Overexpression of O-methyltransferase leads to improved vanillin production in baker's yeast only when complemented with model-guided network engineering.
Brochado, A.R. & Patil, K.R.
Biotechnol Bioeng. 2013 Feb;110(2):656-9. doi: 10.1002/bit.24731. Epub 2012 Oct23.
Overproduction of a desired metabolite is often achieved via manipulation of the pathway directly leading to the product or through engineering of distant nodes within the metabolic network. Empirical examples illustrating the combined effect of these local and global strategies have been so far limited in eukaryotic systems. In this study, we compared the effects of overexpressing a key gene in de novo vanillin biosynthesis (coding for O-methyltransferase, hsOMT) in two yeast strains, with and without model-guided global network modifications. Overexpression of hsOMT resulted in increased vanillin production only in the strain with model-guided modifications, exemplifying advantage of using a global strategy prior to local pathway manipulation.
Identification of Metabolic Engineering Targets through Analysis of Optimal and Sub-Optimal Routes.
Soons, Z.I., Ferreira, E.C., Patil, K.R. & Rocha, I.
PLoS One. 2013 Apr 23;8(4):e61648. doi: 10.1371/journal.pone.0061648. Print 2013.
Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.
Industrial Systems Biology of Saccharomyces cerevisiae Enables Novel Succinic Acid Cell Factory.
Otero, J.M., Cimini, D., Patil, K.R., Poulsen, S.G., Olsson, L. & Nielsen, J.
PLoS One. 2013;8(1):e54144. doi: 10.1371/journal.pone.0054144. Epub 2013 Jan 21.
Saccharomyces cerevisiae is the most well characterized eukaryote, the preferred microbial cell factory for the largest industrial biotechnology product (bioethanol), and a robust commerically compatible scaffold to be exploitted for diverse chemical production. Succinic acid is a highly sought after added-value chemical for which there is no native pre-disposition for production and accmulation in S. cerevisiae. The genome-scale metabolic network reconstruction of S. cerevisiae enabled in silico gene deletion predictions using an evolutionary programming method to couple biomass and succinate production. Glycine and serine, both essential amino acids required for biomass formation, are formed from both glycolytic and TCA cycle intermediates. Succinate formation results from the isocitrate lyase catalyzed conversion of isocitrate, and from the alpha-keto-glutarate dehydrogenase catalyzed conversion of alpha-keto-glutarate. Succinate is subsequently depleted by the succinate dehydrogenase complex. The metabolic engineering strategy identified included deletion of the primary succinate consuming reaction, Sdh3p, and interruption of glycolysis derived serine by deletion of 3-phosphoglycerate dehydrogenase, Ser3p/Ser33p. Pursuing these targets, a multi-gene deletion strain was constructed, and directed evolution with selection used to identify a succinate producing mutant. Physiological characterization coupled with integrated data analysis of transcriptome data in the metabolically engineered strain were used to identify 2(nd)-round metabolic engineering targets. The resulting strain represents a 30-fold improvement in succinate titer, and a 43-fold improvement in succinate yield on biomass, with only a 2.8-fold decrease in the specific growth rate compared to the reference strain. Intuitive genetic targets for either over-expression or interruption of succinate producing or consuming pathways, respectively, do not lead to increased succinate. Rather, we demonstrate how systems biology tools coupled with directed evolution and selection allows non-intuitive, rapid and substantial re-direction of carbon fluxes in S. cerevisiae, and hence show proof of concept that this is a potentially attractive cell factory for over-producing different platform chemicals.
Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.
Brochado, A.R., Andrejev, S., Maranas, C.D. & Patil, K.R.
PLoS Comput Biol. 2012;8(11):e1002758. doi: 10.1371/journal.pcbi.1002758. Epub2012 Nov 1.
Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae.
Random sampling of elementary flux modes in large-scale metabolic networks.
Machado, D., Soons, Z., Patil, K.R., Ferreira, E.C. & Rocha, I.
Bioinformatics. 2012 Sep 15;28(18):i515-i521.
MOTIVATION: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. RESULTS: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. AVAILABILITY: Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. CONTACT: firstname.lastname@example.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Involvement of a natural fusion of a cytochrome P450 and a hydrolase in mycophenolic acid biosynthesis.
