K-FIT: Parameterizing kinetic models of metabolism using multiple fluxomic datasets

Kinetic models predict the metabolic flows by directly linking metabolite concentrations and enzyme levels to reaction fluxes. Robust parameterization of organism-level kinetic models that faithfully reproduce the effect of different genetic or environmental perturbations remains an open challenge due to the intractability of existing algorithms. This paper introduces K-FIT, an accelerated kinetic parameterization workflow that leverages a novel decomposition approach to identify steady-state fluxes in response to genetic perturbations followed by a gradient-based update of kinetic parameters until predictions simultaneously agree with the fluxomic data in all perturbed metabolic networks. The applicability of K-FIT to large-scale models is demonstrated by parameterizing an expanded kinetic model for E. coli (307 reactions and 258 metabolites) using fluxomic data from six mutants. The achieved thousand-fold speed-up afforded by K-FIT over meta-heuristic approaches is transformational enabling follow-up robustness of inference analyses and optimal design of experiments to inform metabolic engineering strategies

Identifying functional roles of SNPs using metabolic networks for improved plant breeding

Genetic sources of phenotypic variation have been a major focus of studies in plants aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies (GWAS) aim to identify the genetic background behind a trait by examining the associations between specific phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are now commonly performed, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases it introduces. Here, we propose a complementary analysis referred to as SNPeffect that sifts out functional SNPs from the tens of thousands typically identified during a genome sequencing study by integrating biochemical knowledge encoded in metabolic models, superimposed with phenotypic measurements. By design, SNPeffect can handle both monogenic and polygenic traits while offering mechanistic interpretations of the deciphered genotype-to-phenotype relations. SNPeffect was used to explain phenotypic variations such as differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of activating and inactivating SNPs present in the enzyme-coding regions of the genotypes. To this end, we also constructed a non-compartmentalized genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that plant growth is a complex polygenic trait which is primarily governed by carbon and energy partitioning. Growth-affecting SNPs in coding regions were found to primarily be in amino-acid metabolism, glycolysis, TCA cycle, and energy metabolism. Faster-growing Arabidopsis genotypes were predicted to have higher fluxes through the protein metabolism pathways, indicating that increase in amino acid levels has a positive growth effect. Faster genotypes were also seen to preferentially employ the energy-efficient purine salvage pathway as opposed to de novo purine biosynthesis for generating energy metabolites AMP and GMP. We also found putative causal SNPs to be distributed among genes belonging to glycolysis, pyrimidine metabolism, folate biosynthesis, and shikimate metabolism, which can serve as candidate genes for further experimental characterization and/or targeted plant breeding. For both Arabidopsis and poplar, a number of deactivating SNPs were predicted to be in genes belonging to the lignin biosynthetic pathway, indicating that the energetics of producing lignin is a major growth determinant. To further decipher the underlying genetic landscape, we calculated all possible epistatic interactions using flux-balance analysis. Interestingly, we detected a significant positive correlation between the number of negative epistatic interactions in a genotype and its replicative fitness, indicating that functional genetic redundancies are beneficial for growth in Arabidopsis. This possibly serves to increase robustness to mutational and/or environmental perturbations as these can then be buffered by shuttling metabolic flux through unaffected parts of the network. We anticipate that putative causal roles for many more SNPs can be gleaned if this analysis is repeated with additional genotypes, phenotypes (such as genotype-specific rates of photosynthetic oxygen evolution or nutrient exchange fluxes) and/or omics datasets (such as proteomics or transcriptomics). Hence, as genome sequencing and plant phenotyping technologies are rapidly decreasing in cost, undertaking large-scale studies that incorporate diverse datasets is also becoming more feasible. As more such data is made available, the need for complex analytical tools will also rise. We envision SNPeffect to pave the way for more tools that can mechanistically elucidate the genetic landscape underlying the wide phenotypic variations that is characteristic of plants

Genome-scale mapping models and algorithms for stationary and instationary MFA-based metabolic flux elucidation

