Activation of proinflammatory macrophages is associated with the inflammatory state of rheumatoid arthritis. Their polarization and activation are controlled by transcription factors such as NF-κB and the AP-1 transcription factor member c-Fos. Surprisingly, little is known about the role of the AP-1 transcription factor c-Jun in macrophage activation. In this study, we show that mRNA and protein levels of c-Jun are increased in macrophages following pro- or anti-inflammatory stimulations. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment cluster analyses of microarray data using wild-type and c-Jun-deleted macrophages highlight the central function of c-Jun in macrophages, in particular for immune responses, IL production, and hypoxia pathways. Mice deficient for c-Jun in macrophages show an amelioration of inflammation and bone destruction in the serum-induced arthritis model. In vivo and in vitro gene profiling, together with chromatin immunoprecipitation analysis of macrophages, revealed direct activation of the proinflammatory factor cyclooxygenase-2 and indirect inhibition of the anti-inflammatory factor arginase-1 by c-Jun. Thus, c-Jun regulates the activation state of macrophages and promotes arthritis via differentially regulating cyclooxygenase-2 and arginase-1 levels.

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Epigenetic deregulation remarkably triggers mechanisms associated with tumor aggressiveness like epithelial-mesenchymal transition (EMT). Since EMT is a highly complex, but also reversible event, epigenetic processes such as DNA methylation or chromatin alterations must be involved in its regulation. It was recently described that loss of the cell cycle regulator p21 was associated with a gain in EMT characteristics and an upregulation of the master EMT transcription factor ZEB1. In this study, in silico analysis was performed in combination with different in vitro and in vivo techniques to identify and verify novel epigenetic targets of ZEB1, and to proof the direct transcriptional regulation of SETD1B by ZEB1. The chorioallantoic-membrane assay served as an in vivo model to analyze the ZEB1/SETD1B interaction. Bioinformatical analysis of CRC patient data was used to examine the ZEB1/ SETD1B network under clinical conditions and the ZEB1/SETD1B network was modeled under physiological and pathological conditions. Thus, we identified a self-reinforcing loop for ZEB1 expression and found that the SETD1B associated active chromatin mark H3K4me3 was enriched at the ZEB1 promoter in EMT cells. Moreover, clinical evaluation of CRC patient data showed that the simultaneous high expression of ZEB1 and SETD1B was correlated with the worst prognosis. Here we report that the expression of chromatin modifiers is remarkably dysregulated in EMT cells. SETD1B was identified as a new ZEB1 target in vitro and in vivo. Our study demonstrates a novel example of an activator role of ZEB1 for the epigenetic landscape in colorectal tumor cells.

Gene expression data clustering is one of the important tasks of functional genomics as it provides a powerful tool for studying functional relationships of genes in a biological process. Identifying coexpressed groups of genes represents the basic challenge in gene clustering problem. In this regard, a gene clustering algorithm, termed as robust rough-fuzzy c-means, is proposed judiciously integrating the merits of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in cluster definition, the integration of probabilistic and possibilistic memberships of fuzzy sets enables efficient handling of overlapping partitions in noisy environment. The concept of possibilistic lower bound and probabilistic boundary of a cluster, introduced in robust rough-fuzzy c-means, enables efficient selection of gene clusters. An efficient method is proposed to select initial prototypes of different gene clusters, which enables the proposed c-means algorithm to converge to an optimum or near optimum solutions and helps to discover coexpressed gene clusters. The effectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated both qualitatively and quantitatively on 14 yeast microarray data sets.