Biophysical Modeling Reveals How Gene Expression Drives Tissue-Scale Fat Deposition in Beef Breeds.

Area of Science:

• Genomics and Systems Biology
• Biophysics and Computational Modeling
• Animal Science and Beef Quality

Background:

• Intramuscular fat (IMF) marbling is crucial for beef quality, but predicting its development from genetic factors is challenging.
• Understanding the link between breed-specific gene expression and tissue-level fat patterning is a significant hurdle in beef production.

Purpose of the Study:

• To establish a proof-of-concept omics-to-tissue modeling framework for predicting intramuscular adipogenesis and marbling.
• To translate RNA-seq data into biophysically interpretable parameters governing fat patterning in cattle breeds.

Main Methods:

• Utilized transcriptomic profiles from Japanese Black Wagyu and Chinese Red Steppes cattle.
• Derived composite indices for adipogenic commitment (φ) and lipid droplet capacity (ψ) from gene modules.
• Integrated gene-derived parameters into a Cellular Potts Model (CPM) to simulate tissue-scale fat patterning.

Main Results:

• Wagyu cattle were characterized by a high-adipogenic gene expression regime, while Chinese Red Steppes exhibited a low-adipogenic regime.
• The CPM simulations identified a critical threshold (pF→Abase ≈ 0.55) predicting significant IMF accumulation.
• The model accurately predicted robust marbling in Wagyu and a lean phenotype in Chinese Red Steppes without post hoc tuning.

Conclusions:

• Transcriptomic data can quantitatively predict emergent marbling phenotypes using interpretable biophysical parameters.
• The developed framework offers a generalizable approach for forecasting complex tissue traits from omics data.
• This study provides a foundation for precision breeding strategies aimed at enhancing beef quality traits.