A high-resolution bovine mitochondrial co-expression network.

Area of Science:

• Mitochondrial biology
• Systems biology
• Genomics

Background:

• Mitochondrial function is complex and not fully understood.
• Understanding mitochondrial gene regulation is crucial for cellular processes.
• A comprehensive framework for visualizing mitochondrial data is needed.

Purpose of the Study:

• To create a co-expression network representing the mitochondrial proteome.
• To visualize and interpret genome-wide molecular data related to mitochondria.
• To investigate the interplay between mitochondrial DNA (mtDNA) and nuclear gene regulation.

Main Methods:

• Reverse-engineered a co-expression network using 723 RNA-seq datasets from 441 cattle.
• Analyzed mRNA encoding over half of the mitochondrial proteome.
• Integrated transcription factor data to build a regulatory network.

Main Results:

• Constructed a mitochondrial landscape with a main network and sub-networks.
• Identified a distinct mtDNA sub-network including nuclear gene PDHA1, linking glycolysis to the TCA cycle.
• Discovered breed and diet-sensitive expression patterns in cattle muscle transcriptomes.
• Highlighted subtle, coordinated gene expression changes missed by traditional methods.
• Found nuclear hormone receptors enriched among influential transcription factors.

Conclusions:

• The developed network provides a framework for mitochondrial data interpretation.
• The study reveals intricate regulatory relationships within the mitochondrial network, including nuclear-cytoplasmic interactions.
• This approach enhances the detection of complex gene expression patterns relevant to traits like feed efficiency.