Reactive oxygen species ( ROS ) are emerging as important regulators of cancer growth and metastatic spread. However, how cells integrate redox signals to affect cancer progression is not fully understood. Mitochondria are cellular redox hubs, which are highly regulated by interactions with neighboring organelles. Here, we investigated how ROS at the endoplasmic reticulum ( ER )–mitochondria interface are generated and translated to affect melanoma outcome. We show that TMX 1 and TMX 3 oxidoreductases, which promote ER –mitochondria communication, are upregulated in melanoma cells and patient samples. TMX knockdown altered mitochondrial organization, enhanced bioenergetics, and elevated mitochondrial‐ and NOX 4‐derived ROS . The TMX ‐knockdown‐induced oxidative stress suppressed melanoma proliferation, migration, and xenograft tumor growth by inhibiting NFAT 1. Furthermore, we identified NFAT 1‐positive and NFAT 1‐negative melanoma subgroups, wherein NFAT 1 expression correlates with melanoma stage and metastatic potential. Integrative bioinformatics revealed that genes coding for mitochondrial‐ and redox‐related proteins are under NFAT 1 control and indicated that TMX 1, TMX 3, and NFAT 1 are associated with poor disease outcome. Our study unravels a novel redox‐controlled ER –mitochondria– NFAT 1 signaling loop that regulates melanoma pathobiology and provides biomarkers indicative of aggressive disease.

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BackgroundIdentifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. In this work, we reformulate this problem as the optimization problems of computing a Minimum Dominating Set and a Minimum Connected Dominating Set for directed graphs.ResultsBoth MDS and MCDS are applied to the well-studied gene regulatory networks of the model organisms E. coli and S. cerevisiae and to a pluripotency network for mouse embryonic stem cells. The results show that MCDS can capture most of the known key player genes identified so far in the model organisms. Moreover, this method suggests an additional small set of transcription factors as novel key players for governing the cell-specific gene regulatory network which can also be investigated with regard to diseases. To this aim, we investigated the ability of MCDS to define key drivers in breast cancer. The method identified many known drug targets as members of the MDS and MCDS.ConclusionsThis paper proposes a new method to identify key player genes in gene regulatory networks. The Java implementation of the heuristic algorithm explained in this paper is available as a Cytoscape plugin at http://apps.cytoscape.org/apps/mcds. The SageMath programs for solving integer linear programming formulations used in the paper are available at https://github.com/maryamNazarieh/KeyRegulatoryGenesand as supplementary material.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0329-5) contains supplementary material, which is available to authorized users.

Motivation: Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. In particular, complexation of transcription factors (TFs) and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enables us to infer controlled genes as well as the exerted regulatory mechanism. Common approaches for protein complex prediction that only use protein interaction networks, however, are designed to detect self-contained functional complexes and have difficulties to reveal dynamic combinatorial assemblies of physically interacting proteins.Results: We developed the novel algorithm DACO that combines protein–protein interaction networks and domain–domain interaction networks with the cluster-quality metric cohesiveness. The metric is locally maximized on the holistic level of protein interactions, and connectivity constraints on the domain level are used to account for the exclusive and thus inherently combinatorial nature of the interactions within such assemblies. When applied to predicting TF complexes in the yeast Saccharomyces cerevisiae, the proposed approach outperformed popular complex prediction methods by far. Furthermore, we were able to assign many of the predictions to target genes, as well as to a potential regulatory effect in agreement with literature evidence.Availability and implementation: A prototype implementation is freely available at https://sourceforge.net/projects/dacoalgorithm/.Contact: volkhard.helms@bioinformatik.uni-saarland.deSupplementary information: Supplementary data are available at Bioinformatics online.