We present a comprehensive overview of the hierarchical network of intracellular processes revolving around central nitrogen metabolism in Escherichia coli The hierarchy intertwines transport, metabolism, signaling leading to posttranslational modification, and transcription The protein components of the network include an ammonium transporter (AmtB), a glutamine transporter (GlnHPQ), two ammonium assimilation pathways (glutamine synthetase [GS]-glutamate synthase [glutamine 2-oxoglutarate amidotransferase {GOGAT}] and glutamate dehydrogenase [GDH]), the two bifunctional enzymes adenylyl transferase/adenylyl-removing enzyme (ATase) and uridylyl transferase/uridylyl-removing enzyme (UTase), the two trimeric signal transduction proteins (GlnB and GlnK), the two-component regulatory system composed of the histidine protein kinase nitrogen regulator II (NRII) and the response nitrogen regulator I (NRI), three global transcriptional regulators called nitrogen assimilation control (Nac) protein, leucine-responsive regulatory protein (Lrp), and cyclic AMP (cAMP) receptor protein (Crp), the glutaminases, and the nitrogen-phosphotransferase system First, the structural and molecular knowledge on these proteins is reviewed Thereafter, the activities of the components as they engage together in transport, metabolism, signal transduction, and transcription and their regulation are discussed Next, old and new molecular data and physiological data are put into a common perspective on integral cellular functioning, especially with the aim of resolving counterintuitive or paradoxical processes featured in nitrogen assimilation Finally, we articulate what still remains to be discovered and what general lessons can be learned from the vast amounts of data that are available now

Nitrogen Assimilation in Escherichia coli: Putting Molecular Data into a Systems Perspective

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.

https://www.pnas.org/content/pnas/113/12/3401.full.pdf

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

http://csmres.co.uk/cs.public.upd/article-downloads/Metabolomics_a40221.pdf

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

http://dbkgroup.org/Papers/smallbone_glycolysis_fl13.pdf

A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes.

Advancing metabolic models with kinetic information

Does the understanding of the dynamics of biochemical networks in vivo, in terms of the properties of their components determined in vitro, require the latter to be determined all under the same conditions? An in vivo-like assay medium for enzyme activity determination was designed based on the concentrations of the major ionic constituents of the Escherichia coli cytosol: K(+) , Na(+) , Mg(2+) , phosphate, glutamate, sulfate and Cl(-) . The maximum capacities (V(max) ) of the extracted enzymes of two pathways were determined using both this in vivo-like assay medium and the assay medium specific for each enzyme. The enzyme activities differed between the two assay conditions. Most of the differences could be attributed to unsuspected, pleiotropic effects of K(+) and phosphate. K(+) activated some enzymes (aldolase, enolase and glutamate dehydrogenase) and inhibited others (phosphoglucose isomerase, phosphofructokinase, triosephosphate isomerase, glyceraldehyde 3-phosphate dehydrogenase, phosphoglycerate kinase, phosphoglycerate mutase), whereas phosphate inhibited all glycolytic enzymes and glutamine synthetase but only activated glutamine 2-oxoglutarate amidotransferase. Neither a high glutamate concentration, nor macromolecular crowding affected the glycolytic or nitrogen assimilation enzymes, other than through the product inhibition of glutamate dehydrogenase by glutamate. This strategy of assessing all pathway enzymes kinetically under the same conditions may be necessary to avoid inadvertent differences between in vivo and in vitro biochemistry. It may also serve to reveal otherwise unnoticed pleiotropic regulation, such as that demonstrated in the present study by K(+) and phosphate.

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1111/febs.12007

Paul Vos

Papers

Why in vivo may not equal in vitro - new effectors revealed by measurement of enzymatic activities under the same in vivo-like assay conditions.