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The history of Earth’s carbon cycle reflects trends in atmospheric composition convolved with the evolution of photosynthesis. Fortunately, key parts of the carbon cycle have been recorded in the carbon isotope ratios of sedimentary rocks. The dominant model used to interpret this record as a proxy for ancient atmospheric CO 2 is based on carbon isotope fractionations of modern photoautotrophs, and longstanding questions remain about how their evolution might have impacted the record. Therefore, we measured both biomass (ε p ) and enzymatic (ε Rubisco ) carbon isotope fractionations of a cyanobacterial strain ( Synechococcus elongatus PCC 7942) solely expressing a putative ancestral Form 1B rubisco dating to ≫1 Ga. This strain, nicknamed ANC, grows in ambient pCO 2 and displays larger ε p values than WT, despite having a much smaller ε Rubisco (17.23 ± 0.61‰ vs. 25.18 ± 0.31‰, respectively). Surprisingly, ANC ε p exceeded ANC ε Rubisco in all conditions tested, contradicting prevailing models of cyanobacterial carbon isotope fractionation. Such models can be rectified by introducing additional isotopic fractionation associated with powered inorganic carbon uptake mechanisms present in Cyanobacteria, but this amendment hinders the ability to accurately estimate historical pCO 2 from geological data. Understanding the evolution of rubisco and the CO 2 concentrating mechanism is therefore critical for interpreting the carbon isotope record, and fluctuations in the record may reflect the evolving efficiency of carbon fixing metabolisms in addition to changes in atmospheric CO 2 .

Cyanobacteria rely on CO 2 -concentrating mechanisms (CCMs) to grow in today’s atmosphere (0.04% CO 2 ). These complex physiological adaptations require ≈15 genes to produce two types of protein complexes: inorganic carbon (Ci) transporters and 100+ nm carboxysome compartments that encapsulate rubisco with a carbonic anhydrase (CA) enzyme. Mutations disrupting any of these genes prohibit growth in ambient air. If any plausible ancestral form—i.e., lacking a single gene—cannot grow, how did the CCM evolve? Here, we test the hypothesis that evolution of the bacterial CCM was “catalyzed” by historically high CO 2 levels that decreased over geologic time. Using an E. coli reconstitution of a bacterial CCM, we constructed strains lacking one or more CCM components and evaluated their growth across CO 2 concentrations. We expected these experiments to demonstrate the importance of the carboxysome. Instead, we found that partial CCMs expressing CA or Ci uptake genes grew better than controls in intermediate CO 2 levels (≈1%) and observed similar phenotypes in two autotrophic bacteria, Halothiobacillus neapolitanus and Cupriavidus necator . To understand how CA and Ci uptake improve growth, we model autotrophy as colimited by CO 2 and HCO 3 − , as both are required to produce biomass. Our experiments and model delineated a viable trajectory for CCM evolution where decreasing atmospheric CO 2 induces an HCO 3 − deficiency that is alleviated by acquisition of CA or Ci uptake, thereby enabling the emergence of a modern CCM. This work underscores the importance of considering physiology and environmental context when studying the evolution of biological complexity.

From a metabolic perspective, molecular oxygen (O

Enzymes are well known for their catalytic abilities, some even reaching "catalytic perfection" in the sense that the reaction they catalyze has reached the physical bound of the diffusion rate. However, our growing understanding of enzyme superfamilies has revealed that only some share a catalytic chemistry while others share a substrate-handle binding motif, for example, for a particular phosphate group. This suggests that some families emerged through a "substrate-handle-binding-first" mechanism ("binding-first" for brevity) instead of "chemistry-first" and we are, therefore, left to wonder what the role of non-catalytic binders might have been during enzyme evolution. In the last of their eight seminal, back-to-back articles from 1976, John Albery and Jeremy Knowles addressed the question of enzyme evolution by arguing that the simplest mode of enzyme evolution is what they defined as "uniform binding" (parallel stabilization of all enzyme-bound states to the same degree). Indeed, we show that a uniform-binding proto-catalyst can accelerate a reaction, but only when catalysis is already present, that is, when the transition state is already stabilized to some degree. Thus, we sought an alternative explanation for the cases where substrate-handle-binding preceded any involvement of a catalyst. We find that evolutionary starting points that exhibit negative catalysis can redirect the reaction's course to a preferred product without need for rate acceleration or product release; that is, if they do not stabilize, or even destabilize, the transition state corresponding to an undesired product. Such a mechanism might explain the emergence of "binding-first" enzyme families like the aldolase superfamily.