Hansen, B.G., Mnich, E., Nielsen, K.F., Nielsen, J.B., Nielsen, M.T., Mortensen, U.H., Larsen, T.O. & Patil, K.R.
Appl Environ Microbiol. 2012 Jul;78(14):4908-13. doi: 10.1128/AEM.07955-11. Epub2012 Apr 27.
Mycophenolic acid (MPA) is a fungal secondary metabolite and the active component in several immunosuppressive pharmaceuticals. The gene cluster coding for the MPA biosynthetic pathway has recently been discovered in Penicillium brevicompactum, demonstrating that the first step is catalyzed by MpaC, a polyketide synthase producing 5-methylorsellinic acid (5-MOA). However, the biochemical role of the enzymes encoded by the remaining genes in the MPA gene cluster is still unknown. Based on bioinformatic analysis of the MPA gene cluster, we hypothesized that the step following 5-MOA production in the pathway is carried out by a natural fusion enzyme MpaDE, consisting of a cytochrome P450 (MpaD) in the N-terminal region and a hydrolase (MpaE) in the C-terminal region. We verified that the fusion gene is indeed expressed in P. brevicompactum by obtaining full-length sequence of the mpaDE cDNA prepared from the extracted RNA. Heterologous coexpression of mpaC and the fusion gene mpaDE in the MPA-nonproducer Aspergillus nidulans resulted in the production of 5,7-dihydroxy-4-methylphthalide (DHMP), the second intermediate in MPA biosynthesis. Analysis of the strain coexpressing mpaC and the mpaD part of mpaDE shows that the P450 catalyzes hydroxylation of 5-MOA to 4,6-dihydroxy-2-(hydroxymethyl)-3-methylbenzoic acid (DHMB). DHMB is then converted to DHMP, and our results suggest that the hydrolase domain aids this second step by acting as a lactone synthase that catalyzes the ring closure. Overall, the chimeric enzyme MpaDE provides insight into the genetic organization of the MPA biosynthesis pathway.
Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours.
Yamada, T., Waller, A.S., Raes, J., Zelezniak, A., Perchat, N., Perret, A., Salanoubat, M., Patil, K.R., Weissenbach, J. & Bork, P.
Mol Syst Biol. 2012 May 8;8:581. doi: 10.1038/msb.2012.13.
Despite the current wealth of sequencing data, one-third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta)genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta)genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16 345 gene sequences in numerous (meta)genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome-scale metabolic models with these new sequence-function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction.
A new class of IMP dehydrogenase with a role in self-resistance of mycophenolic acid producing fungi.
Hansen, B.G., Genee, H.J., Kaas, C.S., Nielsen, J.B., Regueira, T.B., Mortensen, U.H., Frisvad, J.C. & Patil, K.R.
BMC Microbiol. 2011 Sep 16;11:202.
ABSTRACT: BACKGROUND: Many secondary metabolites produced by filamentous fungi have potent biological activities, to which the producer organism must be resistant. An example of pharmaceutical interest is mycophenolic acid (MPA), an immunosuppressant molecule produced by several Penicillium species. The target of MPA is inosine-5'-monophosphate dehydrogenase (IMPDH), which catalyses the rate limiting step in the synthesis of guanine nucleotides. The recent discovery of the MPA biosynthetic gene cluster from Penicillium brevicompactum revealed an extra copy of the IMPDH-encoding gene (mpaF) embedded within the cluster. This finding suggests that the key component of MPA self resistance is likely based on the IMPDH encoded by mpaF. RESULTS: In accordance with our hypothesis, heterologous expression of mpaF dramatically increased MPA resistance in a model fungus, Aspergillus nidulans, which does not produce MPA. The growth of an A. nidulans strain expressing mpaF was only marginally affected by MPA at concentrations as high as 200 mug/ml. To further substantiate the role of mpaF in MPA resistance, we searched for mpaF orthologs in six MPA producer/non-producer strains from Penicillium subgenus Penicillium. All six strains were found to hold two copies of IMPDH. A cladistic analysis based on the corresponding cDNA sequences revealed a novel group constituting mpaF homologs. Interestingly, a conserved tyrosine residue in the original class of IMPDHs is replaced by a phenylalanine residue in the new IMPDH class. CONCLUSIONS: We identified a novel variant of the IMPDH-encoding gene in six different strains from Penicillium subgenus Penicillium. The novel IMPDH variant from MPA producer P. brevicompactum was shown to confer a high degree of MPA resistance when expressed in a non-producer fungus. Our study provides a basis for understanding the molecular mechanism of MPA resistance and has relevance for biotechnological and pharmaceutical applications.
