Isotopically non-stationary 13 C metabolic flux analysis of two closely related fast-growing cyanobacteria, Synechococcus elongatus PCC 11801 and 11802.

Synechococcus elongatus PCC 11801 and 11802 are closely related cyanobacterial strains that are fast-growing and tolerant to high light and temperature. These strains hold significant promise as chassis for photosynthetic production of chemicals from carbon dioxide. A detailed quantitative understanding of the central carbon pathways would be a reference for future metabolic engineering studies with these strains. We conducted isotopic non-stationary 13 C metabolic flux analysis to quantitively assess the metabolic potential of these two strains. This study highlights key similarities and differences in the central carbon flux distribution between these and other model/non-model strains. The two strains demonstrated a higher Calvin-Benson-Bassham (CBB) cycle flux coupled with negligible flux through the oxidative pentose phosphate pathway and the photorespiratory pathway and lower anaplerosis fluxes under photoautotrophic conditions. Interestingly, PCC 11802 shows the highest CBB cycle and pyruvate kinase flux values among those reported in cyanobacteria. The unique tricarboxylic acid (TCA) cycle diversion in PCC 11801 makes it ideal for the large-scale production of TCA cycle-derived chemicals. Additionally, dynamic labeling transients were measured for intermediates of amino acid, nucleotide, and nucleotide sugar metabolism. Overall, this study provides the first detailed metabolic flux maps of S. elongatus PCC 11801 and 11802, which may aid metabolic engineering efforts in these strains.

Expanding the synthetic biology repertoire of a fast-growing cyanobacterium Synechococcus elongatus PCC 11801.

Synechococcus elongatus PCC 11801 is a fast-growing cyanobacterium, exhibiting high tolerance to environmental stresses. We have earlier characterized its genome and analysed its transcriptome and proteome. However, to deploy it as a potential cell factory, it is necessary to expand its synthetic biology toolbox, including promoter elements and ribosome binding sites (RBSs). Here, based on the global transcriptome analysis, 48 native promoters of the genes with high transcript count were characterized using a fluorescent reporter system. The promoters PcpcB, PpsbA1, and P11770 exhibited consistently high fluorescence under all the cultivation conditions. Similarly, from the genome data and proteome analysis, 534 operons were identified. Fifteen intergenic regions exhibiting higher protein expression from the downstream gene were systematically characterized for identifying RBSs, using an operon construct comprising fluorescent protein genes eyfp and mTurq under PcpcB (PcpcB:eyfp:RBS:mTurq:TrrnB). Overall, the work presents promoter and RBS sequence libraries, with varying strengths, to expedite bioengineering of PCC 11801.

Dynamic flux balance analysis of high cell density fed-batch culture of Escherichia coli BL21 (DE3) with mass spectrometry-based spent media analysis.

Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome-scale metabolic models (GSMM) and by assuming instantaneous pseudo-steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry coupled with liquid and gas chromatography for a fed-batch, high-cell density cultivation of Escherichia coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in tri-carboxylic acid cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

Probing the metabolism of γ-glutamyl peptides in cyanobacteria via metabolite profiling and 13 C labeling.

Cyanobacteria are attractive model organisms for the study of photosynthesis and diurnal metabolism and as hosts for photoautotrophic production of chemicals. Exposure to bright light or environmental pollutants and a diurnal lifestyle of these prokaryotes may result in significant oxidative stress. Glutathione is a widely studied γ-glutamyl peptide that plays a key role in managing oxidative stress and detoxification of xenobiotics in cyanobacteria. The functional role and biosynthesis pathways of this tripeptide have been studied in detail in various phyla, including cyanobacteria. However, other γ-glutamyl peptides remain largely unexplored. We use an integrated approach to identify a number of γ-glutamyl peptides based on signature mass fragments and mass shifts in them in 13 C and 15 N enriched metabolite extracts. The newly identified compounds include γ-glutamyl dipeptides and derivatives of glutathione. Carbon backbones of the former turn over much faster than that of glutathione, suggesting that they follow a distinct biosynthesis pathway. Further, transients of isotopic 13 C enrichment show positional labeling in these peptides, which allows us to delineate the alternative biosynthesis pathways. Importantly, the amino acid of γ-glutamyl dipeptides shows much faster turnover compared to the glutamate moiety. The significant accumulation of γ-glutamyl dipeptides under slow-growth conditions combined with the results from dynamic 13 C labeling suggests that these compounds may act as reservoirs of amino acids in cyanobacteria.

