Metabolism is essential for life, and its alteration is implicated in multiple human diseases. The transformation from a normal to a cancerous cell requires metabolic changes to fuel the high metabolic demands of cancer cells, including but not limited to cell proliferation and cell migration. In recent years, there have been a number of new discoveries connecting known aberrations in oncogenic and tumour suppressor pathways with metabolic alterations required to sustain cell proliferation and migration. However, an understanding of the selective advantage of these metabolic alterations is still lacking. Here, we review the literature on mathematical models of metabolism, with an emphasis on their contribution to the identification of the selective advantage of metabolic phenotypes that seem otherwise wasteful or accidental. We will show how the molecular hallmarks of cancer can be related to cell proliferation and tissue remodelling, the two major physiological requirements for the development of a multicellular structure. We will cover different areas such as genome-wide gene expression analysis, flux balance models, kinetic models, reaction diffusion models and models of the tumour microenvironment. We will also highlight current challenges and how their resolution will help to achieve a better understanding of cancer metabolism and the metabolic vulnerabilities of cancers. In the most recent update to this system [emerging hallmark because metabolism is commonly altered in cancer. The designation “emerging” reflects a sense of ambiguity (neither core nor enabling) concerning the role of metabolism in cancer development. Indeed, the authors note that the metabolic alterations observed in cancer could simply be a consequence of acquisition of the core hallmarks.The hallmarks of cancer  highlighs system , metaboldeveloping tissue developinHere, we review some attempts to harness this metabolism centric framework into mathematical descriptions and quantifiable metrics. The mathematical models are divided into five major categories based on the techniques used and their focus on cell or tumour metabolism. First, we discuss genome-wide gene expression analysis, as our major tool to investigate the heterogeneity of metabolism across cancers of different types. Second, we focus on flux balance models that aim to understand cell metabolism at a steady state. This is followed by a third section reviewing kinetic models of selected cell metabolic pathways and the path to genome-scale models with kinetics. Fourth, we go over metabolic models that take into account the spatial dimensions of the cell, revealing metabolic phenomena that could be determined by spatial heterogeneity within the cell. Finally, we discuss different tumour microenvironment models, focusing on how metabolic interactions between cancer and stroma cells impact tumour growth, invasion and metastasis.Gene expression profiles are a unique resource to understand the differential utilization of metabolic pathways across cancers and genome-wide. As of March 2015, expression profiles from about 300,000 samples were deposited in the Gene Expression Omnibus (GEO) database. In spite of a number of caveats, gene expression profiles have the advantage that they have been obtained using a few standard microarrays and that they interrogate gene expression at a genomic level. Additionally, the abundance of data accumulated by now allows researchers to interrogate metabolic expression patterns in a larger context.SHMT2 and MTHFD2, coding for mitochondrial serine hydromethyltransferase and methylene-tetrahydrofolate dehydrogenase/cyclohydrolase, were identified among the 50 most commonly overexpressed genes. However, the genes SHMT1 and MTHFD1 coding for the corresponding cytosolic enzymes were not. This observation resonates with a previous report indicating that high expression of the mitochondrial one-carbon metabolism enzymes correlates with gene signatures of cell proliferation and Myc activation and is predictive of a good response to the antiproliferative agent methotrexate [Gene expression analysis can be utilized to identify metabolic genes associated with cancer. An investigation of 1981 microarray samples from 19 cancer types identified metabolic genes whose expression is most commonly altered in cancers . Many ofGene expression analysis can also be utilized to uncover metabolic subtypes. The gene expression pattern of metabolism genes in a cancer tissue sample should reflect its metabolic state. Although there are many regulatory mechanisms at the post-translational level, there is generally a subset of metabolic genes that is regulated at the transcriptional level. In fact, many transcription factors with relevance to cancer regulate metabolic genes, including Myc , HIF1α  and p53 2 expression of each gene across all samples of each cohort. Working under the hypothesis that cell proliferation and tissue remodelling are the physiological hallmarks characterizing tumour probes on the molecular level , providing the means to investigate the patterns of DNA alterations genome-wide across several cancer samples. Next-generation sequencing can also be applied to interrogate the whole or subsets of expressed RNAs (RNAseq), and it is replacing microarrays for the quantification of RNA expression. DNAseq and RNAseq, together with microRNA (miRNA) and methylation arrays, have been deployed to characterize the samples collected by The Cancer Genome Atlas (TCGA) project as well as the International Genome Consortium (IGC) project. This provides a unique opportunity to investigate cancer metabolism across multiple cancer subtypes and multiple genomic platforms. The analysis of RNAseq data linked to the TCGA samples corroborates the gene expression microarray analysis: the expression of metabolism genes is primarily dominated by tissue of origin . It is wRNAseq is rapidly replacing gene expression arrays as the standard technique for gene expression profiling. RNAseq provides a better quantification of transcript abundance, and the technology can be tailored to interrogate specific RNA subsets such as miRNAs and long non-coding RNAs. On the other hand, current established methods for gene signature analysis were tailored for gene expression arrays. They assume that the expression distribution for each probe across samples is close to a normal distribution or at least symmetric. RNAseq data violates these assumptions and may require new methodologies to conduct pathway analysis. The capability of RNASeq to resolve the expression of different gene isoforms together with the fact that different isoforms may have different enzymatic activities indicates that further developments are also required from the point of view of gene annotation. This could imply changes to commonly used databases like Gene Ontology, KEGG and other pathway annotation systems.Most published cancer gene expression analyses focus on a snapshot and cell population average sampling of cancer tissue, potentially missing the dynamics and spatial heterogeneity of metabolism. Single-cell expression analysis and collection of samples at multiple time points could overcome this limitation, albeit with a dramatic increase in cost and effort. Therefore, statistical models are needed to infer the expression patterns of mixed cell types in a cancer sample, their metabolic state and their stage of progression.The metabolic pathway analyses described above are based on pathway annotations and gene expression alone. Further elements are required to move from qualitative predictions (active/inactive) to quantitative predictions of the metabolic pathway rates. In principle, the pathway annotations, combined with a quantification of the cell biomass composition and metabolic objectives, could be used to determine which metabolic pathways should be active and at what rate, under specified culture conditions  Fig. 2)2). Any aBound constraints on individual metabolic fluxes can be applied whenever available. For example, exchange fluxes of nutrients between the culture media and cells can be estimated from changes in the media metabolite concentrations and the cell number, also known as consumption and release (CORE) profiles [The steady-state rate of biochemical reactions (change in concentration per unit of time) is often called flux, although it is not a flux as defined in physics and chemistry (rate of flow per unit area). The collection of steady-state rates of all reactions is called flux vector. At a steady state, the rate of production and consumption of every metabolite balances . profiles . Using tFrom an evolutionary point of view, we hypothesize that redundancy evolved to “efficiently” cope with different environmental conditions and constraints acting on cell metabolism. For example, cells with a metabolic flux vector that minimizes nutrient consumption while achieving a specified metabolic objective will be able to carry on that metabolic objective for longer times. In the context where only one nutrient is limiting the metabolic rate, minimizing the nutrient uptake given the metabolic objective rate is equivalent to maximizing the metabolic objective rate given the nutrient uptake rate. In one of the earlier applications of flux balance modelling to study mammalian cell growth, Savinell and Palsson investigated the flux distributions that satisfied a specified growth demand while minimizing the nutrient uptake , 27. TheE. coli [However, most cancer cells generate a significant amount of energy from the metabolism of glycolysis to lactate even when growing in aerobic conditions . The faE. coli  and unpuThe observed saturation in the oxygen consumption rate could be due to a limitation in the oxygen supply or a limitation in the oxidative phosphorylation capacity. Work in the context of muscle cell metabolism has shown that a further increase in the oxygen supply during aerobic conditions does not alter the respiration and aerobic glycolysis rates , 32, rulmacromolecular capacity constraint because it reflects the limited amount of macromolecules that can be allocated in the cell volume.The cell volume is crowded with cytoskeletal filaments, ribosomes, metabolic enzymes and organelles. Overexpression of any component is only possible at the expense of degradation of others. This macromolecular allocation constraint is analogous to the concept of solvent capacity in chemistry, reflecting the limited amount of solute that can be dissolved in a solvent. It was originally introduced under the name of molecular crowding  or solveThe impact of the macromolecular capacity constraint is determined by the size of the macromolecules of interest. In this context, metabolic efficiency aims to minimize the impact of molecular crowding or, equivalently, to maximize metabolic rate per unit of volume occupied by the metabolic machinery. Based on data for in vitro reconstituted glycolysis at 30 °C, aerobic glycolysis can produce 0.73 mol ATP/min/(liters of glycolysis enzymes), calculated as 0.58 mmol lactate/min/(grams of glycolysis enzymes)  divided A mathematical model of energy metabolism based on flux balance, the minimization of nutrient utilization together with the macromolecular capacity constraint, is sufficient to explain the Warburg effect . At low +-dependent dehydrogenases resulting in a net production of NADH. The generated NADH can be used via OxPhos to generate energy. However, once again, when the OxPhos capacity is exceeded, cells should find other means to synthesize precursor metabolites without NADH generation. This can be achieved by importing non-essential amino acids from the media and by synthesizing AcCoA for alternative sources. Among amino acids, only