Mapping phenotypic landscapes using DNA micro-arrays

Inverse metabolic engineering is a useful approach for engineering phenotypes in biological systems. The overarching objective of this approach is to combine the power of evolutionary engineering approaches with the precision of constructive metabolic engineering strategies. Often the difficulty in this approach is elucidating the genetic basis of the phenotypes that emerge as a result of evolutionary mechanisms. As a result of advances in genomics technologies, several techniques now exist that substantially improve researchers ability to identify such genes. Metabolic engineers now have the ability to map phenotypic landscapes of considerable genetic diversity, which should improve understanding of the relationships that exist among phenotype, genotype, and environment. In this mini-review, we will discuss several of such genomics tools that may be useful in developing inverse metabolic engineering strategies and, in particular, mapping phenotypic landscapes.     

Metabolite profiling for plant functional genomics

Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of "metabolic phenotypes" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.     

Reflections on the scope and the future of metabolic engineering and its connections to functional genomics and drug discovery

Concepts, experience, and tools from metabolic engineering are immediately applicable to the challenge of understanding how the genome influences phenotype. However, new experimental approaches and mathematical and computational resources are needed to maximize the contributions of metabolic engineering to general questions in functional genomics. Among the priorities are systems for studying physiology on a microscale, theoretical tools for understanding biological control systems, and metabolic simulators "in silico" which provide reasonable predictions of stimulus-response relationships at engineering and medical resolution, with incomplete information on cellular mechanisms and their parameters. Approaching cells as complex systems, already a well-established principle in metabolic engineering, is essential to surmount stagnation in the rate of pharmaceutical discovery which is still based on a naive single-target paradigm.     

A systematic approach to biochemical profiling

Sequencing of the Arabidopsis thaliana genome is complete. The analytical tools for determining gene function by altering and monitoring gene expression are relatively well developed, and are generating large volumes of valuable data. Recent advances in techniques for the analysis of small molecules allow researchers to apply biochemical profiling as another powerful approach to functional genomics and metabolic research.     

Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems

Metabolic profiling using gas chromatography-mass spectrometry technologies is a technique whose potential in the field of functional genomics is largely untapped. To demonstrate the general usefulness of this technique, we applied to diverse plant genotypes a recently developed profiling protocol that allows detection of a wide range of hydrophilic metabolites within a single chromatographic run. For this purpose, we chose four independent potato genotypes characterized by modifications in sucrose metabolism. Using data-mining tools, including hierarchical cluster analysis and principle component analysis, we were able to assign clusters to the individual plant systems and to determine relative distances between these clusters. Extraction analysis allowed identification of the most important components of these clusters. Furthermore, correlation analysis revealed close linkages between a broad spectrum of metabolites. In a second, complementary approach, we subjected wild-type potato tissue to environmental manipulations. The metabolic profiles from these experiments were compared with the data sets obtained for the transgenic systems, thus illustrating the potential of metabolic profiling in assessing how a genetic modification can be phenocopied by environmental conditions. In summary, these data demonstrate the use of metabolic profiling in conjunction with data-mining tools as a technique for the comprehensive characterization of a plant genotype.     

Evolutionary engineering of Saccharomyces cerevisiae for improved industrially important properties

This article reviews evolutionary engineering of Saccharomyces cerevisiae. Following a brief introduction to the 'rational' metabolic engineering approach and its limitations such as extensive genetic and metabolic information requirement on the organism of interest, complexity of cellular physiological responses, and difficulties of cloning in industrial strains, evolutionary engineering is discussed as an alternative, inverse metabolic engineering strategy. Major evolutionary engineering applications with S. cerevisiae are then discussed in two general categories: (1) evolutionary engineering of substrate utilization and product formation and (2) evolutionary engineering of stress resistance. Recent developments in functional genomics methods allow rapid identification of the molecular basis of the desired phenotypes obtained by evolutionary engineering. To conclude, when used alone or in combination with rational metabolic engineering and/or computational methods to study and analyze processes of adaptive evolution, evolutionary engineering is a powerful strategy for improvement in industrially important, complex properties of S. cerevisiae.     

Understanding mouse models of disease through metabolomics

Metabolomics is widely applicable to a number of fields including toxicology, plant metabolism and functional genomics. In the area of functional genomics, a number of studies have demonstrated the potential of this approach, which combines high-throughput metabolite profiling with computer-assisted pattern recognition approaches. In this review, recent applications of metabolomics to understanding mouse models of disease are considered. This includes studies on the impact of mouse strain on disease models, as well as metabolic profiling of cardiovascular, metabolic and neurodegenerative diseases. This versatile tool is set to increase in popularity as functional genomic approaches produce more mouse models for phenotyping.     

植物萜类代谢工程

植物萜类化合物不仅在植物生命活动中起重要作用,而且具有重要商业价值。随着近年来萜类代谢途径和调控机理研究的深入,代谢工程已成为提高萜类产量最有潜力的途径之一。对萜类代谢工程领域具代表性的研究结果进行了全面回顾,然后讨论了萜类代谢工程的研究方法和策略,其中重点探讨了功能基因组学方法在萜类代谢途径及调控机理研究方面的应用。     

Functional and comparative genomics of pathogenic bacteria

Microarray expression profiling and the development of data-mining tools and new statistical instruments affords an unprecedented opportunity for the genome-scale study of bacterial pathogenicity. Expression profiles obtained from bacteria grown in media simulating host microenvironments yield a portrait of interacting metabolic pathways and multistage developmental programs and disclose regulatory networks. The analysis of closely related strains and species by microarray-based comparative genomics provides a measure of genetic variability within natural populations and identifies crucial differences between pathogen and commensal. In the near future, the combined use of bacterial and host microarrays to study the same infected tissue will reveal the host-pathogen dialogue in a gene-by-gene and site- and time-specific manner. This review discusses the use of microarray-based expression profiling to identify genes of pathogenic bacteria that are differentially regulated in response to host-specific signals. Additionally, the review describes the application of microarray methods to disclose differences in gene content between taxonomically related strains that vary with respect to pathogenic phenotype.     

