Introducing biosynthetic pathways into an organism is both reliant on and challenged by endogenous biochemistry. Here we compared the expansion potential of the metabolic network in the photoautotroph Synechocystis with that of the heterotroph E. coli using the novel workflow POPPY (Prospecting Optimal Pathways with PYthon). First, E. coli and Synechocystis metabolomic and fluxomic data were combined with metabolic models to identify thermodynamic constraints on metabolite concentrations (NET analysis). Then, thousands of automatically constructed pathways were placed within each network and subjected to a network-embedded variant of the max-min driving force analysis (NEM). We found that the networks had different capabilities for imparting thermodynamic driving forces toward certain compounds. Key metabolites were constrained differently in Synechocystis due to opposing flux directions in glycolysis and carbon fixation, the forked tri-carboxylic acid cycle, and photorespiration. Furthermore, the lysine biosynthesis pathway in Synechocystis was identified as thermodynamically constrained, impacting both endogenous and heterologous reactions through low 2-oxoglutarate levels. Our study also identified important yet poorly covered areas in existing metabolomics data and provides a reference for future thermodynamics-based engineering in Synechocystis and beyond. The POPPY methodology represents a step in making optimal pathway-host matches, which is likely to become important as the practical range of host organisms is diversified. 

Biological fixation of atmospheric CO2 via the Calvin-Benson-Bassham cycle has massive ecological impact and offers potential for industrial exploitation, either by improving carbon fixation in plants and autotrophic bacteria, or by installation into new hosts. A kinetic model of the Calvin-Benson-Bassham cycle embedded in the central carbon metabolism of the cyanobacterium Synechocystis sp. PCC 6803 was developed to investigate its stability and underlying control mechanisms. To reduce the uncertainty associated with a single parameter set, random sampling of the steady-state metabolite concentrations and the enzyme kinetic parameters was employed, resulting in millions of parameterized models which were analyzed for flux control and stability against perturbation. Our results show that the Calvin cycle had an overall high intrinsic stability, but a high concentration of ribulose 1,5-bisphosphate was associated with unstable states. Low substrate saturation and high product saturation of enzymes involved in highly interconnected reactions correlated with increased network stability. Flux control, that is the effect that a change in one reaction rate has on the other reactions in the network, was distributed and mostly exerted by energy supply (ATP), but also by cofactor supply (NADPH). Sedoheptulose 1,7-bisphosphatase/fructose 1,6-bisphosphatase, fructose-bisphosphate aldolase, and transketolase had a weak but positive effect on overall network flux, in agreement with published observations. The identified flux control and relationships between metabolite concentrations and system stability can guide metabolic engineering. The kinetic model structure and parameterizing framework can be expanded for analysis of metabolic systems beyond the Calvin cycle.

Cyanobacteria experience both rapid and periodic fluctuations in light and inorganic carbon (C-i) and have evolved regulatory mechanisms to respond to these, including extensive posttranscriptional gene regulation. We report the first genome-wide ribosome profiling data set for cyanobacteria, where ribosome occupancy on mRNA is quantified with codon-level precision. We measured the transcriptome and translatome of Synechocystis during autotrophic growth before (high carbon [HC] condition) and 24 h after removing CO2 from the feedgas (low carbon [LC] condition). Ribosome occupancy patterns in the 5' untranslated region suggest that ribosomes can assemble there and slide to the Shine-Dalgarno site, where they pause. At LC, total translation was reduced by 80% and ribosome pausing was increased at stop and start codons and in untranslated regions, which may be a sequestration mechanism to inactivate ribosomes in response to rapid C-i depletion. Several stress response genes, such as thioredoxin M (sll1057), a putative endonuclease (slr0915), protease HtrA (slr1204), and heat shock protein HspA (sll1514) showed marked increases in translational efficiency at LC, indicating translational control in response to Ci depletion. Ribosome pause scores within open reading frames were mostly constant, though several ribosomal proteins had significantly altered pause score distributions at LC, which might indicate translational regulation of ribosome biosynthesis in response to Ci depletion. We show that ribosome profiling is a powerful tool to decipher dynamic gene regulation strategies in cyanobacteria. IMPORTANCE Ribosome profiling accesses the translational step of gene expression via deep sequencing of ribosome-protected mRNA footprints. Pairing of ribosome profiling and transcriptomics data provides a translational efficiency for each gene. Here, the translatome and transcriptome of the model cyanobacterium Synechocystis were compared under carbon-replete and carbon starvation conditions. The latter may be experienced when cyanobacteria are cultivated in poorly mixed bioreactors or engineered to be product-secreting cell factories. A small fraction of genes (<200), including stress response genes, showed changes in translational efficiency during carbon starvation, indicating condition-dependent translation-level regulation. We observed ribosome occupancy in untranslated regions, possibly due to an alternative translation initiation mechanism in Synechocystis. The higher proportion of ribosomes residing in untranslated regions during carbon starvation may be a mechanism to quickly inactivate superfluous ribosomes. This work provides the first ribosome profiling data for cyanobacteria and reveals new regulation strategies for coping with nutrient limitation.

Knowledge of the genetic basis for autotrophic metabolism is valuable since it relates to both the emergence of life and to the metabolic engineering challenge of incorporating CO₂ as a potential substrate for biorefining. The most common CO₂ fixation pathway is the Calvin cycle, which utilizes Rubisco and phosphoribulokinase enzymes. We searched thousands of microbial genomes and found that 6.0% contained the Calvin cycle. We then contrasted the genomes of Calvin cycle-positive, non-cyanobacterial microbes and their closest relatives by enrichment analysis, ancestral character estimation, and random forest machine learning, to explore genetic adaptations associated with acquisition of the Calvin cycle. The Calvin cycle overlaps with the pentose phosphate pathway and glycolysis, and we could confirm positive associations with fructose-1,6-bisphosphatase, aldolase, and transketolase, constituting a conserved operon, as well as ribulose-phosphate 3-epimerase, ribose-5-phosphate isomerase, and phosphoglycerate kinase. Additionally, carbohydrate storage enzymes, carboxysome proteins (that raise CO₂ concentration around Rubisco), and Rubisco activases CbbQ and CbbX accompanied the Calvin cycle. Photorespiration did not appear to be adapted specifically for the Calvin cycle in the non-cyanobacterial microbes under study. Our results suggest that chemoautotrophy in Calvin cycle-positive organisms was commonly enabled by hydrogenase, and less commonly ammonia monooxygenase (nitrification). The enrichment of specific DNA-binding domains indicated Calvin-cycle associated genetic regulation. Metabolic regulatory adaptations were illustrated by negative correlation to AraC and the enzyme arabinose-5-phosphate isomerase, which suggests a downregulation of the metabolite arabinose-5-phosphate, which may interfere with the Calvin cycle through enzyme inhibition and substrate competition. Certain domains of unknown function that were found to be important in the analysis may indicate yet unknown regulatory mechanisms in Calvin cycle-utilizing microbes. Our gene ranking provides targets for experiments seeking to improve CO₂ fixation, or engineer novel CO₂-fixing organisms.