Metabolic engineering of Synechococcus elongatus for photoautotrophic production of mannitol.

With multiple applications in food, pharmaceutical, and chemical industries as antioxidant or nonmetabolizable sweetener; the bioproduction of d-mannitol is gaining global attention, especially with photosynthetic organisms as hosts. Considering the sustainability prospects, the current work encompasses metabolic engineering of a widely used cyanobacterial strain, Synechococcus elongatus PCC 7942, and two newly isolated fast-growing cyanobacterial strains; S. elongatus PCC 11801 and S. elongatus PCC 11802, for mannitol production. We engineered these strains with a two-step pathway by cloning genes for mannitol-1-phosphate dehydrogenase (mtlD) and mannitol-1-phosphatase (mlp), where the mtlD expression was under the control of different promoters from PCC 7942, namely, Prbc225 , PcpcB300 , PcpcBm1 , PrbcLm17 , and PrbcLm15 . The strains were tested under the "switch conditions," where the growth conditions were switched after the first 3 days, thereby resulting in differential promoter activity. Among the engineered strains of PCC 11801 and PCC 11802, the strains possessing Prbc225 -mtlD module produced relatively high mannitol titers of 401 ± 18 mg/L and 537 ± 18 mg/L, respectively. The highest mannitol titer of 701 ± 15 mg/L (productivity 60 mg/L.d, yield 895 µM/OD730 ) was exhibited by the engineered strain of PCC 7942 expressing PcpcB300 -mtlD module. It is by far the highest obtained mannitol yield from the engineered cyanobacteria.

Precision fermentation with mass spectrometry-based spent media analysis.

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Transmembrane Shuttling of Photosynthetically Produced Electrons to Propel Extracellular Biocatalytic Redox Reactions in a Modular Fashion.

Many biocatalytic redox reactions depend on the cofactor NAD(P)H, which may be provided by dedicated recycling systems. Exploiting light and water for NADPH-regeneration as it is performed, e.g. by cyanobacteria, is conceptually very appealing due to its high atom economy. However, the current use of cyanobacteria is limited, e.g. by challenging and time-consuming heterologous enzyme expression in cyanobacteria as well as limitations of substrate or product transport through the cell wall. Here we establish a transmembrane electron shuttling system propelled by the cyanobacterial photosynthesis to drive extracellular NAD(P)H-dependent redox reactions. The modular photo-electron shuttling (MPS) overcomes the need for cloning and problems associated with enzyme- or substrate-toxicity and substrate uptake. The MPS was demonstrated on four classes of enzymes with 19 enzymes and various types of substrates, reaching conversions of up to 99 % and giving products with >99 % optical purity.

Characterization and Application of a Robust Glucose Dehydrogenase from Paenibacillus pini for Cofactor Regeneration in Biocatalysis.

Glucose dehydrogenases are important auxiliary enzymes in biocatalysis, employed in the regeneration of reduced nicotinamide cofactors for oxidoreductase catalysed reactions. Here we report the identification and characterization of a novel glucose-1-dehydrogenase (GDH) from Paenibacillus pini that prefers NAD+ as cofactor over NADP+. The purified recombinant P. pini GDH displayed a specific activity of 247.5 U/mg. The enzyme was stable in the pH range 4-8.5 and exhibited excellent thermostability till 50 °C for 24 h, even in the absence of NaCl or glycerol. Paenibacillus pini GDH was also tolerant to organic solvents, demonstrating its potential for recycling cofactors for biotransformation. The potential application of the enzyme was evaluated by coupling with a NAD+-dependent alcohol dehydrogenase for the reduction of acetophenone and ethyl-4-chloro-3-oxo-butanoate. Conversions higher than 95% were achieved within 2 h with low enzyme loading using lyophilized cell lysate, suggesting that P. pini GDH could be highly effective for recycling NADH in redox biocatalysis.

