(228dr) Lipidomic and Transcriptomic Biomarkers for Diagnosis of Nonalcoholic Fatty Liver Diseases

In humans, accumulation of fat in the liver and the associated inflammation, over many years, can results in what is referred to as nonalcoholic fatty liver diseases (NAFLD). NAFLD consists of three stages, namely, steatosis, (nonalcoholic) steatohepatitis (or NASH) and cirrhosis. Steatosis is characterized by accumulation of fat and some inflammation and can be reversed (to normality) by controlling diet and lifestyle changes. NASH is characterized by substantial inflammation and may or may not be reversible in which case it progresses to cirrhosis or liver cancer. Since progression of NAFLD may be reversible during the steatosis and NASH stages, it is important to be able to accurately diagnose the stage of NAFLD. With that objective, in this work, we have developed a two-stage linear discriminant analysis (LDA) based approach to identify lipidomic and transcriptomic biomarkers to identify (classify) among people at distinct stages of NAFLD and normal people (Gorden et al., 2015).

Using lipidomic and transcriptomic data from about 90 human liver-tissue samples, we developed a LDA-based classifier. We found that about 75-85 variables (genes or lipids) are needed to accurately classify among all the four classes/conditions (normal, steatosis, NASH and cirrhosis) whereas about 20 genes or lipids were enough to classify between steatosis and NASH samples. These findings are in contrast to our initial expectation to be able to find a small number of biomarkers, for example, 5 or less genes or lipids, for accurate classification. Two likely reasons for the need of a larger number of biomarkers for complete diagnosis are: (1) relatively much larger variability in the measurements in tissue-level samples as compared to that in cell culture, and (2) the complex and systems-level nature of the disease. Towards the latter possibility, we carried out KEGG pathway and GO-term enrichment of the genes differentially regulated among the four classes or between steatosis and NASH. We found that pathways such as focal adhesion and ECM-receptor interaction were enriched in NASH vs. steatosis comparison suggesting increased inflammation during the progression from steatosis to NASH. Fibrosis pathway and collagen-related genes were enriched in the overall comparison, indicating their role during the late stage NASH and cirrhosis. Using PLINK, we also analyzed single nucleotide polymorphism (SNP) data from the samples. The well-known SNP marker in PNPLA3, rs738409, showed a strong statistical significance with a p-value of 1.7E-6 between normal and cirrhosis samples, but not between NASH and steatosis samples. We are continuing further integrated analysis of the gene-expression and SNP data. Overall, we conclude that progression of NAFLD involves many genes, lipids and pathways and only a systems-based classification and analysis approach will lead to accurate/reproducible diagnosis and mechanistic understanding of NAFLD.

Acknowledgements:This study were primarily supported by the National Institutes of Health (NIH) glue grant GM U54069338 to the LIPID MAPS consortium. We would also like to acknowledge the National Science Foundation (NSF) collaborative grant STC-0939370, and NIH R01 grants HL087375-02, HL106579 and HL108735.

References:

Gorden, D. L., D. S. Myers, P. T. Ivanova, E. Fahy, M. R. Maurya, S. Gupta, J. Min, N. J. Spann, J. G. McDonald, S. L. Kelly, J. Duan, M. C. Sullards, T. J. Leiker, R. M. Barkley, O. Quehenberger, A. M. Armando, S. B. Milne, T. P. Mathews, M. D. Armstrong, C. Li, W. V. Melvin, R. H. Clements, M. K. Washington, A. M. Mendonsa, J. L. Witztum, Z. Guan, C. K. Glass, R. C. Murphy, E. A. Dennis, A. H. Merrill, Jr., D. W. Russell, S. Subramaniam and H. A. Brown, â??Biomarkers of NAFLD Progression: A Lipidomics Approach to an Epidemicâ?, Journal of Lipid Research, 56(3), 722-736, 2015.

Corresponding author: Shankar Subramaniam, Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0412, Phone: (858) 822-0986, E-mail: shankar@ucsd.edu.