Mouse anti-Human ZNF24 monoclonal antibody, clone 3H6

Myeloid zinc finger 1 (MZF1) is a transcription factor that plays an important role in blood cell development. Previous reports indicate MZF1 is an essential factor whose abnormal expression results in cancer. However, the molecular mechanisms by which MZF1 functions in development and contributes to cancer progression remain unknown. MZF1 is a member of the SCAN domain family of zinc finger proteins (SCAN-ZFP) that form dimers via their highly conserved SCAN motif. To better understand the molecular mechanism of MZF1 function, we sought to characterize the cellular localization pattern of MZF1 in the context of SCAN dimerization. Here we provide evidence that MZF1 is a constituent of promyelocytic leukemia nuclear bodies (PML-NBs) and that the SCAN domain is necessary for association with these intranuclear structures. In addition, the SCAN-ZFP member ZNF24 was identified as a novel heterodimeric partner of MZF1 that also associates with PML-NBs in a unique ring-type pattern. Finally, we provide support that MZF1 protein may be modified by SUMOylation, which provides further support for localization of MZF1 protein complexes to PML-NBs. Altogether, these data suggest that MZF1 is recruited to PML-NBs and that the SCAN domain may play an integral role in regulating the localization of heterodimeric protein complexes to these intranuclear structures. (c) 2008 Elsevier Ltd. All rights reserved.

Background: Joint modeling and analysis of phenotypic, genotypic and transcriptomic data have the potential to uncover the genetic control of gene activity and phenotypic variation, as well as shed light on the manner and extent of connectedness among these variables. Current studies mainly report associations, i.e. undirected connections among variables without causal interpretation. Knowledge regarding causal relationships among genes and phenotypes can be used to predict the behavior of complex systems, as well as to optimize management practices and selection strategies. Here, we performed a multistep procedure for inferring causal networks underlying carcass fat deposition and muscularity in pigs using multi-omics data obtained from an F-2 Duroc x Pietrain resource pig population. Results: We initially explored marginal associations between genotypes and phenotypic and expression traits through whole-genome scans, and then, in genomic regions with multiple significant hits, we assessed gene-phenotype network reconstruction using causal structural learning algorithms. One genomic region on SSC6 showed significant associations with three relevant phenotypes, off-midline10th-rib backfat thickness, loin muscle weight, and average intramuscular fat percentage, and also with the expression of seven genes, including ZNF24, SSX2IP, and AKR7A2. The inferred network indicated that the genotype affects the three phenotypes mainly through the expression of several genes. Among the phenotypes, fat deposition traits negatively affected loin muscle weight. Conclusions: Our findings shed light on the antagonist relationship between carcass fat deposition and lean meat content in pigs. In addition, the procedure described in this study has the potential to unravel gene-phenotype networks underlying complex phenotypes.