Cascading transcriptional effects of a naturally occurring frameshift mutation in Saccharomyces cerevisiae

Gene-expression variation in natural populations is widespread, and its phenotypic effects can be acted upon by natural selection. Only a few naturally segregating genetic differences associated with expression variation have been identified at the molecular level. We have identified a single nucleotide insertion in a vineyard isolate of Saccharomyces cerevisiae that has cascading effects through the gene-expression network. This allele is responsible for about 45% (103/230) of the genes that show differential gene expression among the homozygous diploid progeny produced by a vineyard isolate. Using isogenic laboratory strains, we confirm that this allele causes dramatic differences in gene-expression levels of key genes involved in amino acid biosynthesis. The mutation is a frameshift mutation in a mononucleotide run of eight consecutive T's in the coding region of the gene SSY1 , which encodes a key component of a plasma-membrane sensor of extracellular amino acids. The potentially high rate of replication slippage of this mononucleotide repeat, combined with its relatively mild effects on growth rate in heterozygous genotypes, is sufficient to account for the persistence of this phenotype at low frequencies in natural populations.     

遗传学与基因组学整合课程探讨

基因组学是遗传学的重要学科分支,具有全新研究思维和技术手段,已经形成一个系统而完整的体系。在本科课程中加强基因组学教学是遗传学学科发展的要求,有利于对学生进行科学思维训练、提高学生生物伦理学修养和学习兴趣。整合遗传学与基因组学课程符合学科发展规律和教学规律。目前国内已经基本具备相关教材,通过调整遗传学教学内容,合理选择教学方法,充分利用计算机辅助教学,在本科教学中整合遗传学与基因组学是可行的。     

Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments

With tens of billions of dollars spent each year on the development of drugs to treat human diseases, and with fewer and fewer applications for investigational new drugs filed each year despite this massive spending, questions now abound on what changes to the drug discovery paradigm can be made to achieve greater success. The high rate of failure of drug candidates in clinical development, where the great majority of these drugs fail due to lack of efficacy, speak directly to the need for more innovative approaches to study the mechanisms of disease and drug discovery. Here we review systems biology approaches that have been devised over the last several years to understand the biology of disease at a more holistic level. By integrating a diversity of data like DNA variation, gene expression, protein–protein interaction, DNA–protein binding, and other types of molecular phenotype data, more comprehensive networks of genes both within and between tissues can be constructed to paint a more complete picture of the molecular processes underlying physiological states associated with disease. These more integrative, systems-level methods lead to networks that are demonstrably predictive, which in turn provides a deeper context within which single genes operate such as those identified from genome-wide association studies or those targeted for therapeutic intervention. The more comprehensive views of disease that result from these methods have the potential to dramatically enhance the way in which novel drug targets are identified and developed, ultimately increasing the probability of success for taking new drugs through clinical development. We highlight a number of the integrative approaches via examples that have resulted not only in the identification of novel genes for diabetes and cardiovascular disease, but in more comprehensive networks as well that describe the context in which the disease genes operate.     

An Expression QTL of Closely Linked Candidate Genes Affects pH of Meat in Chickens

Genetical genomics has been suggested as a powerful approach to study the genotype–phenotype gap. However, the relatively low power of these experiments (usually related to the high cost) has hindered fulfillment of its promise, especially for loci (QTL) of moderate effects.One strategy with which to overcome the issue is to use a targeted approach. It has two clear advantages: (i) it reduces the problem to a simple comparison between different genotypic groups at the QTL and (ii) it is a good starting point from which to investigate downstream effects of the QTL. In this study, from 698 F2 birds used for QTL mapping, gene expression profiles of 24 birds with divergent homozygous QTL genotypes were investigated. The targeted QTL was on chromosome 1 and affected initial pH of breast muscle. The biological mechanisms controlling this trait can be similar to those affecting malignant hyperthermia or muscle fatigue in humans. The gene expression study identified 10 strong local signals that were markedly more significant compared to any genes on the rest of the genome. The differentially expressed genes all mapped to a region <1 Mb, suggesting a remarkable reduction of the QTL interval. These results, combined with analysis of downstream effect of the QTL using gene network analysis, suggest that the QTL is controlling pH by governing oxidative stress. The results were reproducible with use of as few as four microarrays on pooled samples (with lower significance level). The results demonstrate that this cost-effective approach is promising for characterization of QTL.     