Metabolite profiling of fungi and yeast: from phenotype to metabolome by MS and informatics

Filamentous fungi and yeast from the genera Saccharomyces, Penicillium, Aspergillus, and Fusarium are well known for their impact on our life as pathogens, involved in food spoilage by degradation or toxin contamination, and also for their wide use in biotechnology for the production of beverages, chemicals, pharmaceuticals, and enzymes. The genomes of these eukaryotic micro-organisms range from about 6000 genes in yeasts (S. cerevisiae) to more than 10,000 genes in filamentous fungi (Aspergillus sp.). Yeast and filamentous fungi are expected to share much of their primary metabolism; therefore much understanding of the central metabolism and regulation in less-studied filamentous fungi can be learned from comparative metabolite profiling and metabolomics of yeast and filamentous fungi. Filamentous fungi also have a very active and diverse secondary metabolism in which many of the additional genes present in fungi, compared with yeast, are likely to be involved. Although the 'blueprint' of a given organism is represented by the genome, its behaviour is expressed as its phenotype, i.e. growth characteristics, cell differentiation, response to the environment, the production of secondary metabolites and enzymes. Therefore the profile of (secondary) metabolites--fungal chemodiversity--is important for functional genomics and in the search for new compounds that may serve as biotechnology products. Fungal chemodiversity is, however, equally efficient for identification and classification of fungi, and hence a powerful tool in fungal taxonomy. In this paper, the use of metabolite profiling is discussed for the identification and classification of yeasts and filamentous fungi, functional analysis or discovery by integration of high performance analytical methodology, efficient data handling techniques and core concepts of species, and intelligent screening. One very efficient approach is direct infusion Mass Spectrometry (diMS) integrated with automated data handling, but a full metabolic picture requires the combination of several different analytical techniques.     

Implementation of functional genomics for gene discovery in alkaloid producing plants

Two centuries after the discovery of the first alkaloids, many enzymes involved in plant alkaloid biosynthesis have been identified. Nevertheless, the biosynthetic pathways for most of the plant alkaloids still remain incompletely characterised and understanding the regulatory mechanisms controlling the onset and flux of alkaloid biosynthesis is virtually inexistent. This information is however crucial to allow modelling of metabolic networks and predictive metabolic engineering. In the postgenomics era, new functional genomics tools, enabling comprehensive investigations of biological systems, are continuously emerging and are now gradually being implemented in the field of plant secondary metabolism as well. Here we discuss the advances these promising new technologies have already brought and may still bring with regard to the dissection of plant alkaloid biosynthesis. Encouraging results were obtained in alkaloid producing species such as Papaver somniferum, Catharanthus roseus and Nicotiana tabacum. Therefore we anticipate that functional genomics and the knowledge it brings along, will eventually allow a better exploitation of the plant biosynthetic machinery.     

The path to next generation biofuels: successes and challenges in the era of synthetic biology

Volatility of oil prices along with major concerns about climate change, oil supply security and depleting reserves have sparked renewed interest in the production of fuels from renewable resources. Recent advances in synthetic biology provide new tools for metabolic engineers to direct their strategies and construct optimal biocatalysts for the sustainable production of biofuels. Metabolic engineering and synthetic biology efforts entailing the engineering of native and de novo pathways for conversion of biomass constituents to short-chain alcohols and advanced biofuels are herewith reviewed. In the foreseeable future, formal integration of functional genomics and systems biology with synthetic biology and metabolic engineering will undoubtedly support the discovery, characterization, and engineering of new metabolic routes and more efficient microbial systems for the production of biofuels.     

Production of useful secondary metabolites in plants: Functional genomics approaches

The paradigm of biological research has been changed by recent developments in genomics, high-throughput biology, and bioinformatics. Conventional biology often was based on empirical, labor-intensive, and time-consuming methods. In the new paradigm, biological research e is driven by a holistic approach on the basis of rational, automatic, and high-throughput methods. New functional compounds can be discovered by using high-throughput screening systems. Secondary metabolite pathways and the genes involved in those pathways are then determined by studying functional genomics in conjunction with the data-mining tools of bioinformatics. In addition, these advances in metabolic engineering enable researchers to confer new secondary metabolic pathways to crops by transferring three to five, or more, heterologous genes taken from various other species. In the future, engineering for the production of useful compounds will be designed by a set of software tools that allows the user to specify a cell’s genes, proteins, and other molecules, as well as their individual interactions.     

Enabling inverse metabolic engineering through genomics

Inverse metabolic engineering (IME) is a powerful framework for engineering cellular phenotypes. Progress in this field has been limited by a lack of comprehensive methods for efficiently identifying the genetic basis of relevant phenotypes. Advances in genomics technologies, including DNA microarrays and gene sequencing, have dramatically improved our ability to relate changes in phenotype with associated changes in genotype. When applied in the context of IME, these tools should enable the integration of "evolutionary" and "direct" approaches to engineering cell physiology, which should improve our understanding of the complex interactions affecting the expression, evolution and engineering of traits in natural and industrial hosts.