The history of Earth’s carbon cycle reflects trends in atmospheric composition convolved with the evolution of photosynthesis. Fortunately, key parts of the carbon cycle have been recorded in the carbon isotope ratios of sedimentary rocks. The dominant model used to interpret this record as a proxy for ancient atmospheric CO2is based on carbon isotope fractionations of modern photoautotrophs, and longstanding questions remain about how their evolution might have impacted the record. We tested the intersection of environment and evolution by measuring both biomass (εp) and enzymatic (εRubisco) carbon isotope fractionations of a cyanobacterial strain (Synechococcus elongatusPCC 7942) solely expressing a putative ancestral Form 1B rubisco dating to ≫1 Ga. This strain, nicknamed ANC, grows in ambient pCO2and displays larger εpvalues than WT, despite having a much smaller εRubisco(17.23 ± 0.61‰ vs. 25.18 ± 0.31‰ respectively). Measuring both enzymatic and biomass fractionation revealed a surprising result—ANC εpexceeded ANC εRubiscoin all conditions tested, contradicting prevailing models of cyanobacterial carbon isotope fractionation. However, these models were corrected by accounting for cyanobacterial physiology, notably the CO2concentrating mechanism (CCM). Our model suggested that additional fractionating processes like powered inorganic carbon uptake systems contribute to εp, and this effect is exacerbated in ANC. Understanding the evolution of rubisco and the CCM is therefore critical for interpreting the carbon isotope record. Large fluctuations in that record may reflect the evolving efficiency of carbon fixing metabolisms in addition to changes in atmospheric CO2.Significance StatementEarth scientists rely on chemical fossils like the carbon isotope record to derive ancient atmospheric CO2concentrations, but interpretation of this record is calibrated using modern organisms. We tested this assumption by measuring the carbon isotope fractionation of a reconstructed ancestral rubisco enzyme (>1 billion years old)in vivoandin vitro. Our results contradicted prevailing models of carbon flow in Cyanobacteria, but our data could be rationalized if light-driven uptake of CO2is taken into account. Our study showed that the carbon isotope record tracks both the evolution of photosynthesis physiology as well as changes in atmospheric CO2, highlighting the value of considering both evolution and physiology for comparative biological approaches to understanding Earth’s history.

Cyanobacteria rely on CO2 concentrating mechanisms (CCMs) that depend on ≈15 genes to produce two protein complexes: an inorganic carbon (Ci) transporter and a 100+ nm carboxysome compartment that encapsulates rubisco with a carbonic anhydrase (CA) enzyme. Mutations disrupting CCM components prohibit growth in today’s atmosphere (0.04% CO2), indicating that CCMs evolved to cope with declining environmental CO2. Indeed, geochemical data and models indicate that atmospheric CO2 has been generally decreasing from high concentrations over the last ≈3.5 billion years. We used a synthetic reconstitution of a bacterial CCM in E. coli to study the co-evolution of CCMs with atmospheric CO2. We constructed strains expressing putative ancestors of modern CCMs — strains lacking one or more CCM components — and evaluated their growth in a variety of CO2 concentrations. Partial forms expressing CA or Ci uptake genes grew better than controls in intermediate CO2 levels (≈1%); we observed similar phenotypes in genetic studies of two autotrophic bacteria, H. neapolitanus and C. necator. To explain how partial CCMs improve growth, we advance a model of co-limitation of autotrophic growth by CO2 and HCO3-, as both are required to produce biomass. Our model and results delineated a viable trajectory for bacterial CCM evolution where decreasing atmospheric CO2 induces an HCO3- deficiency that is alleviated by acquisition of CAs or Ci uptake genes, thereby enabling the emergence of a modern CCM. This work underscores the importance of considering physiology and environmental context when studying the evolution of biological complexity.SignificanceThe greenhouse gas content of the ancient atmosphere is estimated using models and measurements of geochemical proxies. Some inferred high ancient CO2 levels using models of biological CO2 fixation to interpret the C isotopes found in preserved organic matter. Others argued that elevated CO2 could reconcile a faint young Sun with evidence for liquid water on Earth. We took a complementary “synthetic biological” approach to understanding the composition of the ancient atmosphere by studying present-day bacteria engineered to resemble ancient autotrophs. By showing that it is simpler to rationalize the emergence of modern bacterial autotrophs if CO2 was once high, these investigations provided independent evidence for the view that CO2 concentrations were significantly elevated in the atmosphere of early Earth.