Cyanobacteria: Promising biocatalysts for sustainable chemical production.

Cyanobacteria are photosynthetic prokaryotes showing great promise as biocatalysts for the direct conversion of CO2 into fuels, chemicals, and other value-added products. Introduction of just a few heterologous genes can endow cyanobacteria with the ability to transform specific central metabolites into many end products. Recent engineering efforts have centered around harnessing the potential of these microbial biofactories for sustainable production of chemicals conventionally produced from fossil fuels. Here, we present an overview of the unique chemistry that cyanobacteria have been co-opted to perform. We highlight key lessons learned from these engineering efforts and discuss advantages and disadvantages of various approaches.

SWATH Tandem Mass Spectrometry Workflow for Quantification of Mass Isotopologue Distribution of Intracellular Metabolites and Fragments Labeled with Isotopic 13C Carbon.

Accurate quantification of mass isotopologue distribution (MID) of metabolites is a prerequisite for 13C-metabolic flux analysis. Currently used mass spectrometric (MS) techniques based on multiple reaction monitoring (MRM) place limitations on the number of MIDs that can be analyzed in a single run. Moreover, the deconvolution step results in amplification of error. Here, we demonstrate that SWATH MS/MS, a data independent acquisition (DIA) technique allows quantification of a large number of precursor and product MIDs in a single run. SWATH sequentially fragments all precursor ions in stacked mass isolation windows. Co-fragmentation of all precursor isotopologues in a single SWATH window yields higher sensitivity enabling quantification of MIDs of fragments with low abundance and lower systematic and random errors. We quantify the MIDs of 53 precursor and product ions corresponding to 19 intracellular metabolites from a dynamic 13C-labeling of a model cyanobacterium, Synechococcus sp. PCC 7002. The use of product MIDs resulted in an improved precision of many measured fluxes compared to when only precursor MIDs were used for flux analysis. The approach is truly untargeted and allows additional metabolites to be quantified from the same data.

Recent advances in synthetic biology of cyanobacteria.

Cyanobacteria are attractive hosts that can be engineered for the photosynthetic production of fuels, fine chemicals, and proteins from CO2. Moreover, the responsiveness of these photoautotrophs towards different environmental signals, such as light, CO2, diurnal cycle, and metals make them potential hosts for the development of biosensors. However, engineering these hosts proves to be a challenging and lengthy process. Synthetic biology can make the process of biological engineering more predictable through the use of standardized biological parts that are well characterized and tools to assemble them. While significant progress has been made with model heterotrophic organisms, many of the parts and tools are not portable in cyanobacteria. Therefore, efforts are underway to develop and characterize parts derived from cyanobacteria. In this review, we discuss the reported parts and tools with the objective to develop cyanobacteria as cell factories or biosensors. We also discuss the issues related to characterization, tunability, portability, and the need to develop enabling technologies to engineer this "green" chassis.

Rerouting of carbon flux in a glycogen mutant of cyanobacteria assessed via isotopically non-stationary 13 C metabolic flux analysis.

