Redox Regulatory Mechanism of Transnitrosylation by Thioredoxin

Transnitrosylation and denitrosylation are emerging as key post-translational modification events in regulating both normal physiology and a wide spectrum of human diseases. Thioredoxin 1 (Trx1) is a conserved antioxidant that functions as a classic disulfide reductase. It also catalyzes the transnitrosylation or denitrosylation of caspase 3 (Casp3), underscoring its central role in determining Casp3 nitrosylation specificity. However, the mechanisms that regulate Trx1 transnitrosylation and denitrosylation of specific targets are unresolved. Here we used an optimized mass spectrometric method to demonstrate that Trx1 is itself nitrosylated by S-nitrosoglutathione at Cys⁷³ only after the formation of a Cys³²-Cys³⁵ disulfide bond upon which the disulfide reductase and denitrosylase activities of Trx1 are attenuated. Following nitrosylation, Trx1 subsequently transnitrosylates Casp3. Overexpression of Trx1^C32S/C35S (a mutant Trx1 with both Cys³² and Cys³⁵ replaced by serine to mimic the disulfide reductase-inactive Trx1) in HeLa cells promoted the nitrosylation of specific target proteins. Using a global proteomics approach, we identified 47 novel Trx1 transnitrosylation target protein candidates. From further bioinformatics analysis of this set of nitrosylated peptides, we identified consensus motifs that are likely to be the determinants of Trx1-mediated transnitrosylation specificity. Among these proteins, we confirmed that Trx1 directly transnitrosylates peroxiredoxin 1 at Cys¹⁷³ and Cys⁸³ and protects it from H₂O₂-induced overoxidation. Functionally, we found that Cys⁷³-mediated Trx1 transnitrosylation of target proteins is important for protecting HeLa cells from apoptosis. These data demonstrate that the ability of Trx1 to transnitrosylate target proteins is regulated by a crucial stepwise oxidative and nitrosative modification of specific cysteines, suggesting that Trx1, as a master regulator of redox signaling, can modulate target proteins via alternating modalities of reduction and nitrosylation.Nitric oxide (NO) is an important second messenger for signal transduction in cells. The production of cGMP by guanylyl cyclase, enabled by the binding of NO onto heme, is considered the primary mechanism responsible for the plethora of functions exerted by NO (1). However, S-nitrosylation, the covalent addition of the NO moiety onto cysteine thiols, is increasingly recognized as an important post-translational modification for regulating protein functions (for reviews, see Refs. 2 and 3). S-Nitrosylation is dynamic, reversible, site-specific, and modulated by selected cellular stimuli (4–7). With improved detection sensitivity, an increasing number of S-nitrosylated proteins have been identified by proteomics technologies (5, 8–13). Among the known modified proteins, nitrosylation occurs only on selected cysteines (4, 6, 14–17). Non-enzymatic mechanisms proposed to determine S-nitrosylation specificity include the availability of specific NO donors and protein microenvironments that stabilize the pK_a of acidic target cysteines (18). Furthermore, several enzymes, including hemoglobin (19, 20), superoxide dismutase 1 (21, 22), S-nitrosoglutathione reductase (23–25), and protein-disulfide isomerase (26), have been shown to possess either transnitrosylase or denitrosylase activities. However, an enzymatic system that governs site-specific transnitrosylation and denitrosylation, analogous to the kinase/phosphatase paradigm for regulating protein phosphorylation, has remained largely uncharacterized.Trx1¹ is an important antioxidant protein with protein reductase activity (27, 28). It has been characterized as an antiapoptotic protein because of its ability to suppress proapoptotic proteins, including apoptosis signal-regulating kinase 1 via disulfide reduction and Casp3 via transnitrosylation of Cys¹⁶³ (14, 29). Conversely, Trx1 can denitrosylate Casp3 at Cys¹⁶³, resulting in Casp3 activation (7). Trx1 appears to govern site-specific reversible nitrosylation of selected protein targets (14, 15), but what are the underlying mechanisms that regulate Trx1 transnitrosylation and denitrosylation activities? Are there additional Trx1-mediated transnitrosylation or denitrosylation targets that have not yet been identified? In this study, we used ESI-Q-TOF mass spectrometry (MS) to analyze the nitrosylation of Trx1 and a Casp3 peptide (Casp3p) under different redox conditions. Because of the labile nature of the S–NO bond, direct identification of S-nitrosylated proteins and their specific nitrosylation sites by MS remains challenging (8). A biotin switch method that is based on the derivatization of protein S–NO with a biotinylating agent is typically used for such analyses (8). However, like any indirect method, both false positive and negative identifications have been reported (30). Recently, we developed a method for direct analysis of protein S-nitrosylation by ESI-Q-TOF MS without prior chemical derivatization (31). Here we applied the same technique to determine the regulation of Trx1 by stepwise oxidative and nitrosative modifications of distinct cysteines and its subsequent ability to transnitrosylate target proteins. Nitrosative modification at Cys⁷³ of Trx1 cannot occur without prior attenuation of the Trx1 disulfide reductase and denitrosylase activities via either disulfide bond formation between Cys³² and Cys³⁵ or their mutation to serines. This is a key observation that has never been previously reported. Consequently, we designed a proteomics approach and discovered over 40 putative Trx1 transnitrosylation target proteins. We further characterized the Trx1 transnitrosylation proteome and identified three consensus motifs surrounding the putative Trx1 transnitrosylation sites, suggesting a protein-protein interaction mechanism for determining transnitrosylation specificity.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]