Opa1 and Drp1 reciprocally regulate cristae morphology, ETC function, and NAD+ regeneration in KRas-mutant lung adenocarcinoma.

Oncogenic KRas activates mitochondrial fission through Erk-mediated phosphorylation of the mitochondrial fission GTPase Drp1. Drp1 deletion inhibits tumorigenesis of KRas-driven pancreatic cancer, but the role of mitochondrial dynamics in other Ras-driven malignancies is poorly defined. Here we show that in vitro and in vivo growth of KRas-driven lung adenocarcinoma is unaffected by deletion of Drp1 but is inhibited by deletion of Opa1, the GTPase that regulates inner membrane fusion and proper cristae morphology. Mechanistically, Opa1 knockout disrupts cristae morphology and inhibits electron transport chain (ETC) assembly and activity, which inhibits tumor cell proliferation through loss of NAD+ regeneration. Simultaneous inactivation of Drp1 and Opa1 restores cristae morphology, ETC activity, and cell proliferation indicating that mitochondrial fission activity drives ETC dysfunction induced by Opa1 knockout. Our results support a model in which mitochondrial fission events disrupt cristae structure, and tumor cells with hyperactive fission activity require Opa1 activity to maintain ETC function.

Mitochondrial dynamics in cancer stem cells.

Many tumors are now understood to be heterogenous cell populations arising from a minority of epithelial-like cancer stem cells (CSCs). CSCs demonstrate distinctive metabolic signatures from the more differentiated surrounding tumor bulk that confer resistance to traditional chemotherapeutic regimens and potential for tumor relapse. Many CSC phenotypes including metabolism, epithelial-to-mesenchymal transition, cellular signaling pathway activity, and others, arise from altered mitochondrial function and turnover, which are regulated by constant cycles of mitochondrial fusion and fission. Further, recycling of mitochondria through mitophagy in CSCs is associated with maintenance of reactive oxygen species levels that dictate gene expression. The protein machinery that drives mitochondrial dynamics is surprisingly simple and may represent attractive new therapeutic avenues to target CSC metabolism and selectively eradicate tumor-generating cells to reduce the risks of metastasis and relapse for a variety of tumor types.

Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology.

Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.