A Workflow towards the Reproducible Identification and Quantitation of Protein Carbonylation Sites in Human Plasma.
Ontology highlight
ABSTRACT:
SUBMITTER: Rojas Echeverri JC
PROVIDER: S-EPMC7999155 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Protein carbonylation has been consistently used as a marker of excessive oxidative stress and studied in the context of multiple human oxidative stress related diseases. The variety of carbonyl post-translational modifications (PTMs) and their low abundance on easily accessible diagnostic tissues (e.g., plasma) challenges their reproducible identification and quantitation. However, the use of carbonyl-specific biotinylated derivatization tags (e.g., aldehyde reactive probe, ARP) allows targeting carbonyl PTMs and enriching proteins and/or peptides carrying these modifications that facilitate their characterization. In this study, an oxidized human serum albumin protein model (OxHSA) and plasma proteins from a healthy individual were derivatized with ARP, digested with trypsin, and ARP-derivatized peptides (ARP-peptides) were enriched using biotin-avidin affinity chromatography prior to RPC-TWIMS-MS/MS analysis. The present workflow highlights previously overlooked analytical challenges, shows the use of ARP specific fragment ions to improve identification reliability of ARP-peptide identifications, and displays an extensive validation of the reproducible recovery and relative quantitation of ARP-peptides. HSA was identified as the most heavily modified protein by a variety of direct amino acid oxidation products and adducts from reactive carbonyl species (RCS). Most RCS modification sites were identified in six HSA modification hotspots (Lys10, Lys190, Lys199, Lys281, Lys432, and Lys525).
2021-03-02 | PXD023738 | Pride
Project description:The goal of this work was to develop a label-free, comprehensive, and reproducible high-resolution liquid chromatography-mass spectrometry (LC-MS)-based untargeted lipidomic workflow using a single instrument, which could be applied to biomarker discovery in both basic and clinical studies. For this, we have (i) optimized lipid extraction and elution to enhance coverage of polar and nonpolar lipids as well as resolution of their isomers, (ii) ensured MS signal reproducibility and linearity, and (iii) developed a bioinformatic pipeline to correct remaining biases. Workflow validation is reported for 48 replicates of a single human plasma sample: 1124 reproducible LC-MS signals were extracted (median signal intensity RSD = 10%), 50% of which are redundant due to adducts, dimers, in-source fragmentation, contaminations, or positive and negative ion duplicates. From the resulting 578 unique compounds, 428 lipids were identified by MS/MS, including acyl chain composition, of which 394 had RSD < 30% inside their linear intensity range, thereby enabling robust semiquantitation. MS signal intensity spanned 4 orders of magnitude, covering 16 lipid subclasses. Finally, the power of our workflow is illustrated by a proof-of-concept study in which 100 samples from healthy human subjects were analyzed and the data set was investigated using three different statistical testing strategies in order to compare their capacity in identifying the impact of sex and age on circulating lipids.
| S-EPMC6572761 | biostudies-literature
Project description:The formation of protein carbonyls in the metal-catalyzed oxidation of human serum albumin (HSA) is characterized using a new analytical approach that involves tagging the modification site with multiple hydrazide reagents. Protein carbonyl formation at lysine and arginine residues was catalyzed with copper and iron ions, and the resulting oxidation patterns in HSA are contrasted. A total of 18 modification sites were identified with iron ion catalysis and 14 with copper ion catalysis. However, with the more stringent requirement of identification with at least two tagging reagents, the number of validated modification sites drops to 10 for iron and 9 for copper. Of the 14 total validated sites, there were only five in common for the two metal ions. The results illustrate the value of using multiple tagging agents and highlight the selective and specific nature of metal-catalyzed protein oxidations.
