowledge funding
from the Morgridge
Institute for Research.
Garnet Module
• Uses epigenetic data from open chromatin
experiments (public data)
• Predicts candidate transcription factors (TFs) that
are likely to influence gene expression changes
• Input: gene expression data
• Output: candidate TF with score
Forest Module
• Generates interaction network connecting a
subset of user defined omic data hits
• Uses Prize-collecting Steiner forest algorithm
• Input:
o TFs from Garnet (triangle nodes)
o Significantly phosphorylated proteins (round
nodes)
o A confidence-weighted interactome from
literature (background network)
• Output:
o An ensemble of networks
From [2]
Env
CD4
Cell surface
Phosphoproteomic hit
Transcription factor hit
Steiner node
Known, relevant interaction Unknown, relevant interaction
No change
Our Analysis Reveals Important Env neighbors
Phosphoproteomics
Steiner Nodes
Phosphoproteomics connected to Env
Steiner nodes connected to Env
![An Integrative Approach to Reveal Signaling Information from HIV cell-to-cell contact
Nafisah Islam1,2,3, Eunju Park3,4,5, Mark Horswill3,4,5, James Bruce3,4,5, Alicia Richards6, Gregory Potts6, Alexander Hebert6, Thevaa
Chandereng2,3, Joshua Coon6,7, Nathan Sherer4,5, Paul Ahlquist3,4,5,8, Anthony Gitter1,2,3
1Department of Computer Sciences and 2Department of Biostatistics and Medical Informatics, 3Morgridge Institute for Research, 4McArdle Laboratory for Cancer Research, 5Institute
for Molecular Virology, 6Depts. of Chemistry, 7Biomolecular Chemistry, 8Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, WI
Systems Biology Integrative Approach
Protein Phosphorylation (PP)
• Rapid activation, deactivation and modification of proteins
Gene Expression (GE)
• Genetic instructions synthesize gene products
Protein abundance (PA)
• Relative abundance under different cellular conditions
Background
• Cell-cell interaction transmits HIV-infection
• Infected/donor cell (Jurkat)
• Uninfected/target cell (SupT1)
• Collaboration with Ahlquist lab
Computational Approach
Statistical Analysis
• Extracting the most
significantly changing
genes from each of the
datasets based on
hypothesis testing
Network Modeling
• Integration of significant
genes found from
statistical analyses with a
background network to
reveal signaling pathways
Interpreting Results
• Validating and
interpreting resulting
signaling molecules and
pathways
• Experimental testing in
the Ahlquist lab
Expected Results
• Ensemble of networks where significant proteins are either connected directly or
via intermediate nodes
• Intermediate Steiner nodes are not experimental hits, but predicted to connect
other important proteins
• Networks will reveal novel components in signaling pathways
• Host signaling information could help inform drug discovery
References
1. Alvarez, RA., et al.
"Unique features of
HIV-1 spread through T
cell virological
synapses." PLoS
pathogens 10.12
(2014).
2. Tuncbag, Nurcan, et al.
"Network-Based
Interpretation of
Diverse High-
Throughput Datasets
through the Omics
Integrator Software
Package." PLoS
Comput Biol 12.4
(2016): e1004879.
Integration of Information and Network Construction
PP
GE
PA
From [1]
Acknowledgement
We acknowledge funding
from the Morgridge
Institute for Research.
Garnet Module
• Uses epigenetic data from open chromatin
experiments (public data)
• Predicts candidate transcription factors (TFs) that
are likely to influence gene expression changes
• Input: gene expression data
• Output: candidate TF with score
Forest Module
• Generates interaction network connecting a
subset of user defined omic data hits
• Uses Prize-collecting Steiner forest algorithm
• Input:
o TFs from Garnet (triangle nodes)
o Significantly phosphorylated proteins (round
nodes)
o A confidence-weighted interactome from
literature (background network)
• Output:
o An ensemble of networks
From [2]
Env
CD4
Cell surface
Phosphoproteomic hit
Transcription factor hit
Steiner node
Known, relevant interaction Unknown, relevant interaction
No change
Our Analysis Reveals Important Env neighbors
Phosphoproteomics
Steiner Nodes
Phosphoproteomics connected to Env
Steiner nodes connected to Env](https://image.slidesharecdn.com/3c188498-e74b-4fbf-8a60-f5e078e98b15-161018133940/85/Epigenetics_Poster_Nafisah-v1-1-320.jpg)