PHUSER (Primer Help for USER): a novel tool for USER fusion primer design.
Olsen, L.R., Hansen, N.B., Bonde, M.T., Genee, H.J., Holm, D.K., Carlsen, S., Hansen, B.G., Patil, K.R., Mortensen, U.H. & Wernersson, R.
Nucleic Acids Res. 2011 Jul;39(Web Server issue):W61-7. Epub 2011 May 26.
Uracil-Specific Exision Reagent (USER) fusion is a recently developed technique that allows for assembly of multiple DNA fragments in a few simple steps. However, designing primers for USER fusion is both tedious and time consuming. Here, we present the Primer Help for USER (PHUSER) software, a novel tool for designing primers specifically for USER fusion and USER cloning applications. We also present proof-of-concept experimental validation of its functionality. PHUSER offers quick and easy design of PCR optimized primers ensuring directionally correct fusion of fragments into a plasmid containing a customizable USER cassette. Designing primers using PHUSER ensures that the primers have similar annealing temperature (T(m)), which is essential for efficient PCR. PHUSER also avoids identical overhangs, thereby ensuring correct order of assembly of DNA fragments. All possible primers are individually analysed in terms of GC content, presence of GC clamp at 3'-end, the risk of primer dimer formation, the risk of intra-primer complementarity (secondary structures) and the presence of polyN stretches. Furthermore, PHUSER offers the option to insert linkers between DNA fragments, as well as highly flexible cassette options. PHUSER is publicly available at http://www.cbs.dtu.dk/services/phuser/.
Versatile enzyme expression and characterization system for Aspergillus nidulans, with the Penicillium brevicompactum polyketide synthase gene from the mycophenolic acid gene cluster as a test case.
Hansen, B.G., Salomonsen, B., Nielsen, M.T., Nielsen, J.B., Hansen, N.B., Nielsen, K.F., Regueira, T.B., Nielsen, J., Patil, K.R. & Mortensen, U.H.
Appl Environ Microbiol. 2011 May;77(9):3044-51. doi: 10.1128/AEM.01768-10. Epub2011 Mar 11.
Assigning functions to newly discovered genes constitutes one of the major challenges en route to fully exploiting the data becoming available from the genome sequencing initiatives. Heterologous expression in an appropriate host is central in functional genomics studies. In this context, filamentous fungi offer many advantages over bacterial and yeast systems. To facilitate the use of filamentous fungi in functional genomics, we present a versatile cloning system that allows a gene of interest to be expressed from a defined genomic location of Aspergillus nidulans. By a single USER cloning step, genes are easily inserted into a combined targeting-expression cassette ready for rapid integration and analysis. The system comprises a vector set that allows genes to be expressed either from the constitutive PgpdA promoter or from the inducible PalcA promoter. Moreover, by using the vector set, protein variants can easily be made and expressed from the same locus, which is mandatory for proper comparative analyses. Lastly, all individual elements of the vectors can easily be substituted for other similar elements, ensuring the flexibility of the system. We have demonstrated the potential of the system by transferring the 7,745-bp large mpaC gene from Penicillium brevicompactum to A. nidulans. In parallel, we produced defined mutant derivatives of mpaC, and the combined analysis of A. nidulans strains expressing mpaC or mutated mpaC genes unequivocally demonstrated that mpaC indeed encodes a polyketide synthase that produces the first intermediate in the production of the medically important immunosuppressant mycophenolic acid.
Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803.
Montagud, A., Zelezniak, A., Navarro, E., de Cordoba, P.F., Urchueguia, J.F. & Patil, K.R.
Biotechnol J. 2011 Mar;6(3):330-42. doi: 10.1002/biot.201000109. Epub 2011 Jan11.