glutamine, glutamate, phenylalanine and tyrosine can be used to produce AcCoA without NADH generation [The flux balance modelling approach described above has been applied to genome-scale reconstructions of the human metabolic network. Most of the work has been based on the human metabolic network reconstruction from the Palsson group , althouneration .Fig. 3PrC. cylindrosporum FTHFL [+ dehydrogenases from one-carbon metabolism contribute to NADPH generation in the cytosol and the mitochondria [The genome-scale models also predict an increase in the rate of serine, one-carbon and glycine (SOG) metabolism  Fig. 3f. The ratum FTHFL  and by kum FTHFL . Treatmeum FTHFL . Howeverum FTHFL , 50. It Flux balance models can be further constrained to take into account metabolic enzyme expression patterns that are specific to a given tissue or that are the consequence of molecular alterations present in cancer cells. For example, a given cancer may have a homozygous deletion of a genomic region containing one or more genes coding for metabolic enzymes. A more accurate metabolic model of these cancer cells should have the corresponding biochemical reactions removed. Furthermore, as shown above, the analysis of gene expression patterns across cancers and normal tissues reveals that the metabolism of cancer cells closely resembles the metabolism of the tissue of origin . These mpersonalized model), using as input expression profiles, proteomics or other genomic data [A recent community-driven effort has combined the annotation of the human metabolic network together with protein expression data to obtain 65 cell-type-specific metabolic models . Severamic data –55. Persmic data . Moving mic data , 58. Themic data , to predmic data .It is becoming evident that a realistic flux balance model of cell metabolism should incorporate the macromolecular capacity constraint. However, a precise implementation of the molecular capacity constraint requires reliable estimates of kinetic parameters. Current implementations sample the kinetic parameters from a specified distribution , reportiPersonalized flux balance models can be also limited by the lack of relevant data to constraint the model. In the path from gene expression, protein expression and enzyme activity to reaction rate, there are regulatory points. Post-transcriptional regulation may result in the lack of proportionality between gene expression and reaction rate. This caveat is in part corrected by the fact that the flux balance model searches for solutions with the best consensus agreement between all reaction rates and the expression of genes coding for the corresponding enzymes. In fact, inconsistency between gene expression and predicted reaction flux can be used to infer reaction steps where post-translational regulation may be taking place . Future The application of personalized flux balance models to cancer samples extracted from animal models or patients is subject to the additional caveat of cells with mixed metabolic phenotypes. The gene expression profiles, or proteomic profiles, represent an average over all cells present in the extracted sample. This average expression is informative only if most cells exhibit a dominant expression pattern. However, in the context of two or more population of cells in significant proportions and metabolic differences, any prediction based on population averages can be misleading. To tackle this scenario, we need mathematical methodologies to infer the different cell subtypes and disentangle their expression profiles or to deploy experimental protocols to separate and profile the different cell populations.Whenever available, the kinetic model of biochemical reactions and estimates of the corresponding kinetic parameters can significantly improve model predictions. Kinetic models have been used extensively in the past to investigate selected metabolic pathways. Some examples are highlighted in Table Generic kinetic models of biochemical reactions aim to capture the key features of enzyme kinetics [For the kinetic annotation, we can start compiling all reported kinetic models and parameters in studies like those highlighted in Table kinetics –65. The Flux balance models can be generalized to include kinetic data and improve the estimation of steady-state metabolic fluxes. As before, a metabolic objective (e.g. proliferation), metabolic constraints  and an efficiency principle (e.g. minimize nutrient uptake) are specified. But now we take into account that the amount of enzyme needed to maintain a specified reaction rate depends on the concentration of substrates and products via the corresponding kinetic model. With this, the optimization problem searches not only for the optimal steady-state fluxes but also for the optimal metabolite concentrations.E. coli [E. coli, there was a good agreement between the predicted optimal metabolite concentrations and the typical reported values. The optimal metabolite concentrations depend to a great extent on the reactions equilibrium constant.Flux balance models with kinetics have been applied to the study of yeast  and E. cE. coli , 67 glycElementary flux modes were defined as minimal metabolic flux distributions that are both stoichiometrically and thermodynamically feasible [The extension of flux balance models with kinetics to genome scale is on its way. The major challenge is the estimation of missing kinetic parameters or other information. Workflows to address this problem have been reported and applied to genome-scale models of different organisms , 68, 69.feasible . TherefoSome metabolites may exhibit significant concentration gradients within cellular compartments. Accounting for those concentration gradients can increase the level of realism of metabolic models, albeit with increased model complexity. To model metabolism in this context, we need to resort to reaction diffusion models, characterizing the spatio-temporal variations of the metabolite concentrations coupled to the reaction dynamics consuming/producing metabolites.It has been recently proposed that glycolytic enzymes and mitochondria are located in different cell regions to satisfy different energetic demands . In thisThe point that subcellular localization of the molecular machinery producing/consuming energy may result in concentration gradients should be taken into consideration. Membrane pumps are indeed major sites of energy consumption during osmotic stress as hypothesized above . During Tumour microenvironment models aim to understand how properties of cancer and tumour stroma cells and their cellular interactions determine macroscopic parameters such as tumour growth rate. The remodelling of the tumour microenvironment requires cancer and stroma cells to acquire metabolic capabilities beyond cell proliferation. Furthermore, cancer cells located in different tumour regions may experience different microenvironments, and they may require different metabolic strategies to survive, proliferate and disseminate. In the following, we describe different approaches to model tumours with an emphasis on tumour metabolism.cell proliferation, characterizing the cell population growth, and cell motility, characterizing the migratory movements depending on limited resources. Additional partial differential equations can be added to model different aspects of metabolism, including the spatio-temporal variations of nutrients and excreted by-products.In reaction diffusion models, tumour and stroma cells are represented by spatio-temporal distributions (fields) quantifying the density of normal and cancer cells. The evolution in time and space of these distributions is described by partial differential equations accounting for relevant processes and constraints. The processes/constraints most commonly considered are Using a reaction diffusion model with one type of stroma cell, one type of cancer cell and a spatio-temporal distribution of tumour-secreted protons (a surrogate of acidification), Gatenby et al. were able to reproduce characteristic features of tumours , 79, incThe properties of tumours can be investigated focusing on their mechanical behaviour, building on soft matter models of developing tissues . The majhomeostatic pressure is defined as the pressure that needs to be applied to keep the tumour volume constant. If the applied pressure is smaller than the homeostatic pressure, then the tumour will continue growing; if it is larger, then the tumour will start shrinking. The homeostatic pressure may have some advantage over tumour growth rate as a measure of tumour malignancy. The tumour growth rate is determined by the difference between the tumour homeostatic pressure and the pressure being applied by the surrounding tissue. In that sense, the tumour growth rate is not an intrinsic property of the tumour; it also depends on its surrounding. In contrast, the homeostatic pressure is an intrinsic tumour property. However, there is currently no protocol to measure homeostatic pressure in vivo.Homeostatic pressure is an interesting concept emerging from soft matter models . The homFuture work is required to understand how cancer metabolism influences the mechanical properties of tumours. This relation will be in part dictated by the influence of metabolism on cell proliferation and the relation between cell proliferation and the tumour fluidity described above. In addition, it is well documented that metabolic by-products, most notably lactate and protons, play a role in the remodelling of the tumour microenvironment. The translation of these relationships to models and laws will allow us to use tumour mechanical properties as indirect surrogates to investigate cancer metabolism in vivo, using non-invasive techniques such as ultrasound.When speaking about the tumour environment, we tend to focus on the tissue that is in close spatial proximity to the cancer cells (microenvironment). However, we should not forget that the blood circulation system connects the metabolism in the cancer tissue with the organism metabolism (macroenvironment). In other words, a complete mathematical description of the tumour microenvironment should take into account the interaction between cancer cells and distant organs via the circulatory system and, by extrapolation, with nutrition. The manifestation of the muscle-wasting syndrome of cachexia in cancer patients is a demonstration of these distant interactions . TherefoWe have reviewed five major areas in mathematical models of metabolism, each addressing specific questions at different scales. First, pathway expression analysis is probably the best means to understand the heterogeneity of metabolism across cancers and to create hypotheses about major cancer metabolism subtypes. Second, flux balance models are a flexible framework to investigate the selective advantage of different metabolic pathways depending on the environmental conditions and the metabolic objective. Gene expression and other genomic information can be used to tailor flux balance models to specific cancer metabolic subtypes, allowing us to make predictions of their specific metabolic vulnerabilities. Third, kinetic modelling is required to obtain an accurate model of cell metabolism, and it is essential to understand the relationships between metabolic fluxes and metabolite concentrations. Fourth, there is experimental evidence of patterns of spatial localization of energy metabolism-related enzyme and mitochondria. The relevance of this spatial heterogeneity can only be addressed with reaction diffusion models of cell metabolism. Finally, tumour microenvironment models will help us to understand how metabolic alterations can impact the remodelling of the tumour tissue.Pathway expression analysis indicates