Interplay and integrated regulation of astaxanthin-lipid biosynthesis in microbial systems: A review from metabolic pathway cross-talk to biotechnological optimization.

Chapter 4 - Lipid biochemistry and biosynthesis

Background: Drug-induced cardiotoxicity is a major cause of clinical trial failures and post-market drug withdrawals. Current screening methods rely primarily on cell assays and transcriptomic profiling, but metabolic perturbations may provide additional predictive signals for cardiotoxic liability in drugs not yet tested in humans. Hypothesis: We hypothesize that predicted metabolic flux changes derived from gene expression data would outperform transcriptomic features for predicting drug cardiotoxicity in a machine learning framework. Methods: We developed a novel computational pipeline integrating a modified iCardio genome-scale metabolic model with transcriptomic data from 5 genetically distinct hiPSC-derived cardiomyocyte cell lines treated with a variety of 31 antineoplastic and immunomodulating drugs (accessed from DToxS Center). Our mathematical framework converts gene expression changes to enzyme activity, then to relative metabolic reaction flux change (4122 reactions) using a novel constrained quadratic approach. Ensemble classifiers were trained to predict cardiotoxicity using FDA Adverse Event Reporting System Reporting Odds Ratio (ROR) as a reference, with drugs above median ROR classified as cardiotoxic. Predictive models were generated using 5-fold cross validation for hyperparameter optimization with 25% hold out for quality metric calculation (reported as mean ± SEM, P values calculated from t-test) over 100 independent iterations. Results: The metabolic flux-based classifiers demonstrated fair predictive performance of drug cardiotoxicity with an AUROC of 0.70±0.02. The flux approach produced equivalent accuracy (+0.02, P = 0.56), and significantly higher F1 (+0.10, P = 0.01), AUROC (+0.08, P = 0.01), and AUPRC (+0.07, P = 0.03) than the gene expression approach. Further analysis revealed that perturbations to fatty acid metabolism were most predictive of cardiotoxic liability, with 42 out of top 100 predictive reactions belonging to fatty acid related subsystems ( P < 1e-5 by binomial test). Conclusions: Metabolic flux prediction from transcriptomic data provides enhanced discrimination of drug cardiotoxicity compared to gene expression analysis alone. This approach enables more informative pre-clinical screening of drug candidates before human exposure, potentially reducing late-stage clinical failures and improving drug safety assessment protocols.

Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism

Nematode flavin-containing monooxygenase-2 (fmo-2) is induced by dietary restriction and hypoxia, and is required for the longevity and health benefits of these pathways. It is also sufficient to confer these benefits when overexpressed. As FMOs are well-conserved across taxa, the fmo-2 mechanism has high translational potential. To determine the changes that occur following fmo-2 induction, we performed RNA-seq and untargeted metabolomics analyses. Our data reveal that one-carbon metabolism (OCM) is significantly altered by fmo-2 overexpression. OCM is a nexus for essential metabolic pathways, including transmethylation, transsulfuration, nucleotide synthesis, and amino acid metabolism. We hypothesized that fmo-2 confers longevity benefits by altering key metabolic processes within or downstream of OCM. To test this, we asked whether fmo-2 and OCM interact to regulate longevity by knocking down expression of genes involved with OCM and measuring lifespan and oxidative stress resistance. To understand the biological implications of these interactions, we generated a computational model using qPCR data of key OCM-related genes to predict changes in OCM metabolic flux. Our model predicts significant changes in OCM flux that are regulated by fmo-2 expression levels and are consistent with our RNAi and multi-omics results. We are now testing this model by knocking down genes downstream of OCM to determine their role in fmo-2-mediated benefits. Preliminary results support our hypothesis that changes in metabolic flux through OCM are involved downstream of fmo-2 expression, and may also implicate the UPRER in this pathway. Our future work will elucidate this mechanism and link stress perception and fmo-2-mediated longevity.