Synechocystis sp. PCC6803 is a model cyanobacterium capable of producing biofuels with CO(2) as carbon source and with its metabolism fueled by light, for which it stands as a potential production platform of socio-economic importance. Compilation and characterization of Synechocystis genome-scale metabolic model is a pre-requisite toward achieving a proficient photosynthetic cell factory. To this end, we report iSyn811, an upgraded genome-scale metabolic model of Synechocystis sp. PCC6803 consisting of 956 reactions and accounting for 811 genes. To gain insights into the interplay between flux activities and metabolic physiology, flux coupling analysis was performed for iSyn811 under four different growth conditions, viz., autotrophy, mixotrophy, heterotrophy, and light-activated heterotrophy (LH). Initial steps of carbon acquisition and catabolism formed the versatile center of the flux coupling networks, surrounded by a stable core of pathways leading to biomass building blocks. This analysis identified potential bottlenecks for hydrogen and ethanol production. Integration of transcriptomic data with the Synechocystis flux coupling networks lead to identification of reporter flux coupling pairs and reporter flux coupling groups - regulatory hot spots during metabolic shifts triggered by the availability of light. Overall, flux coupling analysis provided insight into the structural organization of Synechocystis sp. PCC6803 metabolic network toward designing of a photosynthesis-based production platform.
Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium.
Montagud, A., Navarro, E., Fernandez de Cordoba, P., Urchueguia, J.F. & Patil, K.R.
BMC Syst Biol. 2010 Nov 17;4:156. doi: 10.1186/1752-0509-4-156.
BACKGROUND: Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform--an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. RESULTS: We report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, iSyn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, iSyn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes. CONCLUSIONS: iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories.
Improved vanillin production in baker's yeast through in silico design.
Brochado, A.R., Matos, C., Moller, B.L., Hansen, J., Mortensen, U.H. & Patil, K.R.
Microb Cell Fact. 2010 Nov 8;9(1):84.
ABSTRACT: BACKGROUND: Vanillin is one of the most widely used flavouring agents, originally obtained from cured seed pods of the vanilla orchid Vanilla planifolia. Currently vanillin is mostly produced via chemical synthesis. A de novo synthetic pathway for heterologous vanillin production from glucose has recently been implemented in baker's yeast, Saccharamyces cerevisiae. In this study we aimed at engineering this vanillin cell factory towards improved productivity and thereby at developing an attractive alternative to chemical synthesis. RESULTS: Expression of a glycosyltransferase from Arabidopsis thaliana in the vanillin producing S. cerevisiae strain served to decrease product toxicity. An in silico metabolic engineering strategy of this vanillin glucoside producing strain was designed using a set of stoichiometric modelling tools applied to the yeast genome-scale metabolic network. Two targets (PDC1 and GDH1) were selected for experimental verification resulting in four engineered strains. Three of the mutants showed up to 1.5-fold higher vanillin beta-D-glucoside yield in batch mode, while continuous culture of the [increment]pdc1 mutant showed a 2-fold productivity improvement. This mutant presented a 5-fold improvement in free vanillin production compared to the previous work on de novo vanillin biosynthesis in baker's yeast. CONCLUSION: Use of constraints corresponding to different physiological states was found to greatly influence the target predictions given minimization of metabolic adjustment (MOMA) as biological objective function. In vivo verification of the targets, selected based on their predicted metabolic adjustment, successfully led to overproducing strains. Overall, we propose and demonstrate a framework for in silico design and target selection for improving microbial cell factories.
BioMet Toolbox: genome-wide analysis of metabolism.
Cvijovic, M., Olivares-Hernandez, R., Agren, R., Dahr, N., Vongsangnak, W., Nookaew, I., Patil, K.R. & Nielsen, J.
Nucleic Acids Res. 2010 Jul 1;38 Suppl:W144-9. Epub 2010 May 18.
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/.
Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes.
Zelezniak, A., Pers, T.H., Soares, S., Patti, M.E. & Patil, K.R.
PLoS Comput Biol. 2010 Apr 1;6(4):e1000729.
Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites--metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.
OptFlux: an open-source software platform for in silico metabolic engineering.
Rocha, I., Maia, P., Evangelista, P., Vilaca, P., Soares, S., Pinto, J.P., Nielsen, J., Patil, K.R., Ferreira, E.C. & Rocha, M.
