Algal lipid metabolism from de novo fatty acid bio- synthesis to the formation of complex glycerolipids is similar to that of the plant cells. Higher plants have dif- ferentiated organs, each of which performs specific phy- siological functions, and contains specific biochemical pathways. Similarly to higher plants, algae process TAG into lipid droplets which are coated in a large number of proteins. Most of these are typical members of vesicular transport and signaling pathways such as RabGTPases, but a proteomics approach to algal lipid bodies has iden- tified a protein called major lipid droplet protein (MLDP) which affecs the size of lipid droplets and may present a target for immunofluorescence imaging of algal lipid con- tent . Algae species, especially microalgae, have a general biochemical composition of 30-50% DCW pro- teins, 20-40% DCW carbohydrates and 8-15% DCW lipids under optimal growth condition. Most of the algal lipids are glycerinated membrane lipids, with minor contributions to overall lipid content from TAG, wax esters, hydrocarbons, sterols, and prenyl derivatives [30,36]. Under unfavorable growing conditions many algae shift their metabolicpathways toward the biosynth- esis of storage lipids or polysaccharides. TAG accumula- tion in response to environmental stress likely occurs as a means of providing an energy deposit that can be read- ily catabolized in response to a more favorable environ- ment to allow rapid growth . Nutrients, temperature, light, salinity and growing phase have been shown to influence the flux of algal cellular metabolism .
cost; oxidative phosphorylation produces reactive oxygen species as a by-product. The accumulation of reactive oxygen species (ROS) increases oxidative stress to the cell with deleterious effects on function and cell survival . In healthy cells, the harmful effect of ROS is nullified by antioxidant NADPH (generated by pentose phosphate pathway), such that a stable redox homeostasis is maintained. In critical illness, pro- inflammatory cytokines (tumour necrosis factor-α, interleukin-1β) increase the amount of ROS dramatically, thereby tilting the redox balance towards an oxidative environ- ment . Here, in a study of patients with systemic inflammatory response syndrome and sepsis, we showed that circulating leukocytes might have adapted to this oxidative environment by a reprogramming of metabolicpathways. This metabolic reprogram- ming was suggested by gene-expression changes including (1) reduced substrate entry into oxidative phosphorylation, (2) diverting substrate pyruvate away from oxidative phosphorylation, (3) reduced activity of the tricarboxylic acid cycle, (4) increased sub- strate supply to the pentose phosphate pathway, (5) increased NADPH production in the pentose phosphate pathway and (6) reduced biosynthesis. The net outcome was an increase in transcriptional activity of enzymes required for glycolysis, lactate production and NADPH synthesis. A summary of these steps is given in Fig. 5.
Hunger and food insecurity can be minimized by doubling crop yield without increasing cultivated land area and fertilizer applied. Since plant breeding has not genetically doubled photosynthesis per unit leaf area, an approach for doubling crop yield would be through a biotechnology that reprograms metabolicpathways in favor of photosynthesis. The anchor of this biotechnology is glutamate dehydrogenase (GDH) including the RNAs it synthesizes. Peanut was treated with stoichiometric combinations of mineral salt solu- tions to synchronize the GDH subunit polypeptides. Matured seeds were analyzed for fats by HPLC; the RNA biosynthetic activity of GDH, and mRNAs en- coding yield-specific enzymes by Northern hybridiza- tion. In the PK-treated peanut, the GDH-synthesized RNAs silenced the mRNAs encoding granule-bound starch synthase, phosphoglucomutase (glycolysis), glu- cosyltransferase (cellulose biosynthesis), and nitrate reductase leaving unaffected the mRNAs encoding acetylcoenzyme A carboxylase (fatty acid biosynthe- sis), phosphate translocator, and NADH-glutamate synthase resulting to double seed (4342 kg/ha), cellu- lose (1829 kg/ha), and fat (1381 kg/ha) yields com- pared with the controls. Down-regulation of phos- phate