A multiple in silico program approach for the prediction of mutagenicity from chemical structure

We have conducted an evaluation of three of the most widely used commercial toxicity prediction programs, Toxicity Prediction by Komputer Assisted Technology (TOPKAT), Deductive Estimation of Risk from Existing Knowledge (DEREK) for Windows (DfW) and CASETOX. The three programs were evaluated for their ability to predict Ames test mutagenicity using 520 proprietary drug candidate (Test set 1) and 94 commercial (Test set 2) compounds. The study demonstrates that these three commercially available programs are useful, with limitations in their ability to predict mutagenicity over a wide range of chemical space, i.e. global predictivity. Individually, each of the programs performed at an acceptable level for overall accuracy, i.e. the ability to predict the correct outcome. However, analysis of the predictions indicates that the overall accuracy figure is heavily weighted by the ability of the programs to correctly predict non-mutagens, whereas none of the programs individually performed well in the prediction of novel mutagenic structures, i.e. Ames positive compounds. The performance of these programs' in predicting Ames positive mutagens appeared to be independent of the chemical utility of the compound, i.e. industrial, agricultural or pharmaceutical. The combination of program predictions provided some improvement in overall accuracy, sensitivity and specificity.     

MAGIC database and interfaces: an integrated package for gene discovery and expression

The rapidly increasing rate at which biological data is being produced requires a corresponding growth in relational databases and associated tools that can help laboratories contend with that data. With this need in mind, we describe here a Modular Approach to a Genomic, Integrated and Comprehensive (MAGIC) Database. This Oracle 9i database derives from an initial focus in our laboratory on gene discovery via production and analysis of expressed sequence tags (ESTs), and subsequently on gene expression as assessed by both EST clustering and microarrays. The MAGIC Gene Discovery portion of the database focuses on information derived from DNA sequences and on its biological relevance. In addition to MAGIC SEQ-LIMS, which is designed to support activities in the laboratory, it contains several additional subschemas. The latter include MAGIC Admin for database administration, MAGIC Sequence for sequence processing as well as sequence and clone attributes, MAGIC Cluster for the results of EST clustering, MAGIC Polymorphism in support of microsatellite and single-nucleotide-polymorphism discovery, and MAGIC Annotation for electronic annotation by BLAST and BLAT. The MAGIC Microarray portion is a MIAME-compliant database with two components at present. These are MAGIC Array-LIMS, which makes possible remote entry of all information into the database, and MAGIC Array Analysis, which provides data mining and visualization. Because all aspects of interaction with the MAGIC Database are via a web browser, it is ideally suited not only for individual research laboratories but also for core facilities that serve clients at any distance.     

Advances in microbial culturing conditions to activate silent biosynthetic gene clusters for novel metabolite production

Journal of Industrial Microbiology & Biotechnology - Natural products (NPs) produced by bacteria and fungi are often used as therapeutic agents due to their complex structures and wide range of...     

Design-based re-engineering of biosynthetic gene clusters: plug-and-play in practice

1. Download : Download high-res image (258KB)
2. Download : Download full-size image

     

Imagene: an integrated computer environment for sequence annotation and analysis

MOTIVATION: To be fully and efficiently exploited, data coming from sequencing projects together with specific sequence analysis tools need to be integrated within reliable data management systems. Systems designed to manage genome data and analysis tend to give a greater importance either to the data storage or to the methodological aspect, but lack a complete integration of both components. RESULTS: This paper presents a co-operative computer environment (called Imagenetrade mark) dedicated to genomic sequence analysis and annotation. Imagene has been developed by using an object-based model. Thanks to this representation, the user can directly manipulate familiar data objects through icons or lists. Imagene also incorporates a solving engine in order to manage analysis tasks. A global task is solved by successive divisions into smaller sub-tasks. During program execution, these sub- tasks are graphically displayed to the user and may be further re- started at any point after task completion. In this sense, Imagene is more transparent to the user than a traditional menu-driven package. Imagene also provides a user interface to display, on the same screen, the results produced by several tasks, together with the capability to annotate these results easily. In its current form, Imagene has been designed particularly for use in microbial sequencing projects. AVAILABILITY: Imagene best runs on SGI (Irix 6.3 or higher) workstations. It is distributed free of charge on a CD-ROM, but requires some Ilog licensed software to run. Some modules also require separate license agreements. Please contact the authors for specific academic conditions and other Unix platforms. CONTACT: imagene home page: http://wwwabi.snv.jussieu.fr/imagene      

Interdependent evolution of biosynthetic gene clusters for momilactone production in rice