BMC Syst Biol. 2010 Apr 19;4:45.
BACKGROUND: Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. RESULTS: OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. CONCLUSIONS: The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.
Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering.
Asadollahi, M.A., Maury, J., Patil, K.R., Schalk, M., Clark, A. & Nielsen, J.
Metab Eng. 2009 Nov;11(6):328-34. Epub 2009 Jul 18.
A genome-scale metabolic model was used to identify new target genes for enhanced biosynthesis of sesquiterpenes in the yeast Saccharomyces cerevisiae. The effect of gene deletions on the flux distributions in the metabolic model of S. cerevisiae was assessed using OptGene as the modeling framework and minimization of metabolic adjustments (MOMA) as objective function. Deletion of NADPH-dependent glutamate dehydrogenase encoded by GDH1 was identified as the best target gene for the improvement of sesquiterpene biosynthesis in yeast. Deletion of this gene enhances the available NADPH in the cytosol for other NADPH requiring enzymes, including HMG-CoA reductase. However, since disruption of GDH1 impairs the ammonia utilization, simultaneous over-expression of the NADH-dependent glutamate dehydrogenase encoded by GDH2 was also considered in this study. Deletion of GDH1 led to an approximately 85% increase in the final cubebol titer. However, deletion of this gene also caused a significant decrease in the maximum specific growth rate. Over-expression of GDH2 did not show a further effect on the final cubebol titer but this alteration significantly improved the growth rate compared to the GDH1 deleted strain.
Global transcriptional response of Saccharomyces cerevisiae to the deletion of SDH3.
Cimini, D., Patil, K.R., Schiraldi, C. & Nielsen, J.
BMC Syst Biol. 2009 Feb 6;3:17.
BACKGROUND: Mitochondrial respiration is an important and widely conserved cellular function in eukaryotic cells. The succinate dehydrogenase complex (Sdhp) plays an important role in respiration as it connects the mitochondrial respiratory chain to the tricarboxylic acid (TCA) cycle where it catalyzes the oxidation of succinate to fumarate. Cellular response to the Sdhp dysfunction (i.e. impaired respiration) thus has important implications not only for biotechnological applications but also for understanding cellular physiology underlying metabolic diseases such as diabetes. We therefore explored the physiological and transcriptional response of Saccharomyces cerevisiae to the deletion of SDH3, that codes for an essential subunit of the Sdhp. RESULTS: Although the Sdhp has no direct role in transcriptional regulation and the flux through the corresponding reaction under the studied conditions is very low, deletion of SDH3 resulted in significant changes in the expression of several genes involved in various cellular processes ranging from metabolism to the cell-cycle. By using various bioinformatics tools we explored the organization of these transcriptional changes in the metabolic and other cellular functional interaction networks. CONCLUSION: Our results show that the transcriptional regulatory response resulting from the impaired respiratory function is linked to several different parts of the metabolism, including fatty acid and sterol metabolism.
Natural computation meta-heuristics for the in silico optimization of microbial strains.
Rocha, M., Maia, P., Mendes, R., Pinto, J.P., Ferreira, E.C., Nielsen, J., Patil, K.R. & Rocha, I.
BMC Bioinformatics. 2008 Nov 27;9:499.
BACKGROUND: One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution. RESULTS: This work reports on improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of in silico metabolic engineering. Both approaches use a variable size set-based representation, thereby allowing the automatic finding of the best number of gene deletions necessary for achieving a given productivity goal. The work presents extensive computational experiments, involving four case studies that consider the production of succinic and lactic acid as the targets, by using S. cerevisiae and E. coli as model organisms. The proposed algorithms are able to reach optimal/near-optimal solutions regarding the production of the desired compounds and presenting low variability among the several runs. CONCLUSION: The results show that the proposed SA and EA both perform well in the optimization task. A comparison between them is favourable to the SA in terms of consistency in obtaining optimal solutions and faster convergence. In both cases, the use of variable size representations allows the automatic discovery of the approximate number of gene deletions, without compromising the optimality of the solutions.
Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks.
Oliveira, A.P., Patil, K.R. & Nielsen, J.
BMC Syst Biol. 2008 Feb 8;2:17.