Significance The Calvin cycle is the most important carbon fixation pathway in the biosphere. However, its carboxylating enzyme Rubisco also accepts oxygen, thus producing 2-phosphoglycolate. Phosphoglycolate salvage pathways were extensively studied in photoautotrophs but remain uncharacterized in chemolithoautotrophs using the Calvin cycle. Here, we study phosphoglycolate salvage in the chemolithoautotrophic model bacterium Cupriavidus necator H16. We demonstrate that this bacterium mainly reassimilates 2-phosphoglycolate via the glycerate pathway. Upon disruption of this pathway, a secondary route, which we term the malate cycle, supports photorespiration by completely oxidizing 2-phosphoglycolate to CO2. While the malate cycle was not previously known to metabolize 2-phosphoglycolate in nature, a bioinformatic analysis suggests that it may support phosphoglycolate salvage in diverse chemoautotrophic bacteria., Carbon fixation via the Calvin cycle is constrained by the side activity of Rubisco with dioxygen, generating 2-phosphoglycolate. The metabolic recycling of phosphoglycolate was extensively studied in photoautotrophic organisms, including plants, algae, and cyanobacteria, where it is referred to as photorespiration. While receiving little attention so far, aerobic chemolithoautotrophic bacteria that operate the Calvin cycle independent of light must also recycle phosphoglycolate. As the term photorespiration is inappropriate for describing phosphoglycolate recycling in these nonphotosynthetic autotrophs, we suggest the more general term “phosphoglycolate salvage.” Here, we study phosphoglycolate salvage in the model chemolithoautotroph Cupriavidus necator H16 (Ralstonia eutropha H16) by characterizing the proxy process of glycolate metabolism, performing comparative transcriptomics of autotrophic growth under low and high CO2 concentrations, and testing autotrophic growth phenotypes of gene deletion strains at ambient CO2. We find that the canonical plant-like C2 cycle does not operate in this bacterium, and instead, the bacterial-like glycerate pathway is the main route for phosphoglycolate salvage. Upon disruption of the glycerate pathway, we find that an oxidative pathway, which we term the malate cycle, supports phosphoglycolate salvage. In this cycle, glyoxylate is condensed with acetyl coenzyme A (acetyl-CoA) to give malate, which undergoes two oxidative decarboxylation steps to regenerate acetyl-CoA. When both pathways are disrupted, autotrophic growth is abolished at ambient CO2. We present bioinformatic data suggesting that the malate cycle may support phosphoglycolate salvage in diverse chemolithoautotrophic bacteria. This study thus demonstrates a so far unknown phosphoglycolate salvage pathway, highlighting important diversity in microbial carbon fixation metabolism.

Flamholz and Shih explain how photosynthetic organisms on earth have evolved carbon dioxide concentrating mechanisms to contend with an increased abundance of oxygen in the atmosphere. This critical innovation has occurred numerous times and comes in two basic flavors.

