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.     

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.     

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.     

Histone deacetylase inhibition results in a common metabolic profile associated with HT29 differentiation

Cell differentiation is an orderly process that begins with modifications in gene expression. This process is regulated by the acetylation state of histones. Removal of the acetyl groups of histones by specific enzymes (histone deacetylases, HDAC) usually downregulates expression of genes that can cause cells to differentiate, and pharmacological inhibitors of these enzymes have been shown to induce differentiation in several colon cancer cell lines. Butyrate at high (mM) concentration is both a precursor for acetyl-CoA and a known HDAC inhibitor that induces cell differentiation in colon cells. The dual role of butyrate raises the question whether its effects on HT29 cell differentiation are due to butyrate metabolism or to its HDAC inhibitor activity. To distinguish between these two possibilities, we used a tracer-based metabolomics approach to compare the metabolic changes induced by two different types of HDAC inhibitors (butyrate and the non-metabolic agent trichostatin A) and those induced by other acetyl-CoA precursors that do not inhibit HDAC (caprylic and capric acids). [1,2-¹³C₂]-d-glucose was used as a tracer and its redistribution among metabolic intermediates was measured to estimate the contribution of glycolysis, the pentose phosphate pathway and the Krebs cycle to the metabolic profile of HT29 cells under the different treatments. The results demonstrate that both HDAC inhibitors (trichostatin A and butyrate) induce a common metabolic profile that is associated with histone deacetylase inhibition and differentiation of HT29 cells whereas the metabolic effects of acetyl-CoA precursors are different from those of butyrate. The experimental findings support the concept of crosstalk between metabolic and cell signalling events, and provide an experimental approach for the rational design of new combined therapies that exploit the potential synergism between metabolic adaptation and cell differentiation processes through modification of HDAC activity.     

基于约束建模法的结核菌H37Rv代谢网络分析

基于结核分枝杆菌国际标准强毒株H37Rv菌株的基因组尺度代谢网络模型iNJ661进行分析,以寻找代谢网络中培养基的关键成分和必要基因.该研究在Matlab平台上利用COBRA工具箱,采用基于约束的建模方法进行动态生长模拟、解空间抽样在酶活性水平上的具体化和基因删除模拟实验.结果发现培养基成分中铵盐、三价铁盐、磷酸盐、硫酸盐、甘油等可影响H37Rv的生长;培养基中去除磷酸盐后十种酶均在不同程度上受到抑制,其中丙糖磷酸异构酶、3-磷酸甘油醛脱氢酶、磷酸甘油酸变位酶、烯醇酶受限明显.通过基因删除得出188个必要基因以及非必要基因中的16个致死基因对.基于约束建模分析可初步了解结核杆菌H37Rv菌株代谢网络的性质,可为后续相关研究提供参考和借鉴.     

组蛋白不同乙酰化位点突变对酵母细胞生长和Ssa3、Gal1基因表达的影响

组蛋白乙酰化对基因表达和细胞生长非常重要.为揭示组蛋白H3K14和H4K8的乙酰化修饰对不同条件下细胞生长和Ssa3、Gal1基因表达的重要性及二者功能差异.构建了H3K14、H4K8分别突变为精氨酸的单突变株S14、S8及二者同时突变的双突变株D814,并对其在正常、高温、咖啡因存在等条件下生长及Ssa3、Gal1表达进行比较.结果表明,所有突变株对咖啡因敏感性增加;D814对温度敏感,且在供试条件下其生长及Ssa3和Gal1激活均明显慢于野生型和单突变株;除半乳糖和葡萄糖为单一碳源,30℃时两单突变株差别不大外,其它条件下S8生长及Ssa3和Gal1激活均慢于S14.表明H3K14、H4K8乙酰化对细胞生长和适应不利环境非常重要,而且在对不利条件的快速适应方面,H4K8的乙酰化修饰可能更为重要.组蛋白突变株的表型缺陷是因该条件下细胞生存所必需的基因激活延迟所致.     

Metabolic substrates,histone modifications,and heart failure

Histone epigenetic modifications are chemical modification changes to histone amino acid residues that modulate gene expression without altering the DNA sequence. As both the phenotypic and causal factors, cardiac metabolism disorder exacerbates mitochondrial ATP generation deficiency, thus promoting pathological cardiac hypertrophy. Moreover, several concomitant metabolic substrates also promote the expression of hypertrophy-responsive genes via regulating histone modifications as substrates or enzyme-modifiers, indicating their dual roles as metabolic and epigenetic regulators. This review focuses on the cardiac acetyl-CoA-dependent histone acetylation, NAD⁺-dependent SIRT-mediated deacetylation, FAD⁺-dependent LSD-mediated, and α-KG-dependent JMJD-mediated demethylation after briefly addressing the pathological and physiological cardiac energy metabolism. Besides using an “iceberg model” to explain the dual role of metabolic substrates as both metabolic and epigenetic regulators, we also put forward that the therapeutic supplementation of metabolic substrates is promising to blunt HF via re-establishing histone modifications.     

Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics

Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis, as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that CAF-treated CRC cells are especially sensitive to inhibitions of hexokinase and glucose-6-phosphate, the rate limiting steps of glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.     

The activity reaction core and plasticity of metabolic networks

Understanding the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery.     

Histone acetylation in gene regulation.

Genetic information is packaged in the highly dynamic nucleoprotein structure called chromatin. Many biological processes are regulated via post-translational modifications of key proteins. Acetylation of lysine residues at the N-terminal histone tails is one of the most studied covalent modifications influencing gene regulation in eukaryotic cells. This review focuses on the role of enzymes involved in controlling both histone and non-histone proteins acetylation levels in the cell, with particular emphasis on their effects on cancer.     

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor-utilising enzymes and transporters through constraint-based modelling. The first eukaryotic genome-scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron-sulphur (Fe-S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron-recruiting enzyme at a rate of 3.02 × 10 ⁻¹¹ mmol·(g biomass) ⁻¹·h ⁻¹.