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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.

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.

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. Many bacteria are able to produce all the molecules they need to survive from a limited supply of nutrients. This allows the bacteria to thrive even in harsh environments where other organisms struggle to live. The bacteria act as miniature chemical factories to convert nutrients into the desired molecules via a series of chemical reactions. Some molecules are made in sets of reactions termed autocatalytic cycles. These reaction sets require a molecule to be present in the cell in order to produce more of that molecule; like how a savings account needs to contain some money before it can generate more via interest. Bacteria have many different enzymes that each drive specific chemical reactions. In order for an autocatalytic cycle to work properly, the cell needs to maintain adequate supplies of the molecule it is trying to make and all of the “intermediate” molecules in the cycle. If less of an intermediate molecule is produced, for example, the cell needs to reduce the demand for that molecule by controlling later chemical reactions in the cycle. Bacteria control chemical reactions by regulating the activities of the enzymes involved, but it is not clear exactly how they regulate the enzymes that drive autocatalytic cycles. Barenholz et al. combined two approaches called proteomics and fluxomics to study autocatalytic cycles in a bacterium known as E. coli. The experiments suggest several core principles allow autocatalytic cycles to work smoothly in the bacteria. The next step is to apply these principles to different kinds of molecules produced in bacterial cells. A future challenge is to search for other structures that regulate chemical reactions in E. coli and other bacteria. Extending our understanding of autocatalytic cycles and other pathways of chemical reactions is essential for designing and engineering new reactions in bacteria. Such knowledge can be used to modify bacteria to produce valuable chemicals in environmentally friendly ways.

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 CO 2 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 CO 2 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. An E. coli strain able to use CO 2 fixation for sugar synthesis was previously generated by experimental evolution of an engineered strain. Here, Herz et al. show that specific mutations in five genes, encoding carbon metabolism enzymes or key regulators, are sufficient to enable robust growth of the strain.

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. The study of the impact of mutations in the glutamate dehydrogenase enzyme from Bacillus subtilis across multiple settings, including liquid cultures, biofilms and in soil, highlights how bacterial growth states and the environment can differentially impact protein evolution.

SignificanceThe kcat values of enzymes are important for the study of metabolic systems. However, the current use of kcat 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 kcat under in vivo conditions. We present an approach that utilizes omics data to quantitatively analyze the relationship between in vitro kcat 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. 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 kmaxvivo, the observed maximal catalytic rate of an enzyme inside cells. Comparison with kcat values from Escherichia coli, yields a correlation of r2= 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 kmaxvivo 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.

Enzyme kinetics determine the rate of most biological processes. Decades of biochemistry have demonstrated how enzymes vary by orders of magnitude in their kinetic properties. Why are some enzymes faster than others and how are enzyme capacities related to cellular fitness? In my research I have focused on quantitative constraints on enzymes and metabolic networks. I have used Big Data in order to investigate the cellular cost of enzyme production, the efficiency of their utilization, and how design principles of metabolic networks shape enzyme kinetics and expression. I have been particularly interested in studying enzymatic catalysis directly inside cells, as opposed to classical biochemistry approaches, where enzymes are purified and evaluated outside of their cellular context. In the second part of my PhD I have looked into the interplay between the evolutionary drive to maximize enzyme efficiencies and the selection of other enzymatic traits such as substrate specificity - an interplay that results in often unexpected tradeoffs. These research directions have led me to begin to explore enzymatic tradeoffs in the most abundant, and some may say most-important, enzyme in the biosphere, Rubisco. In a large scale project, I am now working on exploring the kinetic space of Rubisco variants from nature - essentially looking for the best Rubisco in nature. Notably, at the end of this report there is a collection of “negative results”. Throughout the years, I have spend many hours on failed attempts. Here, I bring a collection of these attempts, computational or experimental with the hope that such a collection will encourage others to generate their own set of “negative results”. May this be a conceptual change which will accumulate to an invaluable resource for the scientific community

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.

Turnover numbers, also known as k cat values, are fundamental properties of enzymes. However, k cat 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 k cat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate k max vivo , the observed maximal catalytic rate of an enzyme inside cells. Comparison with k cat values from Escherichia coli, yields a correlation of r² = 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 k max vivo and k cat 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.

Turnover numbers, also known as k... values, are fundamental properties of enzymes. However, k... 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 k... values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate k..., the observed maximal catalytic rate of an enzyme inside cells. Comparison with k... values from Escherichia coli, yields a correlation of r...= 0.62 in log scale (p < 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 k... and k... 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. (ProQuest: ... denotes formulae/symbols omitted.)