Frontiers in Nutrition (Nov 2022)

In silico investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia

  • Matti Hoch,
  • Luise Ehlers,
  • Karen Bannert,
  • Christina Stanke,
  • David Brauer,
  • Vanessa Caton,
  • Georg Lamprecht,
  • Olaf Wolkenhauer,
  • Olaf Wolkenhauer,
  • Olaf Wolkenhauer,
  • Robert Jaster,
  • Markus Wolfien,
  • Markus Wolfien

DOI
https://doi.org/10.3389/fnut.2022.989453
Journal volume & issue
Vol. 9

Abstract

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Malnutrition (MN) is a common primary or secondary complication in gastrointestinal diseases. The patient’s nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help unravel mechanisms, underlying such complex molecular processes. From public literature, we manually compiled experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. We integrated two diseases, namely liver cirrhosis (LC), and intestinal dysfunction, by considering their effects on nutrition and blood secretome. We demonstrate the performance of our model by successfully simulating the impact of changing dietary frequency, glycogen storage capacity, and disease severity on the carbohydrate and muscle systems. We present the Sarcopenia Map as a publicly available, open-source, and interactive online resource, that links gastrointestinal diseases, MN, and sarcopenia. The map provides tools that allow users to explore the information on the map and perform in silico simulations.

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