Key result
An in silico approach identified candidate biomarkers for tissue-specific inflammation, with 9 hepatic and 2 adipose biomarkers highly confirmed in literature and correlated with inflammation in high-fat diet-fed mice.
Why the study?
Tissue-specific chronic low-grade inflammation contributes to metabolic disorder complications, creating a need for blood-based biomarkers to monitor these inflammatory dynamics at the individual level.
An in silico approach successfully identified and preclinically verified novel tissue-specific biomarkers for liver and adipose tissue inflammation in a mouse model of metabolic disease.
Blood-based biomarkers may enable individualized monitoring of tissue inflammation in metabolic disorders; hypothesis-generating pending clinical validation.
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.
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Bilsen et al. (2021) studied Low-grade inflammation in liver and adipose tissue (n=27). High-fat diet vs. Low-fat chow diet was evaluated on Hepatic inflammatory cell aggregates (31.6-fold increase, p=<0.01). An in silico approach identified candidate biomarkers for tissue-specific inflammation, with 9 hepatic and 2 adipose biomarkers highly confirmed in literature and correlated with inflammation in high-fat diet-fed mice.
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