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Bioinformatics. 2019 Feb 14. pii: btz114. doi: 10.1093/bioinformatics/btz114. [Epub ahead of print]

Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease.

Author information

1
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
2
Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
3
Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI, USA.
4
Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
5
Department of Medicine, Translational-Clinical Research, University of Pennsylvania, Philadelphia, PA, USA.
6
Department of Family Medicine and Public Health, University of California at San Diego, San Diego, CA, USA.
7
Department of Internal Medicine, University of Texas Health at San Antonio, San Antonio, TX, USA.
8
Department of Internal Medicine, University of Illinois at Chicago, Chicago, IL, USA.
9
Department of Internal Medicine, Case-Western Reserve University, Cleveland, OH, USA.
10
Department of Epidemiology, Tulane University School of Medicine, Tulane University, New Orleans, LA, USA.
11
School of Medicine, Division of Nephrology and Hypertension, Tulane University, New Orleans, LA, USA.
12
Department of Internal Medicine, Johns Hopkins University, Baltimore, MD, USA.
13
Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA.
14
Department of Internal Medicine, Temple University, Philadelphia, PA, USA.
15
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA.
16
Department of Statistics and the Informatics Institute, University of Florida, Gainesville, FL, USA.
17
Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA.

Abstract

MOTIVATION:

Functional enrichment testing methods can reduce data comprising hundreds of altered biomolecules to smaller sets of altered biological 'concepts' that help generate testable hypotheses. This study leveraged differential network enrichment analysis methodology to identify and validate lipid subnetworks that potentially differentiate chronic kidney disease (CKD) by severity or progression.

RESULTS:

We built a partial correlation interaction network, identified highly connected network components, applied network-based gene-set analysis to identify differentially enriched subnetworks, and compared the subnetworks in patients with early-stage versus late-stage CKD. We identified two subnetworks 'triacylglycerols' and 'cardiolipins-phosphatidylethanolamines (CL-PE)' characterized by lower connectivity, and a higher abundance of longer polyunsaturated triacylglycerols in patients with severe CKD (stage ≥4) from the Clinical Phenotyping Resource and Biobank Core. These finding were replicated in an independent cohort, the Chronic Renal Insufficiency Cohort. Using an innovative method for elucidating biological alterations in lipid networks, we demonstrated alterations in triacylglycerols and cardiolipins-phosphatidylethanolamines that precede the clinical outcome of end-stage kidney disease by several years.

AVAILABILITY AND IMPLEMENTATION:

A complete list of NetGSA results in HTML format can be found at http://metscape.ncibi.org/netgsa/12345-022118/cric_cprobe/022118/results_cric_cprobe/main.html. The DNEA is freely available at https://github.com/wiggie/DNEA. Java wrapper leveraging the cytoscape.js framework is available at http://js.cytoscape.org.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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