A systematic exploration of bacterial form I rubisco maximal carboxylation rates.

Autotrophy is the basis for complex life on Earth. Central to this process is rubisco-the enzyme that catalyzes almost all carbon fixation on the planet. Yet, with only a small fraction of rubisco diversity kinetically characterized so far, the underlying biological factors driving the evolution of fast rubiscos in nature remain unclear. We conducted a high-throughput kinetic characterization of over 100 bacterial form I rubiscos, the most ubiquitous group of rubisco sequences in nature, to uncover the determinants of rubisco's carboxylation velocity. We show that the presence of a carboxysome CO2 concentrating mechanism correlates with faster rubiscos with a median fivefold higher rate. In contrast to prior studies, we find that rubiscos originating from α-cyanobacteria exhibit the highest carboxylation rates among form I enzymes (≈10 s-1 median versus <7 s-1 in other groups). Our study systematically reveals biological and environmental properties associated with kinetic variation across rubiscos from nature.

Association of COVID-19 Quarantine Duration and Postquarantine Transmission Risk in 4 University Cohorts.

Importance: Optimal quarantine length for COVID-19 infection is unclear, in part owing to limited empirical data. Objective: To assess postquarantine transmission risk for various quarantine lengths and potential associations between quarantine strictness and transmission risk. Design, Setting, and Participants: Retrospective cohort study in 4 US universities from September 2020 to February 2021, including 3641 university students and staff who were identified as close contacts to individuals who tested positive for SARS-CoV-2 infection. Individuals were tested throughout the 10 to 14-day quarantine, and follow-up testing continued at least weekly throughout the 2020-2021 academic year. Exposures: Strict quarantine, including designated housing with a private room, private bathroom, and meal delivery, vs nonstrict, which potentially included interactions with household members. Main Outcomes and Measures: Dates of last known exposure, last negative test result, and first positive test result during quarantine. Results: This study included 301 quarantined university students and staff who tested SARS-CoV-2-positive (of 3641 quarantined total). These 301 individuals had a median (IQR) age of 22.0 (20.0-25.0) years; 131 (43.5%) identified as female; and 20 (6.6%) were staff. Of the 287 self-reporting race and ethnicity according to university-defined classifications, 21 (7.3%) were African American or Black, 60 (20.9%) Asian, 17 (5.9%) Hispanic or Latinx, 174 (60.6%) White, and 15 (5.2%) other (including multiracial and/or multiethnic). Of the 301 participants, 40 (13.3%; 95% CI, 9.9%-17.6%) had negative test results and were asymptomatic on day 7 compared with 15 (4.9%; 95% CI, 3.0%-8.1%) and 4 (1.4%; 95% CI, 0.4%-3.5%) on days 10 and 14, respectively. Individuals in strict quarantine tested positive less frequently than those in nonstrict quarantine (10% vs 12%; P = .04). Conclusions and Relevance: To maintain the 5% transmission risk used as the basis for US Centers for Disease Control and Prevention's 7-day test-based quarantine guidance, our data suggest that quarantine with quantitative polymerase chain reaction testing 1 day before intended release should be 10 days for nonstrict quarantine and 8 days for strict quarantine, as ongoing exposure during quarantine may be associated with the higher rate of positive test results following nonstrict quarantine.

Amplicon residues in research laboratories masquerade as COVID-19 in surveillance tests.

Asymptomatic surveillance testing together with COVID-19-related research can lead to positive SARS-CoV-2 tests resulting not from true infections, but non-infectious, non-hazardous by-products of research (amplicons). Amplicons can be widespread and persistent in lab environments and can be difficult to distinguish for true infections. We discuss prevention and mitigation strategies.

Highly active rubiscos discovered by systematic interrogation of natural sequence diversity.

