Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species
| Autor: | Paula Jouhten, Muhammad Fahad Syed, Jana Kludas, Merja Oja, Juho Rousu, Merja Penttilä, Peter Blomberg, Liisa Holm, Mikko Arvas, Esa Pitkänen, Jian Hou |
|---|---|
| Přispěvatelé: | Aalto-yliopisto, Aalto University, Research Programs Unit, Department of Computer Science, Genome-Scale Biology (GSB) Research Program, Department of Medical and Clinical Genetics, Biosciences, Genetics, Institute of Biotechnology, Bioinformatics, Computational genomics |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Aspergillus Nidulans
Proteomics Metabolic network Yeast and Fungal Models FLUX-BALANCE ANALYSIS Genome ANNOTATION Biochemistry Schizosaccharomyces Pombe Gene Knockout Techniques Biomass ta518 Biology (General) ta515 Phylogeny Genetics Kluyveromyces Lactis Ecology ta213 Systems Biology Flux balance analysis Enzymes Computational Theory and Mathematics Modeling and Simulation Genome Fungal Sequence Analysis Algorithms Metabolic Networks and Pathways COMMUNITY APPROACH Research Article Biotechnology Genome evolution QH301-705.5 Systems biology education Computational biology DRUG TARGETS Saccharomyces cerevisiae Biology Models Biological Ustilago Maydis Evolution Molecular Cellular and Molecular Neuroscience Metabolic Networks Industrial Microbiology Model Organisms Exponential growth Species Specificity Candida Albicans Molecular Biology Ecology Evolution Behavior and Systematics Comparative genomics ta113 ta112 Models Statistical Models Genetic Fungi PATHWAYS Computational Biology Neurospora Crassa 113 Computer and information sciences Metabolism Small Molecules ta5141 Computer Science 3111 Biomedicine Gene Function Flux (metabolism) |
| Zdroj: | PLoS Computational Biology, Vol 10, Iss 2, p e1003465 (2014) PLoS Computational Biology Pitkänen, E, Jouhten, P, Hou, J, Syed, M F, Blomberg, P, Kludas, J, Oja, M, Holm, L, Penttilä, M, Rousu, J & Arvas, M 2014, ' Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species ', PLoS Computational Biology, vol. 10, no. 2, e1003465 . https://doi.org/10.1371/journal.pcbi.1003465 |
| ISSN: | 1553-7358 |
| DOI: | 10.1371/journal.pcbi.1003465 |
| Popis: | We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. Author Summary Advances in next-generation sequencing technologies are revolutionizing molecular biology. Sequencing-enabled cost-effective characterization of microbial genomes is a particularly exciting development in metabolic engineering. There, considerable effort has been put to reconstructing genome-scale metabolic networks that describe the collection of hundreds to thousands of biochemical reactions available for a microbial cell. These network models are instrumental in understanding microbial metabolism and guiding metabolic engineering efforts to improve biochemical yields. We have developed a novel computational method, CoReCo, which bridges the growing gap between the availability of sequenced genomes and respective reconstructed metabolic networks. The method reconstructs genome-scale metabolic networks simultaneously for related microbial species. It utilizes the available sequencing data from these species to correct for incomplete and missing data. We used the method to reconstruct metabolic networks for a set of 49 fungal species providing the method protein sequence data and a phylogenetic tree describing the evolutionary relationships between the species. We demonstrate the applicability of the method by comparing a metabolic reconstruction of Saccharomyces cerevisiae to the manually curated, high-quality consensus network. We also provide an easy-to-use implementation of the method, usable both in single computer and distributed computing environments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|