Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms.constraint-based modeling | photobioenergetic | metabolic engineering | biosustainability | metabolic robustness T he recent emphasis on biosustainability has brought attention to photosynthetic microorganisms. Microphototrophs, including cyanobacteria, represent efficient biological systems for producing biomass from inorganic carbon (Ci) and high-value products such as carotenoids, and they are also viewed as a potential source of biofuel (1, 2). On the other hand, photosynthesis is an inherently inefficient process, and photoautotrophic growth is limited by environmental perturbations (3). The success of future light-driven bioengineering approaches requires a systems understanding of the photosynthetic processes, including their bioenergetics and robustness.Optimal photosynthetic performance requires fine-tuning the light/energy conversion by the photosystems and metabolic reactions, with the ATP/NADPH ratio being a particularly important parameter (4, 5). Carbon dioxide (CO 2 ) fixation through the Calvin cycle is the main ATP and NADPH sink in the autotrophic metabolism, requiring an ATP/NADPH ratio of 1.5. Because the photosynthetic linear electron flow (LEF) pathway generates an ATP/NADPH ratio of 1.28, additional ATP is needed (4, 5). Phototrophs have...

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Background: Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions.

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Bacterial transcriptional networks are built on a hierarchy of regulators, on top of which lie the components of the RNA polymerase (in particular the sigma factors) and the global control elements, which play a pivotal role. We have designed a genome-wide oligonucleotide-based DNA microarray for Pseudomonas putida KT2440. In combination with real-time reverse transcription polymerase chain reaction (RT-PCR), we have used it to analyse the expression pattern of the genes encoding the RNA polymerase subunits (the core enzyme and the 24 sigma factors), and various proteins involved in global regulation (Crc, Lrp, Fur, Anr, Fis, CsrA, IHF, HupA, HupB, HupN, BipA and several MvaT-like proteins), during the shift from exponential growth in rich medium into starvation and stress brought about by the entry into stationary phase. Expression of the genes encoding the RNA polymerase core and the vegetative sigma factor decreased in stationary phase, while that of sigma(S) increased. Data obtained for sigma(N), sigma(H), FliA and for the 19 extracytoplasmic function (ECF)-like sigma factors suggested that their mRNA levels change little upon entry into stationary phase. Expression of Crc, BipA, Fis, HupB, HupN and the MvaT-like protein PP3693 decreased in stationary phase, while that of HupA and the MvaT-like protein PP3765 increased significantly. Expression of IHF was indicative of post-transcriptional control. These results provide the first global study of the expression of the transcriptional machinery through the exponential stationary-phase shift in P. putida.

BackgroundThe metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H2/CO2, and more importantly on synthesis gas (H2/CO/CO2) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals.ResultsHere, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism.ConclusionsiHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels.

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knockout library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.

Our view on the bacterial responses to the aerobic degradation of aromatic compounds has been enriched considerably by the current omic methodologies and systems biology approaches, revealing the participation of intricate metabolic and regulatory networks. New enzymes, transporters, and specific/global regulatory systems have been recently characterized, and reveal that the widespread biodegradation capabilities extend to unexpected substrates such as lignin. A completely different biochemical strategy based on the formation of aryl-CoA epoxide intermediates has been unraveled for aerobic hybrid pathways, such as those involved in benzoate and phenylacetate degradation. Aromatic degradation pathways are also an important source of metabolic exchange factors and, therefore, they play a previously unrecognized biological role in cell-to-cell communication. Beyond the native bacterial biodegradation capabilities, pathway evolution as well as computational and synthetic biology approaches are emerging as powerful tools to design novel strain-specific pathways for degradation of xenobiotic compounds.