CO2 is converted into biomass almost solely by the enzyme rubisco. The poor carboxylation properties of plant rubiscos have led to efforts that made it the most kinetically characterized enzyme, yet these studies focused on < 5% of its natural diversity. Here, we searched for fast-carboxylating variants by systematically mining genomic and metagenomic data. Approximately 33,000 unique rubisco sequences were identified and clustered into ≈ 1,000 similarity groups. We then synthesized, purified, and biochemically tested the carboxylation rates of 143 representatives, spanning all clusters of form-II and form-II/III rubiscos. Most variants (> 100) were active in vitro, with the fastest having a turnover number of 22 ± 1 s-1 -sixfold faster than the median plant rubisco and nearly twofold faster than the fastest measured rubisco to date. Unlike rubiscos from plants and cyanobacteria, the fastest variants discovered here are homodimers and exhibit a much simpler folding and activation kinetics. Our pipeline can be utilized to explore the kinetic space of other enzymes of interest, allowing us to get a better view of the biosynthetic potential of the biosphere.

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.

Chance and pleiotropy dominate genetic diversity in complex bacterial environments.

How does environmental complexity affect the evolution of single genes? Here, we measured the effects of a set of Bacillus subtilis glutamate dehydrogenase mutants across 19 different environments-from phenotypically homogeneous single-cell populations in liquid media to heterogeneous biofilms, plant roots and soil populations. The effects of individual gene mutations on organismal fitness were highly reproducible in liquid cultures. However, 84% of the tested alleles showed opposing fitness effects under different growth conditions (sign environmental pleiotropy). In colony biofilms and soil samples, different alleles dominated in parallel replica experiments. Accordingly, we found that in these heterogeneous cell populations the fate of mutations was dictated by a combination of selection and drift. The latter relates to programmed prophage excisions that occurred during biofilm development. Overall, for each condition, a wide range of glutamate dehydrogenase mutations persisted and sometimes fixated as a result of the combined action of selection, pleiotropy and chance. However, over longer periods and in multiple environments, nearly all of this diversity would be lost-across all the environments and conditions that we tested, the wild type was the fittest allele.

Author Correction: Genome-wide SWAp-Tag yeast libraries for proteome exploration.

The version of Supplementary Table 1 originally published online with this article contained incorrect localization annotations for one plate. This error has been corrected in the online Supplementary Information.

Assessment of GFP Tag Position on Protein Localization and Growth Fitness in Yeast.

While protein tags are ubiquitously utilized in molecular biology, they harbor the potential to interfere with functional traits of their fusion counterparts. Systematic evaluation of the effect of protein tags on function would promote accurate use of tags in experimental setups. Here we examine the effect of green fluorescent protein tagging at either the N or C terminus of budding yeast proteins on subcellular localization and functionality. We use a competition-based approach to decipher the relative fitness of two strains tagged on the same protein but on opposite termini and from that infer the correct, physiological localization for each protein and the optimal position for tagging. Our study provides a first of a kind systematic assessment of the effect of tags on the functionality of proteins and provides a step toward broad investigation of protein fusion libraries.

A Bird's-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations.

Enzymes catalyze a vast range of reactions. Their catalytic performances, mechanisms, global folds, and active-site architectures are also highly diverse, suggesting that enzymes are shaped by an entire range of physiological demands and evolutionary constraints, as well as by chemical and physicochemical constraints. We have attempted to identify signatures of these shaping demands and constraints. To this end, we describe a bird's-eye view of the enzyme space from two angles: evolution and chemistry. We examine various chemical reaction parameters that may have shaped the catalytic performances and active-site architectures of enzymes. We test and weigh these considerations against physiological and evolutionary factors. Although the catalytic properties of the "average" enzyme correlate with cellular metabolic demands and enzyme expression levels, at the level of individual enzymes, a multitude of physiological demands and constraints, combined with the coincidental nature of evolutionary processes, result in a complex picture. Indeed, neither reaction type (a chemical constraint) nor evolutionary origin alone can explain enzyme rates. Nevertheless, chemical constraints are apparent in the convergence of active-site architectures in independently evolved enzymes, although significant variations within an architecture are common.

Genome-wide SWAp-Tag yeast libraries for proteome exploration.

Yeast libraries revolutionized the systematic study of cell biology. To extensively increase the number of such libraries, we used our previously devised SWAp-Tag (SWAT) approach to construct a genome-wide library of ~5,500 strains carrying the SWAT NOP1promoter-GFP module at the N terminus of proteins. In addition, we created six diverse libraries that restored the native regulation, created an overexpression library with a Cherry tag, or enabled protein complementation assays from two fragments of an enzyme or fluorophore. We developed methods utilizing these SWAT collections to systematically characterize the yeast proteome for protein abundance, localization, topology, and interactions.

