全基因组范围代谢网络的构建和最小基因组研究

全基因组范围代谢网络（genome-scale metabolic network,GSMN）的构建是合成生物学研究的一个重要研究手段。通过整合各种组学数据和借助计算机进行模拟分析,将基因型与表型的关系进行定量关联,从而为从全局的角度探索和揭示生物代谢机制,进而对生物进行合理的重新设计和工程改造提供了有效的框架。该方法在最小基因组研究中也有着突出的优势,通过计算机辅助的基因组最小化模拟与分析,能够系统鉴定微生物基因组基因的必需性。迄今为止,已有近百个基因组范围的代谢网络发表,覆盖的生物包括原核生物、真核生物和古生生物,并广泛应用于医药、能源、环境、工业和农业等多个领域,展现出了广阔的应用前景。将对全基因组范围代谢网络构建的方法、应用,特别是其在最小基因组研究中的应用作简要的综述。     

Genome-scale expression of proteins from Bacillus subtilis

We have applied high throughput methods for cloning and expression of more than 850 genes from the Bacillus subtilis genome. The process uses 96-well plates and is automated from the level of primer design to the detection of soluble protein by a tag detection screen. This process was applied to a set of cytoplasmic targets from Bacillus subtilis to produce clones expressing soluble protein for incorporation into the structure determination pipeline of the Midwest Center for Structural Genomics. We also evaluated the feasibility of these plate-based methods for domain-based cloning and expression of secretory proteins and putative soluble domains of membrane proteins. This approach shows promise for implementation in a high throughput format and could provide additional target resources for structure determination. The continued development of new technologies that can be implemented in an automated format will be essential for continued success in the structural genomic programs.     

Genome-scale reconstruction and analysis of the metabolic network in the hyperthermophilic archaeon sulfolobus solfataricus

We describe the reconstruction of a genome-scale metabolic model of the crenarchaeon Sulfolobus solfataricus, a hyperthermoacidophilic microorganism. It grows in terrestrial volcanic hot springs with growth occurring at pH 2-4 (optimum 3.5) and a temperature of 75-80°C (optimum 80°C). The genome of Sulfolobus solfataricus P2 contains 2,992,245 bp on a single circular chromosome and encodes 2,977 proteins and a number of RNAs. The network comprises 718 metabolic and 58 transport/exchange reactions and 705 unique metabolites, based on the annotated genome and available biochemical data. Using the model in conjunction with constraint-based methods, we simulated the metabolic fluxes induced by different environmental and genetic conditions. The predictions were compared to experimental measurements and phenotypes of S. solfataricus. Furthermore, the performance of the network for 35 different carbon sources known for S. solfataricus from the literature was simulated. Comparing the growth on different carbon sources revealed that glycerol is the carbon source with the highest biomass flux per imported carbon atom (75% higher than glucose). Experimental data was also used to fit the model to phenotypic observations. In addition to the commonly known heterotrophic growth of S. solfataricus, the crenarchaeon is also able to grow autotrophically using the hydroxypropionate-hydroxybutyrate cycle for bicarbonate fixation. We integrated this pathway into our model and compared bicarbonate fixation with growth on glucose as sole carbon source. Finally, we tested the robustness of the metabolism with respect to gene deletions using the method of Minimization of Metabolic Adjustment (MOMA), which predicted that 18% of all possible single gene deletions would be lethal for the organism.     

Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae

Characterizing gene function is one of the major challenging tasks in the post-genomic era. To address this challenge, we have developed GeneFAS (Gene Function Annotation System), a new integrated probabilistic method for cellular function prediction by combining information from protein-protein interactions, protein complexes, microarray gene expression profiles, and annotations of known proteins through an integrative statistical model. Our approach is based on a novel assessment for the relationship between (1) the interaction/correlation of two proteins' high-throughput data and (2) their functional relationship in terms of their Gene Ontology (GO) hierarchy. We have developed a Web server for the predictions. We have applied our method to yeast Saccharomyces cerevisiae and predicted functions for 1548 out of 2472 unannotated proteins.     

Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential.     

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

Various approaches are used to analyze high-throughput phenotyping data and tools can be developed and assessed using available image-based datasets.     

The application of gene co-expression network reconstruction based on CNVs and gene expression microarray data in breast cancer

Copy number variations (CNVs) are one type of the human genetic variations and are pervasive in the human genome. It has been confirmed that they can play a causal role in complex diseases. Previous studies of CNVs focused more on identifying the disease-specific CNV regions or candidate genes on these CNV regions, but less on the synergistic actions between genes on CNV regions and other genes. Our research combined the CNVs with related gene co-expression to reconstruct gene co-expression network by using single nucleotide polymorphism microarray datasets and gene microarray datasets of breast cancer, and then extracted the modules which connected densely inside and analyzed the functions of modules. Interestingly, all of these modules’ functions were related to breast cancer according to our enrichment analysis, and most of the genes in these modules have been reported to be involved in breast cancer. Our findings suggested that integrating CNVs and gene co-expressed relations was an available way to analyze the roles of CNV genes and their synergistic genes in breast cancer, and provided a novel insight into the pathological mechanism of breast cancer.     

Cloning of the Bacillus subtilis sulfanilamide resistance gene in Bacillus subtilis

A recombinant plasmid was constructed by ligation of chromosomal DNA from a sulfanilamide-resistant strain of Bacillus subtilis to the plasmid vector pUB110 which specifies neomycin resistance. Recombinant molecules generated in vitro were introduced into a B. subtilis recipient strain which carried the recE4 mutation, and selection was for neomycin-sulfanilamide-resistant transformants. A single colony was isolated containing the recombinant plasmid pKO101. This 6.3-megadalton plasmid simultaneously conferred resistance to neomycin and sulfanilamide when transferred into sensitive Rec+ or Rec- cells by either transduction or transformation.     

