Reconstruction of an in silico metabolic model of _Arabidopsis thaliana_ through database integration

The number of genome-scale metabolic models has been rising quickly in recent years, and the scope of their utilization encompasses a broad range of applications from metabolic engineering to biological discovery. However the reconstruction of such models remains an arduous process requiring a high level of human intervention. Their utilization is further hampered by the absence of standardized data and annotation formats and the lack of recognized quality and validation standards.

Plants provide a particularly rich range of perspectives for applications of metabolic modeling. We here report the first effort to the reconstruction of a genome-scale model of the metabolic network of the plant _Arabidopsis thaliana_, including over 2300 reactions and compounds. Our reconstruction was performed using a semi-automatic methodology based on the integration of two public genome-wide databases, significantly accelerating the process. Database entries were compared and integrated with each other, allowing us to resolve discrepancies and enhance the quality of the reconstruction. This process lead to the construction of three models based on different quality and validation standards, providing users with the possibility to choose the standard that is most appropriate for a given application. First, a _core metabolic model_ containing only consistent data provides a high quality model that was shown to be stoichiometrically consistent. Second, an _intermediate metabolic model_ attempts to fill gaps and provides better continuity. Third, a _complete metabolic model_ contains the full set of known metabolic reactions and compounds in _Arabidopsis thaliana_.

We provide an annotated SBML file of our core model to enable the maximum level of compatibility with existing tools and databases. We eventually discuss a series of principles to raise awareness of the need to develop coordinated efforts and common standards for the reconstruction of genome-scale metabolic models, with the aim of enabling their widespread diffusion, frequent update, maximum compatibility and convenience of use by the wider research community and industry

High-Resolution Structural Validation of the Computational Redesign of Human U1A Protein

SummaryAchieving atomic-level resolution in the computational design of a protein structure remains a challenging problem despite recent progress. Rigorous experimental tests are needed to improve protein design algorithms, yet studies of the structure and dynamics of computationally designed proteins are very few. The NMR structure and backbone dynamics of a redesigned protein of 96 amino acids are compared here with the design target, human U1A protein. We demonstrate that the redesigned protein reproduces the target structure to within the uncertainty of the NMR coordinates, even as 65 out of 96 amino acids were simultaneously changed by purely computational methods. The dynamics of the backbone of the redesigned protein also mirror those of human U1A, suggesting that the protein design algorithm captures the shape of the potential energy landscape in addition to the local energy minimum

Multi-omic modeling of translational efficiency for synthetic gene design

Controlled expression of recombinant genes in CHO cells for advanced cell engineering will require precise, coordinated control of the synthetic processes that underpin the production of specific recombinant products or the optimal stoichiometry of functional effector proteins for multigene engineering applications. Although control of recombinant gene transcription in CHO host cells is now possible, technologies that enable control of recombinant mRNA translation rate are lacking. This is undesirable as in eukaryotic cells, cellular mRNA concentration itself may only explain a relatively small proportion of the variation in cellular protein abundance; mRNA translation rate is by far the most important contributor to cellular protein concentration. We have taken a top-down, genome-scale computational modeling approach to develop computational design tools that enable control of recombinant gene translational activity in CHO cells. Through a combination of pulsed stable isotope labelling of amino acids in cell culture (pSILAC) and RNA-Seq based analysis of the CHO cell transcriptome we quantified the translational efficiency of \u3e 4000 mRNAs. Based on informatic reconstruction of CHO mRNAs (to include untranslated and coding sequences) we built and trained a gaussian process regression model using over 250 defined mRNA sequence features to enable validated in silico prediction of mRNA translational efficiency in CHO cells from mRNA sequence. Using this genome-scale empirical modeling we created a computational gene analysis and design platform that permits both prediction of the translational efficiency of natural and recombinant mRNAs in CHO cells and de novo design of synthetic mRNAs with predictable translational activity. This platform will be employed to (i) maximize the efficiency of recombinant mRNA translation for easy-to-express proteins, (ii) optimize the rate of mRNA translation for difficult-to-express proteins and (iii) control the stoichiometry of product synthesis in multigene expression systems

Iron biochemistry is correlated with amyloid plaque morphology in an established mouse model of Alzheimer’s disease

A signature characteristic of Alzheimer's disease (AD) is aggregation of amyloid-beta (Aβ) fibrils in the brain. Nevertheless, the links between Aβ and AD pathology remain incompletely understood. It has been proposed that neurotoxicity arising from aggregation of the Aβ1-42 peptide can in part be explained by metal ion binding interactions. Using advanced X-ray microscopy techniques at sub-micron resolution, we investigated relationships between iron biochemistry and AD pathology in intact cortex from an established mouse model over-producing Aβ. We found a direct correlation of amyloid plaque morphology with iron, and evidence for the formation of an iron-amyloid complex. We also show that iron biomineral deposits in the cortical tissue contain the mineral magnetite, and provide evidence that Aβ-induced chemical reduction of iron could occur in vivo. Our observations point to the specific role of iron in amyloid deposition and AD pathology, and may impact development of iron-modifying therapeutics for AD

