Identification of formaldehyde-induced modifications in proteins: reactions with model peptides

Formaldehyde is a well known cross-linking agent that can inactivate, stabilize, or immobilize proteins. The purpose of this study was to map the chemical modifications occurring on each natural amino acid residue caused by formaldehyde. Therefore, model peptides were treated with excess formaldehyde, and the reaction products were analyzed by liquid chromatography-mass spectrometry. Formaldehyde was shown to react with the amino group of the N-terminal amino acid residue and the side-chains of arginine, cysteine, histidine, and lysine residues. Depending on the peptide sequence, methylol groups, Schiff-bases, and methylene bridges were formed. To study intermolecular cross-linking in more detail, cyanoborohydride or glycine was added to the reaction solution. The use of cyanoborohydride could easily distinguish between peptides containing a Schiff-base or a methylene bridge. Formaldehyde and glycine formed a Schiff-base adduct, which was rapidly attached to primary N-terminal amino groups, arginine and tyrosine residues, and, to a lesser degree, asparagine, glutamine, histidine, and tryptophan residues. Unexpected modifications were found in peptides containing a free N-terminal amino group or an arginine residue. Formaldehyde-glycine adducts reacted with the N terminus by means of two steps: the N terminus formed an imidazolidinone, and then the glycine was attached via a methylene bridge. Two covalent modifications occurred on an arginine-containing peptide: (i) the attachment of one glycine molecule to the arginine residue via two methylene bridges, and (ii) the coupling of two glycine molecules via four methylene bridges. Remarkably, formaldehyde did not generate intermolecular cross-links between two primary amino groups. In conclusion, the use of model peptides enabled us to determine the reactivity of each particular cross-link reaction as a function of the reaction conditions and to identify new reaction products after incubation with formaldehyde.     

Computational analysis of the Phanerochaete chrysosporium v2.0 genome database and mass spectrometry identification of peptides in ligninolytic cultures reveal complex mixtures of secreted proteins

The white-rot basidiomycete Phanerochaete chrysosporium employs extracellular enzymes to completely degrade the major polymers of wood: cellulose, hemicellulose, and lignin. Analysis of a total of 10,048 v2.1 gene models predicts 769 secreted proteins, a substantial increase over the 268 models identified in the earlier database (v1.0). Within the v2.1 'computational secretome,' 43% showed no significant similarity to known proteins, but were structurally related to other hypothetical protein sequences. In contrast, 53% showed significant similarity to known protein sequences including 87 models assigned to 33 glycoside hydrolase families and 52 sequences distributed among 13 peptidase families. When grown under standard ligninolytic conditions, peptides corresponding to 11 peptidase genes were identified in culture filtrates by mass spectrometry (LS-MS/MS). Five peptidases were members of a large family of aspartyl proteases, many of which were localized to gene clusters. Consistent with a role in dephosphorylation of lignin peroxidase, a mannose-6-phosphatase (M6Pase) was also identified in carbon-starved cultures. Beyond proteases and M6Pase, 28 specific gene products were identified including several representatives of gene families. These included 4 lignin peroxidases, 3 lipases, 2 carboxylesterases, and 8 glycosyl hydrolases. The results underscore the rich genetic diversity and complexity of P. chrysosporium's extracellular enzyme systems.     

Reduced kidney lipoprotein lipase and renal tubule triglyceride accumulation in cisplatin-mediated acute kidney injury

