Mitochondrial bioenergetics and structural network organization

TLDR

Mitochondrial networks are dynamic, yet how their structural configurations influence bioenergetics and the metabolic signals linking structure to function remain poorly understood. The study investigates the bidirectional relationship between mitochondrial morphology and function in living human cells. The authors used siRNA to knock down DRP1, rotenone to inhibit complex I, and modulators of oxidative phosphorylation in cultured cells and patient fibroblasts, measuring respiratory rate, membrane potential, and ROS to assess how structural changes affect bioenergetics. The data reveal a bidirectional link between mitochondrial network organization and bioenergetics, and the authors propose a model for the metabolic signals mediating this crosstalk.

Abstract

Mitochondria form a dynamic network, and it remains unclear how the alternate configurations interact with bioenergetics properties. The metabolic signals that link mitochondrial structure to its functional states have not been fully characterized. In this report, we analyze the bidirectional relationships between mitochondrial morphology and function in living human cells. First, we determined the effect of mitochondrial fission on energy production by using small interfering RNA (siRNA) targeting DRP1, which revealed the importance of membrane fluidity on the control of bioenergetics. Second, we followed the effect of rotenone, a specific inhibitor of respiratory chain complex I, which causes large structural perturbations, once a threshold was reached. Last, we followed changes in the mitochondrial network configuration in human cells that had been treated with modulators of oxidative phosphorylation, and in fibroblasts from two patients with mitochondrial disease where the respiratory rate, DeltaPsi and the generation of reactive oxygen species (ROS) were measured. Our data demonstrate that the relationship between mitochondrial network organization and bioenergetics is bidirectional, and we provide a model for analyzing the metabolic signals involved in this crosstalk.