translocator and acetylcoenzyme A carboxylase caused decreased pod yields. GDH-synthesized RNAs that were homologous to yield-specific mRNAs shared extensive plus/plus and plus/minus sequence similari- ties, and they reprogrammed metabolism by permut- ing the partially down-regulated, not down-regulated, and down-regulated yield-specific pathways. Control peanut produced 70, NPKS-treated produced 420, NS-treated produced 1680, and PK-treated produced 280 probable rearrangements of the pathways. There- fore, down-regulation of metabolic reactions followed by permutation of yield-related pathways, and redis- tribution of metabolite load to molecularly connected pathways controls crop yield. Operating as efficient
In our study, gene expression profiles of breast tumors having an “unfavorable” prognosis were compared to breast tumors with a “favorable” prognosis. We wanted to track how the aggressive (unfavorable) tumors have specifically regulated their metabolism to optimize their oncogenetic fitness, and to elucidate ways to severely perturb this process. For this, we used an approach that detects orchestrated regulation of neighboring enzymes in the metabolic network. We mapped gene expression data onto optimally arranged grid representations of pathways of the metabolic network and applied Haar wavelet transforms onto defined pathways of the net- work to combine gene expression values from neighbor- ing enzymes. These combined features were tested using a non-parametric test (Wilcoxon) if they could separate samples from different treatments. Metabolicpathways were selected that had features with the most discrimi- native gene expression patterns. We detected a substan- tially higher number of significant gene expression patterns in comparison to commonly used enrichment tests. We revealed 19 significant metabolicpathways including increased purine and pyrimidine biosynthesis which were needed for increased mitosis cycles. Further- more, we found pathways for increased energy metabo- lism (glycolysis, pyruvate metabolism and fructose/ mannose metabolism). Interestingly, we observed the regulation of a possible a cellular switch in the pathway for bile acid biosynthesis redirecting the metabolic flux to the synthesis of steroids while preventing degradation into bile acids.
The chemotherapeutic compound, cisplatin causes various kinds of DNA lesions but also triggers other pertubations, such as ER and oxidative stress. We and others have shown that treatment of pluripotent stem cells with cisplatin causes a plethora of transcriptional and post-translational alterations that, to a major extent, point to DNA damage response (DDR) signaling. The orchestrated DDR signaling network is important to arrest the cell cycle and repair the lesions or, in case of damage beyond repair, eliminate affected cells. Failure to properly balance the various aspects of the DDR in stem cells contributes to ageing and cancer. Here, we performed metabolic profiling by mass spectrometry of embryonic stem (ES) cells treated for different time periods with cisplatin. We then integrated metabolomics with transcriptomics analyses and connected cisplatin-regulated metabolites with regulated metabolic enzymes to identify enriched metabolicpathways. These included nucleotide metabolism, urea cycle and arginine and proline metabolism. Silencing of identified proline metabolic and catabolic enzymes indicated that altered proline metabolism serves as an adaptive, rather than a toxic response. A group of enriched metabolicpathways clustered around the metabolite S-adenosylmethionine, which is a hub for methylation and transsulfuration reactions and polyamine metabolism. Enzymes and metabolites with pro- or anti- oxidant functions were also enriched but enhanced levels of reactive oxygen species were not measured in cisplatin-treated ES cells. Lastly, a number of the differentially regulated metabolic enzymes were identified as target genes of the transcription factor p53, pointing to p53-mediated alterations in metabolism in response to genotoxic stress. Altogether, our findings reveal interconnecting metabolicpathways that are responsive to cisplatin and may serve as signaling modules in the DDR in pluripotent stem cells.