| S-EPMC5347518 | biostudies-literature
Project description:The focus of this study was on the assessment of technology that might be of clinical utility in identification, quantification, characterization of carbonylation in human plasma proteins. Carbonylation is widely associated with oxidative stress diseases. Breast cancer patient samples were chosen as a stress positive case based on the fact that oxidative stress has been reported to be elevated in this disease. Measurements of 8-isoprostane in plasma confirmed that breast cancer patients in this study were indeed experiencing significant oxidative stress. Carbonyl groups in proteins from freshly drawn blood were derivatized with biotin hydrazide after which the samples were dialyzed and the biotinylated proteins subsequently selected, digested and labeled with iTRAQ™ heavy isotope coding reagent(s). Four hundred sixty proteins were identified and quantified, 95 of which changed 1.5 fold or more in concentration. Beyond confirming the utility of the analytical method, association of protein carbonylation was examined as well. Nearly one fourth of the selected proteins were of cytoplasmic, nuclear, or membrane origin. Analysis of the data by unbiased knowledge assembly methods indicated the most likely disease associated with the proteins was breast neoplasm. Pathway analysis showed the proteins which changed in carbonylation were strongly associated with Brca1, the breast cancer type-1 susceptibility protein. Pathway analysis indicated the major molecular functions of these proteins are defense, immunity and nucleic acid binding.
| S-EPMC3199367 | biostudies-literature
Project description:BACKGROUND:Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson's disease, and Alzheimer's disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures. RESULTS:By manually curating experimental data from research articles, we obtained 332, 144, 135, and 140 verified substrate sites for K (lysine), R (arginine), T (threonine), and P (proline) residues, respectively, from 241 carbonylated proteins. In order to examine the informative attributes for classifying between carbonylated and non-carbonylated sites, multifarious features including composition of twenty amino acids (AAC), composition of amino acid pairs (AAPC), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM) were investigated in this study. Additionally, in an attempt to explore the motif signatures of carbonylation sites, an iterative statistical method was adopted to detect statistically significant dependencies of amino acid compositions between specific positions around substrate sites. Profile hidden Markov model (HMM) was then utilized to train a predictive model from each motif signature. Moreover, based on the method of support vector machine (SVM), we adopted it to construct an integrative model by combining the values of bit scores obtained from profile HMMs. The combinatorial model could provide an enhanced performance with evenly predictive sensitivity and specificity in the evaluation of cross-validation and independent testing. CONCLUSION:This study provides a new scheme for exploring potential motif signatures at substrate sites of protein carbonylation. The usefulness of the revealed motifs in the identification of carbonylated sites is demonstrated by their effective performance in cross-validation and independent testing. Finally, these substrate motifs were adopted to build an available online resource (MDD-Carb, http://csb.cse.yzu.edu.tw/MDDCarb/ ) and are also anticipated to facilitate the study of large-scale carbonylated proteomes.
| S-EPMC5763492 | biostudies-literature
Project description:Motivation:Oxidative stress and protein damage have been associated with over 200 human ailments including cancer, stroke, neuro-degenerative diseases and aging. Protein carbonylation, a chemically diverse oxidative post-translational modification, is widely considered as the biomarker for oxidative stress and protein damage. Despite their importance and extensive studies, no database/resource on carbonylated proteins/sites exists. As such information is very useful to research in biology/medicine, we have manually curated a data-resource (CarbonylDB) of experimentally-confirmed carbonylated proteins/sites. Results:The CarbonylDB currently contains 1495 carbonylated proteins and 3781 sites from 21 species, with human, rat and yeast as the top three species. We have made further analyses of these carbonylated proteins/sites and presented their occurrence and occupancy patterns. Carbonylation site data on serum albumin, in particular, provides a fine model system to understand the dynamics of oxidative protein modifications/damage. Availability and implementation:The CarbonylDB is available as a web-resource and for download at http://digbio.missouri.edu/CarbonylDB/. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6041944 | biostudies-literature
