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PubMed2002

The family of toxin-related ecto-ADP-ribosyltransferases in humans and the mouse.

ADP-ribosyltransferases including toxins secreted by Vibrio cholera, Pseudomonas aerurginosa, and other pathogenic bacteria inactivate the function of human target proteins by attaching ADP-ribose onto a critical amino acid residue. Cross-species polymerase chain reaction (PCR) and database mining identified the orthologs of these ADP-ribosylating toxins in humans and the mouse. The human genome contains four functional toxin-related ADP-ribosyltransferase genes (ARTs) and two related intron-containing pseudogenes; the mouse has six functional orthologs. The human and mouse ART genes map to chromosomal regions with conserved linkage synteny. The individual ART genes reveal highly restricted expression patterns, which are largely conserved in humans and the mouse. We confirmed the predicted extracellular location of the ART proteins by expressing recombinant ARTs in insect cells. Two human and four mouse ARTs contain the active site motif (R-S-EXE) typical of arginine-specific ADP-ribosyltransferases and exhibit the predicted enzyme activities. Two other human ARTs and their murine orthologues deviate in the active site motif and lack detectable enzyme activity. Conceivably, these ARTs may have acquired a new specificity or function. The position-sensitive iterative database search program PSI-BLAST connected the mammalian ARTs with most known bacterial ADP-ribosylating toxins. In contrast, no related open reading frames occur in the four completed genomes of lower eucaryotes (yeast, worm, fly, and mustard weed). Interestingly, these organisms also lack genes for ADP-ribosylhydrolases, the enzymes that reverse protein ADP-ribosylation. This suggests that the two enzyme families that catalyze reversible mono-ADP-ribosylation either were lost from the genomes of these nonchordata eucaryotes or were subject to horizontal gene transfer between kingdoms.

ADP Ribose Transferases↗
PubMed2006

Theodor Bücher Lecture. Metabolomics, modelling and machine learning in systems biology - towards an understanding of the languages of cells. Delivered on 3 July 2005 at the 30th FEBS Congress and the 9th IUBMB conference in Budapest.

The newly emerging field of systems biology involves a judicious interplay between high-throughput 'wet' experimentation, computational modelling and technology development, coupled to the world of ideas and theory. This interplay involves iterative cycles, such that systems biology is not at all confined to hypothesis-dependent studies, with intelligent, principled, hypothesis-generating studies being of high importance and consequently very far from aimless fishing expeditions. I seek to illustrate each of these facets. Novel technology development in metabolomics can increase substantially the dynamic range and number of metabolites that one can detect, and these can be exploited as disease markers and in the consequent and principled generation of hypotheses that are consistent with the data and achieve this in a value-free manner. Much of classical biochemistry and signalling pathway analysis has concentrated on the analyses of changes in the concentrations of intermediates, with 'local' equations - such as that of Michaelis and Menten v=(Vmax x S)/(S+K m) - that describe individual steps being based solely on the instantaneous values of these concentrations. Recent work using single cells (that are not subject to the intellectually unsupportable averaging of the variable displayed by heterogeneous cells possessing nonlinear kinetics) has led to the recognition that some protein signalling pathways may encode their signals not (just) as concentrations (AM or amplitude-modulated in a radio analogy) but via changes in the dynamics of those concentrations (the signals are FM or frequency-modulated). This contributes in principle to a straightforward solution of the crosstalk problem, leads to a profound reassessment of how to understand the downstream effects of dynamic changes in the concentrations of elements in these pathways, and stresses the role of signal processing (and not merely the intermediates) in biological signalling. It is this signal processing that lies at the heart of understanding the languages of cells. The resolution of many of the modern and postgenomic problems of biochemistry requires the development of a myriad of new technologies (and maybe a new culture), and thus regular input from the physical sciences, engineering, mathematics and computer science. One solution, that we are adopting in the Manchester Interdisciplinary Biocentre (http://www.mib.ac.uk/) and the Manchester Centre for Integrative Systems Biology (http://www.mcisb.org/), is thus to colocate individuals with the necessary combinations of skills. Novel disciplines that require such an integrative approach continue to emerge. These include fields such as chemical genomics, synthetic biology, distributed computational environments for biological data and modelling, single cell diagnostics/bionanotechnology, and computational linguistics/text mining.

Artificial Intelligence↗
PubMed2001

BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes.

