Proteomic analysis of arginine adducts on glyoxal-modified ribonuclease

Accumulation of advanced glycation end-products (AGEs) on proteins is associated with the development of diabetic complications. Although the overall extent of modification of protein by AGEs is limited, localization of these modifications at a few critical sites might have a significant effect on protein structure and function. In the present study, we describe the sites of modification of RNase by glyoxal under physiological conditions. Arg(39) and Arg(85), which are closest to the active site of the enzyme, were identified as the primary sites of formation of the glyoxal-derived dihydroxyimidazolidine and hydroimidazolone adducts. Lower amounts of modification were detected at Arg(10), while Arg(33) appeared to be unmodified. We conclude that dihydroxyimidazolidine adducts are the primary products of modification of protein by glyoxal, that Arg(39) and Arg(85) are the primary sites of modification of RNase by glyoxal, and that modification of arginine residues during Maillard reactions of proteins is a highly selective process.

Mass spectrometric analysis of glucose-modified ribonuclease

RNase A (1 mM) was incubated with glucose (0.4 M) at 37°C for up to 14 days in phosphate buffer (0.2 M, pH 7.4), digested with trypsin and analysed by LC-MS. The major sites of fructoselysine formation were Lys1, Lys7, Lys37 and Lys41. Three of these sites (Lys7, Lys37 and Lys41) were also the major sites of Ne-(carboxymethyl)lysine formation.

Proteomic analysis of the site specificity of glycation and carboxymethylation of ribonuclease

Proteomic analysis using electrospray liquid chromatography-mass spectrometry (ESI-LC-MS) has been used to compare the sites of glycation (Amadori adduct formation) and carboxymethylation of RNase and to assess the role of the Amadori adduct in the formation of the advanced glycation end-product (AGE), N-is an element of-(carboxymethyl)lysine (CIVIL). RNase (13.7 mg/mL, 1 mM) was incubated with glucose (0.4 M) at 37 degreesC for 14 days in phosphate buffer (0.2 M, pH 7.4) under air. On the basis of ESI-LC-MS of tryptic peptides, the major sites of glycation of RNase were, in order, K41, K7, K1, and K37. Three of these, in order, K41, K7, and K37 were also the major sites of CIVIL formation. In other experiments, RNase was incubated under anaerobic conditions (1 mM DTPA, N-2 purged) to form Amadori-modified protein, which was then incubated under aerobic conditions to allow AGE formation. Again, the major sites of glycation were, in order, K41, K7, K1, and K37 and the major sites of carboxymethylation were K41, K7, and K37. RNase was also incubated with 1-5 mM glyoxal, substantially more than is formed by autoxidation of glucose under experimental conditions, but there was only trace modification of lysine residues, primarily at K41. We conclude the following: (1) that the primary route to formation of CIVIL is by autoxidation of Amadori adducts on protein, rather than by glyoxal generated on autoxidation of glucose; and (2) that carboxymethylation, like glycation, is a site-specific modification of protein affected by neighboring amino acids and bound ligands, such as phosphate or phosphorylated compounds. Even when the overall extent of protein modification is low, localization of a high proportion of the modifications at a few reactive sites might have important implications for understanding losses in protein functionality in aging and diabetes and also for the design of AGE inhibitors.

Bayesian estimation of ancestral character states on phylogenies

Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree and its branch lengths when making inferences about ancestral states or other comparative parameters. Here we present a general Bayesian approach for testing comparative hypotheses across statistically justified samples of phylogenies, focusing on the specific issue of reconstructing ancestral states. The method uses Markov chain Monte Carlo techniques for sampling phylogenetic trees and for investigating the parameters of a statistical model of trait evolution. We describe how to combine information about the uncertainty of the phylogeny with uncertainty in the estimate of the ancestral state. Our approach does not constrain the sample of trees only to those that contain the ancestral node or nodes of interest, and we show how to reconstruct ancestral states of uncertain nodes using a most-recent-common-ancestor approach. We illustrate the methods with data on ribonuclease evolution in the Artiodactyla. Software implementing the methods ( BayesMultiState) is available from the authors.