The breast cancer oncogene IKKε coordinates mitochondrial function and serine metabolism.

IκB kinase ε (IKKε) is a key molecule at the crossroads of inflammation and cancer. Known to regulate cytokine secretion via NFκB and IRF3, the kinase is also a breast cancer oncogene, overexpressed in a variety of tumours. However, to what extent IKKε remodels cellular metabolism is currently unknown. Here, we used metabolic tracer analysis to show that IKKε orchestrates a complex metabolic reprogramming that affects mitochondrial metabolism and consequently serine biosynthesis independently of its canonical signalling role. We found that IKKε upregulates the serine biosynthesis pathway (SBP) indirectly, by limiting glucose-derived pyruvate utilisation in the TCA cycle, inhibiting oxidative phosphorylation. Inhibition of mitochondrial function induces activating transcription factor 4 (ATF4), which in turn drives upregulation of the expression of SBP genes. Importantly, pharmacological reversal of the IKKε-induced metabolic phenotype reduces proliferation of breast cancer cells. Finally, we show that in a highly proliferative set of ER negative, basal breast tumours, IKKε and PSAT1 are both overexpressed, corroborating the link between IKKε and the SBP in the clinical context.

Inositol trisphosphate receptor-mediated Ca2+ signalling stimulates mitochondrial function and gene expression in core myopathy patients.

Core myopathies are a group of childhood muscle disorders caused by mutations of the ryanodine receptor (RyR1), the Ca2+ release channel of the sarcoplasmic reticulum. These mutations have previously been associated with elevated inositol trisphosphate receptor (IP3R) levels in skeletal muscle myotubes derived from patients. However, the functional relevance and the relationship of IP3R mediated Ca2+ signalling with the pathophysiology of the disease is unclear. It has also been suggested that mitochondrial dysfunction underlies the development of central and diffuse multi-mini-cores, devoid of mitochondrial activity, which is a key pathological consequence of RyR1 mutations. Here we used muscle biopsies of central core and multi-minicore disease patients with RyR1 mutations, as well as cellular and in vivo mouse models of the disease to characterize global cellular and mitochondrial Ca2+ signalling, mitochondrial function and gene expression associated with the disease. We show that RyR1 mutations that lead to the depletion of the channel are associated with increased IP3-mediated nuclear and mitochondrial Ca2+ signals and increased mitochondrial activity. Moreover, western blot and microarray analysis indicated enhanced mitochondrial biogenesis at the transcriptional and protein levels and was reflected in increased mitochondrial DNA content. The phenotype was recapitulated by RYR1 silencing in mouse cellular myotube models. Altogether, these data indicate that remodelling of skeletal muscle Ca2+ signalling following loss of functional RyR1 mediates bioenergetic adaptation.

MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections.

The potential to understand fundamental biological processes from gene expression data has grown in parallel with the recent explosion of the size of data collections. However, to exploit this potential, novel analytical methods are required, capable of discovering large co-regulated gene networks. We found current methods limited in the size of correlated gene sets they could discover within biologically heterogeneous data collections, hampering the identification of multi-gene controlled fundamental cellular processes such as energy metabolism, organelle biogenesis and stress responses. Here we describe a novel biclustering algorithm called Massively Correlated Biclustering (MCbiclust) that selects samples and genes from large datasets with maximal correlated gene expression, allowing regulation of complex networks to be examined. The method has been evaluated using synthetic data and applied to large bacterial and cancer cell datasets. We show that the large biclusters discovered, so far elusive to identification by existing techniques, are biologically relevant and thus MCbiclust has great potential in the analysis of transcriptomics data to identify large-scale unknown effects hidden within the data. The identified massive biclusters can be used to develop improved transcriptomics based diagnosis tools for diseases caused by altered gene expression, or used for further network analysis to understand genotype-phenotype correlations.

Multistate Gene Cluster Switches Determine the Adaptive Mitochondrial and Metabolic Landscape of Breast Cancer.

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labeled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome.

Macrophages induce malignant traits in mammary epithelium via IKKε/TBK1 kinases and the serine biosynthesis pathway.

During obesity, macrophages infiltrate the breast tissue leading to low-grade chronic inflammation, a factor considered responsible for the higher risk of breast cancer associated with obesity. Here, we formally demonstrate that breast epithelial cells acquire malignant properties when exposed to medium conditioned by macrophages derived from human healthy donors. These effects were mediated by the breast cancer oncogene IKKε and its downstream target-the serine biosynthesis pathway as demonstrated by genetic or pharmacological tools. Furthermore, amlexanox, an FDA-approved drug targeting IKKε and its homologue TBK1, delayed in vivo tumour formation in a combined genetic mouse model of breast cancer and high-fat diet-induced obesity/inflammation. Finally, in human breast cancer tissues, we validated the link between inflammation-IKKε and alteration of cellular metabolism. Altogether, we identified a pathway connecting obesity-driven inflammation to breast cancer and a potential therapeutic strategy to reduce the risk of breast cancer associated with obesity.

Biclustering Analysis of Co-regulation Patterns in Nuclear-Encoded Mitochondrial Genes and Metabolic Pathways.

Transcription of a large set of nuclear-encoded genes underlies biogenesis of mitochondria, regulated by a complex network of transcription factors and co-regulators. A remarkable heterogeneity can be detected in the expression of these genes in different cell types and tissues, and the recent availability of large gene expression compendiums allows the quantification of specific mitochondrial biogenesis patterns. We have developed a method to effectively perform this task. Massively correlated biclustering (MCbiclust) is a novel bioinformatics method that has been successfully applied to identify co-regulation patterns in large genesets, underlying essential cellular functions and determining cell types. The method has been recently evaluated and made available as a package in Bioconductor for R. One of the potential applications of the method is to compare expression of nuclear-encoded mitochondrial genes or larger sets of metabolism-related genes between different cell types or cellular metabolic states. Here we describe the essential steps to use MCbiclust as a tool to investigate co-regulation of mitochondrial genes and metabolic pathways.

The mitochondrial calcium uniporter regulates breast cancer progression via HIF-1α.

Triple-negative breast cancer (TNBC) represents the most aggressive breast tumor subtype. However, the molecular determinants responsible for the metastatic TNBC phenotype are only partially understood. We here show that expression of the mitochondrial calcium uniporter (MCU), the selective channel responsible for mitochondrial Ca(2+) uptake, correlates with tumor size and lymph node infiltration, suggesting that mitochondrial Ca(2+) uptake might be instrumental for tumor growth and metastatic formation. Accordingly, MCU downregulation hampered cell motility and invasiveness and reduced tumor growth, lymph node infiltration, and lung metastasis in TNBC xenografts. In MCU-silenced cells, production of mitochondrial reactive oxygen species (mROS) is blunted and expression of the hypoxia-inducible factor-1α (HIF-1α) is reduced, suggesting a signaling role for mROS and HIF-1α, downstream of mitochondrial Ca(2+) Finally, in breast cancer mRNA samples, a positive correlation of MCU expression with HIF-1α signaling route is present. Our results indicate that MCU plays a central role in TNBC growth and metastasis formation and suggest that mitochondrial Ca(2+) uptake is a potential novel therapeutic target for clinical intervention.