The genetic basis for the adaptation of E. coli to sugar synthesis from CO2.

Understanding the evolution of a new metabolic capability in full mechanistic detail is challenging, as causative mutations may be masked by non-essential "hitchhiking" mutations accumulated during the evolutionary trajectory. We have previously used adaptive laboratory evolution of a rationally engineered ancestor to generate an Escherichia coli strain able to utilize CO2 fixation for sugar synthesis. Here, we reveal the genetic basis underlying this metabolic transition. Five mutations are sufficient to enable robust growth when a non-native Calvin-Benson-Bassham cycle provides all the sugar-derived metabolic building blocks. These mutations are found either in enzymes that affect the efflux of intermediates from the autocatalytic CO2 fixation cycle toward biomass (prs, serA, and pgi), or in key regulators of carbon metabolism (crp and ppsR). Using suppressor analysis, we show that a decrease in catalytic capacity is a common feature of all mutations found in enzymes. These findings highlight the enzymatic constraints that are essential to the metabolic stability of autocatalytic cycles and are relevant to future efforts in constructing non-native carbon fixation pathways.

Lessons on enzyme kinetics from quantitative proteomics.

Enzyme kinetics are fundamental to an understanding of cellular metabolism and for crafting synthetic biology applications. For decades, enzyme characterization has been based on in vitro enzyme assays. However, kinetic parameters are only available for <10% of reactions, and this data scarcity limits the predictive power of metabolic models. Here we review recent studies that leverage quantitative proteomics to gain insight into in vivo enzyme kinetics. We discuss findings on the relationship between in vivo and in vitro enzyme catalysis and show how proteomics can be used to characterize the efficiency of enzyme utilization across conditions. Lastly, the efficient use of enzymes is shown to rationalize preference for low energy-yield metabolic strategies, such as aerobic fermentation at high growth rate.

Design principles of autocatalytic cycles constrain enzyme kinetics and force low substrate saturation at flux branch points.

A set of chemical reactions that require a metabolite to synthesize more of that metabolite is an autocatalytic cycle. Here, we show that most of the reactions in the core of central carbon metabolism are part of compact autocatalytic cycles. Such metabolic designs must meet specific conditions to support stable fluxes, hence avoiding depletion of intermediate metabolites. As such, they are subjected to constraints that may seem counter-intuitive: the enzymes of branch reactions out of the cycle must be overexpressed and the affinity of these enzymes to their substrates must be relatively weak. We use recent quantitative proteomics and fluxomics measurements to show that the above conditions hold for functioning cycles in central carbon metabolism of E. coli. This work demonstrates that the topology of a metabolic network can shape kinetic parameters of enzymes and lead to seemingly wasteful enzyme usage.

The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization.

Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.

Sugar Synthesis from CO2 in Escherichia coli.

Can a heterotrophic organism be evolved to synthesize biomass from CO2 directly? So far, non-native carbon fixation in which biomass precursors are synthesized solely from CO2 has remained an elusive grand challenge. Here, we demonstrate how a combination of rational metabolic rewiring, recombinant expression, and laboratory evolution has led to the biosynthesis of sugars and other major biomass constituents by a fully functional Calvin-Benson-Bassham (CBB) cycle in E. coli. In the evolved bacteria, carbon fixation is performed via a non-native CBB cycle, while reducing power and energy are obtained by oxidizing a supplied organic compound (e.g., pyruvate). Genome sequencing reveals that mutations in flux branchpoints, connecting the non-native CBB cycle to biosynthetic pathways, are essential for this phenotype. The successful evolution of a non-native carbon fixation pathway, though not yet resulting in net carbon gain, strikingly demonstrates the capacity for rapid trophic-mode evolution of metabolism applicable to biotechnology. PAPERCLIP.

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements.

Turnover numbers, also known as kcat values, are fundamental properties of enzymes. However, kcat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are kcat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate kmax(vivo), the observed maximal catalytic rate of an enzyme inside cells. Comparison with kcat values from Escherichia coli, yields a correlation ofr(2)= 0.62 in log scale (p < 10(-10)), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between kmax(vivo) and kcat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a high-throughput method for extracting enzyme kinetic constants from omics data.