that the tissue of origin is the first dominant signature in the genome-wide and metabolism-specific gene expression profiles of cancers. After correcting for the tissue of origin, cell proliferation and tissue remodelling emerge as secondary major signatures. Gene signatures associated with key metabolic pathways such as glycolysis, the pentose phosphate pathway, oxidative phosphorylation and one-carbon metabolism are correlated to a great extent with the degree of proliferation. In contrast, a gene signature representing autophagy manifests the strongest expression in cancers with a high degree of tissue remodelling. Other parts of the metabolic system, such as fatty acid metabolism, are not generally associated with these major factors. This could be due to high levels of redundancy and feedback obscuring the pathway analysis; however, there could also be other factors associated with the utilization of these metabolic regimes. More detailed studies within individual tissues and tissue classes are needed to gain a better understanding of the variation and dependencies of metabolic pathways.The evolution of flux balance models is getting closer to the development of genome-scale flux balance models with kinetics. It is an exciting time in this area. The increase in model complexity and burden in parameter estimation is balanced by a significant reduction of the space of possible flux distributions to elementary flux modes. The next step is to develop methods to estimate large sets of kinetic parameters from proteomic, phospho-proteomic and metabolomic profiles. Once these methods have been applied, we will be in a better position to address open questions regarding the selective advantage of metabolic phenotypes of cancer cells.The investigation of tumour metabolism using tumour microenvironment models is in its early days, as it is the case in the experimental field as well. However, these models are required to understand the selective advantage of metabolic interactions between cancer cells and the stroma. There are reports of both glycolytic cancer cells feeding lactate to the stroma and glycolytic stroma feeding lactate to the cancer cells . It rema


Pseudomonas fluorescens switches to an alginate-producing phenotype when the pleiotropic anti-sigma factor MucA is inactivated. The inactivation is accompanied by an increased biomass yield on carbon sources when grown under nitrogen-limited chemostat conditions. A previous metabolome study showed significant changes in the intracellular metabolite concentrations, especially of the nucleotides, in mucA deletion mutants compared to the wild-type. In this study, the P. fluorescens SBW25 wild-type and an alginate non-producing mucA- ΔalgC double-knockout mutant are investigated through model-based 13C-metabolic flux analysis (13C-MFA) to explore the physiological consequences of MucA inactivation at the metabolic flux level. Intracellular metabolite extracts from three carbon labelling experiments using fructose as the sole carbon source are analysed for 13C-label incorporation in primary metabolites by gas and liquid chromatography tandem mass spectrometry.The bacterium mucA- ΔalgC mutant, while flux through the pentose phosphate pathway was increased. Our findings also indicated flexibility of the anaplerotic reactions through down-regulation of the pyruvate shunt in the mucA- ΔalgC mutant and up-regulation of the glyoxylate shunt.From mass isotopomer distribution datasets, absolute intracellular metabolic reaction rates for the wild type and the mutant are determined, revealing extensive reorganisation of carbon flux through central metabolic pathways in response to MucA inactivation. The carbon flux through the Entner-Doudoroff pathway was reduced in the mucA- mutant as a cell factory for alginate production would be to use non-growing conditions and nitrogen deprivation.Absolute metabolic fluxes and metabolite levels give detailed, integrated insight into the physiology of this industrially, medically and agriculturally important bacterial species and suggest that the most efficient way of using a The online version of this article (doi:10.1186/s12918-015-0148-0) contains supplementary material, which is available to authorized users. Pseudomonas is a bacterial genus containing species of industrial, agricultural and medical relevance due to their metabolic diversity and ability to colonise a wide variety of ecological niches, including not only soil and water but also insects, plants, and animals  mmol/(gDW h) of the MAL influx goes to PYR)  and normalised to fructose uptake for relative (B) comparison between the mutant and the wild type . On a relative scale, compared to the influx of the network  is 60% higher in the mucA- ΔalgC strain. Thus, the wildtype either uses more of the fructose for maintenance processes, has a less energy-efficient metabolism , and/or disposes of surplus energy through futile cycling, which was frequently described for other organisms such as Escherichia coli, Bacillus subtilis, and Corynebacterium glutamicum [Interestingly, the wild-type strain has a 60% higher fructose uptake rate than the  Figures . Becauseutamicum ,21-24.mucA- ΔalgC strain cannot be a direct effect of MucA inactivation alone, as an alginate-producing mucA- single-deletion mutant had a two-fold increased fructose uptake rate in the metabolome study (18.3 mmol C/(gDW h) in the mucA- mutant vs. 9.4 mmol C/(gDW h) in the wild type) [F6P and G6P alone.The reduction in fructose uptake in the ATP + 0.5ADP)/(ATP + ADP + AMP)) in both the alginate-non-producing mucA- ΔalgC mutant used in this fluxome study and an alginate-producing mucA- single-knockout mutant [ATP concentration and an increase in ADP and AMP concentrations, the latter almost 10 times higher. The higher EC of the wild type correlates with its larger NADH production . From this perspective, both P. fluorescens strains preferably use the less energy-efficient EDP route. Flamholz and co-workers analysed glycolysis and the EDP in terms of thermodynamic and kinetic constraints and concluded that glycolysis incurs a higher protein cost  than the EDP [In theory,  the EDP . The lowP. fluorescens is negatively regulated by AMP, as it is in other bacteria, e.g., E. coli [F6P to M6P, the induction of alginate synthesis in the mucA- mutant must alleviate the possible rate-limitation of the FBPase. The eight-fold increase in the mucA- mutants’ AMP concentration could have led to the direct conversion of FBP to DHAP and GAP by FBP aldolase (EC 4.1.2.13) in the mucA- ΔalgC strain, but that was not observed in the current fluxome study.Concerning the preferred route for fructose uptake, it is also interesting to note that FBPase in  E. coli . Because2, with energy conservation taking place through oxidative phosphorylation. This occurs with no obvious use of this extra energy compared to the mucA- ΔalgC strain. Based on current knowledge of co-factor dependencies [NADPH as well as NADH production are higher in the wild type  and Gram-positive (B. subtilis and P. versutus) bacteria [NADH production is not required.According to our fluxome study, a large proportion of the excess fructose uptake of the wild type is shuttled via EDP to TCA and complete oxidation to COndencies , the NADe Figure . Strikin species -29. The bacteria . Thus, tNADP if needed? Activation of a transhydrogenase (EC 1.6.1.1) may provide an explanation to this question [mucA- ΔalgC strain, it is unlikely that this mutant is energy limited. It is puzzling that the mutant activates the GLX shunt, which is mostly associated with growth on two-carbon substrates, and generates one net OAA from acetyl-CoA without any energy conservation. Additionally, and similarly to the wild type, the ANA cycling of the mucA- ΔalgC strain through PEP carboxylase (EC 4.1.1.31) and PEP carboxykinase (EC 4.1.1.49) could be regarded as a futile cycle because one net ATP is consumed. However, the extent of futile cycling could not be quantified because, as mentioned, ANA reactions were not resolved in the forward and backward directions with statistical confidence. To this end, further CLEs with specially designed 13C-label mixtures are needed [Yet there remain open questions: how does the cell regenerate or allocate question . Due to e needed ,31.The comparative investigation of changes in the transcriptome  and the mucA- ΔalgC strain and the glyoxylate shunt was activated, the pool sizes of the TCA metabolites were not altered, except for the pool size of succinate, which increased by a factor of 2.4 . Cra is a global sensor and regulator of primary metabolic fluxes in Gram-negative bacteria, with a dual role as both a transcriptional repressor and an activator [E. coli cra- mutant, indicating that Cra represses these pathways [F1P is the preferred effector of Cra in P. putida, and FBP is probably a minor effector as well [F1P mediated by Cra, or directly through MucA inactivation, was observed in P. fluorescens [F1P pool changes and metabolic flux reorganisation. Hence, metabolome and fluxome data provide a “true” orthogonal view of the metabolism of P. fluorescens, in the sense that understanding how cells modulate metabolic fluxes in response to interventions requires integrative multi-omics studies.Additionally, and not surprisingly, direct comparison of changes in the metabolome and the fluxome do not show any clear correlation: while the net flux through TCA was reduced by 50% in the ctivator ,33. For pathways . F1P is  as well . No stromucA- ΔalgC double-knockout P. fluorescens mutant was compared to the wild-type strain at the metabolic flux level to illuminate the global metabolic response to MucA inactivation on central carbon metabolism without a biasing carbon re-routing by alginate production. Corroborating transcriptome and metabolome results from former studies, a strong influence of the mucA- ΔalgC double-knockout mutant was shown, involving fine-tuned and significant reorganisation of carbon utilisation. Salient alterations lead to a decrease of the overall fructose uptake and an activation of the PPP and the GLX shunt that is accompanied by a deactivation of the EDP. Although both strains produce a similar amount of energy (NADH and ATP) per mole of substrate, the mutant seems to exhibit more energy-efficient behaviour than the wildtype.In this work, a mucA- mutants need to be further investigated. These experiments bear the potential to elucidate the complex control mechanisms of MucA on alginate production and central carbon metabolism. These control points may become important targets for drug development to prevent the formation of the mucoid phenotype, which leads to pulmonary infection in cystic fibrosis patients, or to increase industrial alginate production. This requires extending the multi-omics characterisation to further mutants, including catabolite repressor/activator cra mutants, and experimental conditions, i.e., growth rates and carbon sources, that are clinically and industrially relevant. From the modelling point of view, the application of non-stationary 13C-MFA in combination with advanced experimental design strategies would be advantageous to advance the integrative analysis of metabolite levels and fluxes and increase the statistical significance of the results, particularly with regard to anaplerosis.Clearly, to mechanistically elucidate the metabolic control mechanisms of alginate production on fluxes, P. fluorescens SBW25 and P. fluorescens SBW25 mucA-ΔalgC. Construction of the mutant strain was previously presented in detail [mucA-ΔalgC strain to look for effects of MucA inactivation other than alginate production is that it produces all biosynthetic enzymes required for alginate production