Intermolecular Structure Determination of Amyloid Fibrils with 2 Magic-Angle Spinning and Dynamic Nuclear Polarization NMR

We describe magic-angle spinning NMR experiments designed to elucidate the interstrand architecture of amyloid fibrils. Three methods are introduced for this purpose, two being based on the analysis of long-range [superscript 13]C–[superscript 13]C correlation spectra and the third based on the identification of intermolecular interactions in [superscript 13]C–[superscript 15]N spectra. We show, in studies of fibrils formed by the 86-residue SH3 domain of PI3 kinase (PI3-SH3 or PI3K-SH3), that efficient [superscript 13]C–[superscript 13]C correlation spectra display a resonance degeneracy that establishes a parallel, in-register alignment of the proteins in the amyloid fibrils. In addition, this degeneracy can be circumvented to yield direct intermolecular constraints. The [superscript 13]C–[superscript 13]C experiments are corroborated by [superscript 15]N–[superscript 13]C correlation spectra obtained from a mixed [[superscript 15]N,[superscript 12]C]/[[superscript 14]N,[superscript 13]C] sample which directly quantify interstrand distances. Furthermore, when the spectra are recorded with signal enhancement provided by dynamic nuclear polarization (DNP) at 100 K, we demonstrate a dramatic increase (from 23 to 52) in the number of intermolecular [superscript 15]N–[superscript 13]C constraints detectable in the spectra. The increase in the information content is due to the enhanced signal intensities and to the fact that dynamic processes, leading to spectral intensity losses, are quenched at low temperatures. Thus, acquisition of low temperature spectra addresses a problem that is frequently encountered in MAS spectra of proteins. In total, the experiments provide 111 intermolecular [superscript 13]C–[superscript 13]C and [superscript 15]N–[superscript 13]C constraints that establish that the PI3-SH3 protein strands are aligned in a parallel, in-register arrangement within the amyloid fibril.National Institutes of Health (U.S.) (Grant EB-003151)National Institutes of Health (U.S.) (Grant EB-002804)National Institutes of Health (U.S.) (Grant EB-002026

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)

Assessing the long-term effectiveness of interferon-beta and glatiramer acetate in multiple sclerosis: final 10-year results from the UK multiple sclerosis risk-sharing scheme

Background Because multiple sclerosis (MS) is a chronic disease causing disability over decades, it is crucial to know if the short-term effects of disease-modifying therapies reported in randomised controlled trials reduce long-term disability. This 10-year prospective observational study of disability outcomes (Expanded Disability Status Scale (EDSS) and utility) was set up, in conjunction with a risk-sharing agreement between payers and producers, to investigate this issue. Methods The outcomes of the UK treated patients were compared with a modelled untreated control based on the British Columbia MS data set to assess the long-term effectiveness of these treatments. Two complementary analysis models were used: a multilevel model (MLM) and a continuous Markov model. Results 4862 patients with MS were eligible for the primary analysis (mean and median follow-up times 8.7 and 10 years). EDSS worsening was reduced by 28% (MLM), 7% (Markov) and 24% time-adjusted Markov in the total cohort, and by 31% (MLM) and 14% (Markov) for relapsing remitting patients. The utility worsening was reduced by 23%–24% in the total cohort and by 24%–31% in the RR patients depending on the model used. All sensitivity analyses showed a treatment effect. There was a 4-year (CI 2.7 to 5.3) delay to EDSS 6.0. An apparent waning of treatment effect with time was seen. Subgroup analyses suggested better treatment effects in those treated earlier and with lower EDSS scores. Conclusions This study supports a beneficial effect on long-term disability with first-line MS disease-modifying treatments, which is clinically meaningful. However the waning effect noted requires further study

Controlling <i>In Planta</i> Gold Nanoparticle Synthesis and Size for Catalysis

Gold nanoparticles (Au-NPs) are used as catalysts for a diverse range of industrial applications. Currently, Au-NPs are synthesized chemically, but studies have shown that plants fed Au deposit, this element naturally as NPs within their tissues. The resulting plant material can be used to make biomass-derived catalysts. In vitro studies have shown that the addition of specific, short (∼10 amino acid) peptide/s to solutions can be used to control the NP size and shape, factors that can be used to optimize catalysts for different processes. Introducing these peptides into the model plant species, Arabidopsis thaliana (Arabidopsis), allows us to regulate the diameter of nanoparticles within the plant itself, consequently influencing the catalytic performance in the resulting pyrolyzed biomass. Furthermore, we show that overexpressing the copper and gold COPPER TRANSPORTER 2 (COPT2) in Arabidopsi sincreases the uptake of these metals. Adding value to the Au-rich biomass offers the potential to make plant-based remediation and stabilization of mine wastes financially feasible. Thus, this study represents a significant step toward engineering plants for the sustainable recovery of finite and valuable elements from our environment