Conserved homeodomain proteins interact with MADS box protein Mcm1 to restrict ECB-dependent transcription to the M/G1 phase of the cell cycle

Two homeodomain proteins, Yox1 and Yhp1, act as repressors at early cell cycle boxes (ECBs) to restrict their activity to the M/G1 phase of the cell cycle in budding yeast. These proteins bind to Mcm1 and to a typical homeodomain binding site. The expression of Yox1 is periodic and directly correlated with its binding to, and repression of, ECB activity. The absence of Yox1 and Yhp1 or the constitutive expression of Yox1 leads to the loss of cell-cycle regulation of ECB activity. Therefore, the cell-cycle-regulated expression of these repressors defines the interval of ECB-dependent transcription. Twenty-eight genes, including MCM2-7, CDC6, SWI4, CLN3, and a number of genes required during late M phase have been identified that are coordinately regulated by this pathway

The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle

Transcription patterns shift dramatically as cells transit from one phase of the cell cycle to another. To better define this transcriptional circuitry, we collected new microarray data across the cell cycle of budding yeast. The combined analysis of these data with three other cell cycle data sets identifies hundreds of new highly periodic transcripts and provides a weighted average peak time for each transcript. Using these data and phylogenetic comparisons of promoter sequences, we have identified a late S-phase-specific promoter element. This element is the binding site for the forkhead protein Hcm1, which is required for its cell cycle-specific activity. Among the cell cycle-regulated genes that contain conserved Hcm1-binding sites, there is a significant enrichment of genes involved in chromosome segregation, spindle dynamics, and budding. This may explain why Hcm1 mutants show 10-fold elevated rates of chromosome loss and require the spindle checkpoint for viability. Hcm1 also induces the M-phase-specific transcription factors FKH1, FKH2, and NDD1, and two cell cycle-specific transcriptional repressors, WHI5 and YHP1. As such, Hcm1 fills a significant gap in our understanding of the transcriptional circuitry that underlies the cell cycle

Conserved homvedTqWR proteins

lins,whli directth phctUYNb)[UVY localization and/or substrate specificity of th kinase. Thna is a critical distinction between G1phYE and th rest of th cell cycle, inthw G1 is expandable in response to th environment (Rupe 2002).Th length of G1 is influenced by age,growth conditions, and th size of th cell (Hartwell and Unger 1977;Joh;Uw) et al. 1979). In contrast, onceth cells exit G1, th length of th rest of th cycle is fairly constant(Jagadish and Carter 1977), even after severe nutrient limitation(Johtati et al. 1977). Accumulation of G1 cyclins (Clns) is rate-limiting for th G1 to S transition, and Clns are regulated at virtually every level (Wittenberg et al. 1990; Gallego et al. 1997; Polymenis andSchw][ 1997; MacKay et al. 2001; Newcomb et al. 2002). However, one of th great remaining mysteries iswhw triggersth rapid accumulation of Clns and causesth irreversible transition into Sphb[ in th normal mitotic cycle. Entry into G1 requiresthu Clb kinase act

Next-generation sequencing of human mitochondrial reference genomes uncovers high heteroplasmy frequency

We describe methods for rapid sequencing of the entire human mitochondrial genome (mtgenome), which involve long-range PCR for specific amplification of the mtgenome, pyrosequencing, quantitative mapping of sequence reads to identify sequence variants and heteroplasmy, as well as de novo sequence assembly. These methods have been used to study 40 publicly available HapMap samples of European (CEU) and African (YRI) ancestry to demonstrate a sequencing error rate <5.63×10(-4), nucleotide diversity of 1.6×10(-3) for CEU and 3.7×10(-3) for YRI, patterns of sequence variation consistent with earlier studies, but a higher rate of heteroplasmy varying between 10% and 50%. These results demonstrate that next-generation sequencing technologies allow interrogation of the mitochondrial genome in greater depth than previously possible which may be of value in biology and medicine

Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer.

The head and neck squamous cell carcinoma (HNSCC) transcriptome has been profiled extensively, nevertheless, identifying biomarkers that are clinically relevant and thereby with translational benefit, has been a major challenge. The objective of this study was to use a meta-analysis based approach to catalog candidate biomarkers with high potential for clinical application in HNSCC. Data from publically available microarray series (N = 20) profiled using Agilent (4X44K G4112F) and Affymetrix (HGU133A, U133A_2, U133Plus 2) platforms was downloaded and analyzed in a platform/chip-specific manner (GeneSpring software v12.5, Agilent, USA). Principal Component Analysis (PCA) and clustering analysis was carried out iteratively for segregating outliers; 140 normal and 277 tumor samples from 15 series were included in the final analysis. The analyses identified 181 differentially expressed, concordant and statistically significant genes; STRING analysis revealed interactions between 122 of them, with two major gene clusters connected by multiple nodes (MYC, FOS and HSPA4). Validation in the HNSCC-specific database (N = 528) in The Cancer Genome Atlas (TCGA) identified a panel (ECT2, ANO1, TP63, FADD, EXT1, NCBP2) that was altered in 30% of the samples. Validation in treatment naïve (Group I; N = 12) and post treatment (Group II; N = 12) patients identified 8 genes significantly associated with the disease (Area under curve>0.6). Correlation with recurrence/re-recurrence showed ANO1 had highest efficacy (sensitivity: 0.8, specificity: 0.6) to predict failure in Group I. UBE2V2, PLAC8, FADD and TTK showed high sensitivity (1.00) in Group I while UBE2V2 and CRYM were highly sensitive (>0.8) in predicting re-recurrence in Group II. Further, TCGA analysis showed that ANO1 and FADD, located at 11q13, were co-expressed at transcript level and significantly associated with overall and disease-free survival (p<0.05). The meta-analysis approach adopted in this study has identified candidate markers correlated with disease outcome in HNSCC; further validation in a larger cohort of patients will establish their clinical relevance

A. Heteroplasmic sites per sample.

<p>Total number of heteroplasmic sites found per sample for each YRI and CEU sample. <b>B</b>. <b>Level of heteroplasmy per position across the mitochondrial genome</b>. Top and bottom displays the level of heteroplasmy for all sites found in YRI and CEU samples, respectively, across the mitochondrial genome.</p

Validation of the markers in patients.

<p>Quantitative gene expression profiling of the selected markers was carried out in Group I (primary; A) and the Group II (recurrent; B) cohort. <i>PLAC8</i> and <i>UBE2V2</i> were validated in all the samples (100%) of Group I with regard to regulation trends whereas other genes showed similar trend in >60% of the samples. In Group II, >60% of the patients showed concordant regulation trends for four genes. Based on the patient follow-up, the Group I was sub-categorized into non-recurrent (C) and recurrent (D) and the expression was further evaluated. ROC curve analysis in the Group I patients showed that <i>PLAC8</i> (E), <i>FOS</i> (F), <i>ANO1</i> (G) and <i>UBE2V2</i> (H) had highest association with the disease (AUC >0.8). Bar represents the median fold change of Normals.</p

Identification of Protein-Protein Interaction.

<p>Analysis for protein-protein interaction by STRING network identified two major interconnecting clusters with high degree interactions between the genes (N = 122). These 2 major clusters were interconnected by the nodes MYC, FN1, FOS and HSPA4. The number of lines represent the levels of evidence as indicated in the color legend. The different sizes of the node are based on the extent of protein structural information available for each gene while the colors of the node are a visual aid used for better representation. The markers from this analysis selected for patient validation are encircled.</p