BACKGROUND: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. RESULTS: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription Factors, Reporter Proteins and Reporter Complexes, and use this to decipher the logic of regulatory circuits playing a key role in yeast glucose repression and human diabetes. CONCLUSION: Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks that are significantly affected between or across conditions. Results of the Reporter Feature analysis not only provide a snapshot of the transcriptional regulatory program but also are biologically easy to interpret and provide a powerful way to generate new hypotheses. Our Reporter Features analyses of yeast glucose repression and human diabetes data brings hints towards the understanding of the principles of transcriptional regulation controlling these two important and potentially closely related systems.
Optimal fed-batch cultivation when mass transfer becomes limiting.
Villadsen, J. & Patil, K.R.
Biotechnol Bioeng. 2007 Oct 15;98(3):706-10.
In the design of an aerobic fed-batch process to produce, for example, a pharmaceutical protein, the volumetric production rate will eventually become limited by mass transfer when the biomass concentration exceeds a certain upper limit x*. It appears to be common practice to switch from exponential feed of substrate to a constant feed rate when x* is reached. This is done to avoid oxygen starvation with a potential risk of undesired stress responses. But with a constant feed rate the carbon source (glucose) concentration may decrease to a low level with a resulting loss of viability and an undesired production of endotoxins. It is shown that an exponential feeding strategy may be continued, but with a smaller exponent than the one used before oxygen limitation occurs. This will diminish the potential detrimental effects on the culture due to low glucose concentration, and the total time to reach a given final biomass concentration will be reduced.
The metabolic response of heterotrophic Arabidopsis cells to oxidative stress
Baxter C.J., Redestig H., Schauer N., Repsilber D., Patil K.R., Nielsen J., Selbig J., Liu J., Fernie A.R. & Sweetlove L.J.
Plant Physiology. 143 (1), 312-325 (2007).
Global transcriptional and physiological response of Saccharomyces cerevisiae to nitrogen limitation
Usaite R., Patil K.R., Grotkjær T., Nielsen J. & Regenberg B.
Appl. Env. Microbiol., 72 (9), 6194-6203 (2006).
Hap4 is not essential for activation of respiration at low specific growth rates in Saccharomyces cerevisiae
Raghevendran V., Patil K.R., Olsson L. & Nielsen J.
J. Biol. Chem., 281 (18), 12308-12314 (2006).
Integration of metabolome data with metabolic networks reveals reporter reactions.
Cakir, T., Patil, K.R., Onsan, Z., Ulgen, K.O., Kirdar, B. & Nielsen, J.
Mol Syst Biol. 2006;2:50. Epub 2006 Oct 3.
Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.
Evolutionary programming as a platform for in silico metabolic engineering.
Patil, K.R., Rocha, I., Forster, J. & Nielsen, J.
BMC Bioinformatics. 2005 Dec 23;6:308.
BACKGROUND: Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. RESULTS: In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. CONCLUSION: We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.
Uncovering transcriptional regulation of metabolism by using metabolic network topology.
Patil, K.R. & Nielsen, J.
Proc Natl Acad Sci U S A. 2005 Feb 22;102(8):2685-9. Epub 2005 Feb 14.
Cellular response to genetic and environmental perturbations is often reflected and/or mediated through changes in the metabolism, because the latter plays a key role in providing Gibbs free energy and precursors for biosynthesis. Such metabolic changes are often exerted through transcriptional changes induced by complex regulatory mechanisms coordinating the activity of different metabolic pathways. It is difficult to map such global transcriptional responses by using traditional methods, because many genes in the metabolic network have relatively small changes at their transcription level. We therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns in the metabolic network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic or environmental perturbations. We find that cells respond to perturbations by changing the expression pattern of several genes involved in the specific part(s) of the metabolism in which a perturbation is introduced. These changes then are propagated through the metabolic network because of the highly connected nature of metabolism.
The role of high-throughput transcriptome analysis in metabolic engineering
Jewett M., Oliveira A. P., Patil K.R. & Nielsen J.
Biotechnol. Bioproc. Eng. 10, 385-399 (2005).
Use of genome-scale microbial models for metabolic engineering
Patil K. R., Akesson M. & Nielsen J.
Curr. Opin. Biotechnol. 15, 64-69 (2004).