Making sense of the metabolism of microbial communities is a daunting task. Using denitrification as a model metabolism, a new paper shows that the rate of denitrification can often be predicted from genome contents, and dynamical models can be composed to predict denitrification rates of communities of two to five species.

eQuilibrator (equilibrator.weizmann.ac.il) is a database of biochemical equilibrium constants and Gibbs free energies, originally designed as a web-based interface. While the website now counts around 1,000 distinct monthly users, its design could not accommodate larger compound databases and it lacked a scalable Application Programming Interface (API) for integration into other tools developed by the systems biology community. Here, we report on the recent updates to the database as well as the addition of a new Python-based interface to eQuilibrator that adds many new features such as a 100-fold larger compound database, the ability to add novel compounds, improvements in speed and memory use, and correction for Mg2+ ion concentrations. Moreover, the new interface can compute the covariance matrix of the uncertainty between estimates, for which we show the advantages and describe the application in metabolic modelling. We foresee that these improvements will make thermodynamic modelling more accessible and facilitate the integration of eQuilibrator into other software platforms., Nucleic Acids Research, 50 (D1), ISSN:1362-4962, ISSN:0301-5610

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies., Graphical abstract Image, graphical abstract

Coenzymes distribute a variety of chemical moieties throughout cellular metabolism, participating in group (e.g., phosphate, acyl) and electron transfer. For a variety of reactions requiring acceptors or donors of specific resources, there often exist degenerate sets of molecules (e.g., NAD(H) and NADP(H)) that carry out similar functions. Although the physiological roles of various coenzyme systems are well established, it is unclear what selective pressures may have driven the emergence of coenzyme redundancy. Here we use genome-wide metabolic modeling approaches to decompose the selective pressures driving enzymatic specificity for either NAD(H) or NADP(H) in the metabolic network ofEscherichia coli. We found that few enzymes are thermodynamically constrained to using a single coenzyme, and in principle, a metabolic network relying on only NAD(H) is feasible. However, structural and sequence analyses revealed widespread conservation of residues that retain selectivity for either NAD(H) or NADP(H), suggesting that additional forces may shape specificity. Using a model accounting for the cost of oxidoreductase enzyme expression, we found that coenzyme redundancy universally reduces the minimal amount of protein required to catalyze coenzyme-coupled reactions, inducing individual reactions to strongly prefer one coenzyme over another when reactions are near thermodynamic equilibrium. We propose that protein minimization generically promotes coenzyme redundancy, and that coenzymes typically thought to exist in a single pool (e.g., CoA) may exist in more than one form (e.g., dephospho-CoA).Significance statementMetabolism relies on a small class of molecules (coenzymes) that serve as universal donors and acceptors of key chemical groups and electrons. Although metabolic networks crucially depend on structurally redundant coenzymes (e.g., NAD(H) and NADP(H)) associated with different enzymes, the criteria that led to the emergence of this redundancy remain poorly understood. Our combination of modeling, and structural and sequence analysis indicates that coenzyme redundancy is not essential for metabolism, but rather an evolved strategy promoting efficient usage of enzymes when biochemical reactions are near equilibrium. Our work suggests that early metabolism may have operated with fewer coenzymes, and that adaptation for metabolic efficiency may have driven the rise of coenzyme diversity in living systems.

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility, and wastewater SARS-CoV-2 concentrations were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA were evaluated as normalization biomarkers, and crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall’s Tau-b (τ)=0.43 and 0.38, respectively). Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents approaches that could be applied to make wastewater signal more interpretable and comparable across studies.

Viral genome sequencing has guided our understanding of the spread and extent of genetic diversity of SARS-CoV-2 during the COVID-19 pandemic. SARS-CoV-2 viral genomes are usually sequenced from nasopharyngeal swabs of individual patients to track viral spread., Viral genome sequencing has guided our understanding of the spread and extent of genetic diversity of SARS-CoV-2 during the COVID-19 pandemic. SARS-CoV-2 viral genomes are usually sequenced from nasopharyngeal swabs of individual patients to track viral spread. Recently, RT-qPCR of municipal wastewater has been used to quantify the abundance of SARS-CoV-2 in several regions globally. However, metatranscriptomic sequencing of wastewater can be used to profile the viral genetic diversity across infected communities. Here, we sequenced RNA directly from sewage collected by municipal utility districts in the San Francisco Bay Area to generate complete and nearly complete SARS-CoV-2 genomes. The major consensus SARS-CoV-2 genotypes detected in the sewage were identical to clinical genomes from the region. Using a pipeline for single nucleotide variant calling in a metagenomic context, we characterized minor SARS-CoV-2 alleles in the wastewater and detected viral genotypes which were also found within clinical genomes throughout California. Observed wastewater variants were more similar to local California patient-derived genotypes than they were to those from other regions within the United States or globally. Additional variants detected in wastewater have only been identified in genomes from patients sampled outside California, indicating that wastewater sequencing can provide evidence for recent introductions of viral lineages before they are detected by local clinical sequencing. These results demonstrate that epidemiological surveillance through wastewater sequencing can aid in tracking exact viral strains in an epidemic context.