other than AlgC.The strains used in this study were n detail  for the mucA- ΔalgC strain, respectively. These carbon source concentrations lead to a fructose concentration of 2.5 g/L or above in the fermenters at steady state. In brief, the chemostat cultivations were nitrogen limited using fructose as a carbon source with a dilution rate of 0.04 h-1. Temperature was maintained at 25°C, pH was maintained at 7.8 by automated sodium hydroxide addition, air flow was 1.5 VVM, and oxygen saturation was maintained above 20% by adjustment of the stirring speed. Cultivations were operated as batches for 24 h before initiating continuous operation with a feed containing naturally labelled fructose. After 1.5-2.0 volume exchanges, the feed stock was changed. Afterwards, the feedstock contained 13C-labelled fructose  and isotope enrichments of 99% (mole/mole); and one lot of U-13C-fructose with chemical purity of 100% (w/w) and isotopic enrichment of 99.5% (mole/mole), Cambridge Isotope Laboratories, Inc, Andover). Sampling was performed after an additional 3 volume exchanges. Process data of double knock out mutant is given in Additional file Chemostat cultivations were performed as described earlier , except 13C-labelled feed. In the first cultivation, the wild-type strain was grown using a 60.04% 1-13C-, 20.31% U-13C- and 19.65% 12C-fructose isotopomer composition . In the two main cultivations, the wild type and the mucA-ΔalgC strain were grown with the designed fructose isotopomer mixture of 60.08% 1-13C-, 39.92% U-13C-fructose.Three cultivations were performed; the only difference between the first and the two last was the fructose isotopomer composition used in the 660 and residual fructose measurement. The CO2 concentration in the off gas was measured with a Rosemount Binos 100 CO2 analyser. These data were used to calculate the carbon balance as described previously [Immediately prior to sampling for metabolite extracts, an aliquot of 5 mL was taken from the culture for ODeviously , except P. fluorescens SBW25 wild-type and the mucA-ΔalgC strains. Prior to LC-MS/MS analysis, samples were reconstituted in 500 μL 60% (v/v) methanol. Analysis was performed on an Agilent 1200 series LC connected via an electrospray ion source to an Agilent 6410 triple-quadrupole MS using an adaptation of the reverse phase tributylamine ion exchange method introduced by Luo et al. [P. fluorescens [n-m + 1)(m + 1) MRM-transitions for each compound, where n is the number of carbons in the precursor ion and m is the number of carbons in the product ion, respectively [Metabolite extracts were analysed by an LC-MS/MS method and a GC-MS/MS method to generate mass isotopomer distribution datasets for the o et al. . The samorescens . A totalectively . A list Samples for GC-MS/MS analysis were derivatised prior to analysis as described previously  using anmucA-ΔalgC, respectively. For mass isotopomers detected by the GC-MS/MS method, these STD values were 10%, 12% and 15%, respectively.The resulting mass isotopomer fractions are listed in Additional file P. fluorescens SBW25 was derived from the genome-scale metabolic model iSB1139 reconstructed by Borgos et al. [http://www.genome.jp/kegg). Construction of our model was guided by 1) representing degradation pathways by effluxes from the system and excluding low-abundance biomass components .A biochemical reaction network of central carbon metabolism of P. fluorescens central carbon metabolism consists of routes for fructose uptake (Carbon Uptake), EMP, EDP, PPP, TCA cycle including the GLX shunt, the ANA section, as well as the biomass equation, which defines the net fluxes of metabolites into amino acid and fatty acid synthesis. To formulate the latter effluxes, the biomass composition for P. putida devised by Nogales et al. [P. fluorescens wild type and the mucA-ΔalgC mutant strain.The model of s et al.  was adop13C-MFA protocols, we refer to classical review papers, e.g., [The generation of isotopomer balances, the simulation of measurements, the experimental mixture design, the flux estimation , and the statistical assessment of resulting flux confidence intervals were performed using the software tool 13CFLUX2 . For furs, e.g., . Finallys, e.g., .P. fluorescens SBW25 wild type. The main CLEs for the wild type and the mucA-ΔalgC strain were performed using the calculated OED mixture consisting of 60.08% 1-13C-, 39.92% U-13C-fructose isotopomers. When inspecting the data thoroughly with the isotopomer network at hand, some peak measurements have to be excluded due to low peak quality. Specifically, we used the following procedure to achieve an acceptable fit: 1) we assumed a minimum STD of 1% to account for the noise of measurements close to the detection limit of the MS instruments, 2) we obtained the best fit by minimising the difference between simulated and measured data, and 3) we excluded those measurements with low abundance that have extraordinary large discrepancies  study to determine an information-optimised fructose label mixture was performed based on the fluxes estimated from the pre-CLE with


Escherichia coli strains in the human microbial community to be profiled en masse. However, there are two challenges to address: (1) exploring the genetic differences between E. coli strains in human gut and (2) dynamic responses of E. coli to diverse stress conditions. As a result, we investigated the E. coli strains in human gut microbiome using deep sequencing data and reconstructed genome-wide metabolic networks for the three most common E. coli strains, including E. coli HS, UTI89, and CFT073. The metabolic models show obvious strain-specific characteristics, both in network contents and in behaviors. We predicted optimal biomass production for three models on four different carbon sources  and found that these stress-associated genes were involved in host-microbial interactions and increased in human obesity. Besides, it shows that the growth rates are similar among the models, but the flux distributions are different, even in E. coli core reactions. The correlations between human diabetes-associated metabolic reactions in the E. coli models were also predicted. The study provides a systems perspective on E. coli strains in human gut microbiome and will be helpful in integrating diverse data sources in the following study.The recent high-throughput sequencing has enabled the composition of  Escherichia coli (E. coli) is the most widely studied prokaryotic model organism and an important species in the fields of biotechnology and microbiology. E. coli constitutes about 0.1% of human gut flora [ E. coli in the stool, while diarrhea was apparent [ut flora . The stuut flora , and of apparent . E. coli strains is of value to human health; for example, changes in the E. coli composition were observed associated with intestinal inflammatory disorders in human and mice [In the recent five years, the flood of deep sequencing data has set the latest wave of microbiome research apart from earlier studies, with the ability to enumerate all of the cells in a complex microbial community at once . For insand mice , 9. Secoand mice –12. Howe E. coli [To solve these problems, considerable efforts have been made to develop metabolic networks of E. coli , 13, 14. E. coli , express E. coli , proteom E. coli , or meta E. coli . Goals o E. coli strains in human gut microbiome. First, we determined E. coli strains in human gut microbiome using 148 fecal metagenomes. Next, we reconstructed genome-wide metabolic network of common E. coli strains in human gut. Then, the cellular phenotypes were predicted and validated using the genome variation of E. coli and diet changes. The findings of the study will help in developing new technologies and tools for computational analysis and exploring the relationship between disease and changes in the human microbiome.In this study, we performed in silico modeling of metabolic networks ofhttp://www.hmpdacc.org/). The sequenced and well-annotated E. coli genomes  deposited in GenBank were downloaded from NCBI database (http://www.ncbi.nlm.nih.gov/), to build a reference genome database. The reads were aligned against the E. coli reference genome using BLASTN (version 2.2.27+) with E < 0.01, minimal 99% identity cutoff and considering the reads that were aligned onto only a single position in the reference genome.High-quality short reads of 148 human gut samples were retrieved from Human Microbiome Project , following the protocol in HMP . The ass E. coli pan-genome  metabolic network has been generated in a recent study [ E. coli strains in human gut microbiome based on the pan-genome metabolic network.Thent study . The strIn the process, we derived the strain-specific metabolic models using two commonly used algorithms of top-down metabolic reconstructions, including GIMME  and iMAT E. coli models and were the same as that in iAF1260 model [x > 1.1 × flux.y or flux.x < 0.9 × flux.y) between two conditions, we defined the reactions to be associated with the diet stress.Fluxes through reactions in the metabolic models can be predicted using flux balance analysis (FBA) . In the 60 model . When th E. coli metabolic models in any environmental condition is a rapid and scalable way to characterize the structure of the allowed space of metabolic fluxes [ E. coli network were on each other.Uniform random sampling of the solution space forc fluxes . The set E. coli models determined through FBA. The maximum value of the objective function is first computed and this value is used with multiple optimizations to calculate the maximum and minimum flux values through each reaction.Biological systems often contain redundancies that contribute to their robustness. FVA can be used to examine these redundancies by calculating the full range of numerical values for each reaction flux in a network . In FVA, E. coli species in human gut microbiome.Deep metagenomic sequencing provides us the opportunity to explore the existence of a common set of E. coli genomes. After aligning the reads of each human gut microbial sample onto the reference database, we determined the proportion of the genomes covered by the reads (Methods). At a 99% identity threshold and 10-fold coverage (the genomes of E. coli strains are 5 M on average), we detected one in all gut samples, three in 80%, and seven in 60% of the 148 human gut samples .Besides the genome-guided methods, the reads were used to perform de novo assembly, which can recover transcript fragments from regions missing in the genome assembly . We firs E. coli  from metabolic model of E. coli pan-genome using GIMME and iMAT algorithms.We generated genome-wide metabolic network of three commonhttp://dx.doi.org/10.1155/2014/694967). We then explored the differences in network properties among the three models. It shows that these models are different in network structure , PTAr (phosphotransacetylase), and ACKr (acetate kinase) in CFTO73 model are obviously different from that in the other two models.In the previous studies, one of the most fundamental genome-scale phenotypic calculations is the simulation of cellular growth using flux balance analysis (FBA) . As a re E. coli CFTO73 and the other two models . Besides, the growth rate is sharply reduced after reaching the optimal value in HS and UTI89 models.We then estimated the effect of reducing flux through metabolic reactions on biomass production of three models. Two reactions ACOAD6F  and PGK (phosphoglycerate kinase) were taken as examples here . It shows that the growth rate is sustained near the optimal value over a range of values for PGK in all three models, indicating the same network robustness with respect to flux changes in the reaction . However, the effects