Prospects for Biotechnology in Oil Palm (Elaeis guineensis) and Coconut (Cocos nucifera) Improvement

Abstract

Research into human metabolism is expanding rapidly due to the emergence of metabolism as a key factor in common diseases. Mathematical modeling of human cellular metabolism has traditionally been performed via kinetic approaches whose applicability for large-scale systems is limited by lack of kinetic constants data. An alternative computational approach bypassing this hurdle called constraint-based modeling (CBM) serves to analyze the function of large-scale metabolic networks by solely relying on simple physical-chemical constraints. However, while extensive research has been performed on constraint-based modeling of microbial metabolism, large-scale modeling of human metabolism is still in its infancy. Utilizing constraint-based modeling to model human cellular metabolism is significantly more complicated than modeling microbial metabolism as in multi-cellular organisms the metabolic behavior varies across cell-types and tissues. It is further complicated due to lack of data on cell type- and tissue-specific metabolite uptake from the surrounding microenvironments and tissue-specific metabolic objective functions. To overcome these problems, several studies suggested CBM methods that integrate metabolic networks with gene expression data that is easily measurable under various conditions. This specific objective functions are expected to improve the prediction accuracy of the presented methods. Such objective functions may be derived based on computational learning that would give optimal correspondence between predicted and measured metabolic phenotypes (Burgard, 2003).

The CBM methods presented here open the way for future computational investigations of metabolic disorders given the relevant expression data. A first attempt to visualize and interpret changes in gene expression data measured following gastric bypass surgery via a genome-scale metabolic network was done by Duarte et al (Duarte, 2007). Another potential application would be the prediction of diagnostic biomarkers for metabolic diseases that could be identified via biofluid metabolomics (Kell, 2007). Towards this goal, we have recently developed a CBM method for predicting metabolic biomarkers for in-born errors of metabolism by searching for changes in metabolite uptake and secretion rate due to genetic alterations (Shlomi, 2009). Incorporating cell type- and tissue-specific gene expression data within this framework can potentially improve the identification of diagnostic biomarkers. Overall, the methods presented here lay the foundation for studying normal and abnormal human cellular metabolism in tissue-specific manner based on commonly measured gene expression data.     

Incorporating qualitative knowledge in enzyme kinetic models using fuzzy logic

Modeling of metabolic pathway dynamics requires detailed kinetic equations at the enzyme level. In particular, the kinetic equations must account for metabolite effectors that contribute significantly to the pathway regulation in vivo. Unfortunately, most kinetic rate laws available in the literature do not consider all the effectors simultaneously, and much kinetic information exists in a qualitative or semiquantitative form. In this article, we present a strategy to incorporate such information into the kinetic equation. This strategy uses fuzzy logic‐based factors to modify algebraic rate laws that account for partial kinetic characteristics. The parameters introduced by the fuzzy factors are then optimized by use of a hybrid of simplex and genetic algorithms. The resulting model provides a flexible form that can simulate various kinetic behaviors. Such kinetic models are suitable for pathway modeling without complete enzyme mechanisms. Three enzymes in Escherichia coli central metabolism are used as examples: phosphoenolpyruvate carboxylase; phosphoenolpyruvate carboxykinase; and pyruvate kinase I. Results show that, with fuzzy logic‐augmented models, the kinetic data can be much better described. In particular, complex behavior, such as allosteric inhibition, can be captured using fuzzy rules. The resulting models, even though they do not provide additional physical meaning in enzyme mechanisms, allow the model to incorporate semiquantitative information in metabolic pathway models. © 1999 John Wiley & Sons, Inc. Biotechnol Bioeng 62: 722–729, 1999.     

Genomically and biochemically accurate metabolic reconstruction of Methanosarcina barkeri Fusaro,iMG746

Methanosarcina barkeri is an Archaeon that produces methane anaerobically as the primary byproduct of its metabolism. M. barkeri can utilize several substrates for ATP and biomass production including methanol, acetate, methyl amines, and a combination of hydrogen and carbon dioxide. In 2006, a metabolic reconstruction of M. barkeri, iAF692, was generated based on a draft genome annotation. The iAF692 reconstruction enabled the first genome-Scale simulations for Archaea. Since the publication of the first metabolic reconstruction of M. barkeri, additional genomic, biochemical, and phenotypic data have clarified several metabolic pathways. We have used this newly available data to improve the M. barkeri metabolic reconstruction. Modeling simulations using the updated model, iMG746, have led to increased accuracy in predicting gene knockout phenotypes and simulations of batch growth behavior. We used the model to examine knockout lethality data and make predictions about metabolic regulation under different growth conditions. Thus, the updated metabolic reconstruction of M. barkeri metabolism is a useful tool for predicting cellular behavior, studying the methanogenic lifestyle, guiding experimental studies, and making predictions relevant to metabolic engineering applications.     