Significance Knowing the absolute numbers of virions in an infection promotes better understanding of disease dynamics and response of the immune system. Here we use current knowledge on the concentrations of virions in infected individuals to estimate the total number and mass of SARS-CoV-2 virions in an infected person. Although each infected person carries an estimated 1 billion to 100 billion virions during peak infection, their total mass is no more than 0.1 mg. This curiously implies that all SARS-CoV-2 virions currently in all human hosts have a mass of between 100 g and 10 kg. Combining the known mutation rate and our estimate of the number of infectious virions, we quantify the formation rate of genetic variants., Quantitatively describing the time course of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection within an infected individual is important for understanding the current global pandemic and possible ways to combat it. Here we integrate the best current knowledge about the typical viral load of SARS-CoV-2 in bodily fluids and host tissues to estimate the total number and mass of SARS-CoV-2 virions in an infected person. We estimate that each infected person carries 109 to 1011 virions during peak infection, with a total mass in the range of 1 μg to 100 μg, which curiously implies that all SARS-CoV-2 virions currently circulating within human hosts have a collective mass of only 0.1 kg to 10 kg. We combine our estimates with the available literature on host immune response and viral mutation rates to demonstrate how antibodies markedly outnumber the spike proteins, and the genetic diversity of virions in an infected host covers all possible single nucleotide substitutions.

Many photosynthetic organisms employ a CO2concentrating mechanism (CCM) to increase the rate of CO2fixation via the Calvin cycle. CCMs catalyze ≈50% of global photosynthesis, yet it remains unclear which genes and proteins are required to produce this complex adaptation. We describe the construction of a functional CCM in a non-native host, achieved by expressing genes from an autotrophic bacterium in anEscherichia colistrain engineered to depend on rubisco carboxylation for growth. Expression of 20 CCM genes enabledE. colito grow by fixing CO2from ambient air into biomass, with growth in ambient air depending on the components of the CCM. Bacterial CCMs are therefore genetically compact and readily transplanted, rationalizing their presence in diverse bacteria. Reconstitution enabled genetic experiments refining our understanding of the CCM, thereby laying the groundwork for deeper study and engineering of the cell biology supporting CO2assimilation in diverse organisms.

Many photosynthetic organisms employ a CO2concentrating mechanism (CCM) to increase the rate of CO2fixation via the Calvin cycle. CCMs catalyze ≈50% of global photosynthesis, yet it remains unclear which genes and proteins are required to produce this complex adaptation. We describe the construction of a functional CCM in a non-native host, achieved by expressing genes from an autotrophic bacterium in an engineeredE. colistrain. Expression of 20 CCM genes enabledE. colito grow by fixing CO2from ambient air into biomass, with growth depending on CCM components. Bacterial CCMs are therefore genetically compact and readily transplanted, rationalizing their presence in diverse bacteria. Reconstitution enabled genetic experiments refining our understanding of the CCM, thereby laying the groundwork for deeper study and engineering of the cell biology supporting CO2assimilation in diverse organisms.One Sentence SummaryA bacterial CO2concentrating mechanism enablesE. colito fix CO2from ambient air.