of reducing flux through ACOAD6F on growth are different between E. coli's response to diet using metabolic modeling.Although a few human gut microbiome projects have been launched, the interrelationships between our diets and the structure and operations of our gut microbial communities are poorly understood. Here, we predicted the human gut−1 h−1). The average growth rates of three metabolic models corresponding to four diet conditions are shown in −1 h−1) growth with the same glucose uptake rate, which was supported by recent studies that E. coli requires aerobic respiration to compete successfully in the mouse intestine [ E. coli strains in human gut, carbon sources are diverse, but glucose is most suitable for their growth.We simulated the optimal growth rates for three models on carbon source as acetate, ethanol, glucose, and succinate, respectively  closely associated with the diet stress (P < 10−3 using Fisher's exact test). Besides, we found that these enzymes were evolutionarily conserved from E. coli to human and were involved in the interactions between human and E. coli [ GPT, glutamic-pyruvate transaminase) were found to be increased in human obesity [These responses oft stress . The gly E. coli , 33. Esp obesity .E. coli acts as an integral component) has become a major research area in human type 2 diabetes, the most prevalent endocrine disease worldwide. A recent metagenomic research identified and validated over 400 type-2-diabetes-associated markers in E. coli, including over 100 metabolic genes [Assessment and characterization of gut microbiota (ic genes . In the  E. coli models that were associated with human type 2 diabetes (Table S2). It shows that these reactions participate in many subsystems, of which over 30% are associated with lipid metabolism and cofactor/prosthetic group biosynthesis. Correlation ships between some metabolic reactions can be observed in  E. coli HS model, whereas PGL shows negative correlation with RPI (ribose-5-phosphate isomerase) fluxes. The correlation ships between these diabetes-associated reaction fluxes are the same in other two models.We detected 158 metabolic reactions in E. coli models under glucose-limited aerobic growth conditions .FBA returns a single flux distribution that corresponds to maximal biomass production under given growth conditions. However, alternate optimal solutions may exist, which correspond to maximal growth. As a result, we performed FVA for the three E. coli models are different. It shows that the minimum and maximum fluxes for the reactions in E. coli strains in human gut microbial communities based on HMP datasets. We applied two widely used algorithms (GIMME and iMAT) to reconstruct genome-wide metabolic models for three common E. coli strains  and compared the network characteristics of these models. These models were then used to predict the cellular phenotypes and dynamic responses to the diverse gut microenvironment. The models were also applied in exploring the relationships between E. coli and human diabetes. The results will be helpful in exploring the dynamic responses of gut microbiome to the environmental perturbations.In our study, we determined the common E. coli strains have been proven to be significantly different among individuals, although the species is abundant in human gut [ E. coli strains in human gut flora is associated with health status, the exact molecular mechanism is still unclear. We detected the common E. coli strains in human gut and systematically compared their functions through in silico modeling, which have two advantages over the traditional methods. First, the sequencing data allows for a much more accurate determination of microbiome composition. The advent of next-generation sequencing (NGS) enabled several high-profile collaborative projects including the HMP Consortium  and MetaHIT Consortium (http://www.sanger.ac.uk/resources/downloads/bacteria/metahit/), which have released a wide range of data on the human microbiome. Using these datasets, we applied different methods (genome-guided mapping and de novo assembly) to determine the common E. coli strains, making the following study of interconnectivity between gut microbiota, diet, and cell molecular responses available. Second, the metabolic modeling can allow us to see how a biological system might respond [ E. coli (Table S2) can be integrated with experimental validations to detect the causal genes in human diabetes.Theuman gut . Althoug respond . This wi respond . For exa E. coli is regarded as the prototypical pluripotent pathogens capable of causing a wide variety of illnesses in a broad array of species, including pyelonephritis, diarrhea, dysentery, and the hemolytic-uremic syndrome [ E. coli and its relationship to complex diseases, such as cancer [ E. coli with variable pathogenic potential [ E. coli strains respond physiologically to the complex gut microenvironment. Our study can provide valuable information based on the systematic comparisons of different E. coli strains. It shows that although the optimal growth rates are similar for three E. coli strains, the optimal flux distributions are different for three models, even in E. coli core reactions. The detected different reactions, such as ACS (acetyl-CoA synthetase) and PTAr (phosphotransacetylase) were approved to be involved in the virulence of E. coli and be associated with human complex diseases [ E. coli [Thesyndrome . In parts cancer  and diabs cancer , has atts cancer . The ansotential . Howeverdiseases , 42. The E. coli  and esti E. coli responded distinctly to different gut diets and the stress-associated genes were closely associated with obesity. With the high prevalence of diet-induced health concerns, such as diabetes and obesity, there remains a need for approaches that treat the causal factors. Among these factors, gut microbiome is drawing more attention [ ADH5, alcohol dehydrogenase 5), which trigger the downstream signaling pathways. These signaling pathways mainly associated with immune responses and development [We found that thettention , 44 for ttention . The undelopment . It is celopment . The dyn E. coli metabolic network in human gut. This work will enable a wider spectrum of studies focused on microbe-host interactions and serve as a means of integrating other omics sets in systems biology.In summary, the findings here represent a significantly expanded and comprehensive reconstruction of theFigure S1: Robustness analysis of metabolic reactions in E. coli models.Table S1: The reactions and metabolites in three E. coli metabolic networks.Table S2: Metabolic reactions associated with human type 2 diabetes.TEXT S1: Metabolic model for E. coli CFTO73 in SBML format.TEXT S2: Metabolic model for E. coli HS in SBML format.TEXT S3: Metabolic model for E. coli UTI89 in SBML format.


Synechocystis sp. PCC 6803 that produces no alkanes, which grew poorly at low temperatures. We analyzed this defect by assessing the redox kinetics of PSI. The mutant exhibited enhanced cyclic electron flow (CEF), especially at low temperature. CEF raises the ATP:NADPH ratio from photosynthesis and balances reductant requirements of biosynthesis with maintaining the redox poise of the electron transport chain. We conducted in silico flux balance analysis and showed that growth rate reaches a distinct maximum for an intermediate value of CEF equivalent to recycling 1 electron in 4 from PSI to the plastoquinone pool. Based on this analysis, we conclude that the lack of membrane alkanes causes higher CEF, perhaps for maintenance of redox poise. In turn, increased CEF reduces growth by forcing the cell to use less energy-efficient pathways, lowering the quantum efficiency of photosynthesis. This study highlights the unique and universal role of medium-chain hydrocarbons in cyanobacterial thylakoid membranes: they regulate redox balance and reductant partitioning in these oxygenic photosynthetic cells under stress.All cyanobacterial membranes contain diesel-range C15-C19 hydrocarbons at concentrations similar to chlorophyll. Recently, two universal but mutually exclusive hydrocarbon production pathways in cyanobacteria were discovered. We engineered a mutant of  The two known pathways for cyanobacterial alka(e)ne biosynthesis either reduce and then deformylate fatty acids using a pair of soluble enzymes (ADO-type), or elongate and then decarboxylate fatty acids using a polyketide synthase-like complex (PKS-type). One of these pathways, but never both, is present in all fully sequenced cyanobacteria7n-heptadecane produced by the unicellular cyanobacterium Synechocystis sp. PCC 6803 (hereafter Synechocystis 6803). This strain harbors the ADO-type pathway and is easily amenable to genetic manipulation. It was the first photosynthetic organism to have its genome completely sequencedn-heptadecane as a biofuel molecule, they have met with limited success111213Cyanobacteria are the most ancient group of oxygenic photosynthetic organisms. They have a specialized intracellular thylakoid membrane system that contains components of the photosynthetic apparatus involved in conversion of solar energy to chemical energy with concomitant oxidation of water to molecular oxygen. These membranes universally include alkanes and/or alkenes of 15–19 carbons. Recently, two pathways for production of these metabolites have been discovered1235Synechococcus sp. PCC 7002, to utilize nitrate, and requires urea as a reduced nitrogen source for optimal growth22The cyanobacterial thylakoid membrane is unique because it houses both an oxygen-evolving photosynthetic apparatus and a full complement of respiratory enzymes15161819+. They are first excited by light at photosystem II (PSII) where water is split and O2 is evolved. These excited electrons are then transported by plastoquinone (PQ) inside the thylakoid membrane to the cytochrome b6f complex. Next, they are transported by soluble acceptors such as plastocyanin in the thylakoid lumen to PSI, where they are again excited by light before reaching the final acceptors in the cytoplasm, including NADP+, nitrate, and others. Along the way, various steps in the pathway are coupled to transport of protons into the thylakoid lumen to power ATP synthesis by an F1F0 ATP synthase. This ATP synthesis requires 14 protons to generate 3 ATP, unlike those found in most heterotrophs, which require only 12 protons282 fixation25262-concentrating mechanism+172631Synechocystis 6803 for ATP and NADPH production. Because of its prominent role as a model system for photosynthesis studies, much more is known about such pathways in Synechocystis 6803 as compared with any other cyanobacterium.25Synechococcus sp. PCC 7002 play a role in cold-tolerance of this cyanobacteriumn-heptadecane in Synechocystis 6803. We show that membrane alkanes support optimal growth at low temperatures. Beyond this growth phenotype, we show that a strain that does not produce alkanes relies more on cyclic electron transport, especially at low temperatures. We examine this result in the context of a genome-scale metabolic model that we generated previously for this strainUntil recently, physiological roles of the ubiquitous alkanes and alkenes found in cyanobacterial membranes had remained elusive. It was recently found that the α-olefins produced by the PKS-type pathway in n-heptadecane biosynthesis in Synechocystis 6803 with a kanamycin resistance cassette. After approximately six months and many rounds of patching on kanamycin-containing BG-11 mediaSynechocystis 6803 has a high and flexible genome copy number and genes that confer a fitness advantage can be maintained in a merodiploid state when replaced by selective markers. Usually most mutant strains fully segregate