The development of genome sequencing and DNA microarray analysis of gene expression gives rise to the demand for data-mining tools. BioProspector, a C program using a Gibbs sampling strategy, examines the upstream region of genes in the same gene expression pattern group and looks for regulatory sequence motifs. BioProspector uses zero to third-order Markov background models whose parameters are either given by the user or estimated from a specified sequence file. The significance of each motif found is judged based on a motif score distribution estimated by a Monte Carlo method. In addition, BioProspector modifies the motif model used in the earlier Gibbs samplers to allow for the modeling of gapped motifs and motifs with palindromic patterns. All these modifications greatly improve the performance of the program. Although testing and development are still in progress, the program has shown preliminary success in finding the binding motifs for Saccharomyces cerevisiae RAP1, Bacillus subtilis RNA polymerase, and Escherichia coli CRP. We are currently working on combining BioProspector with a clustering program to explore gene expression networks and regulatory mechanisms.

Bacillus subtilis↗
PubMed2006

The UCSC genome browser database: update 2007.

The University of California, Santa Cruz Genome Browser Database contains, as of September 2006, sequence and annotation data for the genomes of 13 vertebrate and 19 invertebrate species. The Genome Browser displays a wide variety of annotations at all scales from the single nucleotide level up to a full chromosome and includes assembly data, genes and gene predictions, mRNA and EST alignments, and comparative genomics, regulation, expression and variation data. The database is optimized for fast interactive performance with web tools that provide powerful visualization and querying capabilities for mining the data. In the past year, 22 new assemblies and several new sets of human variation annotation have been released. New features include VisiGene, a fully integrated in situ hybridization image browser; phyloGif, for drawing evolutionary tree diagrams; a redesigned Custom Track feature; an expanded SNP annotation track; and many new display options. The Genome Browser, other tools, downloadable data files and links to documentation and other information can be found at http://genome.ucsc.edu/.

Animals↗
PubMed2025

RNAi screening of uncharacterized genes identifies promising druggable targets in Schistosoma japonicum.

Schistosomiasis affects more than 250 million people worldwide and is one of the neglected tropical diseases. Currently, the treatment of schistosomiasis relies on a single drug-praziquantel-which has led to increasing pressure from drug resistance. Therefore, there is an urgent need to find new treatments. The development of genome sequencing has provided valuable information for understanding the biology of schistosomes. In the genome of Schistosoma japonicum, approximately 11% of the protein-coding sequences are uncharacterized genes (UGs) annotated as "hypothetical protein" or "protein of unknown function." These poorly understood genes have been unjustifiably neglected, although some may be essential for the survival of the parasites and serve as potential drug targets. In this study, we systematically mined the highly expressed UGs in both genders of this parasite throughout key developmental stages in their mammalian host, using our previously published S. japonicum genome and RNA-seq data. By employing in vitro RNA interference (RNAi), we screened 126 UGs that lack homologs in Homo sapiens and identified 8 that are essential for the parasite vitality. We further investigated two UGs, Sjc_0002003 and Sjc_0009272, which resulted in the most severe phenotypes. Fluorescence in situ hybridization demonstrated that both genes were expressed throughout the body without sex bias. Silencing either Sjc_0002003 or Sjc_0009272 reduced the cell proliferation in the body. Furthermore, in vivo RNAi indicated both genes are required for the growth and survival of the parasites in the mammalian host. For Sjc_0002003, we further characterize the underlying molecular cause of the observed phenotype. Through RNA-seq analysis and functional studies, we revealed that silencing Sjc_0002003 reduces the expression of a series of intestinal genes, including Sjc_0007312 (hypothetical protein), Sjc_0008276 (vha-17), Sjc_0002942 (PLA2G15), and Sjc_0003646 (SJCHGC09134 protein), leading to gut dilation. Our work highlights the importance of UGs in schistosomes as promising targets for drug development in the treatment of the schistosomiasis.

Schistosoma japonicum↗
PubMed2005

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.

Ecosystem↗
PubMed1994

Characterization of the diversity of sulfate-reducing bacteria in soil and mining waste water environments by nucleic acid hybridization techniques.

Nucleic acid hybridization techniques were used to characterize the sulfate-reducing bacterial communities at seven waste water and two soil sites in Canada. Genomic DNA was obtained from liquid enrichment cultures of samples taken from these nine sites. The liquid enrichment protocol favored growth of the sulfate-reducing bacterial component of the communities at these sites. The genomic DNA preparations were analyzed with (i) a specific gene probe aimed at a single genus (Desulfovibrio), (ii) a general 16S rRNA gene probe aimed at all genera of sulfate-reducing bacteria and other bacteria, and (iii) whole genome probes aimed at specific bacteria. This three-pronged approach provided information on the sulfate-reducing bacterial community structures for the nine sites. These were compared with each other and with the sulfate-reducing bacterial communities of western Canadian oil field production waters, studied previously. It was found that there is considerable diversity in the sulfate-reducing bacterial community at each site. Most sulfate-reducing bacteria isolated from distinct sites are genomically different and differ also from sulfate-reducing bacteria found in oil field production waters.