Plants can contain biosynthetic gene clusters (BGCs) that nominally resemble those found in microbes. However, while horizontal gene transmission is often observed in microbes, plants are limited to vertical gene transmission, implying that their BGCs may exhibit distinct inheritance patterns. Rice (Oryza sativa) contains two unlinked BGCs involved in diterpenoid phytoalexin metabolism, with one clearly required for momilactone biosynthesis, while the other is associated with production of phytocassanes. Here, in the process of elucidating momilactone biosynthesis, genetic evidence was found demonstrating a role for a cytochrome P450 (CYP) from the other “phytocassane” BGC. This CYP76M8 acts after the CYP99A2/3 from the “momilactone” BGC, producing a hemiacetal intermediate that is oxidized to the eponymous lactone by a short-chain alcohol dehydrogenase also from this BGC. Thus, the “momilactone” BGC is not only incomplete, but also fractured by the need for CYP76M8 to act in between steps catalyzed by enzymes from this BGC. Moreover, as supported by similar activity observed with orthologs from the momilactone-producing wild-rice species Oryza punctata, the presence of CYP76M8 in the other “phytocassane” BGC indicates interdependent evolution of these two BGCs, highlighting the distinct nature of BGC assembly in plants.

Investigation of momilactone production in rice demonstrates roles for two unlinked biosynthetic clusters, requiring interdependent evolution and highlighting the distinct nature of their assembly.     

GeneBuilder: interactive in silico prediction of gene structure.

MOTIVATION: Prediction of gene structure in newly sequenced DNA becomes very important in large genome sequencing projects. This problem is complicated due to the exon-intron structure of eukaryotic genes and because gene expression is regulated by many different short nucleotide domains. In order to be able to analyse the full gene structure in different organisms, it is necessary to combine information about potential functional signals (promoter region, splice sites, start and stop codons, 3' untranslated region) together with the statistical properties of coding sequences (coding potential), information about homologous proteins, ESTs and repeated elements. RESULTS: We have developed the GeneBuilder system which is based on prediction of functional signals and coding regions by different approaches in combination with similarity searches in proteins and EST databases. The potential gene structure models are obtained by using a dynamic programming method. The program permits the use of several parameters for gene structure prediction and refinement. During gene model construction, selecting different exon homology levels with a protein sequence selected from a list of homologous proteins can improve the accuracy of the gene structure prediction. In the case of low homology, GeneBuilder is still able to predict the gene structure. The GeneBuilder system has been tested by using the standard set (Burset and Guigo, Genomics, 34, 353-367, 1996) and the performances are: 0.89 sensitivity and 0.91 specificity at the nucleotide level. The total correlation coefficient is 0.88. AVAILABILITY: The GeneBuilder system is implemented as a part of the WebGene a the URL: http://www.itba.mi. cnr.it/webgene and TRADAT (TRAncription Database and Analysis Tools) launcher URL: http://www.itba.mi.cnr.it/tradat.     

RNAspace.org: An integrated environment for the prediction, annotation, and analysis of ncRNA

The annotation of noncoding RNA genes remains a major bottleneck in genome sequencing projects. Most genome sequences released today still come with sets of tRNAs and rRNAs as the only annotated RNA elements, ignoring hundreds of other RNA families. We have developed a web environment that is dedicated to noncoding RNA (ncRNA) prediction, annotation, and analysis and allows users to run a variety of tools in an integrated and flexible manner. This environment offers complementary ncRNA gene finders and a set of tools for the comparison, visualization, editing, and export of ncRNA candidates. Predictions can be filtered according to a large set of characteristics. Based on this environment, we created a public website located at http://RNAspace.org. It accepts genomic sequences up to 5 Mb, which permits for an online annotation of a complete bacterial genome or a small eukaryotic chromosome. The project is hosted as a Source Forge project (http://rnaspace.sourceforge.net/).     

Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach

Large, complex data sets that are generated from microarray experiments, create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene's mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analysing in silico steady-state changes in the activities of only the module outputs, communicating intermediates, that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, the accuracy of the modular approach and its sensitivity to key assumptions are evaluated.     

Protein function prediction and annotation in an integrated environment powered by web services (AFAWE)

Many sequenced genes are mainly annotated through automatic transfer of annotation from similar sequences. Manual comparison of results or intermediate results from different tools can help avoid wrong annotations and give hints to the function of a gene even if none of the automated tools could return any result. AFAWE simplifies the task of manual functional annotation by running different tools and workflows for automatic function prediction and displaying the results in a way that facilitates comparison. Because all programs are executed as web services, AFAWE is easily extensible and can directly query primary databases, thereby always using the most up-to-date data sources. Visual filters help to distinguish trustworthy results from non-significant results. Furthermore, an interface to add detailed manual annotation to each gene is provided, which can be displayed to other users.     

Natural disesquiterpenoids: an overview of their chemical structures,pharmacological activities,and biosynthetic pathways

Phytochemistry Reviews - Disesquiterpenoids are constructed by two sesquiterpenoid molecules, containing at least 30 carbons with various connection patterns, and numerous new skeletons have been...     