Automation of gene assignments to metabolic pathways using high-throughput expression data

Background  

Accurate assignment of genes to pathways is essential in order to understand the functional role of genes and to map the existing pathways in a given genome. Existing algorithms predict pathways by extrapolating experimental data in one organism to other organisms for which this data is not available. However, current systems classify all genes that belong to a specific EC family to all the pathways that contain the corresponding enzymatic reaction, and thus introduce ambiguity.     

Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation

Background  

Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties.     

Control of key metabolic intersections in Bacillus subtilis

The remarkable ability of bacteria to adapt efficiently to a wide range of nutritional environments reflects their use of overlapping regulatory systems that link gene expression to intracellular pools of a small number of key metabolites. By integrating the activities of global regulators, such as CcpA, CodY and TnrA, Bacillus subtilis manages traffic through two metabolic intersections that determine the flow of carbon and nitrogen to and from crucial metabolites, such as pyruvate, 2-oxoglutarate and glutamate. Here, the latest knowledge on the control of these key intersections in B. subtilis is reviewed.     

枯草芽孢杆菌代谢工程改造的策略与工具

作为一种食品安全级的典型工业模式微生物,枯草芽孢杆菌Bacillus subtilis由于具有非致病性、胞外分泌蛋白能力强以及无明显的密码子偏爱性等特点,现已被广泛应用于代谢工程领域。近年来,随着分子生物学和基因工程技术等的迅速发展,多种研究策略和工具被用于构建枯草芽孢杆菌底盘细胞进行生物制品的高效合成。文中从启动子工程、基因编辑、基因回路、辅因子工程以及途径酶组装等方面介绍枯草芽孢杆菌在代谢工程领域的研究历程,并总结其在生物制品生产中的相关应用,最后对其未来的研究方向进行展望。     

D-Tyrosine as a metabolic inhibitor of Bacillus subtilis

The d-isomer of tyrosine is a potent inhibitor of growth in transformable strain 168 of Bacillus subtilis. A d-tyrosine-resistant mutant of the inhibited strain was isolated which excreted l-tyrosine, had a diminished growth rate, and required l-phenylalanine to attain the growth rate of the wild-type parent. Mapping by deoxyribonucleate transformation located this resistance in the gene coding for prephenate dehydrogenase. This enzyme in the d-tyrosine-resistant mutant was insensitive to the usual feedback inhibition exerted by l-tyrosine in extracts of strain 168. In contrast, the growth of poorly transformable strain 23 of B. subtilis, as well as that of several other Bacillus species, was not affected by the analogue. Transformation mapping demonstrated no linkage of this latter "natural resistance" to several different aromatic markers. Prephenate dehydrogenase in extracts from strain 23 was as sensitive as that from strain 168 to feedback inhibition by l-tyrosine in vitro. The relationships of the latter results to the regulation of tyrosine biosynthesis and the possible nature of strain differences in d-tyrosine sensitivity are discussed.     

Phylogenetic analysis of Bacillus subtilis and related taxa based on partial gyrA gene sequences

Partial gyrA sequences were determined for twelve strains belonging to Bacillus amyloliquefaciens, B. atrophaeus, B. licheniformis, B. mojavensis,B. subtilis subsp. subtilis, B. subtilissubsp. spizizenii and B. vallismortis. The average nucleotide and translated amino acid similarities for the seven type strains were 83.7 and 95.1%, respectively, whereas the corresponding value for the 16S rRNA sequences was 99.1%. All of the type strains were sharply separated; the closest relationship was found between B. atrophaeus and B. mojavensis which shared a nucleotide similarity of 95.8%. Phylogenetic trees were inferred from gyrA nucleotide sequences using the neighbor-joining, Fitch–Margoliash and maximum parsimony algorithms. The test strains were divided into four groups, which generally reflected results previously reported in restriction digest and DNA-DNA hybridization studies. It is concluded from the comparative sequence analysis that the gyrA sequences provide a firm framework for the rapid and accurate classification and identification of Bacillus subtilis and related taxa.     

Glutamine synthetase gene of Bacillus subtilis

The glutamine synthetase gene (glnA) of Bacillus subtilis was purified from a library of B. subtilis DNA cloned in phage lambda. By mapping the locations of previously identified mutations in the glnA locus it was possible to correlate the genetic and physical maps. Mutations known to affect expression of the glnA gene and other genes were mapped within the coding region for glutamine synthetase, as determined by measuring the sizes of truncated, immunologically cross-reacting polypeptides coded for by various sub-cloned regions of the glnA gene. When the entire B. subtilis glnA gene was present on a plasmid it was capable of directing synthesis in Escherichia coli of B. subtilis glutamine synthetase as judged by enzymatic activity, antigenicity, and ability to allow growth of a glutamine auxotroph. By use of the cloned B. subtilis glnA gene as a hybridization probe, it was shown that the known variability of glutamine synthetase specific activity during growth in various nitrogen sources is fully accounted for by changes in glnA mRNA levels.     

iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations

Background  

Bacillus subtilis is an organism of interest because of its extensive industrial applications, its similarity to pathogenic organisms, and its role as the model organism for Gram-positive, sporulating bacteria. In this work, we introduce a new genome-scale metabolic model of B. subtilis 168 called iBsu1103. This new model is based on the annotated B. subtilis 168 genome generated by the SEED, one of the most up-to-date and accurate annotations of B. subtilis 168 available.