Application of metabolome data in functional genomics: a conceptual strategy

A gene with yet unknown physiological function can be studied by changing its expression level followed by analysis of the resulting phenotype. This type of functional genomics study can be complicated by the occurrence of 'silent mutations', the phenotypes of which are not easily observable in terms of metabolic fluxes (e.g., the growth rate). Nevertheless, genetic alteration may give rise to significant yet complicated changes in the metabolome. We propose here a conceptual functional genomics strategy based on microbial metabolome data, which identifies changes in in vivo enzyme activities in the mutants. These predicted changes are used to formulate hypotheses to infer unknown gene functions. The required metabolome data can be obtained solely from high-throughput mass spectrometry analysis, which provides the following in vivo information: (1) the metabolite concentrations in the reference and the mutant strain; (2) the metabolic fluxes in both strains and (3) the enzyme kinetic parameters of the reference strain. We demonstrate in silico that changes in enzyme activities can be accurately predicted by this approach, even in 'silent mutants'.     

From genotype to phenotype in bovine functional genomics

In past 5 years, the promise that came with genome sequencing has revolutionized the functional genomics research field at unprecedented manner. It would soon know what all known genes do, particularly genes involved in genetic improvement of animal health and increase food animal production. With the availability of full bovine genomic sequence, yet we still have a lot of daunting tasks on 'genotype-to-phenotype problem' particularly about the phenotypic variations and trying to predict what genes are likely to be involved, and improved integrated interactive database. This article outlined and discussed about the current status of bovine functional genomics, recent development in bovine genome databases particularly in annotation of bovine genome, bovine quantitative trait loci database and its potential impact to unveil the from genotype-to-phenotype problem.     

Predicting the functional impact of protein mutations: application to cancer genomics

As large-scale re-sequencing of genomes reveals many protein mutations, especially in human cancer tissues, prediction of their likely functional impact becomes important practical goal. Here, we introduce a new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns. The information in these patterns is derived from aligned families and sub-families of sequence homologs within and between species using combinatorial entropy formalism. The score performs well on a large set of human protein mutations in separating disease-associated variants (∼19 200), assumed to be strongly functional, from common polymorphisms (∼35 600), assumed to be weakly functional (area under the receiver operating characteristic curve of ∼0.86). In cancer, using recurrence, multiplicity and annotation for ∼10 000 mutations in the COSMIC database, the method does well in assigning higher scores to more likely functional mutations (‘drivers’). To guide experimental prioritization, we report a list of about 1000 top human cancer genes frequently mutated in one or more cancer types ranked by likely functional impact; and, an additional 1000 candidate cancer genes with rare but likely functional mutations. In addition, we estimate that at least 5% of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.     

Integrating systems biology data to yield functional genomics insights

A report of the recent EMBO Conference 'From Functional Genomics to Systems Biology' held at the EMBL Advanced Training Centre, Heidelberg, Germany, 13-16 November 2010.     

Structural and functional genomics in domestic animals: the way to understand the phenotype

Molecular approaches to genome analysis in livestock are reviewed by discussing the contribution of molecular genome analysis to the identification of the genetic variation underlying phenotypic variation (structural genome analysis) and to the definition of the trait-associated and environment-affected gene expression (functional genome analysis) as an important prerequisite to understanding the formation of a phenotype. Aspects of using mapped 'quantitative trait loci' (QTL) or gene variants as well as the identified trait-associated and environment-affected gene expression profile in livestock production are expounded.     

A metabolome pipeline: from concept to data to knowledge

Metabolomics, like other omics methods, produces huge datasets of biological variables, often accompanied by the necessary metadata. However, regardless of the form in which these are produced they are merely the ground substance for assisting us in answering biological questions. In this short tutorial review and position paper we seek to set out some of the elements of “best practice” in the optimal acquisition of such data, and in the means by which they may be turned into reliable knowledge. Many of these steps involve the solution of what amount to combinatorial optimization problems, and methods developed for these, especially those based on evolutionary computing, are proving valuable. This is done in terms of a “pipeline” that goes from the design of good experiments, through instrumental optimization, data storage and manipulation, the chemometric data processing methods in common use, and the necessary means of validation and cross-validation for giving conclusions that are credible and likely to be robust when applied in comparable circumstances to samples not used in their generation.This revised version was published online in June 2005. The previous version did not contain colour images.     

A screen for mutations that suppress the phenotype of Drosophila armadillo, the beta-catenin homolog

During development signaling pathways coordinate cell fates and regulate the choice between cell survival or programmed cell death. The well-conserved Wingless/Wnt pathway is required for many developmental decisions in all animals. One transducer of the Wingless/Wnt signal is Armadillo/beta-catenin. Drosophila Armadillo not only transduces Wingless signal, but also acts in cell-cell adhesion via its role in the epithelial adherens junction. While many components of both the Wingless/Wnt signaling pathway and adherens junctions are known, both processes are complex, suggesting that unknown components influence signaling and junctions. We carried out a genetic modifier screen to identify some of these components by screening for mutations that can suppress the armadillo mutant phenotype. We identified 12 regions of the genome that have this property. From these regions and from additional candidate genes tested we identified four genes that suppress arm: dTCF, puckered, head involution defective (hid), and Dpresenilin. We further investigated the interaction with hid, a known regulator of programmed cell death. Our data suggest that Wg signaling modulates Hid activity and that Hid regulates programmed cell death in a dose-sensitive fashion.     