Bacteria↗
PubMed2004

Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling.

Current applications of microarrays focus on precise classification or discovery of biological types, for example tumor versus normal phenotypes in cancer research. Several challenging scientific tasks in the post-genomic epoch, like hunting for the genes underlying complex diseases from genome-wide gene expression profiles and thereby building the corresponding gene networks, are largely overlooked because of the lack of an efficient analysis approach. We have thus developed an innovative ensemble decision approach, which can efficiently perform multiple gene mining tasks. An application of this approach to analyze two publicly available data sets (colon data and leukemia data) identified 20 highly significant colon cancer genes and 23 highly significant molecular signatures for refining the acute leukemia phenotype, most of which have been verified either by biological experiments or by alternative analysis approaches. Furthermore, the globally optimal gene subsets identified by the novel approach have so far achieved the highest accuracy for classification of colon cancer tissue types. Establishment of this analysis strategy has offered the promise of advancing microarray technology as a means of deciphering the involved genetic complexities of complex diseases.

Acute Disease↗
PubMed2001

Advances in sequence analysis.

In its early days, the entire field of computational biology revolved almost entirely around biological sequence analysis. Over the past few years, however, a number of new non-sequence-based areas of investigation have become mainstream, from the analysis of gene expression data from microarrays, to whole-genome association discovery, and to the reverse engineering of gene regulatory pathways. Nonetheless, with the completion of private and public efforts to map the human genome, as well as those of other organisms, sequence data continue to be a veritable mother lode of valuable biological information that can be mined in a variety of contexts. Furthermore, the integration of sequence data with a variety of alternative information is providing valuable and fundamentally new insight into biological processes, as well as an array of new computational methodologies for the analysis of biological data.

Computational Biology↗
PubMed2026

Allelic variation and light-responsive regulation of FaMYB10-2 underlie tissue-specific anthocyanin accumulation in strawberry.

Anthocyanins critically determine fruit color, nutrition, and stress resilience in cultivated strawberry (Fragaria × ananassa), directly influencing consumer preference. Despite complex genetic and environmental regulation of their biosynthesis, the basis for tissue-specific pigmentation, notably the widespread occurrence of red skin and pale flesh, remains poorly understood. We integrated genomic, transcriptomic, and functional analyses across 200 cultivars to dissect receptacle pigmentation regulation. Approaches included FaMYB10-2 allele mining, promoter structural variant (SV) identification, expression profiling, regulatory interaction assays, and characterization of upstream light-responsive factors. FaMYB10-2 was identified as the key R2R3-MYB regulator of fruit anthocyanin biosynthesis. Alleles FaMYB10-2.2 and FaMYB10-2.3 encode truncated proteins retaining bHLH-binding capacity but lacking activation domains, functioning as dominant-negative repressors. A promoter SV 986 bp upstream of FaMYB10-2 was associated with reduced pale fruit due to cis-regulatory divergence. The SV (Alt) allele is prevalent in Asian cultivars, while the Ref allele is enriched in Western germplasm. Crucially, a light-responsive FaHYH-FaWRKY71 cascade activates FaMYB10-2 and structural genes haplotype-dependently, compensating for weak MYB activity in the skin. Our findings reveal a multilayered regulatory system integrating allelic variation, cis-regulatory divergence, and environmental signals, advancing anthocyanin understanding and providing engineering targets for polyploid crop color improvement.

Fragaria↗
PubMed2002

Accessing genomic data through XML-based remote procedure calls.

As the amount of data in public genomic databases grows, interoperability among them is becoming an increasingly critical feature. The ability for automated systems to mine and integrate data will be crucial to extracting knowledge from sources of data whose volume far exceeds the capabilities of human researchers. The currently dominant paradigm of presenting information as Web pages and using hyperlinks to describe relationships between pieces of information favors usability, but makes interoperability and automated data exchange more difficult. In this paper we describe how SNPper, a web-based system for the retrieval and analysis of Single Nucleotide Polymorphisms (SNPs), was augmented with a Remote Procedure Call interface, allowing client applications to query our program for SNP data and to receive the response as an XML document. Data represented in this form can be easily parsed by the requesting program, and thus reused for other applications. In this paper we describe the implementation of the interface and we show examples of its usage in a number of existing applications.

Databases, Genetic↗
PubMed2005

[Strategy for the protein identification of human proteome expression profile: selection of searching database].

Widely used method of protein identification for high-throughout proteome expression profile studies was database-dependent, so the selection of databases for the protein identification was very important. Despite the deficiency of available human protein databases, the complementarity of human proteins could be got mainly from human genome but not from the protein databases of other organisms. According to the comparison of the current protein databases from different aspects, IPI was recommended for the basic identification for the studies of human proteome expression profile, and other human protein or nucleic acid databases were needed for the complementary identification and novel protein mining.