within 2–3 patchings. However, to obtain the NOalk mutant, even after several months we had to screen several mutant lines to identify one that was fully segregated. We used GC-MS to confirm that the strain did not produce detectable n-heptadecane, and regularly reconfirmed this throughout our experiments.We constructed a plasmid vector, pNOalk , to replSynechocystis grows optimally at 30 Cn-heptadecane as when grown at 30 C (n-heptadecane at low temperature and poor growth in its absence indicate that n-heptadecane plays an important role in cold tolerance. at 30 C . This in700+) have much lower absorption of red light than the reduced form (P700), so that a decrease in absorbance at 705 nm signifies increased oxidation of P700. In the presence of a long actinic flash, significant net oxidation of P700 can occur, especially in the presence of inhibitors of photosynthetic electron transfer reactions. Probing the kinetics of light-induced P700 oxidation and its subsequent reduction in darkness is a powerful method for studying in vivo photosynthetic electron flow, and in particular, cyclic electron transport38700, then switched off the light and measured its re-reduction in the dark over 10 seconds ne production are never found to occur together in the same strainSimilarly to the alkane produced by Synechocystis 6803 exhibits increased cyclic electron flow at low temperature, especially in the NOalk mutant strain. Cyclic electron flow is known to serve diverse roles in autotrophs. Recent evidence suggests that cyclic electron transport in Chlamydomonas chloroplasts is regulated directly by the redox state of the cell and acts to prevent over-reduction of the stromaArabidopsis thaliana to fluctuating light2 fixation2 fixation by the Calvin Cycle26322 fixation, although the optimal number depends on the exact mix of nutrients consumed for biomass production. Different nitrogen and carbon sources, especially, will require different inputs of ATP and NADPH for biomass synthesis, as discussed below in the context of our FBA analysis. Additionally, because cyanobacterial thylakoid membranes include a cytochrome c terminal oxidase (CtaI/II)However, the high rates of cyclic electron flow in the absence of alkanes appear to be beyond the range to which this flexible system can adapt while maintaining optimal growth. It remains unclear what exactly the source of this limitation might be. One possibility is that the lack of alkanes reduces membrane fluidity at low temperature, leading to some loss of photosynthetic activity or restriction in electron transport, such as the intramembrane trafficking of reducing equivalents by plastoquinone. In turn, this metabolic inflexibility leads to non-ideal thylakoid reductant partitioning. For example, the lack of alkanes could disable the more efficient NDH-1 CEF pathway or kinetically favor a less efficient pathway, especially at low temperature. This switch to a less efficient pathway could explain the slow growth phenotype and increased CEF requirement. While the mutant displayed a similar effect on its redox kinetics at either ideal or low temperature, the mutant only grew more slowly at low temperature. It seems that with alkanes present, cellular metabolism was able to accommodate the challenge of maintaining a near-maximal growth rate at 25 or 20 C. However without alkanes these low temperatures exceeded the capacity of the cell to adapt See . Taken t700+ in the NOalk mutant with DBMIB might be explained in several ways. The re-reduction of P700+ in the presence of DBMIB proceeds primarily by charge recombination within PSI in green algae700+ via cytochrome c553 under these conditions. Because alkane production from fatty acids is a source of formate in the cell, this pathway might be slowed in the mutant. While it is unlikely that this pathway is a major contributor to P700+ reduction during normal growth, it might have an effect in low temperature.The slow re-reduction of PIn vivo, this activity would lead to an underestimation of CEF rates by intercepting some of the electrons donated to cytochrome b6f from reaching PSI. Others have attempted to block cytochrome oxidase activity using potassium cyanide in combination with DCMU for estimation of cyclic electron flow in cyanobacteria, but this inhibitor has given inconsistent results. In some cases, it leads to faster re-reduction of P700+383846In silico, by restricting the activity of cytochrome oxidase  to explore the effect of the dominant cyclic pathway via NDH-1The FBA derived optimal recycle rate agrees with our observations of cyclic electron flow. We measured a recycle rate of approximately 1 electron in 5 in the mutant at 20 C compared to an optimal recycle ratio of 1 electron in 4 via FBA. While the measured recycle rate for optimal growth was somewhat lower (1 electron in 17 in WT cells at 30 C), it is expected that experimental measurements would be underestimates of the actual rate and that modeling results would overestimate the ideal rate. Both of these discrepancies are caused by the potential activity of cytochrome oxidase. n-heptadecane. However, by inputting our observations of increased CEF into the model, we identified a likely secondary outcome of activation of inefficient metabolic cycles for growth. These metabolic reactions would have been impossible to directly measure using available technologies. Thus, an in silico approach helped us to understand the mechanistic role of alkanes in a unique way.In future work, FBA will serve as a valuable tool for studying the effect of different environmental conditions on cyanobacteria in general and on cyclic electron transport specifically, as we believe that this is a key process that allows photosynthetic organisms to acclimate to a dynamic environment. We have demonstrated here that FBA can serve to analyze phenotypes beyond what is explicitly included in the model. Our model contains no information related to the effects of temperature or a regulatory role of Synechocystis 6803. Redox balance must be tightly controlled by cyanobacteria to avoid redox stressEspecially at low temperatures, alkanes modulate reductant partitioning and their absence leads to increased reliance on CEF. We hypothesize that this increase serves to maintain the redox balance of the photosynthetic electron transport chain. In the absence of alkanes, increased CEF represents a less energy-efficient pathway that leads to slower growth at suboptimal temperatures. The low temperature of 20 C used in this study is well within the normal daily range that might be expected in a temperate climate where mid-day temperatures reach or exceed the optimum of 30 C for in vivo and in silico that a range of CEF rates can accommodate growth and may be the best option for an organism under different environmental conditions. We propose that CEF is a flexible redox and nutritional strategy that may be a key way in which cyanobacteria, as well as other photosynthetic organisms, adapt to the demands of generating energy from sunlight under varied environmental conditions.The new model we have presented for the role of alkanes in modulating cylic electron flow across environmental conditions challenges previous notions about CEF as an energy generation pathway. This new model should not only enable production of alkanes as biofuels in the future. It also expands our understanding of cyanobacterial photosynthesis and will enable making a wide range of products whose reductant requirements do not exactly match those of normal cyanobacterial growth, including highly reduced, energy dense next generation biofuels like biodiesel and isoprenoids. Beyond any metabolic engineering aims, we have shown both Synechocystis 6803 genes sll0208 and sll0209 encoding aldehyde deformylating oxygenase (ADO) and fatty acyl-ACP reductase (FAR), respectively, were cloned into the pUC118 backbone on either side of a kanamycin-resistance (kanR) cassette from pUC4K. Primers were designed using j5 softwarePlasmid pNOalk was constructed via SLICSynechocystis 6803 was transformed with this plasmid via natural transformation and transformants were isolated on BG-11 mediasll0208 or sll0209 loci as shown in Wild-type −2s−1 of white light. For growth experiments, cultures were pre-incubated at their respective growth temperature for 48 hours before being diluted to OD730 = 0.05 (~107 cells/mL) in fresh BG-11 media. Cell growth was monitored by measuring OD730 daily on a BioTek plate reader . For n-heptadecane analysis, samples were collected after 8 days of growth.Cultures were grown in shake flasks at 200 rpm with BG-11 mediag, and then the upper ethyl acetate layer was removed for analysis. Chlorophyll a was determined on a DW-2000 spectrophotometer according to the formula [Chl a] (μg/mL) = (16.29 × A665) − (8.24 × A652)n-heptadecane standard .2 mL of culture was pelleted by centrifugation and combined with 1 mL of ethyl acetate and 0.5 mL of 0.1 mm glass beads. Cells were lysed by bead beating  for 3 cycles of 1 minute, with 5 minutes rest between cycles. Glass beads and debris were pelleted by centrifugation for 10 minutes at 16,000 × 700 redox kinetics, cultures were grown as above for 5 days, then diluted 2-fold in fresh media and grown for 24 hours at either 20 C or 30 C. Cells were harvested by centrifugation and resuspended in fresh media to a chlorophyll concentration of 10 μg/mL. Samples were then maintained in the light at their growth temperature until ready for analysis . Before each measurement, any required inhibitors were added and then the sample was dark-adapted for 2 minutes. A sub-saturating (130 μmoles.m−2s−1) pulse of actinic light from an orange LED source was used to illuminate the sample for 5 seconds. During that pulse and for 10 seconds of recovery afterwards, the redox state of P700 was monitored by absorption at 705 nm.For analysis of PSynechocystis iSyn731 modelFlux balance analysis (FBA)Subject toSij is the stoichiometric coefficient of metabolite i in reaction j and vj is the flux value of reaction j. Parameters vj,min and vj,max denote the minimum and maximum allowable fluxes for reaction j, respectively. vBiomass, vATPm,vPSIphoton_uptake, vPSIIphoton_uptake, vNDH, vPSI and vPSI_2 represent the flux of biomass formation, ATP maintenance, bicarbonate, carbon-di-oxide, PSI photon and PSII photon uptake, NAD(P)H dehydrogenase, PSI (involving reduced/oxidized plastocyanin) and PSI_2 (involving ferro/ferri-cytochrome) reactions, respectively. Note that n is the ratio of NDH-1 activity to PSI activity that is varied in the range of 0.01 and 2 with an increment of 0.01 . Following established modeling practices32Synechocystis sp. PCC 6803 are represented via setting a ratio of photon uptake (in the form PSI and PSII photon) to carbon uptake (in the form of CO2 and H2CO3) to 1000:100. Also the ATP maintenance requirement of the cell is set to be 10 mmole/g-DW-h and the activity of both the PSI reactions is assumed to be equalε was set to 0.01.Here, Synechocystis iSyn731 model, flux variability analysisOnce we had the values of optimal biomass for various ratios of NDH to PSI activity (as discussed above) from the Subject toSynechocystis iSyn731 model.Here, CPLEX solver  was used in the GAMS  environment for solving the aforementioned optimization models. All computations were carried out on Intel Xeon E5450 Quad-Core 3.0 GHz and Intel Xeon X5675 Six-Core 3.06 GH that are part of the lionxj and lionxf clusters  of High Performance Computing Group of The Pennsylvania State University.How to cite this article: Berla, B. M. et al. Cyanobacterial Alkanes Modulate Photosynthetic Cyclic Electron Flow to Assist Growth under Cold Stress. Sci. Rep. 5, 14894; doi: 10.1038/srep14894 (2015).