Cyanobacteria, which constitute a quantitatively dominant phylum, have attracted attention in biofuel applications due to favorable physiological characteristics, high photosynthetic efficiency and amenability to genetic manipulations. However, quantitative aspects of cyanobacterial metabolism have received limited attention. In the present study, we have performed isotopically non-stationary 13 C metabolic flux analysis (INST-13 C-MFA) to analyze rerouting of carbon in a glycogen synthase deficient mutant strain (glgA-I glgA-II) of the model cyanobacterium Synechococcus sp. PCC 7002. During balanced photoautotrophic growth, 10-20% of the fixed carbon is stored in the form of glycogen via a pathway that is conserved across the cyanobacterial phylum. Our results show that deletion of glycogen synthase gene orchestrates cascading effects on carbon distribution in various parts of the metabolic network. Carbon that was originally destined to be incorporated into glycogen gets partially diverted toward alternate storage molecules such as glucosylglycerol and sucrose. The rest is partitioned within the metabolic network, primarily via glycolysis and tricarboxylic acid cycle. A lowered flux toward carbohydrate synthesis and an altered distribution at the glucose-1-phosphate node indicate flexibility in the network. Further, reversibility of glycogen biosynthesis reactions points toward the presence of futile cycles. Similar redistribution of carbon was also predicted by Flux Balance Analysis. The results are significant to metabolic engineering efforts with cyanobacteria where fixed carbon needs to be re-routed to products of interest. Biotechnol. Bioeng. 2017;114: 2298-2308. © 2017 Wiley Periodicals, Inc.

Challenges and opportunities for microalgae-mediated CO2 capture and biorefinery.

Aquacultures of microalgae are frontrunners for photosynthetic capture of CO2 from flue gases. Expedient implementation mandates coupling of microalgal CO2 capture with synthesis of fuels and organic products, so as to derive value from biomass. An integrated biorefinery complex houses a biomass growth and harvesting area and a refining zone for conversion to product(s) and separation to desired purity levels. As growth and downstream options require energy and incur loss of carbon, put together, the loop must be energy positive, carbon negative, or add substantial value. Feasibility studies can, thus, aid the choice from among the rapidly evolving technological options, many of which are still in the early phases of development. We summarize basic engineering calculations for the key steps of a biorefining loop where flue gases from a thermal power station are captured using microalgal biomass along with subsequent options for conversion to fuel or value added products. An assimilation of findings from recent laboratory and pilot-scale experiments and life cycle analysis (LCA) studies is presented as carbon and energy yields for growth and harvesting of microalgal biomass and downstream options. Of the biorefining options, conversion to the widely studied biofuel, ethanol, and manufacture of the platform chemical, succinic acid are presented. Both processes yield specific products and do not demand high-energy input but entail 60-70% carbon loss through fermentative respiration. Thermochemical conversions, on the other hand, have smaller carbon and energy losses but yield a mixture of products.

A model of the circadian clock in the cyanobacterium Cyanothece sp. ATCC 51142.

BACKGROUND: The over consumption of fossil fuels has led to growing concerns over climate change and global warming. Increasing research activities have been carried out towards alternative viable biofuel sources. Of several different biofuel platforms, cyanobacteria possess great potential, for their ability to accumulate biomass tens of times faster than traditional oilseed crops. The cyanobacterium Cyanothece sp. ATCC 51142 has recently attracted lots of research interest as a model organism for such research. Cyanothece can perform efficiently both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen--a byproduct of nitrogen fixation--at very high rates of several folds higher than previously described hydrogen-producing photosynthetic microbes. Since the key enzyme for nitrogen fixation is very sensitive to oxygen produced by photosynthesis, Cyanothece employs a sophisticated temporal separation scheme, where nitrogen fixation occurs at night and photosynthesis at day. At the core of this temporal separation scheme is a robust clocking mechanism, which so far has not been thoroughly studied. Understanding how this circadian clock interacts with and harmonizes global transcription of key cellular processes is one of the keys to realize the inherent potential of this organism. RESULTS: In this paper, we employ several state of the art bioinformatics techniques for studying the core circadian clock in Cyanothece sp. ATCC 51142, and its interactions with other key cellular processes. We employ comparative genomics techniques to map the circadian clock genes and genetic interactions from another cyanobacterial species, namely Synechococcus elongatus PCC 7942, of which the circadian clock has been much more thoroughly investigated. Using time series gene expression data for Cyanothece, we employ gene regulatory network reconstruction techniques to learn this network de novo, and compare the reconstructed network against the interactions currently reported in the literature. Next, we build a computational model of the interactions between the core clock and other cellular processes, and show how this model can predict the behaviour of the system under changing environmental conditions. The constructed models significantly advance our understanding of the Cyanothece circadian clock functional mechanisms.