Peroxisome proliferator-activated receptor-α (PPARα) activation attenuates cisplatin (CP)-mediated acute kidney injury by increasing fatty acid oxidation, but mechanisms leading to reduced renal triglyceride (TG) accumulation could also contribute. Here, we investigated the effects of PPARα and CP on expression and enzyme activity of kidney lipoprotein lipase (LPL) as well as on expression of angiopoietin protein-like 4 (Angptl4), glycosylphosphatidylinositol-anchored-HDL-binding protein (GPIHBP1), and lipase maturation factor 1 (Lmf1), which are recognized as important proteins that modulate LPL activity. CP caused a 40% reduction in epididymal white adipose tissue (WAT) mass, with a reduction of LPL expression and activity. CP also reduced kidney LPL expression and activity. Angptl4 mRNA levels were increased by ninefold in liver and kidney tissue and by twofold in adipose tissue of CP-treated mice. Western blots of two-dimensional gel electrophoresis identified increased expression of a neutral pI Angptl4 protein in kidney tissue of CP-treated mice. Immunolocalization studies showed reduced staining of LPL and increased staining of Angptl4 primarily in proximal tubules of CP-treated mice. CP also increased TG accumulation in kidney tissue, which was ameliorated by PPARα ligand. In summary, a PPARα ligand ameliorates CP-mediated nephrotoxicity by increasing LPL activity via increased expression of GPHBP1 and Lmf1 and by reducing expression of Angptl4 protein in the proximal tubule.     

Re-evaluating lipotoxic triggers in skeletal muscle: relating intramyocellular lipid metabolism to insulin sensitivity

Ectopic fat accumulation has been linked to lipotoxic events, including the development of insulin resistance in skeletal muscle. Indeed, intramyocellular lipid storage is strongly associated with the development of type 2 diabetes. Research during the last two decades has provided evidence for a role of lipid intermediates like diacylglycerol and ceramide in the induction of lipid-induced insulin resistance. However, recently novel data has been gathered that suggest that the relation between lipid intermediates and insulin resistance is less straightforward than has been previously suggested, and that there are several routes towards lipid-induced insulin resistance. For example, research in this field has shifted towards imbalances in lipid metabolism and lipid droplet dynamics. Next to imbalances in key lipogenic and lipolytic proteins, lipid droplet coat proteins appear to be essential for proper intramyocellular lipid storage, turnover and protection against lipid-induced insulin resistance.Here, we discuss the current knowledge on lipid-induced insulin resistance in skeletal muscle with a focus on the evidence from human studies. Furthermore, we discuss the available data that provides supporting mechanistic information.     

Unbiased Selective Isolation of Protein N-terminal Peptides from Complex Proteome Samples Using Phospho Tagging (PTAG) and TiO2-based Depletion

A positional proteomics strategy for global N-proteome analysis is presented based on phospho tagging (PTAG) of internal peptides followed by depletion by titanium dioxide (TiO(2)) affinity chromatography. Therefore, N-terminal and lysine amino groups are initially completely dimethylated with formaldehyde at the protein level, after which the proteins are digested and the newly formed internal peptides modified with the PTAG reagent glyceraldhyde-3-phosphate in nearly perfect yields (> 99%). The resulting phosphopeptides are depleted through binding onto TiO(2), keeping exclusively a set of N-acetylated and/or N-dimethylated terminal peptides for analysis by liquid chromatography-tandem MS. Analysis of peptides derivatized with differentially labeled isotopic analogs of the PTAG reagent revealed a high depletion efficiency (> 95%). The method enabled identification of 753 unique N-terminal peptides (428 proteins) in N. meningitidis and 928 unique N-terminal peptides (572 proteins) in S. cerevisiae. These included verified neo-N termini from subcellular-relocalized membrane and mitochondrial proteins. The presented PTAG approach is therefore a novel, versatile, and robust method for mass spectrometry-based N-proteome analysis and identification of protease-generated cleavage products.     

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties¹. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties².Many innovative and useful methods currently exist for creating novel objects and object categories^3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings.First, shape variations are generally imposed by the experimenter^5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints.Second, the existing methods have difficulty capturing the shape complexity of natural objects^11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases.Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or ''tuned''). This allows one to formulate the underlying object recognition tasks in quantitative terms.Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called ''digital embryos'' by simulating the biological process of embryogenesis¹⁴. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection^9,12,13. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics^15,16. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects^9,13. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be ''printed'' as haptic objects using a conventional 3-D prototyper.We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a ''proof of principle'' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have.Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.