Cellulose Nanocrystals: Tensile Strength and Failure Mechanisms Revealed Using Reactive Molecular Dynamics.

Cellulose nanocrystals (CNCs) offer excellent mechanical properties. However, measuring the strength by performing reliable experiments at the nanoscale is challenging. In this paper, we model Iß crystalline cellulose using reactive molecular dynamics simulations. Taking the fibril twist into account, structural changes and hydrogen-bonding characteristics of CNCs during the tensile test are inspected and the failure mechanism of CNCs is analyzed down to the scale of individual bonds. The C4-O4 glycosidic bond is found to be responsible for the failure of CNCs. Finally, the effect of strain rate on ultimate properties is analyzed and a nonlinear model is used to predict the ultimate strength of 9.2 GPa and ultimate strain of 8.5% at a 1 s-1 strain rate. This study sheds light on the applications of cellulose in nanocomposites and further modeling of cellulose nanofibres.

GNP/Al-MOF nanocomposite as an efficient fiber coating of headspace solid-phase micro-extraction for the determination of organophosphorus pesticides in food samples.

The synthesis and utilization of a high porous nanocomposite comprising MIL-53(Al) metal-organic framework (Al-MOF) and graphene nanopowder (GNP) is reported as a fiber coating for headspace solid-phase micro-extraction (HS-SPME) of selected organophosphorus pesticides (OPPs) from apple, potato, grape juice, tomato, and river water. The adsorbed OPPs on the coated fiber were subsequently determined using GC-MS. Several parameters affecting the efficiency of extraction including time and temperature of extraction, desorption condition of extracted analytes, pH and agitation of sample solution, and salt concentration were investigated. The optimum extraction condition was achieved at 70 °C with an extraction time of 40 min, pH = 4-8, and NaCl concentration of 6.0% (w/v). The best condition of desorption were observed at 280 °C for 2.0 min under a flow of helium gas in the GC inlet. Under optimal conditions, the detection limits ranged from 0.2 to 1.5 ng g-1 and the linear ranges between 0.8 and 600 ng g-1. The proposed method showed very good repeatability with RSD values ranging from 4.5 to 7.3% (n = 5). The relative recoveries were between 88% and 109% at the spiked level of 25.0 ng g-1 for the tomato sample. The fabricated fiber exhibited good enrichment factor (62-195) at optimum condition of HS-SPME. The applied HS-SPME technique is facile, fast, and inexpensive. The thermally stable GNP/Al-MOF exhibited a high sensitivity toward OPPs. So, this nanocomposite can be considered as a sorbent for the micro-extraction of other pesticides in food.

Development of a SPE/GC-MS method for the determination of organophosphorus pesticides in food samples using syringe filters packed by GNP/MIL-101(Cr) nanocomposite.

In this study, we report the synthesis and application of a nanocomposite comprising metal-organic framework MIL-101(Cr) and graphene nanopowder (GNP) as a promising sorbent for the extraction of organophosphorus pesticides (OPPs) in juices, water, vegetables and honey samples. A syringe filter, for the first time, was used to host the synthesized nanocomposite and extract the OPPs followed by GC-MS analysis. Different characterization methods including XRD, FTIR, TGA, BET and SEM were employed to confirm the formation of studied nanocomposite. The results indicated that the GNP/MIL-101(Cr) could provide higher capacity for adsorption of OPPs and lower detection limit compared to pristine MIL-101(Cr). The detection limits were 0.005 to 15.0 µg/Kg and the linear range found between 0.05 and 400 µg/Kg. The proposed method showed very good repeatability with the RSD values ranging from 2.9% to 7.1%. The recoveries were between 84% -110% with the spiked levels of 2.0-100.0 µg/Kg.

Cellulose-hemicellulose interactions - A nanoscale view.

In this work, we study interactions of five different hemicellulose models, i.e. Galactoglucomannan, O-Acetyl-Galactoglucomannan, Fuco-Galacto-Xyloglucan, 4-O-Methylglucuronoxylan, and 4-O-Methylglucuronoarabinoxylan, and their respective binding strength to cellulose nanocrystals by molecular dynamics simulations. Glucuronoarabinoxylan showed the highest free energy of binding, whereas Xyloglucan had the lowest interaction energies amongst the five models. We further performed simulated shear tests and concluded that failure mostly happens at the inter-molecular interaction level within the hemicellulose fraction, rather than at the interface with cellulose. The presence of water molecules seems to have a weakening effect on the interactions of hemicellulose and cellulose, taking up the available hydroxyl groups on the surface of the cellulose for hydrogen bonding. We believe that these studies can shed light on better understanding of plant cell walls, as well as providing evidence on variability of the structures of different plant sources for extractions, purification, and operation of biorefineries.

