A map of the rubisco biochemical landscape.

Rubisco is the primary CO2 fixing enzyme of the biosphere yet has slow kinetics. The roles of evolution and chemical mechanism in constraining the sequence landscape of rubisco remain debated. In order to map sequence to function, we developed a massively parallel assay for rubisco using an engineered E. coli where enzyme function is coupled to growth. By assaying >99% of single amino acid mutants across CO2 concentrations, we inferred enzyme velocity and CO2 affinity for thousands of substitutions. We identified many highly conserved positions that tolerate mutation and rare mutations that improve CO2 affinity. These data suggest that non-trivial kinetic improvements are readily accessible and provide a comprehensive sequence-to-function mapping for enzyme engineering efforts.

The proteome is a terminal electron acceptor.

Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. E. coli, for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of metabolic flexibility, we developed a mathematical model coupling redox chemistry with principles of cellular resource allocation. Our integrated model clarifies key phenomena, including demonstrating that autotrophs grow slower than heterotrophs because of constraints imposed by intracellular production of reduced carbon. Our model further indicates that growth is improved by adapting the redox state of biomass to nutrients, revealing an unexpected mode of evolution where proteins accumulate mutations benefiting organismal redox balance.

Closed ecosystems extract energy through self-organized nutrient cycles.

Our planet is a self-sustaining ecosystem powered by light energy from the sun, but roughly closed to matter. Many ecosystems on Earth are also approximately closed to matter and recycle nutrients by self-organizing stable nutrient cycles, e.g., microbial mats, lakes, open ocean gyres. However, existing ecological models do not exhibit the self-organization and dynamical stability widely observed in such planetary-scale ecosystems. Here, we advance a conceptual model that explains the self-organization, stability, and emergent features of closed microbial ecosystems. Our model incorporates the bioenergetics of metabolism into an ecological framework. By studying this model, we uncover a crucial thermodynamic feedback loop that enables metabolically diverse communities to almost always stabilize nutrient cycles. Surprisingly, highly diverse communities self-organize to extract [Formula: see text]10[Formula: see text] of the maximum extractable energy, or [Formula: see text]100 fold more than randomized communities. Further, with increasing diversity, distinct ecosystems show strongly correlated fluxes through nutrient cycles. However, as the driving force from light increases, the fluxes of nutrient cycles become more variable and species-dependent. Our results highlight that self-organization promotes the efficiency and stability of complex ecosystems at extracting energy from the environment, even in the absence of any centralized coordination.

Matching metabolic supply to demand optimizes microbial growth.

Recent research has strengthened the notion that microbes allocate their biosynthetic capacity to maximize the growth rate, λ. Yet many microbes can grow substantially faster after laboratory evolution. Chure and Cremer advance a resource-allocation model, which they derive from first principles, that offers resolution to this conundrum.

Carbon isotope fractionation by an ancestral rubisco suggests that biological proxies for CO2 through geologic time should be reevaluated.

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 CO2 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 (εp) and enzymatic (εRubisco) 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 pCO2 and displays larger εp values than WT, despite having a much smaller εRubisco (17.23 ± 0.61‰ vs. 25.18 ± 0.31‰, respectively). Surprisingly, ANC εp exceeded ANC εRubisco 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 pCO2 from geological data. Understanding the evolution of rubisco and the CO2 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 CO2.

Competing constraints shape the nonequilibrium limits of cellular decision-making.

Gene regulation is central to cellular function. Yet, despite decades of work, we lack quantitative models that can predict how transcriptional control emerges from molecular interactions at the gene locus. Thermodynamic models of transcription, which assume that gene circuits operate at equilibrium, have previously been employed with considerable success in the context of bacterial systems. However, the presence of ATP-dependent processes within the eukaryotic transcriptional cycle suggests that equilibrium models may be insufficient to capture how eukaryotic gene circuits sense and respond to input transcription factor concentrations. Here, we employ simple kinetic models of transcription to investigate how energy dissipation within the transcriptional cycle impacts the rate at which genes transmit information and drive cellular decisions. We find that biologically plausible levels of energy input can lead to significant gains in how rapidly gene loci transmit information but discover that the regulatory mechanisms underlying these gains change depending on the level of interference from noncognate activator binding. When interference is low, information is maximized by harnessing energy to push the sensitivity of the transcriptional response to input transcription factors beyond its equilibrium limits. Conversely, when interference is high, conditions favor genes that harness energy to increase transcriptional specificity by proofreading activator identity. Our analysis further reveals that equilibrium gene regulatory mechanisms break down as transcriptional interference increases, suggesting that energy dissipation may be indispensable in systems where noncognate factor interference is sufficiently large.

Trajectories for the evolution of bacterial CO2-concentrating mechanisms.

Cyanobacteria rely on CO2-concentrating mechanisms (CCMs) to grow in today's atmosphere (0.04% CO2). 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 CO2 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 CO2 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 CO2 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 CO2 and HCO3-, as both are required to produce biomass. Our experiments and model delineated a viable trajectory for CCM evolution where decreasing atmospheric CO2 induces an HCO3- 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.