Issues impacting genetic network reverse engineering algorithm validation using small networks.

Genetic network reverse engineering has been an area of intensive research within the systems biology community during the last decade. With many techniques currently available, the task of validating them and choosing the best one for a certain problem is a complex issue. Current practice has been to validate an approach on in-silico synthetic data sets, and, wherever possible, on real data sets with known ground-truth. In this study, we highlight a major issue that the validation of reverse engineering algorithms on small benchmark networks very often results in networks which are not statistically better than a randomly picked network. Another important issue highlighted is that with short time series, a small variation in the pre-processing procedure might yield large differences in the inferred networks. To demonstrate these issues, we have selected as our case study the IRMA in-vivo synthetic yeast network recently published in Cell. Using Fisher's exact test, we show that many results reported in the literature on reverse-engineering this network are not significantly better than random. The discussion is further extended to some other networks commonly used for validation purposes in the literature. The results presented in this study emphasize that studies carried out using small genetic networks are likely to be trivial, making it imperative that larger real networks be used for validating and benchmarking purposes. If smaller networks are considered, then the results should be interpreted carefully to avoid over confidence. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

Metabolic modeling for multi-objective optimization of ethanol production in a Synechocystis mutant.

Cyanobacteria have potential to produce drop-in bio-fuels such as ethanol via photoautotrophic metabolism. Although model cyanobacterial strains have been engineered to produce such products, systematic metabolic engineering studies to identify optimal strains for the same have not been performed. In this work, we identify optimal ethanol producing mutants corresponding to appropriate gene deletions that result in a suitable redirection in the carbon flux. In particular, we systematically simulate exhaustive single and double gene deletions considering a genome scale metabolic model of a mutant strain of the unicellular cyanobacterium Synechocystis species strain PCC 6803. Various optimization based metabolic modeling techniques, such as flux balance analysis (FBA), method of minimization of metabolic adjustment (MOMA) and regulatory on/off minimization (ROOM) were used for this analysis. For single gene deletion MOMA simulations, the Pareto front with biomass and ethanol fluxes as the two objectives to be maximized was obtained and analyzed. Points on the Pareto front represent maximal utilization of resources constrained by substrate uptake thereby representing an optimal trade-off between the two fluxes. Pareto analysis was also performed for double gene deletion MOMA and single and double gene deletion ROOM simulations. Based on these analyses, two mutants, with combined gene deletions in ethanol and purine metabolism pathways, were identified as promising candidates for ethanol production. The relevant genes were adk, pta and ackA. An ethanol productivity of approximately 0.15 mmol/(gDW h) was predicted for these mutants which appears to be reasonable based on experimentally reported values in literature for other strains.

Metabolic flux analysis of Cyanothece sp. ATCC 51142 under mixotrophic conditions.

Cyanobacteria are a group of photosynthetic prokaryotes capable of utilizing solar energy to fix atmospheric carbon dioxide to biomass. Despite several "proof of principle" studies, low product yield is an impediment in commercialization of cyanobacteria-derived biofuels. Estimation of intracellular reaction rates by (13)C metabolic flux analysis ((13)C-MFA) would be a step toward enhancing biofuel yield via metabolic engineering. We report (13)C-MFA for Cyanothece sp. ATCC 51142, a unicellular nitrogen-fixing cyanobacterium, known for enhanced hydrogen yield under mixotrophic conditions. Rates of reactions in the central carbon metabolism under nitrogen-fixing and -non-fixing conditions were estimated by monitoring the competitive incorporation of (12)C and (13)C from unlabeled CO2 and uniformly labeled glycerol, respectively, into terminal metabolites such as amino acids. The observed labeling patterns suggest mixotrophic growth under both the conditions, with a larger fraction of unlabeled carbon in nitrate-sufficient cultures asserting a greater contribution of carbon fixation by photosynthesis and an anaplerotic pathway. Indeed, flux analysis complements the higher growth observed under nitrate-sufficient conditions. On the other hand, the flux through the oxidative pentose phosphate pathway and tricarboxylic acid cycle was greater in nitrate-deficient conditions, possibly to supply the precursors and reducing equivalents needed for nitrogen fixation. In addition, an enhanced flux through fructose-6-phosphate phosphoketolase possibly suggests the organism's preferred mode under nitrogen-fixing conditions. The (13)C-MFA results complement the reported predictions by flux balance analysis and provide quantitative insight into the organism's distinct metabolic features under nitrogen-fixing and -non-fixing conditions.

