The "knallgas" bacterium Cupriavidus necator is attracting interest due to its extremely versatile metabolism. C. necator can use hydrogen or formic acid as an energy source, fixes CO₂ via the Calvin-Benson-Bassham (CBB) cycle, and grows on organic acids and sugars. Its tripartite genome is notable for its size and duplications of key genes (CBB cycle, hydrogenases, and nitrate reductases). Little is known about which of these isoenzymes and their cofactors are actually utilized for growth on different substrates. Here, we investigated the energy metabolism of C. necator H16 by growing a barcoded transposon knockout library on succinate, fructose, hydrogen (H₂/CO₂), and formic acid. The fitness contribution of each gene was determined from enrichment or depletion of the corresponding mutants. Fitness analysis revealed that (i) some, but not all, molybdenum cofactor biosynthesis genes were essential for growth on formate and nitrate respiration. (ii) Soluble formate dehydrogenase (FDH) was the dominant enzyme for formate oxidation, not membrane-bound FDH. (iii) For hydrogenases, both soluble and membrane-bound enzymes were utilized for lithoautotrophic growth. (iv) Of the six terminal respiratory complexes in C. necator H16, only some are utilized, and utilization depends on the energy source. (v) Deletion of hydrogenase-related genes boosted heterotrophic growth, and we show that the relief from associated protein cost is responsible for this phenomenon. This study evaluates the contribution of each of C. necator's genes to fitness in biotechnologically relevant growth regimes. Our results illustrate the genomic redundancy of this generalist bacterium and inspire future engineering strategies.

IMPORTANCE The soil bacterium Cupriavidus necator can grow on gas mixtures of CO₂, H₂, and O₂. It also consumes formic acid as carbon and energy source and various other substrates. This metabolic flexibility comes at a price, for example, a comparatively large genome (6.6 Mb) and a significant background expression of lowly utilized genes. In this study, we mutated every non-essential gene in C. necator using barcoded transposons in order to determine their effect on fitness. We grew the mutant library in various trophic conditions including hydrogen and formate as the sole energy source. Fitness analysis revealed which of the various energy-generating iso-enzymes are actually utilized in which condition. For example, only a few of the six terminal respiratory complexes are used, and utilization depends on the substrate. We also show that the protein cost for the various lowly utilized enzymes represents a significant growth disadvantage in specific conditions, offering a route to rational engineering of the genome. All fitness data are available in an interactive app at https://m-jahn.shinyapps.io/ShinyLib/.

Barcoded mutant libraries are a powerful tool for elucidating gene function in microbes, particularly when screened in multiple growth conditions. Here, we screened a pooled CRISPR interference library of the model cyanobacterium Synechocystis sp. PCC 6803 in 11 bioreactor-controlled conditions, spanning multiple light regimes and carbon sources. This gene repression library contained 21,705 individual mutants with high redundancy over all open reading frames and noncoding RNAs. Comparison of the derived gene fitness scores revealed multiple instances of gene repression being beneficial in 1 condition while generally detrimental in others, particularly for genes within light harvesting and conversion, such as antennae components at high light and PSII subunits during photoheterotrophy. Suboptimal regulation of such genes likely represents a tradeoff of reduced growth speed for enhanced robustness to perturbation. The extensive data set assigns condition-specific importance to many previously unannotated genes and suggests additional functions for central metabolic enzymes. Phosphoribulokinase, glyceraldehyde-3-phosphate dehydrogenase, and the small protein CP12 were critical for mixotrophy and photoheterotrophy, which implicates the ternary complex as important for redirecting metabolic flux in these conditions in addition to inactivation of the Calvin cycle in the dark. To predict the potency of sgRNA sequences, we applied machine learning on sgRNA sequences and gene repression data, which showed the importance of C enrichment and T depletion proximal to the PAM site. Fitness data for all genes in all conditions are compiled in an interactive web application.

The class Frizzled of G protein-coupled receptors (GPCRs), consisting of ten Frizzled (FZD(1-10)) paralogs and Smoothened, remains one of the most enigmatic GPCR families. This class mediates signaling predominantly through Disheveled (DVL) or heterotrimeric G proteins. However, the mechanisms underlying pathway selection are elusive. Here we employ a structure-driven mutagenesis approach in combination with an extensive panel of functional signaling readouts to investigate the importance of conserved state-stabilizing residues in FZD(5) for signal specification. Similar data were obtained for FZD(4) and FZD(10) suggesting that our findings can be extrapolated to other members of the FZD family. Comparative molecular dynamics simulations of wild type and selected FZD(5) mutants further support the concept that distinct conformational changes in FZDs specify the signal outcome. In conclusion, we find that FZD(5) and FZDs in general prefer coupling to DVL rather than heterotrimeric G proteins and that distinct active state micro-switches in the receptor are essential for pathway selection arguing for conformational changes in the receptor protein defining transducer selectivity. Signaling pathway selectivity downstream of GPCRs is not fully understood. Here, authors perform functional analysis of Frizzled mutants to uncover state-stabilizing residues or 'micro-switches' mediating selectivity towards Disheveled over G proteins.