BackgroundIt has long been recognized that analyzing the behaviour of the complex intracellular biological networks is important for breeding industrially useful microorganisms. However, because of the complexity of these biological networks, it is currently not possible to obtain all the desired microorganisms. In this study, we constructed a system for analyzing the effect of gene expression perturbations on the behavior of biological networks in Escherichia coli. Specifically, we utilized 13 C metabolic flux analysis (13 C-MFA) to analyze the effect of perturbations to the expression levels of pgi and eno genes encoding phosphoglucose isomerase and enolase, respectively on metabolic fluxes.ResultsWe constructed gene expression-controllable E. coli strains using a single-copy mini F plasmid. Using the pgi expression-controllable strain, we found that the specific growth rate correlated with the pgi expression level. 13 C-MFA of this strain revealed that the fluxes for the pentose phosphate pathway and Entner-Doudoroff pathway decreased, as the pgi expression lelvel increased. In addition, the glyoxylate shunt became active when the pgi expression level was almost zero. Moreover, the flux for the glyoxylate shunt increased when the pgi expression level decreased, but was significantly reduced in the pgi-knockout cells. Comparatively, eno expression could not be decreased compared to the parent strain, but we found that increased eno expression resulted in a decreased specific growth rate. 13 C-MFA revealed that the metabolic flux distribution was not altered by an increased eno expression level, but the overall metabolic activity of the central metabolism decreased. Furthermore, to evaluate the impact of perturbed expression of pgi and eno genes on changes in metabolic fluxes in E. coli quantitatively, metabolic sensitivity analysis was performed. As a result, the perturbed expression of pgi gene had a great impact to the metabolic flux changes in the branch point between the glycolysis and pentose phosphate pathway, isocitrate dehydrogenase reaction, anaplerotic pathways and Entner-Doudoroff pathway. In contrast, the impact of perturbed eno expression to the flux changes in E. coli metabolic network was small.ConclusionsOur results indicate that the response of metabolic fluxes to perturbation to pgi expression was different from that to eno expression; perturbations to pgi expression affect the reaction related to the Pgi protein function, the isocitrate dehydrogenase reaction, anaplerotic reactions and Entner-Doudoroff pathway. Meanwhile, eno expression seems to affect the overall metabolic activity, and the impact of perturbed eno expression on metabolic flux change is small. Using the gene expression control system reported here, it is expected that we can analyze the response and adaptation process of complex biological networks to gene expression perturbations.

Computational strategies, that integrate genomic-level information and metabolic flux data, have improved both prediction of metabolic fluxes and metabolic network identification. Due to the tight interplay between hierarchical (transcriptional) and metabolic control it is not clear how changes in gene expression drive changes in cellular phenotypes manifested through changes in metabolic fluxes. This raises the questions to what extent a change in a metabolic flux should be attributed to changes in gene expression and/or changes in metabolite concentrations and what kind of conclu- sions can be drawn by comparing gene expression profiles with the associated metabolic fluxes. This review addresses is- sues related to modeling approaches that attempt to integrate gene expression and metabolic flux data.

The non‐conventional yeast Yarrowia lipolytica have been receiving growing attention due to its excellent lipid accumulation capacity. Microbial lipid have attracted widespread interest due to their broad applications as dietary supplements, cosmetic additives, oleochemicals, and renewable starting materials for the production of fossil fuel. With the development of whole‐genome sequencing, many effective genetic tools, including transformation systems, promoter systems, genomic integration and genome editing tools, have been applied in Y. lipolytica to enhance the overproduction of lipid. It can be genetically engineered for high lipid production via the upregulation of synthetic precursor and lipid synthesis pathways, the downregulation or disruption of competing pathways such as β‐oxidation, and elimination of inhibitory factors. In this review, the lipid metabolism, available genetic tools, and recent advances in metabolic engineering of Y. lipolytica for the overproduction of lipid and lipid‐derived chemicals is summarized. Future prospects of lipid biosynthesis in Y. lipolytica are discussed in light of the current progress, challenges, and trends in this field. Guidelines for future studies are also proposed.Practical Applications: General concerns about climate change, oil price crisis, and the increasing study for renewable energy are driving bio‐lipid as promising alternatives to fossil fuel. Over the past few decades, microbial lipid have been widely applied in dietary supplements, cosmetic additives, oleochemicals, and renewable starting materials for the production of fossil fuel. The non‐conventional yeast Yarrowia lipolytica have become an attractive metabolic engineering host for the production of microbial lipids due to its ability to synthesize them in large quantities. This review illuminates the lipid biosynthesis and degradation of Y. lipolytica, and summarizes the metabolic engineering efforts which have targeted a variety of biosynthetic biosynthetic pathway to efficiently convert carbon source to lipid in oleaginous Y. lipolytica.Schematic diagram of lipid and triacylglyceride biosynthesis, including the native and heterologous biosynthesis pathways of fatty acids in Y. lipolytica.

The prevailing concept of the heart’s response to changes in its environment is a complex network of inter-connecting signal transduction cascades.1 In such a scheme, the focus is on communication of various cell surface receptors, heterotrimeric G-proteins, protein kinases, and transcription factors.2–4⇓⇓ Diabetes is a disorder of metabolic dysregulation. At first glance it appears that metabolism and the metabolic consequences of diabetes do not fit into this signal-response coupling scheme. Two questions arise. First, is metabolism simply an “effect” rather than a “cause” of adaptation? Second, is metabolism only a by-product of signal transduction-induced adaptation, allowing equilibrium (and therefore maintenance of function) in the presence of the other adaptational responses? An alternative is to take a new, less restricted view of metabolism. Beyond its stereotypical function as a provider of ATP, alterations in metabolic flux within the cell create essential signals for the adaptation of the heart to situations such as diabetes. This concept is novel for the heart, but has already been considered in the liver. Like the phosphorylation events occurring in signal transduction cascades, changes in metabolic flux are extremely rapid. For example, translocation of GLUT4 to the cell surface in response to insulin occurs within a second.5 We have previously found that increases or decreases in workload also change metabolic fluxes in seconds.6,7⇓ Therefore, changes in metabolites are rapid enough to allow them to act as signaling molecules. Many of these acute changes in metabolic flux are brought about by the same signal transduction cascades believed to be involved in the adaptation of the heart to changes in its environment. Phosphatidylinositol 3-kinase, Ca2+, and protein kinase C, all of which play a role in cardiac adaptation, regulate metabolism in the heart.8,9⇓ Metabolic signals therefore provide a …