Escherichia coli. However, we argue that the existence of a maximum or optimal macromolecular density is another essential requirement. Here we re-evaluate our previous theory of overflow metabolism based on molecular crowding following the proteomic fractions formulation. We show that molecular crowding is a key factor in explaining the switch from OxPhos to overflow metabolism.Overflow metabolism is a metabolic phenotype of cells characterized by mixed oxidative phosphorylation (OxPhos) and fermentative glycolysis in the presence of oxygen. Recently, it was proposed that a combination of a protein allocation constraint and a higher proteome fraction cost of energy generation by OxPhos relative to fermentation form the basis of overflow metabolism in the bacterium,  E. coli metabolism and average estimates of enzyme kinetic parameters, we have shown that incorporating a molecular crowding constraint into FBA results in theoretical predictions that agree closely with experimental observations. Molecular crowding explains four phenomena: 1- hierarchy of substrate utilization by E. coli cells growing in a mix of carbon sources3E. coli on different carbon sources and 3- their reduction upon genetic perturbationsLife, at its most basic level, is a series of biochemical processes executed and constrained by the physicochemical properties and makeup of the cell. One key feature is that metabolic processes occur within the highly crowded, gel-like interior of the cell and its organelles1et al. have demonstrated that more precise estimations of enzyme kinetic parameters significantly improves the agreement between model predictions and experimental measurements in E. coli7More recently, Adadi et al.A recent theoretical analysis of Basan et al.Following Basan that are associated with proteins in non-metabolic pathways, ribosomes, fermentation and OxPhos, respectively. We further assume that the cell growth is in a steady state where the production and consumption of proteins, energy and carbon atoms is balancedλ is the growth rate, JP is the rate of protein synthesis (moles of amino acids per cell volume and time), JF is the rate of fermentation (in units of moles of ATP produced per unit of cell volume and time), JR is the rate of OxPhos (in units of moles of ATP produced per unit of cell volume and time), JC is the rate of carbon uptake (in units of carbon atoms per unit of cell volume and time), ρ is the protein density (mass of protein per cell volume), ma is the average molecular weight of amino acids in expressed proteins, ζP is the energy consumption rate per unit of amino acid added to proteins, and ei are the yields per unit of carbon atom. We consider the following kinetic models relating biochemical rates to enzyme concentrations.where ki are effective turnover rates per unit of enzyme, Ci are enzyme concentrations, mi are enzyme molar masses, and εi are effective turnover rates per unit of enzyme proteome fraction. By eliminating Jp, ϕB, ϕF and ϕR from equations (1–7) we obtain.where wherenB is the number of amino acids per ribosome. Except for the pre-factor ρ in equations (8–10), these equations are equivalent to the equations derived by Basan et al.and b, σ and β are effective parameters. ρbλ is the biomass fraction of the biosynthetic machinery. σ is the energy required to duplicate the cell biomass per unit of dry weight. β is the number of carbon atoms required to duplicate the cell biomass per unit of dry weight. Finally, the pre-factor ρ in equations (8–10) takes into account that we express metabolic rates per cell volume instead of per cell mass.More generally, the cell biomass is composed of proteins, lipids, ribonucleotides, nucleotides, and sugars. Therefore, instead of considering proteome fractions it is better to define mass fractions relative to the cell dry weight. In this generalized context ϕ0 + bλ is the biomass fraction occupied by non-metabolic biomass components plus the biosynthetic machinery (mostly ribosomes). When ϕ0 + bλ = 1, all the biomass is represented by the non-metabolic biomass and the biosynthetic machinery and there is no biomass fraction left for energy generating pathways. Therefore, growth can be sustained only for ϕ0 + bλ < 1, defining the maximum growth rateEquation (8) is only valid when the right hand side is greater than zero. whereλmax is the maximum growth rate determined by the autocatalytic nature of the biosynthetic machinery: ribosomes synthesize the ribosomes, which is proportional to the ribosome efficiency (kB) and inversely proportional to the number of amino acids in a ribosome (nB). In turn, λmax,0 is the correction to λmax taking into account that a fraction of the biomass accounts for non-metabolic components. As expected, adding non-metabolic components reduces the maximum feasible growth rate (14).et al.ϕ0 is independent of the growth rate, which is essential to obtain a mixed OxPhos/fermentation in their model. However, while the proteome can indeed be divided into those four fractions, their subdivision in itself does not impose any constraint on the growth dependence (or independence) of ϕ0. Indeed, in the calculations presented above we have made no assumption about the growth dependence, or independence, of the biomass fraction associated with non-metabolic components ϕ0. In the following we analyse the consequences of accounting for a variable ϕ0.Basan ϕ0 becomes an additional variable and there is no unique solution to equations (8–10). The full solution space as a function of  can be obtained by analysing the extreme cases of energy generation by OxPhos or fermentation alone and taking into account that 0 < λ < ∞ and 0 < ϕ0 < 1 . For E. coli eF = 2 and eR = 4.4In this case < ϕ0 < 1 . Since tλ < λmax,R. If εR < εF  the metabolic biomass has reached its maximum capacity (the whole biomass) and feasible solutions can only take place by using metabolic pathways with a lower requirement of biomass fraction.The biological interpretation of equations (19–21) is as follows. Starting for a zero growth rate, as the growth rate increases there is an increase in the biomass fraction allocated to metabolism (biosynthesis and energy generation), with a concomitant decrease of the non-metabolic biomass fraction. In this regime, the biomass fraction associated with metabolism is allowed to expand and, therefore, we say it is unconstrained. However, once the biomass fraction allocated to metabolism occupies the whole biomass, and, therefore, we say it is constrained. In the mixed OxPhos  must be nonzero or, more precisely, larger than a minimum value ρ0,min. Regarding ρ, molecular crowding imposes a maximum macromolecular density ρmax.Biological and physical considerations exclude the possibility of The maximum macromolecular density together with a non-zero density of non-metabolic biomass components implies a minimum biomass fraction of non-metabolic components:ϕ0 reduces the space of feasible solutions  predicts a growth independent ϕ0 in the range λC,R < λ < λC,F. Not surprisingly, for λC,R < λ < λC,F the expressions for the fermentation and OxPhos rates (equations (26) and (27)) are equivalent to those obtained by Basan et al.ϕ0 is fixed.are the corrections to ϕ0,min), there is no cytoplasmic space for further increase and, therefore, we say it is constrained. In the mixed OxPhos  the metabolic biomass has reached its maximum capacity  and feasible solutions can only take place by using metabolic pathways with a lower requirement of biomass fraction. In this case the threshold for the switch is λC,R. Based on the parameter estimates reported by Basan et al.λC,R is about 0.8/h, close to the experimentally observed value of 0.78/h.The biological interpretation of equations (25–28) is similar to the previous case, with one correction. Starting for a zero growth rate, as the growth rate increases there is an increase in the biomass fraction allocated to metabolism (biosynthesis and energy generation), with a concomitant decrease of the non-metabolic biomass fraction. In this regime, the biomass fraction associated with metabolism is allowed to expand and, therefore, we say it is unconstrained. However, once the biomass fraction allocated to metabolism occupies the maximum allowed value  to a constrained  metabolic biomass; and the maximum metabolic biomass is determined the ratio between the minimum possible density of non-metabolic biomass and the maximum macromolecular density.Since ϕ0 with increasing the growth rate cannot continue beyond a threshold growth rate.The analysis presented here does not provide any evidence in favour or in contradiction with these two scenarios. The key conclusion of our analysis is that, because of molecular crowding, the decrease in E. coli metabolism recently developed by Basan et al.ϕ0  is a switch from unconstrained to constrained metabolic biomass. More generally, a satisfactory explanation of the switch from high- to low-yield metabolism with an increasing metabolic rate requires three major components: (i) a higher pathway rate per unit of pathway biomass for the low-yield pathway, (ii) a non-zero density of non-metabolic macromolecules, and (iii) an upper bound in the cell macromolecular density. Since molecular crowding explains (iii), molecular crowding is a key factor in explaining overflow metabolism.How to cite this article: Vazquez, A. and Oltvai, Z.N. Macromolecular crowding explains overflow metabolism in cells. Sci. Rep.6, 31007; doi: 10.1038/srep31007 (2016).