The chemolithotroph Cupriavidus necator H16 is known as a natural producer of the bioplastic-polymer PHB, as well as for its metabolic versatility to utilize different substrates, including formate as the sole carbon and energy source. Depending on the entry point of the substrate, this versatility requires adjustment of the thermodynamic landscape to maintain sufficiently high driving forces for biological processes. Here we employed a model of the core metabolism of C. necator H16 to analyze the thermodynamic driving forces and PHB yields from formate for different metabolic engineering strategies. For this, we enumerated elementary flux modes (EFMs) of the network and evaluated their PHB yields as well as thermodynamics via Max-min driving force (MDF) analysis and random sampling of driving forces. A heterologous ATP:citrate lyase reaction was predicted to increase driving force for producing acetyl-CoA. A heterologous phosphoketolase reaction was predicted to increase maximal PHB yields as well as driving forces. These enzymes were then verified experimentally to enhance PHB titers between 60 and 300% in select conditions. The EFM analysis also revealed that PHB production from formate may be limited by low driving forces through citrate lyase and aconitase, as well as cofactor balancing, and identified additional reactions associated with low and high PHB yield. Proteomics analysis of the engineered strains confirmed an increased abundance of aconitase and cofactor balancing. The findings of this study aid in understanding metabolic adaptation. Furthermore, the outlined approach will be useful in designing metabolic engineering strategies in other non-model bacteria.

Bacteria must balance the different needs for substrate assimilation, growth functions, and resilience in order to thrive in their environment. Of all cellular macromolecules, the bacterial proteome is by far the most important resource and its size is limited. Here, we investigated how the highly versatile 'knallgas' bacterium Cupriavidus necator reallocates protein resources when grown on different limiting substrates and with different growth rates. We determined protein quantity by mass spectrometry and estimated enzyme utilization by resource balance analysis modeling. We found that C. necator invests a large fraction of its proteome in functions that are hardly utilized. Of the enzymes that are utilized, many are present in excess abundance. One prominent example is the strong expression of CBB cycle genes such as Rubisco during growth on fructose. Modeling and mutant competition experiments suggest that CO2-reassimilation through Rubisco does not provide a fitness benefit for heterotrophic growth, but is rather an investment in readiness for autotrophy.

Metabolically engineered cyanobacteria have the potential to mitigate anthropogenic CO2 emissions by converting CO2 into renewable fuels and chemicals. Yet, better understanding of metabolic regulation in cyanobacteria is required to develop more productive strains that can make industrial scale-up economically feasible. The aim of this study was to find the cause for the previously reported inconsistency between oscillating transcription and constant protein levels under day-night growth conditions. To determine whether translational regulation counteracts transcriptional changes, Synechocystis sp. PCC 6803 was cultivated in an artificial day-night setting and the level of transcription, translation and protein was measured across the genome at different time points using mRNA sequencing, ribosome profiling and quantitative proteomics. Furthermore, the effect of protein turnover on the amplitude of protein oscillations was investigated through in silico simulations using a protein mass balance model. Our experimental analysis revealed that protein oscillations were not dampened by translational regulation, as evidenced by high correlation between translational and transcriptional oscillations (r = 0.88) and unchanged protein levels. Instead, model simulations showed that these observations can be attributed to a slow protein turnover, which reduces the effect of protein synthesis oscillations on the protein level. In conclusion, these results suggest that cyanobacteria have evolved to govern diurnal metabolic shifts through allosteric regulatory mechanisms in order to avoid the energy burden of replacing the proteome on a daily basis. Identification and manipulation of such mechanisms could be part of a metabolic engineering strategy for overproduction of chemicals.

Cyanobacteria are model organisms for photosynthesis and are attractive for biotechnology applications. To aid investigation of genotype-phenotype relationships in cyanobacteria, we develop an inducible CRISPRi gene repression library in Synechocystis sp. PCC 6803, where we aim to target all genes for repression. We track the growth of all library members in multiple conditions and estimate gene fitness. The library reveals several clones with increased growth rates, and these have a common upregulation of genes related to cyclic electron flow. We challenge the library with 0.1 M L-lactate and find that repression of peroxiredoxin bcp2 increases growth rate by 49%. Transforming the library into an L-lactate-secreting Synechocystis strain and sorting top lactate producers enriches clones with sgRNAs targeting nutrient assimilation, central carbon metabolism, and cyclic electron flow. In many examples, productivity can be enhanced by repression of essential genes, which are difficult to access by transposon insertion.

Cyanobacteria must balance separate demands for energy generation, carbon assimilation, and biomass synthesis. We used shotgun proteomics to investigate proteome allocation strategies in the model cyanobacterium Synechocystis sp. PCC 6803 as it adapted to light and inorganic carbon (C-i) limitation. When partitioning the proteome into seven functional sectors, we find that sector sizes change linearly with growth rate. The sector encompassing ribosomes is significantly smaller than in E. coli, which may explain the lower maximum growth rate in Synechocystis. Limitation of light dramatically affects multiple proteome sectors, whereas the effect of C-i limitation is weak. Carbon assimilation proteins respond more strongly to changes in light intensity than to C-i. A coarse-grained cell economy model generally explains proteome trends. However, deviations from model predictions suggest that the large proteome sectors for carbon and light assimilation are not optimally utilized under some growth conditions and may constrain the proteome space available to ribosomes.

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.

Photoautotrophic production of fuels and chemicals by cyanobacteria typically gives lower volumetric productivities and titers than heterotrophic production. Cyanobacteria cultures become light limited above an optimal cell density, so that this substrate is not supplied to all cells sufficiently. Here, we investigate genetic strategies for a two-phase cultivation, where biofuel-producing Synechocystis cultures are limited to an optimal cell density through inducible CRISPR interference (CRISPRi) repression of cell growth. Fixed CO2 is diverted to ethanol or n-butanol. Among the most successful strategies was partial repression of citrate synthase gltA. Strong repression (>90%) of gitA at low culture densities increased carbon partitioning to n-butanol 5-fold relative to a nonrepression strain, but sacrificed volumetric productivity due to severe growth restriction. CO2 fixation continued for at least 3 days after growth was arrested. By targeting sgRNAs to different regions of the gitA gene, we could modulate GItA expression and carbon partitioning between growth and product to increase both specific and volumetric productivity. These growth arrest strategies can be useful for improving performance of other photoautotrophic processes.