MIL-53(Al)/Fe2O3 nanocomposite for solid-phase microextraction of organophosphorus pesticides followed by GC-MS analysis.

A novel aluminum terephthalate/Fe2O3 nanocomposite was synthesized by the addition of Fe2O3 nanoparticles into a reaction solution containing aluminum terephthalate MOF. The synthesized nanocomposite was successfully used as a fiber coating material for solid-phase microextraction (SPME) of six organophosphorus compounds (OPPs) from river water, grape juice, and tea samples. The effect of different parameters on the efficiency of SPME including desorption temperature and time, extraction temperature and time, salt concentration, pH, and agitation were thoroughly studied. The OPPs were detected and determined using GC-MS. According to the findings, a wide linear range (0.15-800 µg kg-1), low limit of detection (0.04-10 µg kg-1), and high recoveries from spiked samples (87.5-112%) were achieved with low inter-day relative standard deviation (3.2-6.7%, n = 5). The MIL-53(Al)/Fe2O3 nanocomposite showed  a high extraction ability towards OPPs, and hence, it can be considered a promising adsorbent for the extraction of various pesticides in complex matrices like tea and juice.Graphical abstract.

Tensile behaviour of dislocated/crystalline cellulose fibrils at the nano scale.

Atomistic modelling of cellulose has widely been investigated for years using molecular dynamics simulations. In this paper, we model Iß crystalline cellulose as well as develop a model including dislocations in between the crystal regions. The model including dislocations shows a tensile modulus of 109 GPa, 25% lower than that of the fully crystalline model (146 GPa). The change in dihedral angle preferences is analysed, and its effect on hydrogen bonding pattern is assessed. How presence of hydrogen bonds contributes to elastic properties of cellulose nano-fibrils is shown. Effect of water on the elastic modulus of fibrils is also investigated. Moreover, an illustration is given of how the tensile behaviour of fibrils is controlled by a synergy between the geometry changes occurring at the glycosidic linkage, reflected by specific torsional and glycosidic angles. These findings can be useful in further modelling of cellulosic fibrils at the atomistic and coarse-grained scales.

Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations.

BACKGROUND: Clostridium thermocellum is a Gram-positive anaerobe with the ability to hydrolyze and metabolize cellulose into biofuels such as ethanol, making it an attractive candidate for consolidated bioprocessing (CBP). At present, metabolic engineering in C. thermocellum is hindered due to the incomplete description of its metabolic repertoire and regulation within a predictive metabolic model. Genome-scale metabolic (GSM) models augmented with kinetic models of metabolism have been shown to be effective at recapitulating perturbed metabolic phenotypes. RESULTS: In this effort, we first update a second-generation genome-scale metabolic model (iCth446) for C. thermocellum by correcting cofactor dependencies, restoring elemental and charge balances, and updating GAM and NGAM values to improve phenotype predictions. The iCth446 model is next used as a scaffold to develop a core kinetic model (k-ctherm118) of the C. thermocellum central metabolism using the Ensemble Modeling (EM) paradigm. Model parameterization is carried out by simultaneously imposing fermentation yield data in lactate, malate, acetate, and hydrogen production pathways for 19 measured metabolites spanning a library of 19 distinct single and multiple gene knockout mutants along with 18 intracellular metabolite concentration data for a Δgldh mutant and ten experimentally measured Michaelis-Menten kinetic parameters. CONCLUSIONS: The k-ctherm118 model captures significant metabolic changes caused by (1) nitrogen limitation leading to increased yields for lactate, pyruvate, and amino acids, and (2) ethanol stress causing an increase in intracellular sugar phosphate concentrations (~1.5-fold) due to upregulation of cofactor pools. Robustness analysis of k-ctherm118 alludes to the presence of a secondary activity of ketol-acid reductoisomerase and possible regulation by valine and/or leucine pool levels. In addition, cross-validation and robustness analysis allude to missing elements in k-ctherm118 and suggest additional experiments to improve kinetic model prediction fidelity. Overall, the study quantitatively assesses the advantages of EM-based kinetic modeling towards improved prediction of C. thermocellum metabolism and develops a predictive kinetic model which can be used to design biofuel-overproducing strains.