The great phylogenetic diversity of microalgae is corresponded by a wide arrange of interesting and useful metabolites. Nonetheless metabolic engineering in microalgae has been limited, since specific transformation tools must be developed for each species for either the nuclear or chloroplast genomes. Microalgae as production platforms for metabolites offer several advantages over plants and other microorganisms, like the ability of GMO containment and reduced costs in culture media, respectively. Currently, microalgae have proved particularly well suited for the commercial production of omega-3 fatty acids and carotenoids. Therefore most metabolic engineering strategies have been developed for these metabolites. Microalgal biofuels have also drawn great attention recently, resulting in efforts for improving the production of hydrogen and photosynthates, particularly triacylglycerides. Metabolic pathways of microalgae have also been manipulated in order to improve photosynthetic growth under specific conditions and for achieving trophic conversion. Although these pathways are not strictly related to secondary metabolites, the synthetic biology approaches could potentially be translated to this field and will also be discussed. Microalgae, defined as a polyphyletic group of unicellular photosynthetic eukaryotes, are among the most ancient and diverse organisms on the planet. There are at least 40,000–70,000 species belonging to nine different phyla. Additionally, some estimates propose that there could be up to eight times the amount of undiscovered or unclassified species .Figure 1 shows that the polyphyletic nature of microalgae constitutes them as an excellent target for discovering unique groups of protein orthologs , sterols, mycosporine-like amino acids, phycobilins, polyketides, pectins, halogenated compounds, toxins, etc., which have been extensively reviewed previously . CurrentWhile the potential of microalgae as a source of a wide range of products is high, optimization of cultivation and processing technologies will be required before algal-derived biofuels and some of the strain-specific biomolecules can be profitable on a large scale. Techno-economic analysis has identified that three main factors that significantly contribute to the overall cost of production for a microalgal metabolite are product content, growth rate, and cultivation cell density . These bChlamydomonas reinhardtii  from the diatom Cyclotella cryptica. ACCase codes for the enzyme that carboxylates acetyl-CoA to malonyl-CoA, the first committed step for FA synthesis. Expression vectors and transformation protocols were developed for C. cryptica and the diatom Navicula saprophila. A two–threefold increase in the level of ACCase activity was reported for the transformed diatoms, but no increase in FA accumulation was detected. However, no experimental data was presented for the increase of ACCase activity , resulting in 72% increase of total FA without modification of the FA chain-lengths profile  resulted in higher levels of short FA, while no increase of lipid accumulation was achieved. The latter study also focused in the importance of achieving appropriate protein–protein interactions between the components of the FA synthesis machinery in order to obtain favorable metabolic engineering results  has been increased by overexpressing two higher plant FA acyl-carrier protein thioesterases (TE) in mulation . On the  profile . This ac results .C. reinhardtii  didn’t result in increased TAG accumulation or changes in TAG profiles  accumulated 76% more than in the control  in the green microalga Chlorella minutissima resulted in a twofold increase in TAG accumulation. The expression of each enzyme by itself didn’t result in significant changes in lipid accumulation, thus pointing toward the effectiveness of system level approaches  in profiles . RNAi simulation . In contmulation , overexp control . The simproaches .C. reinhardtii, thus resulting in 11% more accumulation of total extractable lipids in nutrient-replete medium. This TF was chosen based on comparative transcriptomic data between nutrient-replete and nitrogen-deplete cells  can also be manipulated as an alternative to “classic” genetic engineering, which could prove to be more effective for generating global metabolic changes. A patent from Sapphire Energy, Inc. describes that the overexpression of the SN03 TF produces the effect of a “lipid-trigger” in te cells . LikewisC. reinhardtii, overexpression of the endogenous Δ4 desaturase (CrΔ4FAD) resulted in increased accumulation of its specific product hexadeca-4,7,10,13-tetraenoic acid (16:4). Alternatively, silencing through microRNA resulted in decreased 16:4 accumulation. This shows that classical genetic engineering approaches can result in rational results when modifying the expression of FA desaturases .The complexity of lipid biosynthesis poses a great challenge if the final goal is to enhance accumulation of not only a single type of lipid, but overall lipid accumulation for biofuels. It is then necessary to investigate in further details the regulatory mechanisms in order to deploy system level metabolic engineering of lipid biosynthesis. The studies from , and Zh involvinChlamydomona, Scenedesmus, Lobochlamys, and Chlorella can reduce protons to produce hydrogen gas due to their hydrogenase activity, determined to derive predominantly from the [FeFe]-hydrogenase  by sulfur deprivation or by using herbicides , leading to the accumulation of hydrogen even in the presence of 100 μM sulfate (Chlorella sp. strain DT expression knock-down of PsbO from PSII (part of the oxygen evolution center) through short interference antisense RNA resulted in up to 10-fold higher hydrogen evolution under semi-aerobic conditions  with three co-transformed RNAi constructs (each one specific for a single gene) resulted in a twofold increase in hydrogen production when compared to the parental strain  and sulfur-replete conditions  resulted in a fourfold increase of hydrogen production in nhardtii . Alternanditions .C. kessleri  was expressed in C. reinhardtii, resulting in a strain that could use glucose as a carbon and electron source (see Trophic Conversion below). This modification also translated into 1.5-fold higher hydrogen production rate  is notoriously downregulated under sulfur deprivation in C. reinhardtii. RNAi knockdown of FNR resulted in decrease of Rubisco activity (60%) and oxygen evolution (44%), accompanied by increase in starch degradation (140%), under sulfur deprivation. These metabolic changes resulted in 2.5-fold increase in hydrogen production when compared to sulfur-starved WT  was expressed in the chloroplast of C. reinhardtii. Unfortunately, there were no measurable effects on the isoprenoid profile of the algae  and C. reinhardtii itself (CRBKT). Following several efforts using different expression vectors, no keto-carotenoids could be detected  was targeted, resulting in a 93% reduction of its mRNA. Nonetheless, the carotenoid content didn’t change significantly, pointing toward the existence of additional rate-limiting processes (psy), which codes for the committing step enzyme for carotenoid synthesis, has been transformed in the C. reinhardtii nucleus causing an increase in carotenoid accumulation. Transformed strains overexpressing psy from Dunaliella salina and C. zofingiensis stored 2.6 and 2.2-fold more lutein than the wild-type, respectively  resulted in accumulation of up to 1.1 μg/mg dry weight of squalene. Transformation of squalene synthase into these knockdown strains didn’t result in enhanced squalene accumulation  to form 3-phosphoglycerate, catalyzed by the famous enzyme RuBP carboxylase/oxygenase (Rubisco). Additional enzymes are also required to regenerate RuBP in a process named the Calvin–Benson–Bassham cycle (CBB). Significant amounts of ATP and NADPH are also required for this process, which are supplied by the light-driven activity of photosystems I and II  or high-temperature conditions , which are all present in commercial scale ponds for algae biomass production in desert areas  of Arabidopsis and sunflower have been transformed independently into an rbcS deficient strain of C. reinhardtii , while preserving the endogenous large subunit gene in the chloroplast (rbcL). The in vitro CO2/O2 specificity or discrimination factor (Ω) was improved by up to 11% while maintaining the Vmax of carboxylation catalysis (Vc). Nonetheless the cells displayed slower autotrophic growth rates and lacked pyrenoids , presumably due to mistargeting of Rubisco caused by the heterologous small subunits , and in organic carbon content per cell  from per cell .The future trends in Rubisco engineering should consider integrating the advances achieved in both RbcS and RbcL in the same microalgae strain. Future mutational studies should be performed in both genes simultaneously in order to capture co-variations that further enhance functionality. Additionally, there are two wide fields that have not been exploited in eukaryotic microalgae: the engineering of Rubisco activase and the carbon concentrating mechanisms.C. reinhardtii). Trophic conversion has been achieved, allowing heterotophy in previously obligate phototrophic species as a proof of concept for simple metabolic engineering. V. carteri has been transformed with a hexose transporter from C. kessleri , resulting in a strain that can survive on glucose under dark conditions  and C. reinhardtii have also been successfully transformed with HUP1, resulting in glucose transport into the cells (glut1 transporter gene (erythrocyte glucose transporter 1) has also been transformed into P. tricornutum, which could also perform glucose uptake into the cell  conditions has several advantages over autotrophic growth. Fermentation systems have been widely studied and successfully applied in industry for several years. The culture conditions are highly controlled and reproducible. Moreover, heterotrophic cultures of microalgae achieve higher cell densities, thus resulting in lower harvesting costs. In waste water treatment applications, trophic conversion allows to diversify the nutrients that can be degraded by algae . In addinditions . P. triche cells . The humthe cell . It is wTrophic conversion is a good proof of concept for microalgae metabolic engineering, but despite the advantages of heterotrophic culture, it might not to be optimal for the production of low-value metabolites . Additional costs for adding a carbon source and the requirement for enclosed bioreactors (given the higher risk of contamination) are major drawbacks compared to phototrophic systems. Furthermore, some metabolites accumulate upon the presence of high-light , excluding the possibility of saving costs associated with illumination.C. reinhardtii. These cells have lower mRNA (0.1–26% relative to the control) and protein accumulation for all LHC genes and 68% less chlorophyll than the parental strain, resulting in 290% higher light transmittance in the culture. Furthermore, they present less dissipation energy through fluorescence quenching, which leads to an increase in photosynthetic quantum yield. Under high-light conditions, transformed cells were less susceptible to photoinhibition and grew at a 65% faster rate; however, they did not reach a higher cell density  of C. reinhardtii. Overexpression and silencing (RNAi) of tla1 resulted in 13% increase and 70% reduction of chlorophyll/cell, respectively. This confirms that tla1 is an attractive target for modifying phototrophic growth performance of algae  have been knocked-down with three co-transformed RNAi constructs (each one specific for a single gene). This resulted in 50% reduction of chlorophyll/cell, and four times more light penetration at equal cell density. Additionally, the transformed strain grew 85% faster at a 5 mm culture depth under 450 μE m-2 s-1 light intensity  for maximizing light absorption in low-light environments, where they naturally occur. Under artificial culture conditions (saturating light) excess energy is dissipated through heat and fluorescence quenching in the LHC. Excess energy that cannot be dissipated usually results in direct photodamage and the production of reactive oxygen species (photoinhibition). The large size of the LHC also limits light penetration into the culture medium, therefore lowering the maximum cell density that can be achieved in large scale facilities . In the  density . Later,  density , and its density . A constious 65% . Even soof algae . As prevntensity .C. reinhardtii D1 protein that can evolve up to ∼4.5-fold more oxygen in vivo under high-light conditions (50% midday sunlight) compared to the control. This has been achieved by transforming error-prone PCR-amplified D1 coding sequences followed by selection under ionizing radiation. Unfortunately the mutant strains perform slightly worse under laboratory light conditions (10% midday sunlight), suggesting that this strategy may not translate to increased biomass yield for commercial biofuel production. The cyanobacteria Synechoccocus sp. PCC 7942 contains two isoforms of the D1 protein which are differentially expressed under low-light and high-light conditions. These two proteins were expressed independently in C. reinhardtii resulting in reconstitution of the low-light and high-light phenotypes associated with each D1 isoform. Interestingly, C. reinhardtii expressing the cyanobacterial low-light isoform yielded 11% more dry weight biomass than the strains expressing the high-light isoform or the endogenous D1 protein, which is a highly desirable for reducing harvesting costs  from the filamentous fungus Neurospora crassa was codon-optimized and expressed in the plastid genome resulting in up to 0.38 g/L xylitol accumulation . Nuclear genome editing would allow for precise gene deletion and gene integration, therefore enabling to reroute metabolic networks and to obtain predictable expression levels of transgenes , and the inorganic carbon membrane transporter HLA3 (C. reinhardtii, S. cerevisiae, and E. coli (S. cerevisiae allows for extensive DNA manipulations through gene replacement which would be required for simultaneous engineering of multiple enzymes in a metabolic pathway. E. coli replication serves for generating multiple copies of the genome for algae transformation (C. reinhardtii plastid, which could tune the expression of the multiple components of an engineered pathway (P. tricornutum and T. pseudonana have been transformed with DNA episomes that replicate independently in the nucleus. These constructs carry a yeast-derived maintaining region that allows for stable replication even in the absence of antibiotic selection. Furthermore, these vectors can be transformed by trans-kingdom conjugation between E. coli and the diatoms (Novel genetic tools will also be a major driving force for metabolic engineering in eukaryotic microalgae (ansgenes . Unfortuchnology , and by chnology . On the in W2J3B . Targeteter HLA3 , 2015. C E. coli . Replicaormation . This “s pathway . Recentl diatoms . These rThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.