Segmented linear modeling of CHO fed‐batch culture and its application to large scale production

We describe a systematic approach to model CHO metabolism during biopharmaceutical production across a wide range of cell culture conditions. To this end, we applied the metabolic steady state concept. We analyzed and modeled the production rates of metabolites as a function of the specific growth rate. First, the total number of metabolic steady state phases and the location of the breakpoints were determined by recursive partitioning. For this, the smoothed derivative of the metabolic rates with respect to the growth rate were used followed by hierarchical clustering of the obtained partition. We then applied a piecewise regression to the metabolic rates with the previously determined number of phases. This allowed identifying the growth rates at which the cells underwent a metabolic shift. The resulting model with piecewise linear relationships between metabolic rates and the growth rate did well describe cellular metabolism in the fed‐batch cultures. Using the model structure and parameter values from a small‐scale cell culture (2 L) training dataset, it was possible to predict metabolic rates of new fed‐batch cultures just using the experimental specific growth rates. Such prediction was successful both at the laboratory scale with 2 L bioreactors but also at the production scale of 2000 L. This type of modeling provides a flexible framework to set a solid foundation for metabolic flux analysis and mechanistic type of modeling. Biotechnol. Bioeng. 2017;114: 785–797. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.     

In Silico Metabolic Model and Protein Expression of Haemophilus influenzae Strain Rd KW20 in Rich Medium

The intermediary metabolism of Haemophilus influenzae strain Rd KW20 was studied by a combination of protein expression analysis using a recently developed direct proteomics approach, mutational analysis, and mathematical modeling. Special emphasis was placed on carbon utilization, sugar fermentation, TCA cycle, and electron transport of H. influenzae cells grown microaerobically and anaerobically in a rich medium. The data indicate that several H. influenzae metabolic proteins similar to Escherichia coli proteins, known to be regulated by low concentrations of oxygen, were well expressed in both growth conditions in H. influenzae. An in silico model of the H. influenzae metabolic network was used to study the effects of selective deletion of certain enzymatic steps. This allowed us to define proteins predicted to be essential or non-essential for cell growth and to address numerous unresolved questions about intermediary metabolism of H. influenzae. Comparison of data from in vivo protein expression with the protein list associated with a genome-scale metabolic model showed significant coverage of the known metabolic proteome. This study demonstrates the significance of an integrated approach to the characterization of H. influenzae metabolism.     

Radiotracer and computer modeling evidence that phospho-base methylation is the main route of choline synthesis in tobacco

Among flowering plants, the synthesis of choline (Cho) from ethanolamine (EA) can potentially occur via three parallel, interconnected pathways involving methylation of free bases, phospho-bases, or phosphatidyl-bases. We investigated which pathways operate in tobacco (Nicotiana tabacum L.) because previous work has shown that the endogenous Cho supply limits accumulation of glycine betaine in transgenic tobacco plants engineered to convert Cho to glycine betaine. The kinetics of metabolite labeling were monitored in leaf discs supplied with [(33)P]phospho-EA, [(33)P]phospho-monomethylethanolamine, or [(14)C]formate, and the data were subjected to computer modeling. Because partial hydrolysis of phospho-bases occurred in the apoplast, modeling of phospho-base metabolism required consideration of the re-entry of [(33)P]phosphate into the network. Modeling of [(14)C]formate metabolism required consideration of the labeling of the EA and methyl moieties of Cho. Results supported the following conclusions: (a) The first methylation step occurs solely at the phospho-base level; (b) the second and third methylations occur mainly (83%-92% and 65%-85%, respectively) at the phospho-base level, with the remainder occurring at the phosphatidyl-base level; and (c) free Cho originates predominantly from phosphatidylcholine rather than from phospho-Cho. This study illustrates how computer modeling of radiotracer data, in conjunction with information on chemical pool sizes, can provide a coherent, quantitative picture of fluxes within a complex metabolic network.     

