Hooman Hefzi

Recon 2.2: from reconstruction to model of human metabolism

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

Quantitative feature extraction from the Chinese hamster ovary bioprocess bibliome using a novel meta-analysis workflow

The scientific literature concerning Chinese hamster ovary (CHO) cells grows annually due to the importance of CHO cells in industrial bioprocessing of therapeutics. In an effort to start to catalogue the breadth of CHO phenotypes, or phenome, we present the CHO bibliome. This bibliographic compilation covers all published CHO cell studies from 1995 to 2015, and each study is classified by the types of phenotypic and bioprocess data contained therein. Using data from selected studies, we also present a quantitative meta-analysis of bioprocess characteristics across diverse culture conditions, yielding novel insights and addressing the validity of long held assumptions. Specifically, we show that bioprocess titers can be predicted using indicator variables derived from viable cell density, viability, and culture duration. We further identified a positive correlation between the cumulative viable cell density (VCD) and final titer, irrespective of cell line, media, and other bioprocess parameters. In addition, growth rate was negatively correlated with performance attributes, such as VCD and titer. In summary, despite assumptions that technical diversity among studies and opaque publication practices can limit research re-use in this field, we show that the statistical analysis of diverse legacy bioprocess data can provide insight into bioprocessing capabilities of CHO cell lines used in industry. The CHO bibliome can be accessed at http://lewislab.ucsd.edu/cho-bibliome/.

Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting

Several antimalarial drugs exist, but differences between life cycle stages among malaria species pose challenges for developing more effective therapies. To understand the diversity among stages and species, we reconstructed genome-scale metabolic models (GeMMs) of metabolism for five life cycle stages and five species of Plasmodium spanning the blood, transmission, and mosquito stages. The stage-specific models of Plasmodium falciparum uncovered stage-dependent changes in central carbon metabolism and predicted potential targets that could affect several life cycle stages. The species-specific models further highlight differences between experimental animal models and the human-infecting species. Comparisons between human- and rodent-infecting species revealed differences in thiamine (vitamin B1), choline, and pantothenate (vitamin B5) metabolism. Thus, we show that genome-scale analysis of multiple stages and species of Plasmodium can prioritize potential drug targets that could be both anti-malarials and transmission blocking agents, in addition to guiding translation from non-human experimental disease models.

Systemic post-translational control of bacterial metabolism regulates adaptation in dynamic environments

Across all domains of life, elaborate control mechanisms regulate proteins, pathways, and cell phenotypes as organisms adapt to ever-changing environments. Post-translational modifications (PTMs) allow cells to rapidly and reversibly regulate molecular pathways, but it remains unclear how individual PTMs regulate fitness. Here, we studied >130 PTM sites in Escherichia coli to unravel how PTMs regulate cell metabolism and fitness in response to environmental changes, such as the glucose-acetate diauxie. Using a new metabolic modeling approach, we found a significant fraction of post-translationally modified enzymes are predicted to control shifts in pathway usage following evolutionarily-important environmental changes. Genetic screens using Multiplex Automated Genome Engineering confirmed that these PTMs impact cellular fitness, especially under dynamically changing environments. Finally, mechanisms of how individual PTMs impact protein function were detailed using molecular dynamics simulations and enzyme assays for enolase, transaldolase, and serine hydroxymethyltransferase. Thus, by integrating whole-cell data and pathway modeling with detailed biochemical analysis, we unraveled how individual PTMs regulate enzymes, pathways, and phenotypes to adapt to sudden environmental changes.

WormJam: A consensus C. elegans Metabolic Reconstruction and Metabolomics Community and Workshop Series