Genome mining for natural product biosynthetic gene clusters in the Subsection V cyanobacteria

Background

Cyanobacteria are well known for the production of a range of secondary metabolites. Whilst recent genome sequencing projects has led to an increase in the number of publically available cyanobacterial genomes, the secondary metabolite potential of many of these organisms remains elusive. Our study focused on the 11 publically available Subsection V cyanobacterial genomes, together with the draft genomes of Westiella intricata UH strain HT-29-1 and Hapalosiphon welwitschii UH strain IC-52-3, for their genetic potential to produce secondary metabolites. The Subsection V cyanobacterial genomes analysed in this study are reported to produce a diverse range of natural products, including the hapalindole-family of compounds, microcystin, hapalosin, mycosporine-like amino acids and hydrocarbons.

Results

A putative gene cluster for the cyclic depsipeptide hapalosin, known to reverse P-glycoprotein multiple drug resistance, was identified within three Subsection V cyanobacterial genomes, including the producing cyanobacterium H. welwitschii UH strain IC-52-3. A number of orphan NRPS/PKS gene clusters and ribosomally-synthesised and post translationally-modified peptide gene clusters (including cyanobactin, microviridin and bacteriocin gene clusters) were identified. Furthermore, gene clusters encoding the biosynthesis of mycosporine-like amino acids, scytonemin, hydrocarbons and terpenes were also identified and compared.

Conclusions

Genome mining has revealed the diversity, abundance and complex nature of the secondary metabolite potential of the Subsection V cyanobacteria. This bioinformatic study has identified novel biosynthetic enzymes which have not been associated with gene clusters of known classes of natural products, suggesting that these cyanobacteria potentially produce structurally novel secondary metabolites.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1855-z) contains supplementary material, which is available to authorized users.     

Streptomycetes: Surrogate hosts for the genetic manipulation of biosynthetic gene clusters and production of natural products

Due to the worldwide prevalence of multidrug-resistant pathogens and high incidence of diseases such as cancer, there is an urgent need for the discovery and development of new drugs. Nearly half of the FDA-approved drugs are derived from natural products that are produced by living organisms, mainly bacteria, fungi, and plants. Commercial development is often limited by the low yield of the desired compounds expressed by the native producers. In addition, recent advances in whole genome sequencing and bioinformatics have revealed an abundance of cryptic biosynthetic gene clusters within microbial genomes. Genetic manipulation of clusters in the native host is commonly used to awaken poorly expressed or silent gene clusters, however, the lack of feasible genetic manipulation systems in many strains often hinders our ability to engineer the native producers. The transfer of gene clusters into heterologous hosts for expression of partial or entire biosynthetic pathways is an approach that can be used to overcome this limitation. Heterologous expression also facilitates the chimeric fusion of different biosynthetic pathways, leading to the generation of “unnatural” natural products. The genus Streptomyces is especially known to be a prolific source of drugs/antibiotics, its members are often used as heterologous expression hosts. In this review, we summarize recent applications of Streptomyces species, S. coelicolor, S. lividans, S. albus, S. venezuelae and S. avermitilis, as heterologous expression systems.     

New strategies and approaches for engineering biosynthetic gene clusters of microbial natural products

With the rapidly growing number of sequenced microbial (meta)genomes, enormous cryptic natural product (NP) biosynthetic gene clusters (BGCs) have been identified, which are regarded as a rich reservoir for novel drug discovery. A series of powerful tools for engineering BGCs has accelerated the discovery and development of pharmaceutically active NPs. Here, we describe recent advances in the strategies for BGCs manipulation, which are driven by emerging technologies, including efficient DNA recombination systems, versatile CRISPR/Cas9 genome editing tools and diverse DNA assembly methods. We further discuss how these approaches could be used for genome mining studies and industrial strain improvement.     

MetMAP: an integrated Matlab package for analysis and optimization of metabolic systems

In previous works we have presented and applied a method to predict the parameter profile that optimizes biochemical systems regarding either a single or a set of metabolic responses within physiological constraints [Vera et al., 2003a]. This optimization technique requires a previous model definition and a translation to S-system form and the use of widely available linear programming packages. However, in dealing with these issues the interested researcher has to confront additional difficulties because of a lack of connectivity among available software packages or routines specifically designed to perform different tasks. In addition to this difficulty is the unavailability of any automated package which is capable of performing such optimizations and the previous required analysis. This situation prompted us to develop an integrated software package able to deal with these tasks in a single program environment. In this paper we present a software package for the model definition, analysis and optimization of a biochemical system. It starts with a given model definition that is directly translated to its equivalent S-system form. Once the model quality assessment is performed (stability and sensitivity analysis) the program determines the parameter profile that yields the optimized response compatible with a predefined set of constraints. Moreover the package finds the set of solutions obtained when more than one system's responses are to be optimized (multiobjective optimization).     

K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space

Background  

Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.     

Unlocking the trove of metabolic treasures: activating silent biosynthetic gene clusters in bacteria and fungi

1. Download : Download high-res image (84KB)
2. Download : Download full-size image