Background

Identifying a predictive gene signature and signaling networks associated with acute kidney injury using

gene co-expression modules Mohamed Diwan M. AbdulHameed,1* Danielle L. Ippolito,2 Jonathan D. Stallings,2 and Anders Wallqvist1

*mabdulhameed@bhsai.org

301-619-1304 www.bhsai.org 1Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and

Materiel Command, Fort Detrick, MD, USA; and 2Environmental Health, U.S. Army Center for Environmental Health Research, Fort Detrick, MD, USA

Acknowledgements: This work was supported by the Military Operational Medicine Research Program and the U.S. Army’s Network Science Initiative, U.S. Army Medical Research and Materiel Command (Ft. Detrick, MD).

• Acute kidney injury (AKI) is a serious clinical condition associated with high morbidity and mortality rates

• There is a need for new AKI biomarkers - current markers such as serum creatinine has many limitations and fails to diagnose the injury at early stages

Objectives • Utilize toxicogenomics and systems approaches to identify co-expressed genes

(gene modules) associated with AKI • Explore the application of co-expression modules to identify a predictive

signature of kidney injury as well as obtain mechanistic insights into the disease

Approach1,2 AKI-relevant module clusters

Application

Conclusions

Havcr1 - frequently co-expressed genes

• We identified AKI-relevant co-expression modules and used it to develop a predictive gene signature

• Co-expression modules enabled us to characterize molecular mechanisms involved in AKI and identify new mechanism-based biomarker candidates

Predictive gene signature AKI-relevant sub-network (AKI-SN)

Disclaimer: The opinions and assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the U.S. Army or of the U.S. Department of Defense. This poster has been approved for public release with unlimited distribution.

BHSAI

• MC7 and MC11 prioritized as AKI-relevant module cluster and genes in this module set were chosen for further analysis

1AbdulHameed et al. (2016) (Submitted); 2Tawa et al. (2014) PLOS ONE 9(9): e107230; 3DrugMatrix. National Institute of Environmental Health Sciences. https://ntp.niehs.nih.gov/drugmatrix/index.html; 4Csardi et al. (2010) Bioinformatics 26(10): 1376-1377.

Module clusters - MC7 and MC11 specifically activated in AKI and maps known genes such as Havcr1 and Clu

Activation score

• Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment captured pathways known to be involved in AKI such as p53 signaling, cell adhesion, and focal adhesion

• We mapped the AKI-relevant gene set to high confidence human protein-protein interaction (PPI) network5,6 and extracted the connected component as the AKI-relevant sub-network (AKI-SN)

• We computed key network properties of AKI-SN and identified critical components of the network such as hubs, high traffic nodes, and highly inter-connected regions

• Our network analysis identified the involvement of immunoproteasomes in AKI

Hubs: includes genes such as ISG15 not previously associated with AKI

High traffic nodes: CLU, CD44, and GSN

• We utilized the AKI-relevant modules to identify a robust gene set that frequently co-express with Havcr1. It is statistically different from random and not affected by excluding 5% of the data

• This set shows positive correlation (r2=0.72) with external ischemic kidney injury data (GSE58438) obtained from Gene Expression Omnibus

• In this gene set, CD44 is a potential non-invasive biomarker candidate as it is up-regulated during AKI, undergoes cleavage of its ectodomain, and is secreted in urine

AKI-relevant co-expression

modules

DrugMatrix3

•Tissue: Kidney; Chip: Affymetrix rat2302 •220 chemical exposure experiments •9,222 genes

Exhaustive gene module generation

Iterative Signature Algorithm (ISA)4

Modules

Module clustering

Prioritization 1) Activation specificity 2) Enrichment of known genes

Applications 1) Development of predictive gene signature 2) Pathway and network analysis 3) Identification of frequently co-expressed genes

with known biomarkers

• Modules are set of genes that are co-expressed under specific disease conditions, e.g., AKI • We generated 137 gene modules from DrugMatrix using the ISA bi-clustering approach • We clustered the modules based on the overlap of genes and chemical exposures into 16 clusters

(MC1-16)

Kidney injury phenotypes

Non-kidney injury phenotypes

0 5

AKI-relevant pathways

• We utilized the AKI-relevant gene set along with random forest approach and developed a gene signature consisting of 30 genes, allowing for earlier predictions of kidney injury

• The signature includes known genes such as Havcr1, as well as several novel genes such as Guaca2a, Ly96, and Irf6

Highly interconnected region in AKI-SN: Immunoproteasomes

IRF1 transcription factor known to up-regulate immunoproteasomes

5Yu et al. (2012) BMC Bioinformatics, 13:79; 6AbdulHameed et al. (2014) PLOS ONE 9(11): e112193.

True positive rate

False positive rate