Animals↗
PubMed2004

Functional genomics approaches in arthritis.

The post-genomic era of functional genomics and target validation will allow us to narrow the bridge between clinically correlative data and causative data for complex diseases, such as arthritis, for which the etiological agent remains elusive. The availability of human and other annotated genome sequences, and parallel developments of new technologies that allow analysis of minute amounts of human and animal cells (peripheral blood cells and infiltrating cells) and tissues (synovium and cartilage) under different pathophysiological conditions, has facilitated high-throughput gene mining approaches that can generate vast amounts of clinically correlative data. Characterizing some of the correlative/causative genes will require reverting to the hypothesis-driven, low throughput method of complementary experimental biology using genomic approaches as a tool. This will include in silico gene expression arrays, genome-wide scans, comparative genomics using various animal models (such as rodents and zebrafish), bioinformatics and a team of well trained translational scientists and physicians. For the first time, the "genomic tools" will allow us to analyze small amounts of surgical samples (such as needle biopsies) and clinical samples in the context of the whole genome. Preliminary genomic analysis in osteoarthritis has already resurrected the debate on the semantic issues in the definition of inflammation. Further analyses will not only facilitate the development of unbiased hypotheses at the molecular level, but also assist us in the identification and characterization of novel targets and disease markers for pharmacological intervention, gene therapy, and diagnosis.

Animals↗
PubMed2005

Discover protein sequence signatures from protein-protein interaction data.

BACKGROUND: The development of high-throughput technologies such as yeast two-hybrid systems and mass spectrometry technologies has made it possible to generate large protein-protein interaction (PPI) datasets. Mining these datasets for underlying biological knowledge has, however, remained a challenge. RESULTS: A total of 3108 sequence signatures were found, each of which was shared by a set of guest proteins interacting with one of 944 host proteins in Saccharomyces cerevisiae genome. Approximately 94% of these sequence signatures matched entries in InterPro member databases. We identified 84 distinct sequence signatures from the remaining 172 unknown signatures. The signature sharing information was then applied in predicting sub-cellular localization of yeast proteins and the novel signatures were used in identifying possible interacting sites. CONCLUSION: We reported a method of PPI data mining that facilitated the discovery of novel sequence signatures using a large PPI dataset from S. cerevisiae genome as input. The fact that 94% of discovered signatures were known validated the ability of the approach to identify large numbers of signatures from PPI data. The significance of these discovered signatures was demonstrated by their application in predicting sub-cellular localizations and identifying potential interaction binding sites of yeast proteins.

Binding Sites↗
PubMed2005

Copy-number polymorphisms: mining the tip of an iceberg.

Copy-number polymorphisms (CNPs) represent a greatly underestimated aspect of human genetic variation. Recently, two landmark studies reported genome-wide analyses of CNPs in normal individuals and represent the beginning of an understanding of this type of large-scale variation. Future array-CGH-based CNP analyses should include standard criteria on a common microarray platform. It is only when parallel analyses of CNPs and SNPs are performed in an integrated format that we will obtain a global picture of our genetic diversity.

Chromosomes, Human, Pair 22↗
PubMed2006

Shotgun proteomics of cyanobacteria--applications of experimental and data-mining techniques.

Cyanobacteria are photosynthetic bacteria notable for their ability to produce hydrogen and a variety of interesting secondary metabolites. As a result of the growing number of completed cyanobacterial genome projects, the development of post-genomics analysis for this important group has been accelerating. DNA microarrays and classical two-dimensional gel electrophoresis (2DE) were the first technologies applied in such analyses. In many other systems, 'shotgun' proteomics employing multi-dimensional liquid chromatography and tandem mass spectrometry has proven to be a powerful tool. However, this approach has been relatively under-utilized in cyanobacteria. This study assesses progress in cyanobacterial shotgun proteomics to date, and adds a new perspective by developing a protocol for the shotgun proteomic analysis of the filamentous cyanobacterium Anabaena variabilis ATCC 29413, a model for N(2) fixation. Using approaches for enhanced protein extraction, 646 proteins were identified, which is more than double the previous results obtained using 2DE. Notably, the improved extraction method and shotgun approach resulted in a significantly higher representation of basic and hydrophobic proteins. The use of protein bioinformatics tools to further mine these shotgun data is illustrated through the application of PSORTb for localization, the grand average hydropathy (GRAVY) index for hydrophobicity, LipoP for lipoproteins and the exponentially modified protein abundance index (emPAI) for abundance. The results are compared with the most well-studied cyanobacterium, Synechocystis sp. PCC 6803. Some general issues in shotgun proteome identification and quantification are then addressed.

Computational Biology↗