Highlights from the Indo-US workshop "Cyanobacteria: molecular networks to biofuels" held at Lonavala, India during December 16-20, 2012.

An Indo-US workshop on "Cyanobacteria: molecular networks to biofuels" was held December 16-20, 2012 at Lagoona Resort, Lonavala, India. The workshop was jointly organized by two of the authors, PPW, a chemical engineer and LAS, a biologist, thereby ensuring a broad and cross-disciplinary participation. The main objective of the workshop was to bring researchers from academia and industry of the two countries together with common interests in cyanobacteria or microalgae and derived biofuels. An exchange of ideas resulted from a series of oral and poster presentations and, importantly, through one-on-one discussions during tea breaks and meals. Another key objective was to introduce young researchers of India to the exciting field of cyanobacterial physiology, modeling, and biofuels. PhD students and early stage researchers were especially encouraged to participate and about half of the 75 participants belonged to this category. The rest were comprised of senior researchers, including 13-15 invited speakers from each country. Overall, twenty-four institutes from 12 states of India were represented. The deliberations, which are being compiled in the present special issue, revolved mainly around molecular aspects of cyanobacterial biofuels including metabolic engineering, networks, genetic regulation, circadian rhythms, and stress responses. Representatives of some key funding agencies and industry provided a perspective and opportunities in the field and for bilateral collaboration. This article summarizes deliberations that took place at the meeting and provides a bird's eye view of the ongoing research in the field in the two countries.

Diurnal rhythm of a unicellular diazotrophic cyanobacterium under mixotrophic conditions and elevated carbon dioxide.

Mixotrophic cultivation of cyanobacteria in wastewaters with flue gas sparging has the potential to simultaneously sequester carbon content from gaseous and aqueous streams and convert to biomass and biofuels. Therefore, it was of interest to study the effect of mixotrophy and elevated CO2 on metabolism, morphology and rhythm of gene expression under diurnal cycles. We chose a diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142 as a model, which is a known hydrogen producer with robust circadian rhythm. Cyanothece 51142 grows faster with nitrate and/or an additional carbon source in the growth medium and at 3 % CO2. Intracellular glycogen contents undergo diurnal oscillations with greater accumulation under mixotrophy. While glycogen is exhausted by midnight under autotrophic conditions, significant amounts remain unutilized accompanied by a prolonged upregulation of nifH gene under mixotrophy. This possibly supports nitrogen fixation for longer periods thereby leading to better growth. To gain insights into the influence of mixotrophy and elevated CO2 on circadian rhythm, transcription of core clock genes kaiA, kaiB1 and kaiC1, the input pathway, cikA, output pathway, rpaA and representatives of key metabolic pathways was analyzed. Clock genes' transcripts were lower under mixotrophy suggesting a dampening effect exerted by an external carbon source such as glycerol. Nevertheless, the genes of the clock and important metabolic pathways show diurnal oscillations in expression under mixotrophic and autotrophic growth at ambient and elevated CO2, respectively. Taken together, the results indicate segregation of light and dark associated reactions even under mixotrophy and provide important insights for further applications.

SHARP: genome-scale identification of gene-protein-reaction associations in cyanobacteria.

Genome scale metabolic model provides an overview of an organism's metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software "Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP)." We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions.