Genetic Pathways in Nonalcoholic Fatty Liver Disease: Insights From Systems Biology

Abstract

Nonalcoholic fatty liver disease (NAFLD) represents a burgeoning worldwide epidemic whose etiology reflects multiple interactions between environmental and genetic factors. Here, we review the major pathways and dominant genetic modifiers known to be relevant players in human NAFLD and which may determine key components of the heritability of distinctive disease traits including steatosis and fibrosis. In addition, we have employed general assumptions which are based on known genetic factors in NAFLD to build a systems biology prediction model that includes functional enrichment. This prediction model highlights additional complementary pathways that represent plausible intersecting signaling networks that we define here as an NAFLD‐Reactome. We review the evidence connecting variants in each of the major known genetic modifiers (variants in patatin‐like phospholipase domain containing 3, transmembrane 6 superfamily member 2, membrane‐bound O‐acyltransferase domain containing 7, glucokinase regulator, and hydroxysteroid 17‐beta dehydrogenase 13) to NAFLD and expand the associated underlying mechanisms using functional enrichment predictions, based on both preclinical and cell‐based experimental findings. These major candidate gene variants function in distinct pathways, including substrate delivery for de novo lipogenesis; mitochondrial energy use; lipid droplet assembly, lipolytic catabolism, and fatty acid compartmentalization; and very low‐density lipoprotein assembly and secretion. The NAFLD‐Reactome model expands these pathways and allows for hypothesis testing, as well as serving as a discovery platform for druggable targets across multiple pathways that promote NAFLD development and influence several progressive outcomes. In conclusion, we summarize the strengths and weaknesses of studies implicating selected variants in the pathophysiology of NAFLD and highlight opportunities for future clinical research and pharmacologic intervention, as well as the implications for clinical practice.

Diagnostic and interventional circulating biomarkers in nonalcoholic steatohepatitis

“…Genomics data can potentially be used to assess risk for fibrosis progression and response to therapy and is likely to enter the clinical arena in the future. [76][77][78]83 Proteomics data have shown potential to differentiate NAFL from NASH, whereas lipidomics, metabolomics and the gut microbiome assessments have also been helpful in distinguishing stages of fibrosis in NASH. 65,102,103,108,109 These 'omics' approaches require further validation in larger, more heterogeneous cohorts before they can be considered for use in clinical practice.…”

Precision medicine in nonalcoholic fatty liver disease: New therapeutic insights from genetics and systems biology

“…Extant studies on the genetic component of NAFLD indicate that, after 12 years following the discovery that an allele in a patatin-like phospholipase domain containing 3 (PNPLA3) variant (rs738409 [G], encoding 148M) was associated with increased hepatic fat 17 and NAFLD disease severity, 18 knowledge of the disease heritability is still incomplete. 3,4,12 The correlation between rs738409 and the risk of developing fatty liver, NASH, and fibrosis is perhaps one of the strongest worldwide-replicated effects for a common variant modifying the individual susceptibility of NAFLD and NASH (explaining ~5.3% of the total variance). 3,4,18,19 Indeed, available evidence indicates that homozygous carriers with the G-risk allele of rs738409 present 3.24-fold greater risk of higher liver necroinflammatory scores and 3.2-fold greater risk of developing fibrosis when compared with homozygous CC carriers.…”

“…The protein encoded by PNPLA3 is a triacylglycerol lipase that mediates triacylglycerol hydrolysis, mostly in adipocytes. 12 The encoded protein, which appears to be membrane-bound, may be involved in the energy usage/storage balance in adipocytes. 12 Figure 3A shows area under the receiver operating characteristics (AUROC; 0.951) for predicted functional processes linked to PNPLA3, in- 57 Tables show the top 10 predictions (the number provided in the first column).…”

“…12 The encoded protein, which appears to be membrane-bound, may be involved in the energy usage/storage balance in adipocytes. 12 Figure 3A shows area under the receiver operating characteristics (AUROC; 0.951) for predicted functional processes linked to PNPLA3, in- 57 Tables show the top 10 predictions (the number provided in the first column). Table explanation: If a gene (gene set) shares high correlation with known members of a gene set, it is assigned a high z-score.…”

“…11 Likewise, it has been hypothesized that, as data on the candidate drugs are not only insufficient but are also not corroborated by genetic inactivation, pharmacological inhibition, antisense oligonucleotides, and/or small interfering RNAs, this poses an additional obstacle to achieve consistent and sustained effects on severe histological outcomes, including improvement in fibrosis scores. 11 The aforementioned pitfalls could potentially be overcome through systems biology analysis, aiming to integrate knowledge of signaling pathways, 12 the genetic information of susceptibility genes, 4 and multiple tissue-specific OMICs-related experiments that include large-scale transcriptomic, proteomic, and metabolomic profiles, 13,14 and more recently metagenomics of the liver tissue. 15 Furthermore, the approach founded on precision medicine is expected to enhance the effectiveness of novel therapies, including elucidation of predictors of drug response.…”