Non-alcoholic fatty liver disease (NAFLD) Cell Models for Research

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an estimated global prevalence of 25% (WHO, 2023). It encompasses a spectrum from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). The incidence of NAFLD is rising in parallel with obesity and type 2 diabetes epidemics. NAFLD is a leading cause of liver-related mortality, and cardiovascular disease is a major cause of death in these patients. The 5-year survival for NAFLD-related cirrhosis is approximately 75%, but drops to 20% once HCC develops (NCI, 2023). Key risk factors include obesity, insulin resistance, dyslipidemia, and metabolic syndrome.

NAFLD is an ideal model for mechanistic studies due to its complex pathophysiology involving lipid metabolism, inflammation, oxidative stress, and genetic susceptibility. Public datasets such as the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) provide extensive transcriptomic and genomic data from NAFLD and NASH patients. Open questions include the molecular drivers of disease progression, the role of genetic variants (e.g., PNPLA3, TM6SF2), and the identification of therapeutic targets. Gene-edited cell models enable precise dissection of these pathways.

NAFLD progression involves multiple interconnected pathways:

• • Lipotoxicity: Accumulation of free fatty acids and diacylglycerols leads to endoplasmic reticulum (ER) stress and mitochondrial dysfunction.
• • Inflammation: Activation of Kupffer cells and hepatic stellate cells via NF-κB and JNK pathways promotes NASH.
• • Fibrosis: TGF-β signaling activates hepatic stellate cells, leading to extracellular matrix deposition.
• • Hepatocarcinogenesis: Chronic inflammation and oxidative stress cause DNA damage, activating oncogenic pathways (e.g., Wnt/β-catenin, PI3K/AKT) and inactivating tumor suppressors (e.g., TP53).

Data from TCGA and COSMIC databases.

Key signaling networks in NAFLD:

• • Insulin/IGF-1 signaling: Insulin resistance leads to increased lipolysis and de novo lipogenesis via SREBP-1c.
• • Wnt/β-catenin pathway: Aberrant activation promotes hepatocyte proliferation and HCC.
• • MAPK/ERK pathway: Activated by growth factors and cytokines, driving inflammation and fibrosis.
• • PI3K/AKT/mTOR pathway: Hyperactivation promotes cell survival and proliferation.
• • NF-κB pathway: Central to inflammatory response, linking steatosis to NASH.

Organoids derived from primary human hepatocytes or liver biopsies recapitulate 3D architecture and can be used for drug testing and disease modeling.

Animal models for NAFLD:

• • High-fat diet (HFD) models: Mice fed a high-fat diet develop steatosis and mild inflammation.
• • Methionine-choline-deficient (MCD) diet: Induces rapid NASH with fibrosis.
• • Genetic models: ob/ob (leptin-deficient) and db/db (leptin receptor-deficient) mice develop obesity and steatosis.
• • Patient-derived xenografts (PDX): Used for HCC, but limited for NAFLD.
• • Genetically engineered mouse models (GEMM): Overexpression of PNPLA3 I148M or knockout of MBOAT7 to study disease mechanisms.

CRISPR-based gene editing enables the creation of isogenic cell lines with precise genetic modifications, such as:

• • Knockout lines: e.g., PNPLA3 knockout in HepG2 to study lipid accumulation.
• • Knock-in lines: e.g., introduction of PNPLA3 I148M mutation to model the common risk variant.
• • Reporter lines: e.g., GFP-tagged lipid droplet proteins to monitor steatosis.

These models are commercially available from various sources and are sequence-verified to ensure accuracy. They provide a controlled system to dissect gene function and validate drug targets.

Gene-edited cell lines are essential for functional genomics studies. For example:

• • Knockout of PNPLA3 in hepatocytes reduces lipase activity, leading to increased triglyceride accumulation, confirming its role in steatosis.
• • Knock-in of TM6SF2 E167K impairs VLDL secretion, providing a model for studying lipid export defects.
• • CRISPR screens using pooled libraries can identify genes that modulate lipid accumulation or inflammation.

Isogenic cell line pairs (wild-type vs. gene-edited) are powerful tools for drug screening:

• • High-throughput screening: Compounds are tested for their ability to reduce lipid accumulation in PNPLA3 knockout cells.
• • Resistance modeling: Chronic exposure to drugs can select for resistant clones, revealing mechanisms of drug resistance.
• • Target validation: Knockdown of a candidate target can confirm its role in drug efficacy.

CRISPR-based synthetic lethality screens can identify novel biomarkers and therapeutic targets:

• • Synthetic lethal partners: In cells with a specific genetic background (e.g., PNPLA3 mutation), knocking out another gene may cause cell death, revealing vulnerabilities.
• • Biomarker identification: Gene expression profiling of gene-edited cells can identify secreted proteins that serve as non-invasive biomarkers for NASH.