Wanwipa Vongsangnak

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production.

De novo sequencing, assembly and analysis of the genome of the laboratory strain Saccharomyces cerevisiae CEN.PK113-7D, a model for modern industrial biotechnology.

Saccharomyces cerevisiae CEN.PK 113-7D is widely used for metabolic engineering and systems biology research in industry and academia. We sequenced, assembled, annotated and analyzed its genome. Single-nucleotide variations (SNV), insertions/deletions (indels) and differences in genome organization compared to the reference strain S. cerevisiae S288C were analyzed. In addition to a few large deletions and duplications, nearly 3000 indels were identified in the CEN.PK113-7D genome relative to S288C. These differences were overrepresented in genes whose functions are related to transcriptional regulation and chromatin remodelling. Some of these variations were caused by unstable tandem repeats, suggesting an innate evolvability of the corresponding genes. Besides a previously characterized mutation in adenylate cyclase, the CEN.PK113-7D genome sequence revealed a significant enrichment of non-synonymous mutations in genes encoding for components of the cAMP signalling pathway. Some phenotypic characteristics of the CEN.PK113-7D strains were explained by the presence of additional specific metabolic genes relative to S288C. In particular, the presence of the BIO1 and BIO6 genes correlated with a biotin prototrophy of CEN.PK113-7D. Furthermore, the copy number, chromosomal location and sequences of the MAL loci were resolved. The assembled sequence reveals that CEN.PK113-7D has a mosaic genome that combines characteristics of laboratory strains and wild-industrial strains.

A trispecies Aspergillus microarray: Comparative transcriptomics of three Aspergillus species

The full-genome sequencing of the filamentous fungi Aspergillus nidulans, Aspergillus niger, and Aspergillus oryzae has opened possibilities for studying the cellular physiology of these fungi on a systemic level. As a tool to explore this, we are making available an Affymetrix GeneChip developed for transcriptome analysis of any of the three above-mentioned aspergilli. Transcriptome analysis of triplicate batch cultivations of all three aspergilli on glucose and xylose media was used to validate the performance of the microarray. Gene comparisons of all three species and cross-analysis with the expression data identified 23 genes to be a conserved response across Aspergillus sp., including the xylose transcriptional activator XlnR. A promoter analysis of the up-regulated genes in all three species indicates the conserved XlnR-binding site to be 5′-GGNTAAA-3′. The composition of the conserved gene-set suggests that xylose acts as a molecule, indicating the presence of complex carbohydrates such as hemicellulose, and triggers an array of degrading enzymes. With this case example, we present a validated tool for transcriptome analysis of three Aspergillus species and a methodology for conducting cross-species evolutionary studies within a genus using comparative transcriptomics.

Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis

With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets.

Unravelling evolutionary strategies of yeast for improving galactose utilization through integrated systems level analysis

Identification of the underlying molecular mechanisms for a derived phenotype by adaptive evolution is difficult. Here, we performed a systems-level inquiry into the metabolic changes occurring in the yeast Saccharomyces cerevisiae as a result of its adaptive evolution to increase its specific growth rate on galactose and related these changes to the acquired phenotypic properties. Three evolved mutants (62A, 62B, and 62C) with higher specific growth rates and faster specific galactose uptake were isolated. The evolved mutants were compared with a reference strain and two engineered strains, SO16 and PGM2, which also showed higher galactose uptake rate in previous studies. The profile of intermediates in galactose metabolism was similar in evolved and engineered mutants, whereas reserve carbohydrates metabolism was specifically elevated in the evolved mutants and one evolved strain showed changes in ergosterol biosynthesis. Mutations were identified in proteins involved in the global carbon sensing Ras/PKA pathway, which is known to regulate the reserve carbohydrates metabolism. We evaluated one of the identified mutations, RAS2Tyr112, and this mutation resulted in an increased specific growth rate on galactose. These results show that adaptive evolution results in the utilization of unpredicted routes to accommodate increased galactose flux in contrast to rationally engineered strains. Our study demonstrates that adaptive evolution represents a valuable alternative to rational design in bioengineering of improved strains and, that through systems biology, it is possible to identify mutations in evolved strain that can serve as unforeseen metabolic engineering targets for improving microbial strains for production of biofuels and chemicals.

BioMet Toolbox: genome-wide analysis of metabolism

The rapid progress of molecular biology tools for directed genetic modifications, accurate quantitative experimental approaches, high-throughput measurements, together with development of genome sequencing has made the foundation for a new area of metabolic engineering that is driven by metabolic models. Systematic analysis of biological processes by means of modelling and simulations has made the identification of metabolic networks and prediction of metabolic capabilities under different conditions possible. For facilitating such systemic analysis, we have developed the BioMet Toolbox, a web-based resource for stoichiometric analysis and for integration of transcriptome and interactome data, thereby exploiting the capabilities of genome-scale metabolic models. The BioMet Toolbox provides an effective user-friendly way to perform linear programming simulations towards maximized or minimized growth rates, substrate uptake rates and metabolic production rates by detecting relevant fluxes, simulate single and double gene deletions or detect metabolites around which major transcriptional changes are concentrated. These tools can be used for high-throughput in silico screening and allows fully standardized simulations. Model files for various model organisms (fungi and bacteria) are included. Overall, the BioMet Toolbox serves as a valuable resource for exploring the capabilities of these metabolic networks. BioMet Toolbox is freely available at www.sysbio.se/BioMet/.

Methodological Review: Biomedical text mining and its applications in cancer research

Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research.

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

BackgroundSince ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an A. oryzae Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of A. oryzae.ResultsOur assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the A. oryzae genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of A. oryzae. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources.ConclusionA much enhanced annotation of the A. oryzae genome was performed and a genome-scale metabolic model of A. oryzae was reconstructed. The model accurately predicted the growth and biomass yield on different carbon sources. The model serves as an important resource for gaining further insight into our understanding of A. oryzae physiology.

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications

BackgroundThe need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering.ResultsIn this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function.ConclusionsWith an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk.

Genome-wide analysis of maltose utilization and regulation in aspergilli

Maltose utilization and regulation in aspergilli is of great importance for cellular physiology and industrial fermentation processes. In Aspergillus oryzae, maltose utilization requires a functional MAL locus, composed of three genes: MALR encoding a regulatory protein, MALT encoding maltose permease and MALS encoding maltase. Through a comparative genome and transcriptome analysis we show that the MAL regulon system is active in A. oryzae while it is not present in Aspergillus niger. In order to utilize maltose, A. niger requires a different regulatory system that involves the AmyR regulator for glucoamylase (glaA) induction. Analysis of reporter metabolites and subnetworks illustrates the major route of maltose transport and metabolism in A. oryzae. This demonstrates that overall metabolic responses of A. oryzae occur in terms of genes, enzymes and metabolites when the carbon source is altered. Although the knowledge of maltose transport and metabolism is far from being complete in Aspergillus spp., our study not only helps to understand the sugar preference in industrial fermentation processes, but also indicates how maltose affects gene expression and overall metabolism.