Overview：　in vivo MASH (NASH) model

Metabolic Dysfunction Associated Steatohepatitis (MASH) is one of the most prevalent chronic liver diseases, which is closely associated with obesity, insulin resistance and dyslipidemia. These important features therefore need to be reflected in a preclinical model as well. Based on 25 years of research on translational metabolic disease models, TNO has developed the Ldlr-/-.Leiden MASH mouse model that accurately mimics the etiology and pathology of MASH and fibrosis in humans. By using a high-fat diet, with a macronutrient composition comparable to human diets (e.g., without added cholesterol), the Ldlr-/-.Leiden MASH mouse model develops obesity, insulin resistance, adipose tissue inflammation, increased gut permeability with altered microbiota composition, and MASH with bridging fibrosis [F3 stage].

Features：the Ldlr-/-.Leiden MASH mouse model

●  Translational disease induction and clinical phenotype
●  Severe obesity (with adipose tissue inflammation)
●  Insulin resistance
●  Hyperlipidemia (humanized lipoprotein profiles), high triglycerides, high (V)LDL, low HDL)
●  Intact de novo cholesterol synthesis and de novo lipogenesis (DNL)
●  Translational histology: steatosis, inflammatory aggregates and bridging fibrosis (up to F3)
●  Atherosclerosis development
●  Translational underlying pathways, verified on transcriptomics, proteomics and metabolomics level
●  Extensive validation with multiple interventions (including the FDA approved drug)
●  Metabolic overload-induced complications in multiple organs
●  the mouse contains 262 high-impact SNPs. In humans, these SNPs associate with human health & disease processes (e.g. mitochondrial function, bilirubin metabolism, detoxification, inflammation, etc).

Additional obesity-associated endpoints:
●   Insulin resistance and visceral adipose tissue inflammation
●   Gut permeability
●   Muscle atrophy
●   Neuroinflammation (Brain)
●   Vascular disease (Atherosclerosis)

Time lines・Readout Parameters ; The Ldlr-/-.Leiden MASH mouse model

The Ldlr-/-.Leiden MASH model has been validated with many different interventions

● Recent paper in the FASEB Journal (Jun, 2025); Fibrogenic Gene Signature as Early Prediction for the Efficacy of Pharmacological Interventions for MASH-Associated Fibrosis.

● Recent paper in Heliyon (Mar, 2023); Translational characterization of the temporal dynamics of metabolic dysfunctions in liver, adipose tissue and the gut during diet-induced NASH development in Ldlr-/-.Leiden mice.


● Recent paper in the FASEB Journal. (Oct, 2024); Therapeutic effects of FGF21 mimetic bFKB1 on MASH and atherosclerosis in Ldlr-/-.Leiden mice.

● Recent paper in Nature comm (May, 2024); Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD.

● Other MASH related publication list (pdf)

AI MASH HISTOPATHOLOGY： Liver AI

TNO's AI histopathology turns liver tissue samples into objective, reproducible MASH and fibrosis read-outs. Scoring steatosis, inflammation and fibrosis similar to pathologists, but across 100% of the tissue and in a fraction of the time.


Quantify liver histology faster and more accurate with AI
Our AI reads whole-slide images of liver tissue and reproduces the pathologist's reasoning. The result is standardized, repeatable quantification that removes inter- and intra-reader variability and scales to hundreds of slides. It is built for preclinical studies, where scoring large cohorts consistently is hard to achieve by manual review.

Three key MASH markers, Quantified by AI
Inflammation
The AI finds and counts inflammatory-cell aggregates much as a pathologist does and reports their density per mm2, estimated across 100% of the tissue (a pathologist samples ~30%). Because individual cells are detected, it also reports cells per aggregate and total counts, statistics infeasible to manually collect at scale.

Fibrosis
It estimates the percentage of tissue affected by fibrosis the way a pathologist does, not as an abstract pixel-level score. The AI scores closely track expert assessments across study groups.

Steatosis
Macro- and microvesicular steatosis are quantified as affected-tissue estimates, reproducing the pathologist's read in an automated, reproducible way. Additional markers are on the roadmap.




