Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue

Abstract

Metabolic disorders, such as obesity and type 2 diabetes have a large impact on global health, especially in industrialized countries. Tissue-specific chronic low-grade inflammation is a key contributor to complications in metabolic disorders. To support therapeutic approaches to these complications, it is crucial to gain a deeper understanding of the inflammatory dynamics and to monitor them on the individual level. To this end, blood-based biomarkers reflecting the tissue-specific inflammatory dynamics would be of great value. Here, we describe an in silico approach to select candidate biomarkers for tissue-specific inflammation by using a priori mechanistic knowledge from pathways and tissue-derived molecules. The workflow resulted in a list of candidate markers, in part consisting of literature confirmed biomarkers as well as a set of novel, more innovative biomarkers that reflect inflammation in the liver and adipose tissue. The first step of biomarker verification was on murine tissue gene-level by inducing hepatic inflammation and adipose tissue inflammation through a high-fat diet. Our data showed that in silico predicted hepatic markers had a strong correlation to hepatic inflammation in the absence of a relation to adipose tissue inflammation, while others had a strong correlation to adipose tissue inflammation in the absence of a relation to liver inflammation. Secondly, we evaluated the human translational value by performing a curation step in the literature using studies that describe the regulation of the markers in human, which identified 9 hepatic (such as Serum Amyloid A, Haptoglobin, and Interleukin 18 Binding Protein) and 2 adipose (Resistin and MMP-9) inflammatory biomarkers at the highest level of confirmation. Here, we identified and pre-clinically verified a set of in silico predicted biomarkers for liver and adipose tissue inflammation which can be of great value to study future development of therapeutic/lifestyle interventions to combat metabolic inflammatory complications.

Keywords: blood-based biomarker; lifestyle intervention; low-grade inflammation; mechanism; metabolic disease.

FIGURE 1

Workflow of the selection of candidate protein biomarkers for tissue inflammation of liver and/or adipose tissue.

FIGURE 2

(A) Panels of immuno-histochemical staining of inflammatory aggregates in liver and adipose tissue; (B) quantification of inflammatory aggregates in HFD-fed mice (black bars) in liver (left) and adipose tissue (right) as compared to chow fed mice (open bars). ^∗∗∗ indicates significance with P-values < 0.05.

FIGURE 3

Heatmap representation of significant differential expressed genes from liver and adipose tissue and after 50 weeks of High Fat Diet feeding as compared to chow fed mice. ^∗indicate significance of gene expression in specific tissue (P < 0.05).

FIGURE 4

(A) Correlation analysis of hepatic inflammation based on immuno-histochemical staining of inflammatory aggregates in liver and hepatic SAA1 expression in mice (R² = 0.82; P < 0.01); (B) Correlation analysis of adipose tissue inflammation based on immuno-histochemical staining of crown-like structures and adipose tissue SAA1 expression in mice (R² = 0.15; P > 0.5); (C) Correlation analysis of hepatic inflammation in liver and plasma SAA1 levels (R² = 0.68; P < 0.01).

FIGURE 5

Workflow of the verification of the curated candidate biomarkers. Curated candidate biomarkers were checked for up/down regulated gene-expression in WAT and livers of obese mice. The curated candidate biomarkers were divided into 2 groups: markers differentially expressed in inflamed tissues; markers not differentially expressed in inflamed tissues. *Cut-off criteria described in M&M; ** Due to absence of murine analog of human candidate biomarker and/or lack of (differential) expression in murine tissues in RNAseq experiment.