Carbon fixation via the Calvin cycle is constrained by the side activity of Rubisco with dioxygen, generating 2-phosphoglycolate. The metabolic recycling of 2-phosphoglycolate, an essential process termed photorespiration, was extensively studied in photoautotrophic organisms, including plants, algae, and cyanobacteria, but remains uncharacterized in chemolithoautotrophic bacteria. Here, we study photorespiration in the model chemolithoautotroph Cupriavidus necator (Ralstonia eutropha) by characterizing the proxy-process of glycolate metabolism, performing comparative transcriptomics of autotrophic growth under low and high CO2 concentrations, and testing autotrophic growth phenotypes of gene deletion strains at ambient CO2. We find that the canonical plant-like C2 cycle does not operate in this bacterium and instead the bacterial-like glycerate pathway is the main photorespiratory pathway. Upon disruption of the glycerate pathway, we find that an oxidative pathway, which we term the malate cycle, supports photorespiration. In this cycle, glyoxylate is condensed with acetyl-CoA to give malate, which undergoes two oxidative decarboxylation steps to regenerate acetyl-CoA. When both pathways are disrupted, autotrophic growth is abolished at ambient CO2. We present bioinformatic data suggesting that the malate cycle may support photorespiration in diverse chemolithoautotrophic bacteria. This study thus demonstrates a so-far unknown photorespiration pathway, highlighting important diversity in microbial carbon fixation metabolism.

Bacterial autotrophs often rely on CO2 concentrating mechanisms (CCMs) to assimilate carbon. Although many CCM proteins have been identified, a systematic screen of the components of CCMs is lacking. Here, we performed a genome-wide barcoded transposon screen to identify essential and CCM-related genes in the γ-proteobacterium Halothiobacillus neapolitanus. Screening revealed that the CCM comprises at least 17 and probably no more than 25 genes, most of which are encoded in 3 operons. Two of these operons (DAB1 and DAB2) contain a two-gene locus that encodes a domain of unknown function (Pfam: PF10070) and a putative cation transporter (Pfam: PF00361). Physiological and biochemical assays demonstrated that these proteins-which we name DabA and DabB, for DABs accumulate bicarbonate-assemble into a heterodimeric complex, which contains a putative β-carbonic anhydrase-like active site and functions as an energy-coupled inorganic carbon (Ci) pump. Interestingly, DAB operons are found in a diverse range of bacteria and archaea. We demonstrate that functional DABs are present in the human pathogens Bacillus anthracis and Vibrio cholerae. On the basis of these results, we propose that DABs constitute a class of energized Ci pumps and play a critical role in the metabolism of Ci throughout prokaryotic phyla.

Significance The k cat values of enzymes are important for the study of metabolic systems. However, the current use of k cat presents major difficulties, as values for most enzymes have not been experimentally measured, and experimentally available values are often measured under nonphysiological conditions, thereby casting doubt on the relevance of k cat under in vivo conditions. We present an approach that utilizes omics data to quantitatively analyze the relationship between in vitro k cat values and the maximal catalytic rate of enzymes in vivo. Our approach offers a high-throughput method to obtain enzyme kinetic constants, which reflect in vivo conditions, and are useful for more accurate and complete cellular metabolic models.

Rubisco is the primary carboxylase of the Calvin cycle, the most abundant enzyme in the biosphere, and one of the best-characterized enzymes. On the basis of correlations between Rubisco kinetic parameters, it is widely posited that constraints embedded in the catalytic mechanism enforce trade-offs between CO2 specificity, SC/O, and maximum carboxylation rate, kcat,C. However, the reasoning that established this view was based on data from ≈20 organisms. Here, we re-examine models of trade-offs in Rubisco catalysis using a data set from ≈300 organisms. Correlations between kinetic parameters are substantially attenuated in this larger data set, with the inverse relationship between kcat,C and SC/O being a key example. Nonetheless, measured kinetic parameters display extremely limited variation, consistent with a view of Rubisco as a highly constrained enzyme. More than 95% of kcat,C values are between 1 and 10 s-1, and no measured kcat,C exceeds 15 s-1. Similarly, SC/O varies by only 30% among Form I Rubiscos and

Many bacterial autotrophs rely on CO2concentrating mechanisms (CCMs) to assimilate carbon. Although many CCM proteins have been identified, including a 200+ MDa protein organelle called the carboxysome, a systematic screen of CCM components has not been carried out. Here, we performed a genome-wide barcoded transposon screen to identify essential and CCM-related genes in the ɣ-proteobacteriumH. neapolitanus. Our screen revealed an operon critical for CCM function which encodes a domain of unknown function (PFAM:PF10070) and putative cation transporter subunit (PFAM:PF00361). These two proteins, which we name DabA and DabB for “DABs accumulate bicarbonate,” function as a heterodimeric, energy-coupled inorganic carbon pump inE. coli. Furthermore, DabA binds zinc and has a an active site homologous to a β-carbonic anhydrase. Based on these results, we propose that DABs function as vectorial CAs coupled to cation gradients and serve as inorganic carbon pumps throughout prokaryotic phyla.

