Uncovering genetic control of variation in ethanol tolerance in natural populations of yeast Saccharomyces cerevisiae is essential for understanding the evolution of fermentation, the dominant lifestyle of the species, and for improving efficiency of selection for strains with high ethanol tolerance, a character of great economic value for the brewing and biofuel industries. To date, as many as 251 genes have been predicted to be involved in influencing this character. Candidacy of these genes was determined from a tested phenotypic effect following gene knockout, from an induced change in gene function under an ethanol stress condition, or by mutagenesis. This article represents the first genomics approach for dissecting genetic variation in ethanol tolerance between two yeast strains with a highly divergent trait phenotype. We developed a simple but reliable experimental protocol for scoring the phenotype and a set of STR/SNP markers evenly covering the whole genome. We created a mapping population comprising 319 segregants from crossing the parental strains. On the basis of the data sets, we find that the tolerance trait has a high heritability and that additive genetic variance dominates genetic variation of the trait. Segregation at five QTL detected has explained ∼50% of phenotypic variation; in particular, the major QTL mapped on yeast chromosome 9 has accounted for a quarter of the phenotypic variation. We integrated the QTL analysis with the predicted candidacy of ethanol resistance genes and found that only a few of these candidates fall in the QTL regions.

The average length of genes in a eukaryote is larger than in a prokaryote, implying that evolution of complexity is related to change of gene lengths. Here, we show that although the average lengths of genes in prokaryotes and eukaryotes are much different, the average lengths of genes are highly conserved within either of the two kingdoms. This suggests that natural selection has clearly set a strong limitation on gene elongation within the kingdom. Furthermore, the average gene size adds another distinct characteristic for the discrimination between the two kingdoms of organisms.

Purpose: Lung cancer is the leading cause of cancer-related death in the world, and emerging evidences suggest that lung cancer stem cells (CSC) are associated with its poor prognosis, tumor recurrence, and therapy resistance. Here we reveal a novel role for miR-708-5p in inhibiting lung CSC–like features. Experimental Design: Phenotypic effects of miR-708-5p on the lung CSC–like properties were examined by in vitro sphere formation assay and in xenografted animal models. Immunoblotting, dual luciferase reporter, and immunocytochemistry were performed to determine the target of miR-708-5p. DNA methylation of CDH1 promoter region was tested using bisulfate sequencing. Genome-wide miRNA sequencing data of 990 patients from The Cancer Genome Atlas (TCGA) dataset and 148 patients from China cohort were analyzed to excavate the pathogenic implications of miR-708-5p. Results: Expression of miR-708-5p inhibits the CSC traits of NSCLC cells in vitro while antagonizing miR-708-5p promotes tumorigenesis in vivo. miR-708-5p directly suppresses the translation of DNMT3A, which results in a substantial reduction of global DNA methylation and the upregulated expression of tumor suppressor CDH1. The upregulation of CDH1 decreased the activity of Wnt/β-catenin signaling and then impaired the stemness characteristics of NSCLC cells. Clinically, patients with high miR-708-5p expression show significantly better survival and lower recurrence. Furthermore, miR-708-5p has a promising potential to apply to differentiating histologic subtypes in NSCLC. Conclusions: Our findings support that miR-708-5p suppresses NSCLC initiation, development, and stemness through interfering DNMT3A-dependent DNA methylation. miR-708-5p may function as a novel diagnostic and prognostic biomarker in NSCLC. Clin Cancer Res; 24(7); 1748–60. ©2017 AACR.

MicroRNAs (miRNAs) are increasingly implicated in regulating tumor malignance through their capacity to coordinately repress expression of tumor-related genes. Here, we show that overexpression of miR-194 in lung cancer cell lines, results in suppressing metastasis of lung cancer cells, while inhibiting its expression through ‘miRNA sponge’ promotes the cancer cells to metastasize. miR-194 expression is also found to be in strongly negative association with metastasis in clinical specimens of non-small cell lung cancer. We demonstrate that miR-194 directly targets both BMP1 and p27^kip1. The resulting downregulation of BMP1 leads to suppression of TGFβ activity and, thus, to downregulation of the expression of key oncogenic genes (matrix metalloproteinases MMP2 and MMP9). This leads, in turn, to decreased tumor invasion. In addition, the miRNA-194-induced suppression of p27^kip1 activates the RhoA pathway, producing enhanced development of actin stress fibers and impaired migration of cancer cells. These findings reveal two structurally independent but functionally linked branches of the regulatory and signaling pathway that together provide a bridge between the metastasis-depressing miRNA and the key genes that govern the malignancy of lung cancers.

Linkage analysis in autotetraploid species has been an historical challenge in quantitative genetics theory and is a stumbling block that urgently needs to be removed in the rapidly emerging genome research on this species, such as cultivated potato. This article presents theory of a full model of tetrasomic linkage and develops a statistical framework for the linkage analysis. The model considers both double reduction and recombination, the most essential features of tetrasomic inheritance with linked loci, whereas the statistical method takes appropriate account of the major complexities in analyzing both dominant and codominant molecular marker data during map reconstruction in tetraploid species. These complexities include the problems arising from multiple dosage of allelic inheritance, the null allele, allelic segregation distortion, mixed bivalent and quadrivalent pairing in meiosis, and incomplete information of marker phenotype data. The theoretical analysis established the relationship between the coefficients of double reduction at linked loci, which is essential in the present tetrasomic linkage analysis and in assessing the impact of double reduction on the evolution of tetraploid populations. The statistical method, based on the combination of theoretical analysis and a computer-based algorithm, provided analytical tools for predicting the maximum-likelihood estimates of the model parameters. A simulation study showed the feasibility of a practical implementation of the method, detailed the procedure of the analysis, validated the power and reliability in the parameter estimation, and compared the present method with those proposed in the current literature.