Metabolic flux distributions of recombinant Escherichia coli BL21 expressing human-like collagen were determined by means of a stoichiometric network and metabolic balancing. At the batch growth stage, the fluxes of the pentose phosphate pathway were higher than the fluxes of the fed-batch growth phase and the production stage. After the temperature was increased, there was a substantially elevated energy demand for synthesizing human-like collagen and heat-shock proteins, which resulted in changes in metabolic fluxes. The activities of the Embden-Meyerhof-Parnas pathway and the tricarboxylic acid cycle were significantly enhanced, leading to a reduction in the fluxes of the pentose phosphate pathway and other anabolic pathways. The temperature upshift also caused an increase in NADPH production by isocitrate dehydrogenase in the tricarboxylic acid cycle. The metabolic model predicted the involvement of a transhydrogenase that generates additional NADH from NADPH, thereby increasing ATP regeneration in the respiratory chain. These data indicated that the maintenance energy for cellular activity increased with the increase in biomass in fed-batch culture, and that cell growth and synthesis of human-like collagen could clearly represent the changes in metabolic fluxes. At the production stage, more NADPH was used to synthesize human-like collagen than for maintaining cellular activity, cell growth, and cell propagation.

L-glutamate overproduction in Corynebacterium glutamicum, a biotin auxotroph, is induced by biotin limitation or by treatment with certain fatty acid ester surfactants or with penicillin. We have analyzed the relationship between the inductions, 2-oxoglutarate dehydrogenase complex (ODHC) activity, and L-glutamate production. Here we show that a strain deleted for odhA and completely lacking ODHC activity produces L-glutamate as efficiently as the induced wild type (27.8 mmol/g [dry weight] of cells for the ohdA deletion strain compared with only 1.0 mmol/g [dry weight] of cells for the uninduced wild type). This level of production is achieved without any induction or alteration in the fatty acid composition of the cells, showing that L-glutamate overproduction can be caused by the change in metabolic flux alone. Interestingly, the L-glutamate productivity of the odhA-deleted strain is increased about 10% by each of the L-glutamate-producing inductions, showing that the change in metabolic flux resulting from the odhA deletion and the inductions have additive effects on L-glutamate overproduction. Tween 40 was indicated to induce drastic metabolic change leading to L-glutamate overproduction in the odhA-deleted strain. Furthermore, optimizing the metabolic flux from 2-oxoglutarate to L-glutamate by tuning glutamate dehydrogenase activity increased the l-glutamate production of the odhA-deleted strain.

Severe injury activates many stress-related and inflammatory pathways that can lead to a systemic hypermetabolic state. Prior studies using perfused hypermetabolic rat livers have identified intrinsic metabolic flux changes that were not dependent upon the continual presence of elevated stress hormones and substrate loads. We investigated the hypothesis that such changes may be due to persistent alterations in gene expression. A systemic hypermetabolic response was induced in rats by applying a moderate burn injury followed 2 days later by cecum ligation and puncture (CLP) to produce sepsis. Control animals received a sham-burn followed by CLP, or a sham-burn followed by sham-CLP. Two days after CLP, livers were analyzed for gene expression changes using DNA microarrays and for metabolism alterations by ex vivo perfusion coupled with Metabolic Flux Analysis. Burn injury prior to CLP increased fluxes while decreases in gene expression levels were observed. Conversely, CLP alone significantly increased metabolic gene expression, but decreased many of the corresponding metabolic fluxes. Burn injury combined with CLP led to the most dramatic changes, where concurrent changes in fluxes and gene expression levels occurred in about 1/3 of the reactions. The data are consistent with the notion that in this model, burn injury prior to CLP increased fluxes through post-translational mechanisms with little contribution of gene expression, while CLP treatment up-regulated the metabolic machinery by transcriptional mechanisms. Overall, these data show that mRNA changes measured at a single time point by DNA microarray analysis do not reliably predict metabolic flux changes in perfused livers.