Cell metabolism: An essential link between cell growth and apoptosis

Growth factor-stimulated or cancerous cells require sufficient nutrients to meet the metabolic demands of cell growth and division. If nutrients are insufficient, metabolic checkpoints are triggered that lead to cell cycle arrest and the activation of the intrinsic apoptotic cascade through a process dependent on the Bcl-2 family of proteins. Given the connections between metabolism and apoptosis, the notion of targeting metabolism to induce cell death in cancer cells has recently garnered much attention. However, the signaling pathways by which metabolic stresses induce apoptosis have not as of yet been fully elucidated. Thus, the best approach to this promising therapeutic avenue remains unclear. This review will discuss the intricate links between metabolism, growth, and intrinsic apoptosis and will consider ways in which manipulation of metabolism might be exploited to promote apoptotic cell death in cancer cells. This article is part of a Special Issue entitled Mitochondria: the deadly organelle.     

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric,thermodynamic and physiological constraints

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.     

Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters

Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.     

Sphingolipid homeostasis in the web of metabolic routes

Sphingolipids play a key role in cells as structural components of membrane lipid bilayers and signaling molecules implicated in important physiological and pathological processes. Their metabolism is tightly regulated. Mechanisms controlling sphingolipid metabolism are far from being completely understood. However, they already reveal the integration of sphingolipids in the whole metabolic network as signaling devices that coordinate different metabolic pathways. A picture of sphingolipids integrated into metabolic networks might help to understand sphingolipid homeostasis. This review describes recent advances in the regulation of de novo sphingolipid synthesis with a focus on the bridges that exist with other metabolic pathways and the importance of this crosstalk in the control of sphingolipid homeostasis. This article is part of a Special Issue entitled New Frontiers in Sphingolipid Biology.     

Combining mechanistic and data‐driven approaches to gain process knowledge on the control of the metabolic shift to lactate uptake in a fed‐batch CHO process

A growing body of knowledge is available on the cellular regulation of overflow metabolism in mammalian hosts of recombinant protein production. However, to develop strategies to control the regulation of overflow metabolism in cell culture processes, the effect of process parameters on metabolism has to be well understood. In this study, we investigated the effect of pH and temperature shift timing on lactate metabolism in a fed‐batch Chinese hamster ovary (CHO) process by using a Design of Experiments (DoE) approach. The metabolic switch to lactate consumption was controlled in a broad range by the proper timing of pH and temperature shifts. To extract process knowledge from the large experimental dataset, we proposed a novel methodological concept and demonstrated its usefulness with the analysis of lactate metabolism. Time‐resolved metabolic flux analysis and PLS‐R VIP were combined to assess the correlation of lactate metabolism and the activity of the major intracellular pathways. Whereas the switch to lactate uptake was mainly triggered by the decrease in the glycolytic flux, lactate uptake was correlated to TCA activity in the last days of the cultivation. These metabolic interactions were visualized on simple mechanistic plots to facilitate the interpretation of the results. Taken together, the combination of knowledge‐based mechanistic modeling and data‐driven multivariate analysis delivered valuable insights into the metabolic control of lactate production and has proven to be a powerful tool for the analysis of large metabolic datasets. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1657–1668, 2015     

Smoothing Splines: Methods and Applications by WANG,Y.

EDITOR: GUILHERME J. M. ROSA Smoothing Splines: Methods and Applications (Y. Wang) Pang Du Statistics for Spatio‐Temporal Data (N. Cressie and C. K. Wikle) Ole F. Christensen Bayesian Modeling in Bioinformatics (D. K. Dey, S. Ghosh, and B. K. Mallick, eds.) Hisashi Noma Randomized Response and Indirect Questioning Techniques in Surveys (A. Chaudhuri) Mariano R. Espejo Designs and Analysis of Experiments, Volume 3: Special Designs and Applications (K. Hinkelmann, ed.) Luzia Trinca     

Plasma Markers of Cholesterol Homeostasis and Apolipoprotein B‐100 Kinetics in the Metabolic Syndrome