Project description:BackgroundProtein carbonylation, an irreversible and non-enzymatic post-translational modification (PTM), is often used as a marker of oxidative stress. When reactive oxygen species (ROS) oxidized the amino acid side chains, carbonyl (CO) groups are produced especially on Lysine (K), Arginine (R), Threonine (T), and Proline (P). Nevertheless, due to the lack of information about the carbonylated substrate specificity, we were encouraged to develop a systematic method for a comprehensive investigation of protein carbonylation sites.ResultsAfter the removal of redundant data from multipe carbonylation-related articles, totally 226 carbonylated proteins in human are regarded as training dataset, which consisted of 307, 126, 128, and 129 carbonylation sites for K, R, T and P residues, respectively. To identify the useful features in predicting carbonylation sites, the linear amino acid sequence was adopted not only to build up the predictive model from training dataset, but also to compare the effectiveness of prediction with other types of features including amino acid composition (AAC), amino acid pair composition (AAPC), position-specific scoring matrix (PSSM), positional weighted matrix (PWM), solvent-accessible surface area (ASA), and physicochemical properties. The investigation of position-specific amino acid composition revealed that the positively charged amino acids (K and R) are remarkably enriched surrounding the carbonylated sites, which may play a functional role in discriminating between carbonylation and non-carbonylation sites. A variety of predictive models were built using various features and three different machine learning methods. Based on the evaluation by five-fold cross-validation, the models trained with PWM feature could provide better sensitivity in the positive training dataset, while the models trained with AAindex feature achieved higher specificity in the negative training dataset. Additionally, the model trained using hybrid features, including PWM, AAC and AAindex, obtained best MCC values of 0.432, 0.472, 0.443 and 0.467 on K, R, T and P residues, respectively.ConclusionWhen comparing to an existing prediction tool, the selected models trained with hybrid features provided a promising accuracy on an independent testing dataset. In short, this work not only characterized the carbonylated substrate preference, but also demonstrated that the proposed method could provide a feasible means for accelerating preliminary discovery of protein carbonylation.
| S-EPMC5374553 | biostudies-literature
Project description:Protein carbonylation is one of the most pervasive oxidative stress-induced post-translational modifications (PTMs), which plays a significant role in the etiology and progression of several human diseases. It has been regarded as a biomarker of oxidative stress due to its relatively early formation and stability compared with other oxidative PTMs. Only a subset of proteins is prone to carbonylation and most carbonyl groups are formed from lysine (K), arginine (R), threonine (T) and proline (P) residues. Recent advancements in analysis of the PTM by mass spectrometry provided new insights into the mechanisms of protein carbonylation, such as protein susceptibility and exact modification sites. However, the experimental approaches to identifying carbonylation sites are costly, time-consuming and capable of processing a limited number of proteins, and there is no bioinformatics method or tool devoted to predicting carbonylation sites of human proteins so far. In the paper, a computational method is proposed to identify carbonylation sites of human proteins. The method extracted four kinds of features and combined the minimum Redundancy Maximum Relevance (mRMR) feature selection criterion with weighted support vector machine (WSVM) to achieve total accuracies of 85.72%, 85.95%, 83.92% and 85.72% for K, R, T and P carbonylation site predictions respectively using 10-fold cross-validation. The final optimal feature sets were analysed, the position-specific composition and hydrophobicity environment of flanking residues of modification sites were discussed. In addition, a software tool named CarSPred has been developed to facilitate the application of the method. Datasets and the software involved in the paper are available at https://sourceforge.net/projects/hqlstudio/files/CarSPred-1.0/.
| S-EPMC4210226 | biostudies-literature
Project description:BackgroundWhole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploid's parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIO was made simple to set up, run and interpret, and its implementation ensures reproducibility by including both package management and containerization.ResultsWe evaluated ARPEGGIO in two ways. First, we tested EAGLE-RC's performance with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. Second, using the same initial dataset, we show agreement between ARPEGGIO's output and published results. Compared to other similar workflows, ARPEGGIO is the only one supporting polyploid data.ConclusionsThe goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation. ARPEGGIO is available at https://github.com/supermaxiste/ARPEGGIO .
| S-EPMC8285871 | biostudies-literature
Project description:SummaryAccurate gene prediction is essential for successful metagenome analysis. We present KOunt, a Snakemake pipeline, that precisely quantifies KEGG orthologue abundance.Availability and implementationKOunt is available on GitHub: https://github.com/WatsonLab/KOunt. The KOunt reference database is available on figshare: https://doi.org/10.6084/m9.figshare.21269715. Test data are available at https://doi.org/10.6084/m9.figshare.22250152 and version 1.2.0 of KOunt at https://doi.org/10.6084/m9.figshare.23607834.
| S-EPMC10423021 | biostudies-literature