Abstract
Cardiolipin (CL) is a phospholipid specific for mitochondrial membranes and crucial for many core tasks of this organelle. Its acyl chain configurations are tissue specific, functionally important, and generated via post-biosynthetic remodeling. However, this process lacks the necessary specificity to explain CL diversity, which is especially evident for highly specific CL compositions in mammalian tissues. To investigate the so far elusive regulatory origin of CL homeostasis in mice, we combine lipidomics, integrative transcriptomics, and data-driven machine learning. We demonstrate that not transcriptional regulation, but cellular phospholipid compositions are closely linked to the tissue specificity of CL patterns allowing artificial neural networks to precisely predict cross-tissue CL compositions in a consistent mechanistic specificity rationale. This is especially relevant for the interpretation of disease-related perturbations of CL homeostasis, by allowing differentiation between specific aberrations in CL metabolism and changes caused by global alterations in cellular (phospho-)lipid metabolism.