Dominance is a form of phenotypic robustness to mutations. Understanding how such robustness can evolve provides a window into how the relation between genotype and phenotype can evolve. As such, the issue of dominance evolution is a question about the evolution of inheritance systems. Attempts at explaining the evolution of dominance have run into two problems. One is that selection for dominance is sensitive to the frequency of heterozygotes. Accordingly, dominance cannot evolve unless special condi- tions lead to the presence of a high frequency of mutant alleles in the population. Second, on the basis of theoretical results in metabolic control analysis, it has been proposed that metabolic systems possess inherent constraints. These hypothetical constraints imply the default manifestation of dominance of the wild type with respect to the effects of mutations at most loci. Hence, some biologists have maintained that an evolutionary explanation is not relevant to dominance. In this article, we put into question the hypothetical assumption of default metabolic constraints. We show that this assumption is based on an exclusion of important nonlinear interactions that can occur between enzymes in a pathway. With an a priori exclusion of such interactions, the possibility of epistasis and hence dominance modification is eliminated. We present a theoretical model that integrates enzyme kinetics and population genetics to address dominance evolution in metabolicpathways. In the case of mutations that decrease enzyme concentrations, and given the mechanistic constraints of Michaelis-Menten-type catalysis, it is shown that dominance of the wild type can be extensively modified in a two-enzyme pathway. Moreover, we discuss analytical results indicating that the conclusions from the two-enzyme case can be generalized to any number of enzymes. Dominance modification is achieved chiefly through changes in enzyme concentrations or kinetic parameters such as k cat , both of which can alter saturation levels. Low saturation translates into
a continuous culturing system to induce rapid evolution (Fig. 1a). Although longer evolution time is required for in vivo evolution compared to in vitro evolution to obtain an improved targeted strain, human intervention is not required for in vivo evolution when an automated con- tinuous culturing approach is employed. This automated continuous cultivation approach has freed the labours from benchwork, hence, increasing the time efficiency of each step in experiment. However, the involvement of intense labour in in vitro evolution, and longer time-con- sumption in in vivo evolution due to its random muta- genic nature have rendered them impractical for deep mesoscale optimisation of large, complex pathway in a short time. Further improvement to accelerate the pro- cess is made by coupling genotype diversification, natu- ral mutation and selection into a single process (Fig. 1b), known as in vivo continuous evolution. With its advan- tage over directed evolution, in vivo continuous evolution is becoming an important tool to evolve large, complex metabolicpathways for chemical production . In this review, we will highlight the latest developments of each aspect in in vivo continuous evolution including in vivo genotype diversification, fitness-coupled selection pres- sure and equipment maintaining continuous culture. We will present a systematic review of recent advances in in vivo genotype diversification technology and the com- parisons of these technology covering modified natural mutagenesis system, plasmid-targeted mutagenesis sys- tem, genome-targeted mutagenesis system and recombi- nation-based mutagenesis system. Next, we will analyse fitness-coupled selection pressure, covering natural and artificial metabolite production/cell fitness coupling for phenotypic selection. As a system mimicking natural continuous evolution mechanism, we will also review
that returns posterior component probabilities for the observations. Taking advantage of this flexibility we pro- pose a HME as a method to supervise the Markov mix- ture model for metabolicpathways 3M . Combining HME with a Markov mixture model first employs the Markov mixture to find dominant pathways. Posterior probabilities are then assigned to each sequence based on its similarity to the dominant pathway. These are then passed as input weights into the parameter estima- tion procedure within the supervised technique. Using the posterior probabilities of 3M to weight the para- meter estimation of each supervised technique is in effect localizing each expert to summarize the predictive capability of each dominant pathway. Therefore incor- porating the 3M Markov mixture model within a HME is creating a method capable of combining network structures with standard data table information. We now formally state the base 3M model and provide the detail of our proposed model, Hierarchical Mixture Experts 3M (HME3M) classifier.
Clearly the situation can become more complicated than this if one considers the possibility of having several shared metabolites for each enzyme. Such a representation can readily be searched for any given enzyme. It has the advantage that each enzyme only occurs once in the diagram, as opposed to the hand-crafted, artistic, versions, such as the Nicholson metabolicpathways charts [see 9, 10] or in the Roche Applied Science "Biochemical Pathways" wall chart, which can be searched, in segments, through ExPASy , where the separation of different metabolic systems in the display can result in the same enzyme occurring in several different places.