Network genomics – A novel approach for the analysis of biological systems in the post-genomic era

Network Genomics studies genomics and proteomics foundations of cellular networks in biological systems. It complements systems biology in providing information on elements, their interaction and their functional interplay in cellular networks. The relationship between genomic and proteomic high-throughput technologies and computational methods are described, as well as several examples of specific network genomic application are presented.     

A New System for Comparative Functional Genomics of Saccharomyces Yeasts

Whole-genome sequencing, particularly in fungi, has progressed at a tremendous rate. More difficult, however, is experimental testing of the inferences about gene function that can be drawn from comparative sequence analysis alone. We present a genome-wide functional characterization of a sequenced but experimentally understudied budding yeast, Saccharomyces bayanus var. uvarum (henceforth referred to as S. bayanus), allowing us to map changes over the 20 million years that separate this organism from S. cerevisiae. We first created a suite of genetic tools to facilitate work in S. bayanus. Next, we measured the gene-expression response of S. bayanus to a diverse set of perturbations optimized using a computational approach to cover a diverse array of functionally relevant biological responses. The resulting data set reveals that gene-expression patterns are largely conserved, but significant changes may exist in regulatory networks such as carbohydrate utilization and meiosis. In addition to regulatory changes, our approach identified gene functions that have diverged. The functions of genes in core pathways are highly conserved, but we observed many changes in which genes are involved in osmotic stress, peroxisome biogenesis, and autophagy. A surprising number of genes specific to S. bayanus respond to oxidative stress, suggesting the organism may have evolved under different selection pressures than S. cerevisiae. This work expands the scope of genome-scale evolutionary studies from sequence-based analysis to rapid experimental characterization and could be adopted for functional mapping in any lineage of interest. Furthermore, our detailed characterization of S. bayanus provides a valuable resource for comparative functional genomics studies in yeast.     

基因表达系列分析技术在真菌功能基因组学中的应用

基因表达系列分析( SAGE)是一种在mRNA水平上高通量、快速、灵敏分析细胞或组织基因表达信息,并在基因组学研究中广泛应用的技术.该技术不仅能够全面地分析特定组织或细胞表达的基因,比较不同时空条件下基因表达的差异,还可以在全基因组范围内获得基因的表达谱,从而发现新基因.综述基因表达系列分析技术在材料用量、标签长度、技术流程和标签测序等方面的研究进展及该技术在病原真菌、工业真菌和食用真菌功能基因组学中的应用.     

A transgenic perspective on plant functional genomics

Transgenic crops are very much in the news due to the increasing public debate on their acceptance. In the scientific community though, transgenic plants are proving to be powerful tools to study various aspects of plant sciences. The emerging scientific revolution sparked by genomics based technologies is producing enormous amounts of DNA sequence information that, together with plant transformation methodology, is opening up new experimental opportunities for functional genomics analysis. An overview is provided here on the use of transgenic technology for the functional analysis of plant genes in model plants and a link made to their utilization in transgenic crops. In transgenic plants, insertional mutagenesis using heterologous maize transposons or Agrobacterium mediated T-DNA insertions, have been valuable tools for the identification and isolation of genes that display a mutant phenotype. To discover functions of genes that do not display phenotypes when mutated, insertion sequences have been engineered to monitor or change the expression pattern of adjacent genes. These gene detector insertions can detect adjacent promoters, enhancers or gene exons and precisely reflect the expression pattern of the tagged gene. Activation tag insertions can mis-express the adjacent gene and confer dominant phenotypes that help bridge the phenotype gap. Employment of various forms of gene silencing technology broadens the scope of recovering knockout phenotypes for genes with redundant function. All these transgenic strategies describing gene-phenotype relationships can be addressed by high throughput reverse genetics methods that will help provide functions to the genes discovered by genome sequencing. The gene functions discovered by insertional mutagenesis and silencing strategies along with expression pattern analysis will provide an integrated functional genomics perspective and offer unique applications in transgenic crops. This revised version was published online in August 2006 with corrections to the Cover Date.