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains.

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).

Improving prediction fidelity of cellular metabolism with kinetic descriptions.

Several modeling frameworks for describing and redirecting cellular metabolism have been developed keeping pace with the rapid development in high-throughput data generation and advances in metabolic engineering techniques. The incorporation of kinetic information within stoichiometry-only modeling techniques offers potential advantages for improved phenotype prediction and consequently more precise computational strain design. In addition to substrate-level kinetic regulatory information, the integration of a number of additional layers of regulation at the transcription, translation, and post-translation levels is sought after by many research groups. However, the practical integration of these complex biological processes into a unified framework amenable to design remains an ongoing challenge.

Advances in de novo strain design using integrated systems and synthetic biology tools.

Recent efforts in expanding the range of biofuel and biorenewable molecules using microbial production hosts have focused on the introduction of non-native pathways in model organisms and the bio-prospecting of non-model organisms with desirable features. Current challenges lie in the assembly and coordinated expression of the (non-)native pathways and the elimination of competing pathways and undesirable regulation. Several systems and synthetic biology approaches providing contrasting top-down and bottom-up strategies, respectively, have been developed. In this review, we discuss recent advances in both in silico and experimental approaches for metabolic pathway design and engineering, with a critical assessment of their merits and remaining challenges.

A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data.

In contrast to stoichiometric-based models, the development of large-scale kinetic models of metabolism has been hindered by the challenge of identifying kinetic parameter values and kinetic rate laws applicable to a wide range of environmental and/or genetic perturbations. The recently introduced ensemble modeling (EM) procedure provides a promising remedy to address these challenges by decomposing metabolic reactions into elementary reaction steps and incorporating all phenotypic observations, upon perturbation, in its model parameterization scheme. Here, we present a kinetic model of Escherichia coli core metabolism that satisfies the fluxomic data for wild-type and seven mutant strains by making use of the EM concepts. This model encompasses 138 reactions, 93 metabolites and 60 substrate-level regulatory interactions accounting for glycolysis/gluconeogenesis, pentose phosphate pathway, TCA cycle, major pyruvate metabolism, anaplerotic reactions and a number of reactions in other parts of the metabolism. Parameterization is performed using a formal optimization approach that minimizes the discrepancies between model predictions and flux measurements. The predicted fluxes by the model are within the uncertainty range of experimental flux data for 78% of the reactions (with measured fluxes) for both the wild-type and seven mutant strains. The remaining flux predictions are mostly within three standard deviations of reported ranges. Converting the EM-based parameters into a Michaelis-Menten equivalent formalism revealed that 35% of Km and 77% of kcat parameters are within uncertainty range of the literature-reported values. The predicted metabolite concentrations by the model are also within uncertainty ranges of metabolomic data for 68% of the metabolites. A leave-one-out cross-validation test to evaluate the flux prediction performance of the model showed that metabolic fluxes for the mutants located in the proximity of mutations used for training the model can be predicted more accurately. The constructed model and the parameterization procedure presented in this study pave the way for the construction of larger-scale kinetic models with more narrowly distributed parameter values as new metabolomic/fluxomic data sets are becoming available for E. coli and other organisms.

Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model.

Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. In this effort, we apply the k-OptForce procedure on a kinetic model of E. coli core metabolism constructed using the Ensemble Modeling (EM) method and parameterized using multiple mutant strains data under aerobic respiration with glucose as the carbon source. Minimal interventions are identified that improve succinate yield under both aerobic and anaerobic conditions to test the fidelity of model predictions under both genetic and environmental perturbations. Under aerobic condition, k-OptForce identifies interventions that match existing experimental strategies while pointing at a number of unexplored flux re-directions such as routing glyoxylate flux through the glycerate metabolism to improve succinate yield. Many of the identified interventions rely on the kinetic descriptions that would not be discoverable by a purely stoichiometric description. In contrast, under fermentative (anaerobic) condition, k-OptForce fails to identify key interventions including up-regulation of anaplerotic reactions and elimination of competitive fermentative products. This is due to the fact that the pathways activated under anaerobic condition were not properly parameterized as only aerobic flux data were used in the model construction. This study shed light on the importance of condition-specific model parameterization and provides insight on how to augment kinetic models so as to correctly respond to multiple environmental perturbations.