Rubisco is the most abundant enzyme in the biosphere and one of the best-characterized enzymes. Based on correlations between Rubisco kinetic parameters, it is widely posited that tradeoffs embedded in the catalytic mechanism constrain its specificity and maximum catalytic rate. However, the reasoning that established this view was based on data from ≈20 organisms. We re-examine these tradeoff models using a dataset from ≈300 organisms. Most correlations are substantially attenuated, with the inverse relationship between carboxylation kcat and specificity SC/O being a key example. Only one tradeoff model survives in our dataset. In this model, increasing catalytic efficiency (kcat/KM) for carboxylation requires increased catalytic efficiency for the competing oxygenation reaction, evidenced by strong power-law correlation between catalytic efficiencies. Our results imply that Rubisco evolution is constrained primarily by the physicochemical limits of O2/CO2 discrimination, which should reframe efforts to engineer this very central enzyme.

Thermodynamics dictates the structure and function of metabolism. Redox reactions drive cellular energy and material flow. Hence, accurately quantifying the thermodynamics of redox reactions should reveal key design principles that shape cellular metabolism. However, only a limited number of redox potentials have been measured experimentally, and mostly with inconsistent, poorly-reported experimental setups. Here, we develop a quantum chemistry approach for the calculation of redox potentials of biochemical reactions. We demonstrate that our method predicts experimentally measured potentials with unparalleled accuracy. We calculate the reduction potentials of all redox pairs that can be generated from biochemically relevant compounds and highlight fundamental thermodynamic trends that define cellular redox biochemistry. We further use the calculated potentials to address the question of why NAD/NADP are used as the primary cellular electron carriers, demonstrating how their physiological redox range specifically fits the reactions of central metabolism and minimizes the concentration of reactive carbonyls. The use of quantum chemistry tools, as demonstrated in this study, can revolutionize our understanding of key biochemical phenomena by enabling fast and accurate calculation of large datasets of thermodynamic values.

Many carbon-fixing bacteria rely on a CO2 concentrating mechanism (CCM) to elevate the CO2 concentration around the carboxylating enzyme ribulose bisphosphate carboxylase/oxygenase (RuBisCO). The CCM is postulated to simultaneously enhance the rate of carboxylation and minimize oxygenation, a competitive reaction with O2 also catalyzed by RuBisCO. To achieve this effect, the CCM combines two features: active transport of inorganic carbon into the cell and colocalization of carbonic anhydrase and RuBisCO inside proteinaceous microcompartments called carboxysomes. Understanding the significance of the various CCM components requires reconciling biochemical intuition with a quantitative description of the system. To this end, we have developed a mathematical model of the CCM to analyze its energetic costs and the inherent intertwining of physiology and pH. We find that intracellular pH greatly affects the cost of inorganic carbon accumulation. At low pH the inorganic carbon pool contains more of the highly cell-permeable H2CO3, necessitating a substantial expenditure of energy on transport to maintain internal inorganic carbon levels. An intracellular pH ≈8 reduces leakage, making the CCM significantly more energetically efficient. This pH prediction coincides well with our measurement of intracellular pH in a model cyanobacterium. We also demonstrate that CO2 retention in the carboxysome is necessary, whereas selective uptake of HCO3 (-) into the carboxysome would not appreciably enhance energetic efficiency. Altogether, integration of pH produces a model that is quantitatively consistent with cyanobacterial physiology, emphasizing that pH cannot be neglected when describing biological systems interacting with inorganic carbon pools.