Janna Hastings, Abraham Mains, Marta Artal-Sanz, Sven Bergmann, Bart P. Braeckman, Jake Bundy , Filipe Cabreiro, Paul Dobson, Paul Ebert, Jake Hattwell, Hooman Hefzi, Riekelt H. Houtkooper, Rob Jelier, Chintan Joshi, Varun B. Kothamachu, Nathan Lewis, Artur Bastos Lourenço, Yu Nie, Povilas Norvaisas, Juliette Pearce, Cristian Riccio, Nicolas Rodriguez, Toon Santermans, Pasquale Scarcia, Horst Joachim Schirra, Ming Sheng, Reuben Smith, Manusnan Suriyalaksh, Benjamin Towbin, Mary Ann Tuli, Michel van Weeghel, David Weinkove, Aleksandra Ze ci c, Johannes Zimmermann, Nicolas le Nov ere, Christoph Kaleta, Michael Witting, and Olivia Casanueva Epigenetics, Babraham Institute, Babraham Research Campus, Cambridge, UK; Developmental Biology, Andalusian Center for Developmental Biology. Consejo Superior de Investigaciones Cient ıficas/Junta de Andalucia/Universidad Pablo de Olavide, Seville, Spain; Computational Biology, University of Lausanne, Lausanne, Switzerland; Department of Biology, University of Gent, Gent, Belgium; Computational and Systems Medicine, Imperial College London, London, UK; Structural and Molecular Biology, University College London, London, UK; School of Computer Science, University of Manchester, Manchester, UK; School of Biological Sciences, University of Queensland, Queensland, Australia; Centre for Advanced Imaging, University of Queensland, Queensland, Australia; Department of Bioengineering, Novo Nordisk Center for Biosustainability at UC San Diego, University of California, San Diego, USA; Laboratory Genetic Metabolic Diseases, Academic Medical Center, Amsterdam, Netherlands; Centre of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium; Department of Pediatrics, Novo Nordisk Center for Biosustainability at UC San Diego, University of California, San Diego, USA; Signalling, Babraham Institute, Babraham Research Campus, Cambridge, UK; School of Human and Life Sciences, Canterbury Christ Church University, Canterbury, UK; Sanger Institute, University of Cambridge, Cambridge, UK; Dep. Biosciences Biotechnologies Biopharmaceutics, University of Bari, Bari, Italy; Neurobiology, MRC LMB, Cambridge, UK; Friedrich Miescher Institute, Basel, Switzerland; WormBase, Caltech, Pasadena, CA, USA; Department of Biosciences, Durham University, Durham, UK; Medical Systems Biology, Christian-Albrechts-University Kiel, Kiel, Germany; Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum Muenchen, Muenchen, Germany

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.

Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow

Significance Understanding roles and mechanisms of protein posttranslational modifications (PTMs) would greatly impact multiple scientific domains, from bioengineering to biomedical science. PTMs are known to interfere with drug action and influence biochemical networks of engineered organisms. Many PTM sites have been identified, but it remains unclear under which conditions these sites are modified. Furthermore, there is a need to understand how the cell utilizes PTMs to increase fitness. Here, we approach this challenge by integrating tools from molecular biology, biochemistry, and systems biology to unravel mechanisms through which PTMs regulate enzymes throughout Escherichia coli metabolism and demonstrate how these individual PTMs further regulate pathways and ultimately cell phenotypes. This workflow could be applied to study PTMs and their roles across species. Understanding the complex interactions of protein posttranslational modifications (PTMs) represents a major challenge in metabolic engineering, synthetic biology, and the biomedical sciences. Here, we present a workflow that integrates multiplex automated genome editing (MAGE), genome-scale metabolic modeling, and atomistic molecular dynamics to study the effects of PTMs on metabolic enzymes and microbial fitness. This workflow incorporates complementary approaches across scientific disciplines; provides molecular insight into how PTMs influence cellular fitness during nutrient shifts; and demonstrates how mechanistic details of PTMs can be explored at different biological scales. As a proof of concept, we present a global analysis of PTMs on enzymes in the metabolic network of Escherichia coli. Based on our workflow results, we conduct a more detailed, mechanistic analysis of the PTMs in three proteins: enolase, serine hydroxymethyltransferase, and transaldolase. Application of this workflow identified the roles of specific PTMs in observed experimental phenomena and demonstrated how individual PTMs regulate enzymes, pathways, and, ultimately, cell phenotypes.

Reconstruction of Genome-Scale Metabolic Networks

Metabolic networks are representations of all biochemical reactions occurring within cells. These often include all reactions that produce and degrade metabolites in a cell, including compounds that are necessary for energy production, growth, and cellular maintenance. Organism-specific genome-scale metabolic network models can be constructed from annotated genomes after an iterative process of curation and validation. This chapter demonstrates how these networks are reconstructed and how they can be used to gain insight into cellular processes after a network is converted into a model. These models can be used for systems-level analysis and simulation of metabolism in areas such as high-throughput data analysis, metabolic engineering, and the discovery of fundamental biological properties. Furthermore, the principles of metabolic network reconstruction and analysis can be extended to include other cellular processes, thereby bringing us closer to the development of whole-cell computational models.