Since the Internet contains such a vast amount of information, there are undoubtedly many other useful resources and it would be foolish to deny the existence of a program similar to the one described in this paper. The current program was designed to help nutrition students learn about the main metabolicpathways, and in particular the ways in which vitamins are used in
I n order to establish a successful infection, intracellular bacterial pathogens must adapt their metabolism to utilize the nutrients available within the host cell, often in direct competition with the host’s own metabolic processes and mechanisms for nutrient sequestration (1). Nevertheless, many of these microorganisms have evolved dedicated mechanisms to harvest and assimilate essential nutrients to proliferate within this specialized niche (2–4). Targeted strategies for carbon acquisition and assimilation fuel bacterial replication and often aid in the evasion of host cell defenses (5–7). Despite their importance, the metabolicpathways and host-derived carbon sources utilized by bacterial pathogens in vivo are generally not well understood (8, 9). Metabolites can be directly acquired from the host, salvaged from similar molecules, or synthesized de novo using host-derived sources of carbon, nitrogen, sulfur, etc. Bacteria that replicate within the host cell cytosol theoretically have access to the products and intermediates produced during major host metabolic processes that take place within this compartment, including glycolysis and amino acid biosynthesis. The actual concentrations of these products within an infected cell, however, are unclear. Rather, most nutrients are stored within complex structures, such as lipid droplets, glycogens, and proteins, and thus are not immediately available to intracellular patho- gens (8).
(measured in air), poor plant growth and alterations in the chloroplast structure (Migge et al. 1999). It has been suggested that photorespiration is important for energy dissipation to prevent photoinhibition (Osmond 1981; Osmond & Grace 1995; Osmond et al. 1997; Kozaki & Takeba 1996; Wu et al. 1991). In addition, photorespiration can generate metabolites, such as serine and glycine, which can be exported out of the leaf (Madore & Grodzinksi 1984) or used in other metabolicpathways, for example, provision of glycine for the synthesis of glutathione (Noctor et al. 1997, 1998, 1999). Since glutathione is a component of the antioxidative system in plants (Noctor & Foyer 1998), photorespiration may provide additional protection against oxidative damage in high light by supplying glycine. Thus, photorespiration, in addition to being wasteful, may also be a useful process in plants.
Metabolicpathways display a high degree of connectivity in larger networks, especially when metabolites are involved in two or more pathways; hence, the introduction of a large number of input genes has the potential to generate unintended and unpre- dicted effects. However, an interesting study showed that transfer of the entire pathway for dhurrin biosynthesis (a tyrosine-derived cyanogenic glucoside) into Arabidopsis had no significant im- pact on the wider transcriptome and metabolome, whereas the transfer of an incomplete pathway induced significant changes in morphology, transcriptome, and metabolome, probably through metabolic crosstalk or detoxification reactions (Kristensen et al., 2005). Monitoring changes at the gene, transcript, protein, and metabolite levels is a challenge. In the future, it will be necessary to integrate these data in the context of systems biology, in which modeling is becoming a standard analytical tool for understanding whole biological systems and predicting gene behavior (Purnick and Weiss, 2009). Systems biology is also a necessary component of synthetic biology, because it is critical to foresee the behavior of synthetic genetic circuits in the context of the wider organism. Advances in systems biology and synthetic biology offer enormous potential in terms of development of novel materials and energy sources, improvement of agronomic traits, human health applica- tions, and a better understanding of natural gene regulation (Naqvi et al., 2009; Zurbriggen et al., 2012). For example, the expression of three genes required for the conversion of acetyl-CoA to PHB in plastids allows the production of bioplastics in plants (Bohmert- Tatarev et al., 2011), and the introduction of five genes of the E. coli glycolate catabolic pathway into Arabidopsis thaliana plastids reduces the loss of fixed carbon and nitrogen during photorespira- tion, increasing plant biomass (Kebeish et al., 2007).
