# Mitochondrial membrane lipids and respiratory efficiency

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $480,076

## Abstract

Project Summary
No energy transfer processes are perfectly efficient. Mitochondrial oxidative phosphorylation (OXPHOS)
consists of a sequence of reactions with known nodes of inefficient energy transfer. Exercise training is known
to improve skeletal muscle mitochondrial efficiency to maximize energy output. The premise of this proposal is
to examine how cardiolipin (CL) in the inner mitochondrial membrane (IMM) modulates OXPHOS efficiency to
alter skeletal muscle and whole-body energy expenditure. CL is a cone-shaped non-bilayer lipid that induces
membrane curvature in cristae, and binds with high affinity to mitochondrial respiratory complexes to regulate
their functions. Exercise or inactivity alters muscle mitochondrial CL content, coincidental to changes in
OXPHOS efficiency. Preliminary tissue-specific gain- or loss-of-function studies suggest that reduced
mitochondrial CL diminishes OXPHOS efficiency to protect mice from diet-induced obesity. The central
hypothesis of this proposal is that exercise promotes CL biosynthesis to improve OXPHOS energy efficiency.
Combining mitochondrial diagnostics, lipidomics, and metabolic phenotyping, the role of CL in energy
transduction will be examined.

## Key facts

- **NIH application ID:** 10833619
- **Project number:** 5R01DK107397-08
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Katsuhiko Funai
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $480,076
- **Award type:** 5
- **Project period:** 2017-02-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833619

## Citation

> US National Institutes of Health, RePORTER application 10833619, Mitochondrial membrane lipids and respiratory efficiency (5R01DK107397-08). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10833619. Licensed CC0.

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