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Suppression of NAF-1 in Breast Cancer Cells Reduces their Tumorigenicity by Interfering with Cellular Iron Distribution and Metabolism and Ensuing ROS Formation and Apoptosis

ABSTRACT: Nutrient autophagy factor 1 (NAF-1) is an iron-sulfur protein found on the outer mitochondrial membrane and the ER. Recent studies highlighted an important role for NAF-1 in regulating autophagy via interaction with BCL-2. We recently reported that the level of NAF-1 is elevated in cancer cells and that NAF-1 is required for tumor growth. Here we report that shRNA suppression of NAF-1 results in the activation of apoptosis in xenograft tumors and cancer cells grown in culture. Suppression of NAF-1 resulted in a depletion in the cytosolic iron pool, facilitated uptake of iron, and accumulation of iron and ROS in mitochondria, a shift to glycolysis and glutaminolysis, and the activation of cellular stress pathways associated with HIF1α, AMPK and mTOR. Suppression of NAF-1 in breast cancer cells appears therefore to reduce their tumorigenicity by interfering with cellular iron distribution and energy metabolism resulting in the activation of apoptosis.

ORGANISM(S): Homo sapiens

PROVIDER: GSE66158 | GEO | 2016/02/05

SECONDARY ACCESSION(S): PRJNA275982

REPOSITORIES: GEO

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Project description:Worldwide, breast cancer is the main cause of cancer mortality in women. Most cases originate in mammary ductal cells that secrete the nipple aspirate fluid (NAF). In cancer patients, this breast secretome contains proteins associated with the tumor microenvironment. NAF studies are challenging because inter-individual variability is substantial. To better address this limitation, we introduced a paired-proteomic strategy that relies on NAF sample analysis from both breasts of patients with unilateral breast cancer. We developed a software extension to the PatternLab for Proteomics software to take advantage of this setup. Briefly, the software relies on a peptide-centric approach and uses the binomial distribution to attribute a probability for each peptide as being linked to the disease or not; these probabilities are then propagated to a final protein p-value according to the Stouffer’s Z-score method. Our approach was applied to both a discovery-driven (shotgun) analysis of NAF samples and a hypothesis-driven (targeted) assessment of 19 cancer-related proteins described in the literature. Shotgun results culminated in the reliable quantitative proteomic profiling of NAF samples from healthy and cancer cohorts. A total of 1,083 proteins were identified, of which 77 were differentially abundant, being mainly involved in glycolysis (Warburg effect) and immune system activation (activated stroma). Additionally, in the estrogen receptor-positive subgroup, proteins related to the lipid metabolism and the complement cascade displayed higher abundance, as expected for this well-differentiated subtype of cancer. The targeted analysis of NAF samples from triple negative patients revealed three differentially abundant proteins related to cell migration/attraction and tumor cell differentiation. In summary, we debuted a paired differential bioinformatics workflow, performing a proof-of-principal differential proteomic analysis of NAF samples in unilateral breast cancers patients. The results revealed a promising statistical paired analysis workflow, thus validating NAF as a treasure-trove for studying this paired-organ cancer type.

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Suppression of NAF-1 in Breast Cancer Cells Reduces their Tumorigenicity by Interfering with Cellular Iron Distribution and Metabolism and Ensuing ROS Formation and Apoptosis

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