从酵母表达时间序列估计基因调控网络

基因调控网络是生命功能在基因表达层面上的展现。用组合线性调控模型、调控元件识别和基因聚类等方法 ,从基因组表达谱解读酵母在细胞周期与环境胁迫中的基因调控网络。结果表明 ,细胞在不同环境条件下会调整基因调控网络。在适应的环境下 ,起主要作用的是细胞生长和增殖有关的基因调控网络 ;而在响应环境胁迫时 ,细胞会再规划调控网络 ,抑制细胞生长和增殖相关的基因 ,诱导跟适应性糖类代谢与结构修复相关的基因 ,还可能启动减数分裂产生孢子。分别从细胞周期和环境胁迫响应相关基因中 ,搜索到转录因子Mcm1结合位点TT CC T GGAAA ,和Dal82在尿囊素代谢途径相关基因上的结合位点TGAAAAWTTT。从而 ,从酵母表达时间序列估计基因调控网络是可行的 ,与至今已知的实验观察相当吻合     

Proteogenomic approaches for the molecular characterization of natural microbial communities

At the present time we know little about how microbial communities function in their natural habitats. For example, how do microorganisms interact with each other and their physical and chemical surroundings and respond to environmental perturbations? We might begin to answer these questions if we could monitor the ways in which metabolic roles are partitioned amongst members as microbial communities assemble, determine how resources such as carbon, nitrogen, and energy are allocated into metabolic pathways, and understand the mechanisms by which organisms and communities respond to changes in their surroundings. Because many organisms cannot be cultivated, and given that the metabolisms of those growing in monoculture are likely to differ from those of organisms growing as part of consortia, it is vital to develop methods to study microbial communities in situ. Chemoautotrophic biofilms growing in mine tunnels hundreds of meters underground drive pyrite (FeS(2)) dissolution and acid and metal release, creating habitats that select for a small number of organism types. The geochemical and microbial simplicity of these systems, the significant biomass, and clearly defined biological-inorganic feedbacks make these ecosystem microcosms ideal for development of methods for the study of uncultivated microbial consortia. Our approach begins with the acquisition of genomic data from biofilms that are sampled over time and in different growth conditions. We have demonstrated that it is possible to assemble shotgun sequence data to reveal the gene complement of the dominant community members and to use these data to confidently identify a significant fraction of proteins from the dominant organisms by mass spectrometry (MS)-based proteomics. However, there are technical obstacles currently restricting this type of "proteogenomic" analysis. Composite genomic sequences assembled from environmental data from natural microbial communities do not capture the full range of genetic potential of the associated populations. Thus, it is necessary to develop bioinformatics approaches to generate relatively comprehensive gene inventories for each organism type. These inventories are critical for expression and functional analyses. In proteomic studies, for example, peptides that differ from those predicted from gene sequences can be measured, but they generally cannot be identified by database matching, even if the difference is only a single amino acid residue. Furthermore, many of the identified proteins have no known function. We propose that these challenges can be addressed by development of proteogenomic, biochemical, and geochemical methods that will be initially deployed in a simple, natural model ecosystem. The resulting approach should be broadly applicable and will enhance the utility and significance of genomic data from isolates and consortia for study of organisms in many habitats. Solutions draining pyrite-rich deposits are referred to as acid mine drainage (AMD). AMD is a very prevalent, international environmental problem associated with energy and metal resources. The biological-mineralogical interactions that define these systems can be harnessed for energy-efficient metal recovery and removal of sulfur from coal. The detailed understanding of microbial ecology and ecosystem dynamics resulting from the proposed work will provide a scientific foundation for dealing with the environmental challenges and technological opportunities, and yield new methods for analysis of more complex natural communities.     

Recent advances in the genomic analysis of Candida albicans

The release of the diploid genomic sequence of Candida albicans and its recent community-based annotation have permitted a number of studies which have significantly advanced our understanding of the biology of this important human pathogen. These advances range from analysis of genomic changes to differential gene expression under a variety of conditions. A few general conclusions can be drawn from the data presently in hand; one can expect more and more new insights as the number and kind of experiments grows.     

Analysis of Genome-scale Expression Network in Four Major Bacterial Residents of Cystic Fibrosis Lung

In polymicrobial communities where several species co-exist in a certain niche and consequently the possibility of interactions among species is very high, gene expression data sources can give better insights in to underlying adaptation mechanisms assumed by bacteria. Furthermore, several possible synergistic or antagonistic interactions among species can be investigated through gene expression comparisons. Lung is one of the habitats harboring several distinct pathogens during severe pulmonary disorders such as chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). Expression data analysis of these lung residents can help to gain a better understanding on how these species interact with each other within the host cells. The first part of this paper deals with introducing available data sources for the major bacteria responsible for causing lung diseases and their genomic relations. In the second part, the main focus is on the studies concerning gene expression analyses of these species.     

Genomics of cellular proliferation in periodic environmental fluctuations

Living systems control cell growth dynamically by processing information from their environment. Although responses to a single environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here, we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out‐of‐equilibrium conditions: periodic oscillations between two environmental conditions. In periodic salt stress, fitness and its genetic variance largely depended on the oscillating period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation under short stress periods, revealing unexpected controllers of growth under fast dynamics. We validated the implication of the high‐affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this group. Periodic oscillations of extracellular methionine, a factor unrelated to salinity, also altered fitness but to a lesser extent and for different genes. The results illustrate how natural selection acts on mutations in a dynamic environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes.     

Strategy of transcription regulation in the budding yeast

Cells must adjust their gene expression in order to compete in a constantly changing environment. Two alternative strategies could in principle ensure optimal coordination of gene expression with physiological requirements. First, characters of the internal physiological state, such as growth rate, metabolite levels, or energy availability, could be feedback to tune gene expression. Second, internal needs could be inferred from the external environment, using evolutionary-tuned signaling pathways. Coordination of ribosomal biogenesis with the requirement for protein synthesis is of particular importance, since cells devote a large fraction of their biosynthetic capacity for ribosomal biogenesis. To define the relative contribution of internal vs. external sensing to the regulation of ribosomal biogenesis gene expression in yeast, we subjected S. cerevisiae cells to conditions which decoupled the actual vs. environmentally-expected growth rate. Gene expression followed the environmental signal according to the expected, but not the actual, growth rate. Simultaneous monitoring of gene expression and growth rate in continuous cultures further confirmed that ribosome biogenesis genes responded rapidly to changes in the environments but were oblivious to longer-term changes in growth rate. Our results suggest that the capacity to anticipate and prepare for environmentally-mediated changes in cell growth presented a major selection force during yeast evolution.