Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

Correlation plot at time 0h  (A) The profile likelihood as a measure of parameter identifiability (Raue et al, 2009) is depicted for all dysregulated parameters before implementation of the DIA data.If the negative log likelihood reaches a statistical threshold in both directions, the parameter has defined confidence bounds and is therefore called identifiable.If this limit is not reached on both sides, the parameter is classified as unidentifiable.Solid lines indicate the profile likelihood of dysregulated parameters for SD (purple) and WD (orange) along with the optimal parameter values as dots.Dashed lines depict thresholds for the confidence interval assessment.(B) The profile likelihood as a measure of parameter identifiability is depicted for all dysregulated parameters after implementation of the DIA data.(A) number of molecules of per primary mouse hepatocyte was determined by quantitative immunoblotting.on a dilution curve of recombinant AKT, the number of molecules of AKT in 1 µg lysate was determined.This was converted with the total content per primary mouse into the number of of AKT cell.Measurements protein abundances from primary mouse were implemented model calibration. of unstimulated hepatocytes subjected to mass analysis.data was normalized and represented boxplot: center indicates median; limits indicate 25th 75th percentiles.lower and upper whiskers extend from the hinge to the smallest or largest value at most 1.5× interquartile range of the hinge.Dots represent the model fit (n = 9 per diet).(C) A list of Hallmark mTORC1 signaling genes was downloaded from Gene Set Enrichment Analysis (GSEA) and used to filter full proteomes of SD and WD mice.Out of 200 listed proteins, 129 were quantified in all samples and used to cluster samples based on protein abundance using the R package pheatmap.

Figure EV4. Influence of dysregulated parameters on protein dynamics.
Individual parameter scan of one dysregulated parameter at a time as explained in Fig. 4A.The value for the indicated parameter was gradually shifted from the SD estimate (purple) to the estimate gray).model simulations for the dynamics of MET, ERK and AKT are displayed in molecules/cell.Solid lines indicate model trajectories after HGF stimulation and dashed lines indicate basal levels.

Molecular Systems Biology
Sebastian Burbano de Lara et al Quantitative data for calibration of the human model.
(A) Proliferation measurements in isolated primary human hepatocytes from patients.Cells were stimulated with 40 ng/ml HGF.DNA content was measured at after 48 I. (B) immunoblot for as MET, AKT, ERK and in primary hepatocytes derived from seven patients.Cells were stimulated with 40 ng/ml HGF or left untreated.Signal is shown in log 10 arbitrary units (a.u.).Data points displayed as dots along with error bars representing 1σ confidence interval estimated from technical replicates (n = 1-3 per patient) using a combined scaling and error model.Model trajectories are depicted as lines.(C) Measurements for abundances derived from primary patient hepatocytes were included as data for model calibration.Lysates of unstimulated hepatocytes were subjected to data-independent mass spectrometry analysis (n = 1-3 per patient).Resulting data was normalized using label-free quantification represented as boxplot: center line indicates the patient median; box limits defined as 1σ, calculated based on the mean spread of the cohort per protein.lower and upper whiskers extend from the center line by Model fits are represented as black dots.

Figure EV2 .
Figure EV2.Impact of the DIA data on identifiability and convergence.

Figure EV3 .
Figure EV3.Absolute quantification of AKT and model-based estimations of total protein Figure EV5.Signal transduction in primary human hepatocytes from steatotic patients.(A) Isolated primary human hepatocytes from patients with different levels of steatosis were analyzed using quantitative immunoblotting.The ratio of basal MET phosphorylation to MET abundance without HGF stimulation was quantified.Error bars represent one standard deviation (n = 3).(B) Primary human hepatocytes from patients with different levels of steatosis were stimulated with 40 ng/ml HGF.Phosphorylation of AKT was quantified by immunoblotting after 10 min.p values were calculated using a two-tailed t test (pMET/tMET *0.011, AKT *0.018).Error bars represent one standard deviation (n = 3).