CASE STUDY ・ LIVER FIBROSIS
AI fibrosis scores track the pathologist
We tested whether AI fibrosis read-outs can capture early treatment effects. Mice on a 28-week high-fat diet (HFD) were then kept on HFD, switched to a lean chow diet, or given a long-acting FGF21 analogue - a direct anti-fibrotic - for 4 or 8 weeks, with a start-of-treatment group as reference. Both pathologist and AI analyses show HFD increases fibrosis, while the dietary switch and FGF21 inhibit its development. Across every group, the AI's scores correlate closely with the pathologist's.

Liver-on-a-Chip・ Cell model for liver fibrosis

We have developed a liver-on-a-chip model that exhibits MASH / liver fibrosis. By adding oleate and palmitate and later fructose, steatosis and steatohepatitis are induced. Adding also LPS leads to the induction of fibrosis. This 3D liver spheroid model uses human hepatocytes, stellate cells and Kupffer cells. The model is sensitive to reference compounds such as pioglitazone and fenofibrate.

Summary:
・A diet-induced disease-mimicking 3D in vitro model, closely resembling the pathophysiology of liver steatosis and early fibrosis was developed;
・Liver spheroids composed of primary hepatocytes, stellate and Kupffer cells exhibit expected cell function for at least 21 days;
・The steatosis and steatohepatitis induced by fatty acids and fructose can be modulated by model drugs;

●   Development of a disease-mimicking model for NASH and fibrosis in a triple cell-type, spheroid-based liver-on-chip platform with microfluidics.(Poster)
● Recent paper in Biomedicine (Jun, 2025); Improved Translational Relevance of In Vitro Fibrosis Models by Integrating IOX2-Mediated Hypoxia-Mimicking Pathways

Flux Analysis using 14C and AMS; De Novo Lipogenesis, Muscle Protein Turnover, etc

Metabolic flux measurements play an important role in advancing our understanding of (patho)physiology, disease mechanisms and the development of new therapeutics. By incorporating stable or radioactive isotopes into specific molecules (tracers), the distribution and fate of the isotope can be followed ? thereby providing insight into the movement and metabolic transformation of biomolecules. An isotope that is frequently used for such analyses is 13C, which is analyzed by isotope ratio mass spectrometry. The high natural abundance of 13C (~1%) requires the use of large amounts of labelled substrates, especially in clinical studies. An alternative isotope is 14C, which has an extremely low natural abundance (ca. one in a trillion). In combination with analysis by extremely sensitive accelerator mass spectrometry (AMS), this enables the detection of very small amounts of 14C-labeled product/biomarker at very low (microtrace) amounts of labelled substrate administration.

Application
●  De Novo Lipogenesis (DNL)
●  HDL funcitionality / Reverse Cholesterol Transport
●  Muscle Protein Synthesis & Breakdown (combined 14C and D2O analysis)
●  Glucose Metabolism (DNL, conversion to fluctose)

  Technical info:
●   Flux Analysis using 14C and AMS

Biomarkers for MASLD・MASH

Fibrosis is the main determinant for mortality and is therefore an important clinical readout. Detection of fibrosis via a liver biopsy is still the golden standard but this is an invasive procedure with risk for complications, sampling and reading errors. Fibrosis is a dynamic process and currently no validated diagnostic/prognostic methods exist, especially biomarkers which can detect the early onset and progression of fibrosis. We aim to identify a mechanism-based biomarker panel which is prognostic for MASLD-related fibrosis and enables early identification of people at risk with active fibrogenesis. By integration of mechanistic mouse studies and human clinical samples we identified a set of circulating proteins related to the active fibrogenesis process.

● Hepatic molecular signature in a translational NASH model as an early screening tool for novel NASH therapeutics.(Poster)
● Early prediction of progressive fibrogenesis in NAFLD-NASH patients using blood-based biomarkers.(Poster)

2025.8.20 | TNO has published a research paper in the International Journal of Molecular Science entitled Biomarkers of Metabolism and Inflammation in Individuals with Obesity and Normal Weight: A Comparative Analysis Exploring Sex Differences.  In this study, we analyzed over 90 biomarkers from healthy normal-weight and obese individuals using ELISA and electrochemiluminescence. The results revealed that many biomarkers associated with chronic inflammation and metabolic disorders, such as adipokines and cytokines, were elevated in obese individuals. The study also suggested potential sex differences in biomarker levels in both groups. This dataset not only assists researchers in selecting appropriate biomarkers for their own studies but also highlights the importance of considering sex differences in future research.