The cyanobacterium Synechococcus elongatus relies upon photosynthesis to drive metabolism and growth. During darkness, Synechococcus stops growing, derives energy from its glycogen stores, and greatly decreases rates of macromolecular synthesis via unknown mechanisms. Here, we show that the stringent response, a stress response pathway whose genes are conserved across bacteria and plant plastids, contributes to this dark adaptation. Levels of the stringent response alarmone guanosine 3'-diphosphate 5'-diphosphate (ppGpp) rise after a shift from light to dark, indicating that darkness triggers the same response in cyanobacteria as starvation in heterotrophic bacteria. High levels of ppGpp are sufficient to stop growth and dramatically alter many aspects of cellular physiology, including levels of photosynthetic pigments and polyphosphate, DNA content, and the rate of translation. Cells unable to synthesize ppGpp display pronounced growth defects after exposure to darkness. The stringent response regulates expression of a number of genes in Synechococcus, including ribosomal hibernation promoting factor (hpf), which causes ribosomes to dimerize in the dark and may contribute to decreased translation. Although the metabolism of Synechococcus differentiates it from other model bacterial systems, the logic of the stringent response remains remarkably conserved, while at the same time having adapted to the unique stresses of the photosynthetic lifestyle.

The clustered, regularly interspaced, short palindromic repeats (CRISPR)-associated protein Cas9 from Streptococcus pyogenes is an RNA-guided DNA endonuclease with widespread utility for genome modification. However, the structural constraints limiting the engineering of Cas9 have not been determined. Here we experimentally profile Cas9 using randomized insertional mutagenesis and delineate hotspots in the structure capable of tolerating insertions of a PDZ domain without disruption of the enzyme's binding and cleavage functions. Orthogonal domains or combinations of domains can be inserted into the identified sites with minimal functional consequence. To illustrate the utility of the identified sites, we construct an allosterically regulated Cas9 by insertion of the estrogen receptor-α ligand-binding domain. This protein showed robust, ligand-dependent activation in prokaryotic and eukaryotic cells, establishing a versatile one-component system for inducible and reversible Cas9 activation. Thus, domain insertion profiling facilitates the rapid generation of new Cas9 functionalities and provides useful data for future engineering of Cas9.

Thermodynamics dictates the structure and function of metabolism. Redox reactions drive cellular energy and material flow. Hence, accurately quantifying the thermodynamics of redox reactions should reveal design principles that shape cellular metabolism. However, only few redox potentials have been measured, and mostly with inconsistent experimental setups. Here, we develop a quantum chemistry approach to calculate redox potentials of biochemical reactions and demonstrate our method predicts experimentally measured potentials with unparalleled accuracy. We then calculate the potentials of all redox pairs that can be generated from biochemically relevant compounds and highlight fundamental trends in redox biochemistry. We further address the question of why NAD/NADP are used as primary electron carriers, demonstrating how their physiological potential range fits the reactions of central metabolism and minimizes the concentration of reactive carbonyls. The use of quantum chemistry can revolutionize our understanding of biochemical phenomena by enabling fast and accurate calculation of thermodynamic values., PLoS Computational Biology, 14 (10), ISSN:1553-734X, ISSN:1553-7358

Single-fluorescent protein biosensors (SFPBs) are an important class of probes that enable the single-cell quantification of analytes in vivo. Despite advantages over other detection technologies, their use has been limited by the inherent challenges of their construction. Specifically, the rational design of green fluorescent protein (GFP) insertion into a ligand-binding domain, generating the requisite allosteric coupling, remains a rate-limiting step. Here, we describe an unbiased approach, termed domain-insertion profiling with DNA sequencing (DIP-seq), that combines the rapid creation of diverse libraries of potential SFPBs and high-throughput activity assays to identify functional biosensors. As a proof of concept, we construct an SFPB for the important regulatory sugar trehalose. DIP-seq analysis of a trehalose-binding-protein reveals allosteric hotspots for GFP insertion and results in high-dynamic range biosensors that function robustly in vivo. Taken together, DIP-seq simultaneously accelerates metabolite biosensor construction and provides a novel tool for interrogating protein allostery., In the construction of single fluorescent protein biosensors, selection of the insertion point of a fluorescent protein into a ligand-binding domain is a rate-limiting step. Here, the authors develop an unbiased, high-throughput approach, called domain insertion profiling with DNA sequencing (DIP-seq), to generate a novel trehalose biosensor.