In summary, our results suggest that metabolic changes in TAp73-/- cells following H 2 O 2 treatment may result in a pro-growth metabolic profile of cells that have undergone severe oxidative damage, rather than in promotion of a cell death response under these conditions. Hence, loss of TAp73 leads, at least under oxidative stress conditions, to a rewiring of the cellular metabolism that partially resembles metabolic changes observed in cancer cells [2, 184-188], such as increase of PPP flux. The findings presented here reinforce the role of TAp73 as tumor suppressor gene and indicate that the regulation of cellular metabolism by TAp73 contributes to its tumor suppressor function. It is also fascinating to speculate that such metabolic regulations might play a role
Subpathway-GMir implements the credible reconstruction of KEGG metabolicpathways by embedding miRNAs with target genes verified by low- throughput experiments. MiRNA-mediated metabolic subpathways are identified by topologically analyzing the positions and cascade regions of condition-specific genes and miRNAs. Furthermore, Subpathway-GMir has been implemented as a freely available R package at http://cran.r-project.org/web/packages/SubpathwayGMir/. It can support the identification of miRNA-mediated metabolic subpathways in six species, abbreviated as follows: cel (Caenorhabditis elegans), dme (Drosophila melanogaster), dre (Danio rerio), hsa (Homo sapiens), mmu (Mus musculus) and rno (Rattus norvegicus). Users can alter the environment variables to identify organism-specific metabolic subpathways mediated by miRNAs. The pipeline overview is depicted in Figure 6. It contains three main components: (i) By converting KEGG metabolicpathways into graphs with genes as nodes, we build reconstructed KEGG metabolic pathway graphs (RMPGs) that integrate miRNA-target interactions supported by low-throughput experiments; (ii) It maps condition-specific genes and miRNAs into RMPGs and identifies miRNA-mediated metabolic subpathways based on the “lenient distance” similarity method; (iii) It evaluates the significance of candidate subpathways using the hypergeometric method. The details of these processes are described below.
gluconeogenesis ’ , and ‘ Pentose phosphate pathway ’ among the most-activated. Not surprisingly, OVR led to marked activation of lipid metabolism (e.g. ‘ Fatty acid biosynthesis ’ and ‘ Glycerolipid metabolism ’ ). Unexpected metabolicpathways that were activated at − 14 d in OVR included several related to metabolism of amino acids (e.g. branched chain) and of cofactors and vitamins (thiamin). Among endocrine and immune system pathways, at − 14 d OVR led to marked activation of ‘ PPAR signalling ’ and ‘ Antigen processing and presentation ’ . Among key pathways affected over time in OVR, a number were related to translation (e.g. mTOR signaling), endocrine/immune signaling (CXCR4 and IGF1), and lipid metabolism (oxidative phosphorylation) with greater activation in OVR vs. CTR specifically at − 14 d. Although statistical differences for several pathways in OVR vs. CTR nearly disappeared at 1 and 14 vs. − 14 d, despite the well-known catabolic state of adipose depots after calving, the bioinformatics analyses suggested important roles for a number of signaling mechanisms at − 14 vs. 14 than 1 vs. -14 d. This was particularly evident in cows fed to meet predicted energy requirements during the dry period (CTR).
transport fuels .Micro algal bio fuels are also likely to have much lower impacts on the environment and the world’s food supply than conventional bio fuel producing crops. In contrast to these previous efforts, we are now equipped with a wide variety of new genetic tools, genome sequences, and high throughput analytical techniques that will allow scientists to analyze and manipulate metabolicpathways with unprecedented precision. Promising advances in metabolic engineering allow for not only the increased production of endogenous carbon storage compound sbut also the direct production, and perhaps secretion, of designer hydrocarbons that may be used directly as fuels. The application of these modern metabolic engineering tools in photosynthetic microalgae has the potential to create important sources of renewable fuel that will not compete with food production or require fresh water and arable land.