Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease

Abstract

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population and can progress to cirrhosis with limited treatment options. As the liver secretes most of the blood plasma proteins, liver disease may affect the plasma proteome. Plasma proteome profiling of 48 patients with and without cirrhosis or NAFLD revealed six statistically significantly changing proteins (ALDOB, APOM, LGALS3BP, PIGR, VTN, and AFM), two of which are already linked to liver disease. Polymeric immunoglobulin receptor (PIGR) was significantly elevated in both cohorts by 170% in NAFLD and 298% in cirrhosis and was further validated in mouse models. Furthermore, a global correlation map of clinical and proteomic data strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis. The prominent diabetic drug target DPP4 is an aminopeptidase like ANPEP, ENPEP, and LAP3, all of which are up-regulated in the human or mouse data. Furthermore, ANPEP and TGFBI have potential roles in extracellular matrix remodeling in fibrosis. Thus, plasma proteome profiling can identify potential biomarkers and drug targets in liver disease.

Keywords: NAFLD; NASH; biomarker discovery; mass spectrometry; plasma proteome profiling.

Figure 1. Design and quality control of the human study

1. In total, 48 participants from three sub‐studies of either NAFLD or cirrhosis with the indicated numbers of patients were included in this study.

2. Fasting plasma was collected and distributed into a 96‐well plate for proteomic analysis. Proteins were denaturized, reduced, alkylated, and digested using the automated plasma proteome profiling pipeline, and purified peptides were analyzed in triplicate measurements in a randomization manner by LC‐MS/MS. The resulting 144 raw files were analyzed together with 168 library files by the MaxQuant and Perseus software programs.

3. Numbers of quantified proteins in the triplicate measurements.

4. Dynamic range of quantified proteins (LFQ, label‐free quantitation values).

5. Assessment of study quality by analyzing erythrocyte‐specific proteins (red circles) and coagulation markers (blue circles). HBA, HBB, HBD: hemoglobin subunits alpha, beta, delta; FGA, FGB, FGG: fibrinogen chains alpha, beta, gamma.

6. Assessment of quantitation accuracy of the LC‐MS/MS instrumentation by the number of proteins with a coefficient of variation (CV) below 30, 20, or 10%, respectively, within three technical replicates.

Figure 2. Reduced protein production and increased immunological response in cirrhotic liver

1. Volcano plot of statistical significance against log2‐fold change between the cirrhosis group (N = 10) and non‐NAFLD group (N = 18), showing significantly differentially expressed proteins shaded in blue and down‐regulated liver‐specific proteins color‐coded according to the classification of Human Protein Atlas (HPA). Significance was defined by independent two‐sample t‐test (two‐sided) corrected by permutation‐based FDR of 0.05. The percentage of down‐ and up‐regulated “liver‐specific” proteins is indicated.

2. Hierarchical clustering of significantly expressed proteins between the cirrhosis group and non‐NAFLD group. Intensities of proteins were log2‐transformed and Z‐scored to normalize across individuals. Proteins involved in different biological processes or belonging to different classes are indicated by color.

3. Violin plot of mean fold changes for down‐ and up‐regulated proteins. The fold changes of down‐regulated proteins were further calculated separately for the three protein classes indicated in panel B.

Figure 3. A panel of proteins strongly associated with NAFLD in human cohorts

1. Volcano plot of statistical significance against log2‐fold change between NAFLD (N = 10) and controls (N = 10) in NAFLD subtype 1: NAFLD in normal glucose tolerance. Significance is controlled by P‐value (independent two‐sample t‐test, two‐sided) and minimum fold change (s0 parameter in Perseus) indicated by the cutoff curve, demonstrating significantly up‐regulation of PIGR, ALDOB, and VTN.

2. Box‐and‐whisker plot showing the distribution of mass spectrometric intensity values of three proteins in the first NAFLD cohort with median fold changes. The yellow line is the median, the top and the bottom of the box represent the upper and lower quartile values of the data and the whiskers represent the upper and lower limits for considering outliers (Q3+1.5*IQR, Q1‐1.5*IQR) where IQR is the interquartile range (Q3–Q1). ***, P < 0.001 (independent two‐sample t‐test, two‐sided). Number of replicates is defined in panel (A).

3. Volcano plot of statistical significance against log2‐fold change between NAFLD (N = 8) and controls in NAFLD (N = 10) subtype 2: NAFLD in T2D, showing that AFM, LGALS3BP, and PIGR are significantly up‐regulated and APOM significantly down‐regulated.

