Dataset Information

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.

ABSTRACT: Cys(2)-His(2) zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

SUBMITTER: Gupta A

PROVIDER: S-EPMC4005693 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.

Gupta Ankit A Christensen Ryan G RG Bell Heather A HA Goodwin Mathew M Patel Ronak Y RY Pandey Manishi M Enuameh Metewo Selase MS Rayla Amy L AL Zhu Cong C Thibodeau-Beganny Stacey S Brodsky Michael H MH Joung J Keith JK Wolfe Scot A SA Stormo Gary D GD

Nucleic acids research 20140212 8

Cys(2)-His(2) zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large numbe ...[more]

PMID: 24523353

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Project description:Osteosarcoma (OS) is the most frequent malignant primary bone tumor in children and young adults. Zinc finger Asp-His-His-Cys palmitoyl-acyltransferase 19 (ZDHHC19) is a key enzyme in protein palmitoylation and plays crucial roles in tumor progression. However, its expression profile and biological function in OS have been unclear. In the present study, the expression level of ZDHHC19 in OS cell lines was determined by qRT-PCR and Western blot. The effect of ZDHHC19 in cell growth, invasion and migration was analyzed by CCK8, EDU, transwell, wound healing assay in vitro, and xenograft tumor model in vivo. In addition, bioinformatics analysis was used to explore the potential mechanism of ZDHHC19 in OS. Furthermore, the luciferase reporter assay was conducted to determine the direct binding between miR-940 and ZDHHC19. We discovered that ZDHHC19 was overexpressed in OS cells compared with the normal cells. The functional investigation demonstrated that ZDHHC19 silencing could inhibit proliferation, invasion and migration of OS in vitro and suppress tumorigenicity and lung metastasis in a xenograft model in vivo. Mechanistically, we identified that ZDHHC19 was a direct target of miR-940 and forced ZDHHC19 expressions partially rescue the suppression of proliferation, migration and invasion induced by miR-940. Moreover, bioinformatics analysis combined with validation experiments revealed that activating wnt/β-catenin pathway contributed to the pro-oncogenic effect induced by ZDHHC19. Furthermore, rescue experiments further verified that miR-940/ZDHHC19 axis regulated wnt/β-catenin pathway. Overall, these findings indicated that miR-940/ZDHHC19 axis played a significant role in OS progression and might be considered as a novel target for OS treatment.Abbreviations: OS, osteosarcoma; miRNAs, microRNAs; 3'-UTR, 3'- untranslated region; TARGET, Therapeutically Applicable Research To Generate Effective Treatments; qRT-PCR, quantitative real-time PCR; IHC, Immunohistochemistry; GSVA, Gene Set Variation Analysis; GSEA, Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Dataset Information

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.

Publications

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.

Similar Datasets

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