Objective: The metabolic syndrome is characterized by defective hepatic apolipoprotein B‐100 (apoB) metabolism. Hepato‐intestinal cholesterol metabolism may contribute to this abnormality. Research Methods and Procedures: We examined the association of cholesterol absorption and synthesis with the kinetics of apoB in 35 obese subjects with the metabolic syndrome. Plasma ratios of campesterol and lathosterol to cholesterol were used to estimate cholesterol absorption and synthesis, respectively. Very‐low‐density lipoprotein (VLDL), intermediate‐density lipoprotein (IDL), and low‐density lipoprotein apoB kinetics were studied using stable isotopy and mass spectrometry. Kinetic parameters were derived using multicompartmental modeling. Results: Compared with controls, the obese subjects had significantly lower plasma ratios of campesterol, but higher plasma ratios of lathosterol (p < 0.05 in both). This was associated with elevated VLDL‐apoB secretion rate (p < 0.05) and delayed fractional catabolism of IDL and low‐density lipoprotein‐apoB (p < 0.01). In the obese group, plasma ratios of campesterol correlated inversely with VLDL‐apoB secretion (r = ?0.359, p < 0.05), VLDL‐apoB (r = ?0.513, p < 0.01) and IDL‐apoB (r = ?0.511, p < 0.01) pool size, and plasma lathosterol ratio (r = ?0.366, p < 0.05). Subjects with low cholesterol absorption had significantly higher VLDL‐apoB secretion, VLDL‐apoB and IDL‐apoB pool size, and plasma lathosterol ratio (p < 0.05 in both) than those with high cholesterol absorption. Discussion: Subjects with the metabolic syndrome have oversecretion of VLDL‐apoB and decreased catabolism of apoB‐containing particles and low absorption and high synthesis rates of cholesterol. These changes in cholesterol homeostasis may contribute to the kinetic defects in apoB metabolism in the metabolic syndrome.     

Modeling <Emphasis Type="Italic">Lactococcus lactis</Emphasis> using a genome-scale flux model

Background  

Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks.     

Systems Metabolic Engineering

Cover illustration Special Issue: Systems Metabolic Engineering. Metabolic engineering combines a mix of approaches, including in silico modeling, omics studies, synthetic biology and protein engineering to improve microorganism strains for increased yields and reduced production costs of desirable chemicals. Such an achievement is exemplified on this Special Issue's cover, which shows an electron microscopy image of Corynebacterium glutamicum that has been engineered to produce a sustainable bio-nylon monomer from hemicellulose sugar found in the cell walls of plants. Image provided by Buschke et al.     

Model‐assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways

Efficient approaches to increase plant lipid production are necessary to meet current industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires an understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom‐up metabolic reconstruction of J. curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint‐based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping stone for future rational optimization of other agronomically relevant traits in J. curcas.     

Fuel availability and fate in cardiac metabolism: A tale of two substrates

The heart's extraordinary metabolic flexibility allows it to adapt to normal changes in physiology in order to preserve its function. Alterations in the metabolic profile of the heart have also been attributed to pathological conditions such as ischemia and hypertrophy; however, research during the past decade has established that cardiac metabolic adaptations can precede the onset of pathologies. It is therefore critical to understand how changes in cardiac substrate availability and use trigger events that ultimately result in heart dysfunction. This review examines the mechanisms by which the heart obtains fuels from the circulation or from mobilization of intracellular stores. We next describe experimental models that exhibit either an increase in glucose use or a decrease in FA oxidation, and how these aberrant conditions affect cardiac metabolism and function. Finally, we highlight the importance of alternative, relatively under-investigated strategies for the treatment of heart failure. This article is part of a Special Issue entitled: Heart Lipid Metabolism edited by G.D. Lopaschuk.     

The Effect of Food Quantity on Feeding and Metabolic Expenditure in Cladocera

The effect of Chlorella concentration on total metabolic expenditure, basal metabolism and filtering and ingestion rates was studied in juvenile and adult females of three cladoceran species. No significant influence of food concentration on basal metabolism was found, whereas total metabolic expenditure depended on Chlorella concentration. Total metabolic expenditure in cladocerans was subdivided into: basal metabolism and expenditure on filtration, locomotion and specific dynamic action (SDA). Significant positive correlations were observed between expenditure on filtration and filtering rate and between expenditures on SDA and ingestion rate.