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。     

The influence of genetics on future crop production strategies: from traits to genes, and genes to traits

This paper discusses how a genetical approach to plant physiology can contribute to research underpinning the production of new crop varieties. It highlights the interactions between genetics and plant breeding and how the current advances in genetics and the new science of genomics can contribute to our understanding of the genetical control of key agronomic traits ‐ the process of ‘translating’ traits to identified and mapped genes. Advances in genomics, such as the sequencing of whole genomes and expressed sequence tags, are producing information on genes and gene structures, but without knowing their function. A great deal more biology will be necessary to translate gene structure to function ‐ the process of translating genes to traits. Combining these ‘forward’ and ‘reverse’ genetic approaches will allow us to get comprehensive knowledge of the biology of agronomic traits at the physiological, biochemical and molecular levels, so that the ‘circuitry’ of our crop plants can be elucidated. This will enable plant breeders to manipulate crop phenotype using marker‐assisted breeding or genetic engineering approaches with a precision not previously possible.     

A functional genomics approach to evaluate candidate genes located in a QTL interval for milk production traits on BTA6

The potential genetic and economic advantage of marker-assisted selection for enhanced production in dairy cattle has provided an impetus to conduct numerous genome scans in order to identify associations between DNA markers and future productive potential. One area of focus has been a quantitative trait locus on bovine chromosome 6 (BTA6) found to be associated with milk yield, milk protein and fat percentage, which has been subsequently fine-mapped to six positional candidate genes. Subsequent investigations have yet to resolve which of the potential positional candidate genes is responsible for the observed associations with productive performance. In this study, we analysed candidate gene expression and the effects of gene knockdown on expression of β- and κ-casein mRNA in a small interfering RNA transfected bovine in vitro mammosphere model. From our expression studies in vivo , we observed that four of the six candidates ( ABCG2 , SPP1 , PKD2 and LAP3 ) exhibited differential expression in bovine mammary tissue over the lactation cycle, but in vitro functional studies indicate that inhibition of only one gene, SPP1 , had a significant impact on milk protein gene expression. These data suggest that the gene product of SPP1 (also known as osteopontin) has a significant role in the modulation of milk protein gene expression. While these findings do not exclude other positional candidates from influencing lactation, they support the hypothesis that the gene product of SPP1 is a significant lactational regulatory molecule.     

Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice

To identify genetic loci influencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identified one significant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to find cis-regulated expression QTL. We then narrowed the list of candidate genes under significant QTL using a "toolbox" of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis-regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, five genes (Polr3 h, Cyp2d22, Cyp2d26, Tspo, and Ttll12) as candidate genes for Chr 15 QTL. This study shows how bioinformatics can be used effectively to reduce candidate gene lists for QTL related to complex traits.     

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.     

Applications of integrative OMICs approaches to gene regulation studies

Background: Functional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.Results: This article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi-dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms. Conclusions: To better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi-dimensional OMICs data will generate novel insights into system-level gene regulations and serves as a foundation for further hypothesis-driven research.     

Resources and transgenesis techniques for functional genomics in Xenopus

Recent developments in genomic resources and high‐throughput transgenesis techniques have allowed Xenopus to ‘metamorphose’ from a classic model for embryology to a leading‐edge experimental system for functional genomics. This process has incorporated the fast‐breeding diploid frog, Xenopus tropicalis, as a new model‐system for vertebrate genomics and genetics. Sequencing of the X. tropicalis genome is nearly complete, and its comparison with mammalian sequences offers a reliable guide for the genome‐wide prediction of cis‐regulatory elements. Unique cDNA sets have been generated for both X. tropicalis and X. laevis, which have facilitated non‐redundant, systematic gene expression screening and comprehensive gene expression analysis. A variety of transgenesis techniques are available for both X. laevis and X. tropicalis, and the appropriate procedure may be chosen depending on the purpose for which it is required. Effective use of these resources and techniques will help to reveal the overall picture of the complex wiring of gene regulatory networks that control vertebrate development.     

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.     

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.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.     

Newly evolved genes: Moving from comparative genomics to functional studies in model systems

Genes are gained and lost over the course of evolution. A recent study found that over 1,800 new genes have appeared during primate evolution and that an unexpectedly high proportion of these genes are expressed in the human brain. But what are the molecular functions of newly evolved genes and what is their impact on an organism's fitness? The acquisition of new genes may provide a rich source of genetic diversity that fuels evolutionary innovation. Although gene manipulation experiments are not feasible in humans, studies in model organisms, such as Drosophila melanogaster, have shown that new genes can quickly become integrated into genetic networks and become essential for survival or fertility. Future studies of new genes, especially chimeric genes, and their functions will help determine the role of genetic novelty in the adaptation and diversification of species.