Noise in gene expression is coupled to growth rate.

Genetically identical cells exposed to the same environment display variability in gene expression (noise), with important consequences for the fidelity of cellular regulation and biological function. Although population average gene expression is tightly coupled to growth rate, the effects of changes in environmental conditions on expression variability are not known. Here, we measure the single-cell expression distributions of approximately 900 Saccharomyces cerevisiae promoters across four environmental conditions using flow cytometry, and find that gene expression noise is tightly coupled to the environment and is generally higher at lower growth rates. Nutrient-poor conditions, which support lower growth rates, display elevated levels of noise for most promoters, regardless of their specific expression values. We present a simple model of noise in expression that results from having an asynchronous population, with cells at different cell-cycle stages, and with different partitioning of the cells between the stages at different growth rates. This model predicts non-monotonic global changes in noise at different growth rates as well as overall higher variability in expression for cell-cycle-regulated genes in all conditions. The consistency between this model and our data, as well as with noise measurements of cells growing in a chemostat at well-defined growth rates, suggests that cell-cycle heterogeneity is a major contributor to gene expression noise. Finally, we identify gene and promoter features that play a role in gene expression noise across conditions. Our results show the existence of growth-related global changes in gene expression noise and suggest their potential phenotypic implications.

Visual account of protein investment in cellular functions.

Proteomics techniques generate an avalanche of data and promise to satisfy biologists' long-held desire to measure absolute protein abundances on a genome-wide scale. However, can this knowledge be translated into a clearer picture of how cells invest their protein resources? This article aims to give a broad perspective on the composition of proteomes as gleaned from recent quantitative proteomics studies. We describe proteomaps, an approach for visualizing the composition of proteomes with a focus on protein abundances and functions. In proteomaps, each protein is shown as a polygon-shaped tile, with an area representing protein abundance. Functionally related proteins appear in adjacent regions. General trends in proteomes, such as the dominance of metabolism and protein production, become easily visible. We make interactive visualizations of published proteome datasets accessible at www.proteomaps.net. We suggest that evaluating the way protein resources are allocated by various organisms and cell types in different conditions will sharpen our understanding of how and why cells regulate the composition of their proteomes.

High-throughput assay for temporal kinetic analysis of lytic coliphage activity.

Plaque analysis allows the determination of phage titer and multiplicity of infection. Yet, this overnight assay provides only endpoint results, ignoring kinetic aspects. We introduce an alternative high-throughput and rapid method for kinetic analysis of lytic coliphage activity. Escherichia coli was infected with serial dilutions of MS2 coliphage, and bacterial growth was monitored using a multi-well plate reader providing within hours the equivalent data as obtained overnight. Additional information is yielded, including phage replication rate, progeny size per cycle, and viral propagation during bacterial growth. This method offers further insights into physicochemical mechanisms of lytic coliphage infection and temporal control. It also provides a virus-host interaction acumen.

A novel, sensitive assay for behavioral defects in Parkinson's disease model Drosophila.

Parkinson's disease is a common neurodegenerative disorder with the pathology of α-synuclein aggregation in Lewy bodies. Currently, there is no available therapy that arrests the progression of the disease. Therefore, the need of animal models to follow α-synuclein aggregation is crucial. Drosophila melanogaster has been researched extensively as a good genetic model for the disease, with a cognitive phenotype of defective climbing ability. The assay for climbing ability has been demonstrated as an effective tool for screening new therapeutic agents for Parkinson's disease. However, due to the assay's many limitations, there is a clear need to develop a better behavioral test. Courtship, a stereotyped, ritualized behavior of Drosophila, involves complex motor and sensory functions in both sexes, which are controlled by large number of neurons; hence, behavior observed during courtship should be sensitive to disease processes in the nervous system. We used a series of traits commonly observed in courtship and an additional behavioral trait-nonsexual encounters-and analyzed them using a data mining tool. We found defective behavior of the Parkinson's model male flies that were tested with virgin females, visible at a much younger age than the climbing defects. We conclude that this is an improved behavioral assay for Parkinson's model flies.