Metabolic models used in 13C metabolic flux analysis (13C-MFA) generally include a limited number of reactions primarily from central metabolism, neglecting degradation pathways and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up core bacterial and cyanobacterial mapping models to a genome-scale carbon mapping (GSCM) models, imEco726 (668 reaction and 566 metabolites) and imSyn711 (731 reactions, 679 metabolites) for E. coli and Synechocystis PCC 6803, respectively, representing a ten-fold increase in model size. The GSCM models are constructed using the CLCA algorithm following reduction of the corresponding metabolic models, iAF1260 and iSyn731, using experimentally measured biomass and product yield during growth on glucose and CO2, respectively. The mapping models are then deployed for flux elucidation using isotopic steady-state MFA for E. coli to recapitulate experimentally observed labeling distributions of 12 measured amino acids, and isotopic instationary MFA for Synechocystis, to recapitulate labeling dynamics of 15 central metabolites. In both models, 80% of all fluxes varies less than onetenth of the basis carbon substrate uptake rate primarily due to the flux coupling with biomass production. Overall, we find that both the topology and estimated values of the metabolic fluxes remain largely consistent between the core and GSMM models for E. coli. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Significant flux range shifts were observed using a GSCM model compared to a core model in Synechocystis arising from the inclusion of 18 novel carbon paths in the GSCM model. In particular, no flux is channeled through the oxidative pentose phosphate pathway, resulting in a reduced carbon fixation flux. In addition, a higher flux is seen through the Transaldolase reaction, serving as a bypass route to Fructose bisphosphatase. Serine and glycine are found to be synthesized from 3-phosphoglycerate and the photorespiratory pathway, respectively. Pyruvate is synthesized exclusively via the malate bypass with trace contributions from pyruvate kinase. Furthermore, trace flux is predicted through the lower TCA cycle indicating either pathway incompleteness or dispensability during photoautotrophic growth. Despite these differences, 80% of all reactions in both genome-scale models are resolved to within 10% of the respective substrate uptake rate due to the presence of 411 and 407 growth-coupled reactions in E. coli and Synechocystis, respectively. Flux ranges obtained with GSCM models are compared with those obtained upon projecting core model ranges on to a genome-scale metabolic model to elucidate the loss of information and erroneous biological inferences about pathway usage arising from assumptions contained within core models, reaffirming the importance of using mapping models with global carbon path coverage in 13C metabolic flux analysis

PoreDesigner: A computational tool for the design of membrane pores for separations

Monodispersed angstrom-size pores embedded in a suitable matrix are promising for highly selective membrane-based separations. They can provide substantial energy savings in water treatment and small molecule bioseparations. Such membrane proteins (primarily aquaporins) are commonplace in biological membranes but difficult to implement in synthetic industrial membranes due to their modest and non-tunable selectivity. Here we describe PoreDesigner, a computational design workflow for the redesign of the robust beta-barrel Outer Membrane Protein F as a scaffold targeting of any specified pore diameter (spanning 3–10 Å), internal geometry and chemistry. PoreDesigner uses a mixed-integer linear program to optimally place long side -chain hydrophobic amino acids at the pore constriction region that yield a smaller and more hydrophobic pore by maximizing the interaction energy between the pore wall and the permeating water wire. We appended a design assessment step in each iteration by accepting only those designs that fit the user-fed pore dimensions. We first ran PoreDesigner to obtain pore sizes lesser than 4 Å that would exhibit aquaporin-like single file water transport yet maintaining high water permeation rates. 40 accepted OmpF redesigns were obtained and were classified as off-center (OCD), uniform closure (UCD), and cork-screw designs (CSD) dictated by their internal pore architecture. The narrowest pore design from each category was chosen and set in a membrane-patch and an all-atom 200ns molecular dynamics forward-osmosis simulation was performed to corroborate the PoreDesigner-predicted pore sizes. Subsequently, stopped-flow light scattering experiments on these three designs revealed complete salt rejection by the UCD mutant and an order of magnitude higher single-channel water permeabilities than any reported aquaporin till date (for all three designs). Follow-up efforts are being made to tune the membrane-pore interactions for various biomimetic membrane materials, by systematic alteration of the hydrophobicity of the membrane-facing residues without altering their pore size. This would enable easier incorporation of these redesigned proteins in 2D planar membrane sheets and serve as viable filtration assemblies for performing precise angstrom-scale separations. PoreDesigner has been made freely downloadable from http://www.maranasgroup.com/software.htm. Please click Additional Files below to see the full abstract

Metabolic modeling for the microbiome

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MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases

<p>Abstract</p> <p>Background</p> <p>Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity).</p> <p>Description</p> <p>MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.</p> <p>Conclusions</p> <p>The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).</p

Adsorption of homopolypeptides on gold investigated using atomistic molecular dynamics

We investigate the role of dynamics on adsorption of peptides to gold surfaces using all-atom molecular dynamics simulations in explicit solvent. We choose six homopolypeptides [Ala 10 , Ser 10 , Thr 10 , Arg 10 , Lys 10 , and Gln 10 ], for which experimental surface coverages are not correlated with amino acid level affinities for gold, with the idea that dynamic properties may also play a role. To assess dynamics we determine both conformational movement and flexibility of the peptide within a given conformation. Low conformational movement indicates stability of a given conformation and leads to less adsorption than homopolypeptides with faster conformational movement. Likewise, low flexibility within a given conformation also leads to less adsorption. Neither amino acid affinities nor dynamic considerations alone predict surface coverage; rather both quantities must be considered in peptide adsorption to gold surfaces.US Department of Energy, Office of Advanced Scientific Computing; grant number DE-FG02-02ER25535. Foundation for Science and Technology for post-doctoral fellowship SFRH/BPD/20555/2004/0GVL. USA National Science Foundation Grant # EEC-0353569 for participation in the REU program in Biomolecular Engineering

\u3ci\u3eZea mays i\u3c/i\u3eRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species