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Bioinformatic identification of genes suppressing genome instability.

ABSTRACT: Unbiased forward genetic screens for mutations causing increased gross chromosomal rearrangement (GCR) rates in Saccharomyces cerevisiae are hampered by the difficulty in reliably using qualitative GCR assays to detect mutants with small but significantly increased GCR rates. We therefore developed a bioinformatic procedure using genome-wide functional genomics screens to identify and prioritize candidate GCR-suppressing genes on the basis of the shared drug sensitivity suppression and similar genetic interactions as known GCR suppressors. The number of known suppressors was increased from 75 to 110 by testing 87 predicted genes, which identified unanticipated pathways in this process. This analysis explicitly dealt with the lack of concordance among high-throughput datasets to increase the reliability of phenotypic predictions. Additionally, shared phenotypes in one assay were imperfect predictors for shared phenotypes in other assays, indicating that although genome-wide datasets can be useful in aggregate, caution and validation methods are required when deciphering biological functions via surrogate measures, including growth-based genetic interactions.

SUBMITTER: Putnam CD

PROVIDER: S-EPMC3511103 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Bioinformatic identification of genes suppressing genome instability.

Putnam Christopher D CD Allen-Soltero Stephanie R SR Martinez Sandra L SL Chan Jason E JE Hayes Tikvah K TK Kolodner Richard D RD

Proceedings of the National Academy of Sciences of the United States of America 20121105 47

Unbiased forward genetic screens for mutations causing increased gross chromosomal rearrangement (GCR) rates in Saccharomyces cerevisiae are hampered by the difficulty in reliably using qualitative GCR assays to detect mutants with small but significantly increased GCR rates. We therefore developed a bioinformatic procedure using genome-wide functional genomics screens to identify and prioritize candidate GCR-suppressing genes on the basis of the shared drug sensitivity suppression and similar g ...[more]

PMID: 23129647

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Project description:Our group has previously identified the activation of a GRAS transcription factor (TF) gene in the gain-of-function mutant population developed through activation tagging in rice (in an indica rice variety, BPT 5204) that was screened for water use efficiency. This family of GRAS transcription factors has been well known for their diverse roles in gibberellin signaling, light responses, root development, gametogenesis etc. Recent studies indicated their role in biotic and abiotic responses as well. Although this family of TFs received significant attention, not many genes were identified specifically for their roles in mediating stress tolerance in rice. Only OsGRAS23 (here named as OsGRAS22) was reported to code for a TF that induced drought tolerance in rice. In the present study, we have analyzed the expression patterns of rice GRAS TF genes under abiotic (NaCl and ABA treatments) and biotic (leaf samples infected with pathogens, Xanthomonas oryzae pv. oryzae that causes bacterial leaf blight and Rhizoctonia solani that causes sheath blight) stress conditions. In addition, their expression patterns were also analyzed in 13 different developmental stages. We studied their spatio-temporal regulation and correlated them with the in-silico studies. Fully annotated genomic sequences available in rice database have enabled us to study the protein properties, ligand interactions, domain analysis and presence of cis-regulatory elements through the bioinformatic approach. Most of the genes were induced immediately after the onset of stress particularly in the roots of ABA treated plants. OsGRAS39 was found to be a highly expressive gene under sheath blight infection and both abiotic stress treatments while OsGRAS8, OsSHR1 and OsSLR1 were also responsive. Our earlier activation tagging based functional characterization followed by the genome-wide characterization of the GRAS gene family members in the present study clearly show that they are highly appropriate candidate genes for manipulating stress tolerance in rice and other crop plants.

Project description:BackgroundBladder cancer (BLCA) is the ninth most common cancer worldwide, with high mortality and recurrence rates. Studies have increasingly reported that molecular diagnosis contributes to the early diagnosis and prognostic assessment of diseases. Thus, this study aims to find new biomarkers for the diagnosis and prognosis of BLCA.MethodsThe microarray datasets GSE147983 and The Cancer Genome Atlas (TCGA)-BLCA mRNA were obtained from the Gene Expression Omnibus (GEO) and TCGA. Differentially expressed genes (DEGs) were screened using the R "Limma" package. The "ClusterProfiler" package was used to conduct Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs. A DEG protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. The functional module was reanalyzed using Cytoscape's Molecular Complex Detection ("MCODE") plugin, and key genes related to BLCA were identified via the "cytoHubba" plugin. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and the Tumor Immune Estimation Resource (TIMER) were used to verify the correlation between hub gene expression and immunity. A survival analysis of hub genes was performed using the Kaplan-Meier Plotter online tool.ResultsA total of 355 DEGs were screened out, including 236 upregulated and 119 downregulated DEGs. Some of the GO terms and pathways, such as chromosome separation, cell cycle, and cell senescence, were found to be significantly enriched in the DEGs. The key genes were kinesin family member 11 (KIF11), DLG associated protein 5 (DLGAP5), non-SMC condensin I complex subunit G (NCAPG), cell division cycle 20 (CDC20), cyclin B2 (CCNB2), BUB1 mitotic checkpoint serine (BUB1B), TPX2 microtubule nucleation factor (TPX2), NUF2 component of NDC80 kinetochore complex (NUF2), kinesin family member 2C (KIF2C), and cyclin B1 (CCNB1). Nine of them were immune-related, including KIF11, DLGAP5, NCAPG, CDC20, CCNB2, BUB1B, NUF2, KIF2C, and CCNB1. Survival analysis showed that the overexpression of BUB1B, CCNB1, CDC20, and DLGAP5 significantly reduced overall survival (OS) in patients with BLCA.ConclusionsThis study provided a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of BLCA by screening potential biomarkers of BLCA.

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Bioinformatic identification of genes suppressing genome instability.

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Bioinformatic identification of genes suppressing genome instability.

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