The study of and interest in microbial lipids go back for at least 140 years. Historically, the production of these microbial oils was expensive and complex as compared to the inexpensive, consistent, and robust production of plant oils. Additionally, the attributes of these microbial oils were often similar to commoditized plant oils. In the 1960s and 1970s, more focus shifted to the discovery of unique attributes of microbial oils and the development of these systems led to some minor commercialization of nutritional microbial lipids in the 1970s and 1980s. Starting in the 1990s, and continuing today, significant success in the development and commercialization of nutritional microbial oils containing Omega-3 and Omega-6 long-chained polyunsaturated fatty (PUFA) acids has been seen. Progress continues to be made in the technology of microbial lipid production in both genetically modified and non-genetically modified systems. While the interest level in microbial lipids and systems continues to run high, there has only been relatively narrow success in commercialization of PUFA microbial oils to date. The presentation will review the history, status, and future challenges and opportunities for microbial lipids for nutrition.

BackgroundMetabolism gained increasing interest for the understanding of diseases and to pinpoint therapeutic intervention points. However, classical metabolomics techniques only provide a very static view on metabolism. Metabolic flux analysis methods, on the other hand, are highly targeted and require detailed knowledge on metabolism beforehand.ResultsWe present a novel workflow to analyze non-targeted metabolome-wide stable isotope labeling data to detect metabolic flux changes in a non-targeted manner. Furthermore, we show how similarity-analysis of isotopic enrichment patterns can be used for pathway contextualization of unidentified compounds. We illustrate our approach with the analysis of changes in cellular metabolism of human adenocarcinoma cells in response to decreased oxygen availability. Starting without a priori knowledge, we detect metabolic flux changes, leading to an increased glutamine contribution to acetyl-CoA production, reveal biosynthesis of N-acetylaspartate by N-acetyltransferase 8-like (NAT8L) in lung cancer cells and show that NAT8L silencing inhibits proliferation of A549, JHH-4, PH5CH8, and BEAS-2B cells.ConclusionsDifferential stable isotope labeling analysis provides qualitative metabolic flux information in a non-targeted manner. Furthermore, similarity analysis of enrichment patterns provides information on metabolically closely related compounds. N-acetylaspartate and NAT8L are important players in cancer cell metabolism, a context in which they have not received much attention yet.Electronic supplementary materialThe online version of this article (doi:10.1186/s40170-016-0150-z) contains supplementary material, which is available to authorized users.

The evolution of Crassulacean acid metabolism (CAM) is thought to be along a C3-CAM continuum including multiple variations of CAM such as CAM cycling and CAM idling. Here, we applied large-scale constraint-based modeling to investigate the metabolism and energetics of plants operating in C3, CAM, CAM cycling, and CAM idling. Our modeling results suggested that CAM cycling and CAM idling could be potential evolutionary intermediates in CAM evolution by establishing a starch/sugar-malate cycle. Our model analysis showed that by varying CO2 exchange during the light period, as a proxy of stomatal conductance, there exists a C3-CAM continuum with gradual metabolic changes, supporting the notion that evolution of CAM from C3 could occur solely through incremental changes in metabolic fluxes. Along the C3-CAM continuum, our model predicted changes in metabolic fluxes not only through the starch/sugar-malate cycle that is involved in CAM photosynthetic CO2 fixation but also other metabolic processes including the mitochondrial electron transport chain and the tricarboxylate acid cycle at night. These predictions could guide engineering efforts in introducing CAM into C3 crops for improved water use efficiency.

Substrate cycles (SC) are formed by a 'forward pathway' (FP) and a 'backward pathway' (BP), the difference between FP and BP forming the 'metabolic flux' (MF) through the route of which the cycle is part. SC modulate regulatory effects, i.e. amplify or reduce the % change in MF compared to the % change in FP and BP, thus affecting the sensitivity to regulatory factors, including hormones. A formula is given to calculate (with an approximation of +/- 0.5) the 'flux response index' (FRI), i.e. the factor by which the % change in FP plus the % change in BP must be multiplied to obtain the % change in metabolic flux, when FP and BP undergo opposite, non-unidirectional changes (as is often the case in metabolic regulation). The formula is: FRI = [( FP + BP)/(FP-BP)]/2. By this formula we evaluated the hepatic activities of glucose-6-phosphatase and glucokinase (which roughly reflect hepatic glucose production and uptake, respectively), i.e. the two enzymes that catalyze the cycle between glucose-6-phosphate (glucose-6-P) and glucose. Based on data obtained in normal, nonobese diabetic and obese diabetic subjects as well as in normal, streptozotocin-diabetic, and obese diabetic (ob/ob) mice, we found that FRI was reduced in non-obese diabetic humans and animals whereas it was increased in obese-diabetic humans and mice, compared to normal controls. Thus, diabetes without obesity decreases, and obesity with diabetes increases, the sensitivity of the glucose-6-P/glucose cycle to regulatory agents.