Optical O2 Sensors Also Respond to Redox Active Molecules Commonly Secreted by Bacteria.

From a metabolic perspective, molecular oxygen (O2) is arguably the most significant constituent of Earth's atmosphere. Nearly every facet of microbial physiology is sensitive to the presence and concentration of O2, which is the most favorable terminal electron acceptor used by organisms and also a dangerously reactive oxidant. As O2 has such sweeping implications for physiology, researchers have developed diverse approaches to measure O2 concentrations in natural and laboratory settings. Recent improvements to phosphorescent O2 sensors piqued our interest due to the promise of optical measurement of spatiotemporal O2 dynamics. However, we found that our preferred bacterial model, Pseudomonas aeruginosa PA14, secretes more than one molecule that quenches such sensors, complicating O2 measurements in PA14 cultures and biofilms. Assaying supernatants from cultures of 9 bacterial species demonstrated that this phenotype is common: all supernatants quenched a soluble O2 probe substantially. Phosphorescent O2 probes are often embedded in solid support for protection, but an embedded probe called O2NS was quenched by most supernatants as well. Measurements using pure compounds indicated that quenching is due to interactions with redox-active small molecules, including phenazines and flavins. Uncharged and weakly polar molecules like pyocyanin were especially potent quenchers of O2NS. These findings underscore that optical O2 measurements made in the presence of bacteria should be carefully controlled to ensure that O2, and not bacterial secretions, is measured, and motivate the design of custom O2 probes for specific organisms to circumvent sensitivity to redox-active metabolites. IMPORTANCE When they are closely packed, as in biofilms, colonies, and soils, microbes can consume O2 faster than it diffuses. As such, O2 concentrations in natural environments can vary greatly over time and space, even on the micrometer scale. Wetting soil, for example, slows O2 diffusion higher in the soil column, which, in concert with microbial respiration, greatly diminishes [O2] at depth. Given that variation in [O2] has outsized implications for microbial physiology, there is great interest in measuring the dynamics of [O2] in microbial cultures and biofilms. We demonstrate that certain classes of bacterial metabolites frustrate optical measurement of [O2] with phosphorescent sensors, but also that some species (e.g., E. coli) do not produce problematic secretions under the conditions tested. Our work therefore offers a strategy for identifying organisms and culture conditions in which optical quantification of spatiotemporal [O2] dynamics with current sensors is feasible.

Anthroponumbers.org: A quantitative database of human impacts on Planet Earth.

The Human Impacts Database (www.anthroponumbers.org) is a curated, searchable resource housing quantitative data relating to the diverse anthropogenic impacts on our planet, with topics ranging from sea-level rise to livestock populations, greenhouse gas emissions, fertilizer use, and beyond. Each entry in the database reports a quantitative value (or a time series of values) along with clear referencing of the primary source, the method of measurement or estimation, an assessment of uncertainty, and links to the underlying data, as well as a permanent identifier called a Human Impacts ID (HuID). While there are other databases that house some of these values, they are typically focused on a single topic area, like energy usage or greenhouse gas emissions. The Human Impacts Database facilitates access to carefully curated data, acting as a quantitative resource pertaining to the myriad ways in which humans have an impact on the Earth, for practicing scientists, the general public, and those involved in education for sustainable development alike. We outline the structure of the database, describe our curation procedures, and use this database to generate a graphical summary of the current state of human impacts on the Earth, illustrating both their numerical values and their intimate interconnections.

Uniform binding and negative catalysis at the origin of enzymes.

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.

Protein cost minimization promotes the emergence of coenzyme redundancy.

SignificanceMetabolism 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 may not be essential for metabolism but could rather constitute 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.

Microbial communities: The metabolic rate is the trait.

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 3.0: a database solution for thermodynamic constant estimation.

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.

Tools for interpretation of wastewater SARS-CoV-2 temporal and spatial trends demonstrated with data collected in the San Francisco Bay Area.

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.

Functional reconstitution of a bacterial CO2 concentrating mechanism in Escherichia coli.

Many photosynthetic organisms employ a CO2 concentrating mechanism (CCM) to increase the rate of CO2 fixation 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 Escherichia coli strain engineered to depend on rubisco carboxylation for growth. Expression of 20 CCM genes enabled E. coli to grow by fixing CO2 from 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 CO2 assimilation in diverse organisms.

Phosphoglycolate salvage in a chemolithoautotroph using the Calvin cycle.

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.

DABs are inorganic carbon pumps found throughout prokaryotic phyla.

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 ß-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.

Revisiting Trade-offs between Rubisco Kinetic Parameters.

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 <10% among C3 plant enzymes. Limited variation in SC/O forces a strong positive correlation between the catalytic efficiencies (kcat/KM) for carboxylation and oxygenation, consistent with a model of Rubisco catalysis in which increasing the rate of addition of CO2 to the enzyme-substrate complex requires an equal increase in the O2 addition rate. Altogether, these data suggest that Rubisco evolution is tightly constrained by the physicochemical limits of CO2/O2 discrimination.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods.

BACKGROUND: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes). RESULTS: We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. CONCLUSION: The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.