4. Box‐and‐whisker plot showing the distribution of mass spectrometric intensity values of four proteins in the second NAFLD cohort with median fold changes. Representation of boxes and whiskers is defined as in panel (B). Number of replicates is defined in panel (C).

Figure EV1. Overlapping significantly regulated proteins in NAFLD and cirrhosis

1. Volcano plot of statistical significance against log2‐fold change between the non‐NAFLD group and the cirrhosis group. Significance is controlled by P‐value and minimum fold change (s0 parameter in the Perseus software) indicated by the cutoff curve, highlighting proteins overlapped with two marker panels for NAFLD subtypes.

2. Box‐and‐whisker plot showing the distribution of LFQ intensity values of PIGR, LGALS3BP, and APOM in plasma. Number of replicates are 18, 20, and 10 in non‐NAFLD, NAFLD, and Cirrhosis, respectively. The yellow line is the median, the top and the bottom of the box represent the upper and lower quartile values of the data and the whiskers represent the upper and lower limits for considering outliers (Q3+1.5*IQR, Q1‐1.5*IQR) where IQR is the interquartile range (Q3–Q1). ***, P < 0.001. (independent two‐sample t‐test, two‐sided).

3. Box‐and‐whisker plot showing the distribution of LFQ intensity values of PIGR in plasma with median fold change indicated. Number of replicates in each group and representation of boxes and whiskers are defined as in panel (B).

4. Box‐and‐whisker plot showing the distribution of LFQ intensity values of ALDOB in with median fold change indicated. Number of replicates in each group and representation of boxes and whiskers are defined as in panel (B).

Figure 4. Global correlation map of the plasma proteome and clinical variables in human cohorts

1. Pairwise correlation of proteins and clinical variables over the 48 study participants, resulting in a matrix of correlation coefficients where each variable is compared to all others. Variables with a high positive correlation to each other will cluster together in groups of red rectangles (high correlations). Negative correlation is indicated in blue patches.

2. The magnified area highlights a cluster of variables that contains the four main clinical measurements for liver diseases (blue names) as well as five proteins, which were quantified by plasma proteome profiling (black gene names).

Figure EV2. Pearson correlation of all proteins to individual liver enzymes

1. A–D

   Four liver enzymes are ALT, AST, ALP, and GGT, and red color‐coded proteins are significant.

Figure EV3. Pearson correlation of plasma proteins to ALT in a separate dataset

This dataset has been published in Wewer Albrechtsen et al (2018) as described in the main text. Red color‐coded proteins are significant.

Figure 5. Plasma proteome changes in a HFD‐induced NAFLD mouse model

1. A

   Mouse cohort design.

2. B

   Box‐and‐whisker plot showing the distribution of log2‐intensity values of body weight across five groups: HFD_1‐2 m (N = 6), HFD > 6 m (N = 6), GIP (N = 7), GLP‐1 (N = 8), and GLP‐1/GIP (N = 6).

3. C

   Volcano plot of statistical significance against log2‐fold change between mice on > 6 months of HFD and mice on 1–2 months of HFD. Significance is controlled by FDR‐corrected P‐value and minimum log2‐fold change of 1 indicated by the blue‐dotted line, demonstrating that Saa1, Pigr, Aldob, Lap3, Enpep, and Dpp4 are significantly up‐regulated.

4. D–O

   Box‐and‐whisker plot showing the distribution of log2‐intensity values of statistical significantly regulated proteins across five groups. Number of replicates is defined in panel (B). The yellow line is the median, the top and the bottom of the box represent the upper and lower quartile values of the data and the whiskers represent the upper and lower limits for considering outliers (Q3+1.5*IQR, Q1‐1.5*IQR) where IQR is the interquartile range (Q3–Q1).

Data information: Significance was defined by independent t‐test (two‐sided) followed by Benjamini–Hochberg correction for multiple hypothesis testing with a significance level of *P < 0.05, **P < 0.01, and ***P < 0.001.

Figure EV4. Protein–protein correlation map of mouse plasma proteome

Pairwise correlation of plasma proteins over 82 mice, resulting in a matrix of correlation coefficients where each variable is compared to all others. Variables with a high positive correlation with each other will cluster together in groups of red rectangles. Negative correlation is indicated in blue patches. The magnified area highlights a cluster of six proteins of special interest that co‐vary under pathophysiological conditions.