A functional genomics approach to the mode of action of apratoxin A

The cyanobacterial metabolite apratoxin A (1) demonstrates potent cytotoxicity against tumor cell lines by a hitherto unknown mechanism. We have used functional genomics to elucidate the molecular basis for this activity. Gene expression profiling and DNA content analysis showed that apratoxin A induces G1-phase cell cycle arrest and apoptosis. Cell-based functional assays with a genome-wide collection of expression cDNAs showed that ectopic induction of fibroblast growth factor receptor (FGFR) signaling attenuates the apoptotic activity of apratoxin A. This natural product inhibited phosphorylation and activation of STAT3, a downstream effector of FGFR signaling. It also caused defects in FGF-dependent processes during zebrafish development, with concomitant reductions in expression levels of the FGF target gene mkp3. We conclude that apratoxin A mediates its antiproliferative activity through the induction of G1 cell cycle arrest and an apoptotic cascade, which is at least partially initiated through antagonism of FGF signaling via STAT3.     

An analysis of extensible modelling for functional genomics data

Background  

Several data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time.     

A functional genomics approach to the study of avian innate immunity

A second-generation 4,959 element cDNA microarray has been created and evaluated for its potential use in examining the avian innate immune response. The elements in this array were obtained from EST libraries of stimulated avian PMNC-derived monocytes/macrophages and supplemented by genes of interest from several specific innate immune pathways. The elements are spotted in triplicate resulting in 14,877 total spots per slide. The avian innate immunity microarray (AIIM) contains 25 avian interleukin, chemokine, and cytokine elements. The array also contains elements for several innate immune pathways, including genes involved in the Toll-like receptor (TLR) pathway (including six of the currently known avian TLR receptors), avian interferon/antiviral response pathway genes, and genes involved in apoptosis, antigen presentation and the oxidative burst. The AIIM can be used to evaluate global gene expression patterns in a number of immunologically relevant tissues and in chickens, turkeys and ducks. The array has also been evaluated for its ability to monitor the avian immune response to both bacterial (avian pathogenic Escherichia coli) and viral (avian influenza) avian pathogens.     

Comparative functional genomics to reveal the molecular basis of phenotypic diversities and guide the genetic breeding of industrial yeast strains

An understanding of the genetic basis underlying the phenotypic variations of yeast strains would guide the breeding of this useful microorganism. Here, comparative functional genomics (CFG) of two bioethanol Saccharomyces cerevisiae strains (YJS329 and ZK2) with different stress tolerances and ethanol fermentation performances were performed. Our analysis indicated that different patterns of gene expression in the central carbon metabolism, antioxidative factors, and membrane compositions of these two strains are the main contributors to their various traits. Some of the differently expressed genes were directly caused by the genomic structural variations between YJS329 and ZK2. Moreover, CFG of these two strains also led to novel insights into the mechanism of stress tolerance in yeast. For example, it was found that more oleic acid in the plasma membrane contributes to the acetic acid tolerance of yeast. Based on the genetic information particular to each strain, strategies to improve their adaptability and ethanol fermentation performances were designed and confirmed. Thus, CFG could not only help reveal basis of phenotypic diversities but also guide the genetic breeding of industrial microorganisms.     

基因组信息的整合与植物功能基因组学研究的策略

近年来,随着许多植物基因组测序和可利用序列的增加,相继建立了一些基于靶基因诱变的“反向”遗传学研究策略,如T—DNA诱变、基因敲除、基因沉默和超表达分析等。同时,DNA微阵列和基因芯片技术的发展使得快速、定量检测植物发育不同时期和不同组织器官的基因转录时空变化成为现实。作图技术的改进和来自不同物种基因组信息的整合也正在加速图谱克隆程序的简化和发展。因此,随着生物基因组测序工作日益增多,整合不同类群植物基因组的信息和资源,在植物功能基因组学研究中的重要性日趋显著。     

A strategy to reveal high-frequency RFLPs along the human X chromosome

Fifteen human X-chromosome-specific DNA fragments, localized to particular regions of that chromosome, were used to search for restriction fragment length polymorphisms. A screening panel prepared by digesting DNA from only two females and one male with 24 restriction enzymes was sufficient to reveal two-allele polymorphisms among one-third of the probes tested. These polymorphisms, as theoretically anticipated, showed minor allele frequencies above 20%, as a rule. Such high-frequency polymorphism allowed identifying females, from pedigrees segregating three X-linked diseases, who were multiply heterozygous for polymorphic loci spread throughout the X chromosome. In addition, two of the 24 enzymes tested with these X-specific probes, Msp I and Taq I, generate fragment sizes in DNA-blotting experiments that, on average, are significantly larger than expected from nearest neighbor predicted recognition site frequencies.     

Simplivariate models: uncovering the underlying biology in functional genomics data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.     

Machine learning of functional class from phenotype data

MOTIVATION: Mutant phenotype growth experiments are an important novel source of functional genomics data which have received little attention in bioinformatics. We applied supervised machine learning to the problem of using phenotype data to predict the functional class of Open Reading Frames (ORFs) in Saccaromyces cerevisiae. Three sources of data were used: TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces (TRIPLES), European Functional Analysis Network (EUROFAN) and Munich Information Center for Protein Sequences (MIPS). The analysis of the data presented a number of challenges to machine learning: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We modified the algorithm C4.5 to deal with these problems. RESULTS: Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 ORFs of unknown function at an estimated accuracy of > or = 80%.     

Deconstructing the neuropathic pain phenotype to reveal neural mechanisms

After nerve injury maladaptive changes can occur in injured sensory neurons and along the entire nociceptive pathway within the CNS, which may lead to spontaneous pain or pain hypersensitivity. The resulting neuropathic pain syndromes present as a complex combination of negative and positive symptoms, which vary enormously from individual to individual. This variation depends on a diversity of underlying pathophysiological changes resulting from the convergence of etiological, genotypic, and environmental factors. The pain phenotype can serve therefore, as a window on underlying pathophysiological neural mechanisms and as a guide for developing personalized pain medicine.