Integrated liver-secreted and plasma proteomics identify a predictive model that stratifies MASH

Abstract

Obesity is a major risk factor for metabolic-associated steatotic liver disease (MASLD), which can progress to metabolic-associated steatohepatitis (MASH). There are no validated non-invasive tests to stratify persons with obesity with a greater risk for MASH. Herein, we assess plasma and liver from 266 obese individuals spanning the MASLD spectrum. Ninety-six human livers were precision-cut, and mass spectrometry-based proteomics identifies 3,333 proteins in the liver-secretion medium, of which 107 are differentially secreted in MASH compared with no pathology. The plasma proteome is markedly remodeled in MASH but is not different between patients with steatosis and no pathology. The APASHA model, comprising plasma apolipoprotein F (APOF), proprotein convertase subtilisin/kexin type 9 (PCSK9), afamin (AFM), S100 calcium-binding protein A6 (S100A6), HbA1c, and zinc-alpha-2-glycoprotein (AZGP1), stratifies MASH (area under receiver operating characteristic [AUROC] = 0.88). Our investigations detail the evolution of liver-secreted and plasma proteins with MASLD progression, providing a rich resource defining human liver-secreted proteins and creating a predictive model to stratify patients with obesity at risk of MASH.

Keywords: APASHA; APOF; AZGP1; MASL; MASLD; hepatokine; protein secretion; proteome; small protein enrichment.

Graphical abstract

Figure 1

Dual proteomic approaches identify candidate biomarkers for MASH in the plasma proteome (A) Schematic of the biomarker discovery workflow. (B and C) Volcano plot of the non-depleted plasma proteome in patients with MASH (n = 33) when compared to those with (B) no pathology (n = 42) (C) and MASL (n = 85). (D–I) (D) Proteomic changes in MASL compared with no pathology. AUROC curves of candidate biomarkers to detect MASH: (E) APOF, (F) FCN3, (G) THBS1, (H) TGFBI, and (I) PPBP derived from the non-depleted proteomics. (J–L) Volcano plot of the SPEA plasma proteome in patients with MASH when compared to those with (J) no pathology and (K) MASL. (L) Volcano plot of the SPEA plasma proteomoe in patients with MASL when compared with no pathology. (M) Venn diagram of proteins significantly remodeled with MASH in the SPEA and non-depleted plasma proteome comparisons. (N–Q) AUROC curves to stratify MASH using the SPEA detected candidate biomarkers: (N) AGT, (O) AZGP1, (P) PCSK9, and (Q) S100A6. Data were determined by two-way t tests with Benjamini-Hochberg false discovery rate (adjusted p value < 0.05) or area under the receiver operative curve.

Figure 2

Protein secretion from the liver is remodeled in MASH (A) Schematic of the study design. (B) The percentage of classically and non-classically secreted proteins secreted from the human liver. (C–E) (C) Volcano plot of human liver-secreted proteins showing remodeling with MASH (n = 11) compared to no pathology (n = 26) and the ingenuity pathway analysis of (D) canonical pathways and (E) upstream regulators. (F and G) (F) Volcano plot depicting the proteins remodeled with MASH compared to MASL (n = 59) and (G) the ingenuity pathway analysis of upstream regulators. (H) Overlay of the liver-secreted proteins remodeled in livers with MASH. (I and J) (I) Spearman’s correlation analysis between the liver-secreted proteins and the pathologist-defined liver steatosis area and (J) the Metascape pathway enrichment analysis of steatosis correlated proteins. (K) Top 10 proteins that correlate with the Kleiner steatosis, ballooning, and nonalcoholic fatty liver disease (NAFLD) activity (NAS) scores, respectively. Significance was tested by two-way t tests or Spearman’s correlation with Benjamini-Hochberg false discovery rate (adjusted p value < 0.05).

Figure 3

Identification of MASH biomarkers that reflect liver secretion (A) Overlay of proteins detected in the non-depleted and SPEA plasma proteome and the human liver-secreted proteome. Proteins detected in both liver-secreted and plasma proteomes are in orange numbers. (B–E) Correlation of the liver-secreted and plasma proteins detected using non-depleted proteomics: (B) AFM, (C) ORM2 (n = 85), (D) SERPINA4, and (E) HRG. Significance was tested by Pearson correlation. n = 86 unless stated otherwise.

Figure 4

Diagnostic utility of the APASHA model to stratify MASH (A and B) (A) AUROC curve of the APASHA model in the discovery cohort to stratify individuals with MASH (n = 30) compared to No MASH (n = 117) and (B) hepatic fibrosis score ≥1 (n = 46) in orange or ≥2 in blue (n = 16). (C) Correlation matrix showing weak correlation between APASHA covariates in the discovery cohort. (D and E) (D) Diagnostic accuracy of the APASHA model vs. AST/ALT (MASH n = 31, No MASH n = 125), plasma TREM2 (MASH n = 31, No MASH n = 125), and CRP detected in the SPEA proteome (MASH n = 33, No MASH n = 127) and (E) non-invasive scores FIB-4 (MASH n = 31, No MASH n = 124) and Forns index (MASH n = 31, No MASH n = 124) to stratify for MASH in the discovery cohort. (F and G) (F) AUROC curve of the APASHA model in the validation cohort (MASH n = 21, No MASH n = 71) to stratify individuals with MASH and (G) hepatic fibrosis score ≥1 (n = 50) in orange or ≥2 (n = 29) in blue. (H) Correlation matrix showing weak correlation between APASHA covariates in the validation cohort. (I and J) (I) Diagnostic accuracy of the APASHA model vs. AST/ALT (MASH n = 24, No MASH n = 80) and CRP detected in the SPEA proteome (MASH n = 24, No MASH n = 82) and (J) non-invasive scores FIB-4 (MASH n = 24, No MASH n = 78) and Forns index (MASH n = 24, No MASH n = 79) in the validation cohort. Correlations were determined by Pearson correlation. Differences in AUROC were assessed by the DeLong test.