Research Article: Dynamic modelling of an ACADS genotype in fatty acid oxidation – Application of cellular models for the analysis of common genetic variants

Date Published: May 23, 2019

Publisher: Public Library of Science

Author(s): Kerstin Matejka, Ferdinand Stückler, Michael Salomon, Regina Ensenauer, Eva Reischl, Lena Hoerburger, Harald Grallert, Gabi Kastenmüller, Annette Peters, Hannelore Daniel, Jan Krumsiek, Fabian J. Theis, Hans Hauner, Helmut Laumen, Ornit Chiba-Falek.


Genome-wide association studies of common diseases or metabolite quantitative traits often identify common variants of small effect size, which may contribute to phenotypes by modulation of gene expression. Thus, there is growing demand for cellular models enabling to assess the impact of gene regulatory variants with moderate effects on gene expression. Mitochondrial fatty acid oxidation is an important energy metabolism pathway. Common noncoding acyl-CoA dehydrogenase short chain (ACADS) gene variants are associated with plasma C4-acylcarnitine levels and allele-specific modulation of ACADS expression may contribute to the observed phenotype.

We assessed ACADS expression and intracellular acylcarnitine levels in human lymphoblastoid cell lines (LCL) genotyped for a common ACADS variant associated with plasma C4-acylcarnitine and found a significant genotype-dependent decrease of ACADS mRNA and protein. Next, we modelled gradual decrease of ACADS expression using a tetracycline-regulated shRNA-knockdown of ACADS in Huh7 hepatocytes, a cell line with high fatty acid oxidation-(FAO)-capacity. Assessing acylcarnitine flux in both models, we found increased C4-acylcarnitine levels with decreased ACADS expression levels. Moreover, assessing time-dependent changes of acylcarnitine levels in shRNA-hepatocytes with altered ACADS expression levels revealed an unexpected effect on long- and medium-chain fatty acid intermediates.

Both, genotyped LCL and regulated shRNA-knockdown are valuable tools to model moderate, gradual gene-regulatory effects of common variants on cellular phenotypes. Decreasing ACADS expression levels modulate short and surprisingly also long/medium chain acylcarnitines, and may contribute to increased plasma acylcarnitine levels.

Partial Text

Genome-wide association studies (GWAS) identified thousands of variants associated with diverse diseases [1]. Although inborn errors of metabolism provided numerous examples how genetics associates with metabolic traits [2], the mechanistic impact of common gene variants, often resulting from a mixture of related processes such as environmental exposures and identified loci [1,3,4], remains challenging. Expression and metabolic quantitative trait loci (eQTL, mQTL) can assist the identification of the underlying biological mechanisms that link a genotype to a phenotype, but this work requires proper cell models with the observed genetic background. However, the availability of human cell models for elucidating the functional role of common gene variants in human disease is limited [5].

Defining the mechanistic basis of how common gene variants affect QTLs or disease states can be difficult and demanding. Moreover, GWAS-inferred variants are frequently noncoding and suggested to affect the expression level of nearby or other genes. Population-based mQTL analysis identified altered plasma levels of C4-acylcarnitine levels associated with a common ACADS locus [10–14] in high linkage with previously described mutations [27,28]. ACADS codes for an enzyme involved in mitochondrial FAO, an important energy metabolism pathway, and these ACADS locus variants may affect the rate of FAO. Here, we selected the ACADS locus for assessing this association at the cellular and molecular level using two cell models, donor-specific genotyped LCLs and an inducible lentiviral hepatocyte knockout cell line. Combining these two cell models allowed the generation of a data set enabling an in vitro assessment on how a gradual, genotype-specific modulation of gene-expression contributes to the FAO pathway and phenotypes.




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