Dataset Information

Comprehensive proteomic quantification of bladder stone progression in a cystinuric mouse model using data-independent acquisitions.

ABSTRACT: Cystinuria is one of various disorders that cause biomineralization in the urinary system, including bladder stone formation in humans. It is most prevalent in children and adolescents and more aggressive in males. There is no cure, and only limited disease management techniques help to solubilize the stones. Recurrence, even after treatment, occurs frequently. Other than a buildup of cystine, little is known about factors involved in the formation, expansion, and recurrence of these stones. This study sought to define the growth of bladder stones, guided by micro-computed tomography imaging, and to profile dynamic stone proteome changes in a cystinuria mouse model. After bladder stones developed in vivo, they were harvested and separated into four developmental stages (sand, small, medium and large stone), based on their size. Data-dependent and data-independent acquisitions allowed deep profiling of stone proteomics. The proteomic signatures and pathways illustrated major changes as the stones grew. Stones initiate from a small nidus, grow outward, and show major enrichment in ribosomal proteins and factors related to coagulation and platelet degranulation, suggesting a major dysregulation in specific pathways that can be targeted for new therapeutic options.

SUBMITTER: Rose J

PROVIDER: S-EPMC9246204 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comprehensive proteomic quantification of bladder stone progression in a cystinuric mouse model using data-independent acquisitions.

Rose Jacob J Basisty Nathan N Zee Tiffany T Wehrfritz Cameron C Bose Neelanjan N Desprez Pierre-Yves PY Kapahi Pankaj P Stoller Marshall M Schilling Birgit B

PloS one 20220630 6

Cystinuria is one of various disorders that cause biomineralization in the urinary system, including bladder stone formation in humans. It is most prevalent in children and adolescents and more aggressive in males. There is no cure, and only limited disease management techniques help to solubilize the stones. Recurrence, even after treatment, occurs frequently. Other than a buildup of cystine, little is known about factors involved in the formation, expansion, and recurrence of these stones. Thi ...[more]

PMID: 35771811

Similar Datasets

Project description:Quantitative analysis of discovery-based proteomic workflows now relies on high-throughput large-scale methods for identification and quantitation of proteins and post-translational modifications. Advancements in label-free quantitative techniques, using either data-dependent or data-independent mass spectrometric acquisitions, have coincided with improved instrumentation featuring greater precision, increased mass accuracy, and faster scan speeds. We recently reported on a new quantitative method called MS1 Filtering (Schilling et al. (2012) Mol. Cell. Proteomics 11, 202-214) for processing data-independent MS1 ion intensity chromatograms from peptide analytes using the Skyline software platform. In contrast, data-independent acquisitions from MS2 scans, or SWATH, can quantify all fragment ion intensities when reference spectra are available. As each SWATH acquisition cycle typically contains an MS1 scan, these two independent label-free quantitative approaches can be acquired in a single experiment. Here, we have expanded the capability of Skyline to extract both MS1 and MS2 ion intensity chromatograms from a single SWATH data-independent acquisition in an Integrated Dual Scan Analysis approach. The performance of both MS1 and MS2 data was examined in simple and complex samples using standard concentration curves. Cases of interferences in MS1 and MS2 ion intensity data were assessed, as were the differentiation and quantitation of phosphopeptide isomers in MS2 scan data. In addition, we demonstrated an approach for optimization of SWATH m/z window sizes to reduce interferences using MS1 scans as a guide. Finally, a correlation analysis was performed on both MS1 and MS2 ion intensity data obtained from SWATH acquisitions on a complex mixture using a linear model that automatically removes signals containing interferences. This work demonstrates the practical advantages of properly acquiring and processing MS1 precursor data in addition to MS2 fragment ion intensity data in a data-independent acquisition (SWATH), and provides an approach to simultaneously obtain independent measurements of relative peptide abundance from a single experiment.

Project description:Wheat is the most important food crop in the world, the unique physiochemical properties of wheat gluten enabling a diverse range of food products to be manufactured. However, genetic and environmental factors affect the technological properties of gluten in unpredictable ways. Although newer proteomic methods have the potential to offer much greater levels of information, it is the older gel-based methods that remain most commonly used to identify compositional differences responsible for the variation in gluten functionality, in part due to the nature of their primary sequences. A combination of platforms were investigated for comprehensive gluten profiling: a QTOF with a data independent schema, which incorporated ion mobility (DIA-IM-MS) and a data dependent acquisition (DDA) workflow using a linear ion trap quadrupole (LTQ) instrument. In conjunction with a manually curated gluten sequence database a total of 2736 gluten peptides were identified with only 157 peptides identified by both platforms. These data showed 127 and 63 gluten protein accessions to be inferred with a minimum of one and three unique peptides respectively. Of the 63 rigorously identified proteins, 26 were gliadin species (4 ω-, 14 α-, and 8 γ-gliadins) and 37 glutenins (including 29 LMW glutenin and 8 HMW glutenins). Of the HMW glutenins, three were 1Dx type and five were 1Bx type illustrating the challenge of unambiguous identification of highly polymorphic proteins without cultivar specific gene sequences. The capacity of the platforms to sequence longer peptides was crucial to achieving the number of identifications, the combination of QTOF-LTQ technology being more important than extraction method to obtain a comprehensive profile. Widespread glutamine deamidation, a post-translational modification, was observed adding complexity to an already highly polymorphic mixture of proteins, with numerous insertions, deletions and substitutions. The data shown is the most comprehensive and detailed proteomic profile of gluten to date.

Project description:BackgroundHalting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking.MethodsPlasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m2/year or worse) and 244 stable patients (∂ eGFR of - 0.5 to + 1 ml/min/1.73 m2/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses.ResultsThe resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin-proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched.ConclusionsThe in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.

Project description:Despite data-independent acquisition (DIA) has been increasingly used for relative protein quantification, DIA-based label-free absolute quantification method has not been fully established. Here we present a novel DIA method using the TPA algorithm (DIA-TPA) for the absolute quantification of protein expressions in human liver microsomal and S9 samples. To validate this method, both data-dependent acquisition (DDA) and DIA experiments were conducted on 36 individual human liver microsome and S9 samples. The MS2-based DIA-TPA was able to quantify approximately twice as many proteins as the MS1-based DDA-TPA method, whereas protein concentrations determined by the two approaches were comparable. To evaluate the accuracy of the DIA-TPA method, we absolutely quantified carboxylesterase 1 concentrations in human liver S9 fractions using an established SILAC internal standard-based proteomic assay; the SILAC results were consistent with those obtained from DIA-TPA analysis. Finally, we employed a unique algorithm in DIA-TPA to distribute the MS signals from shared peptides to individual proteins or isoforms and successfully applied the method to the absolute quantification of several drug-metabolizing enzymes in human liver microsomes. In sum, the DIA-TPA method not only can absolutely quantify entire proteomes and specific proteins, but also has the capability quantifying proteins with shared peptides. SIGNIFICANCE: Data independent acquisition (DIA) has emerged as a powerful approach for relative protein quantification at the whole proteome level. However, DIA-based label-free absolute protein quantification (APQ) method has not been fully established. In the present study, we present a novel DIA-based label-free APQ approach, named DIA-TPA, with the capability absolutely quantifying proteins with shared peptides. The method was validated by comparing the quantification results of DIA-TPA with that obtained from stable isotope-labeled internal standard-based proteomic assays.

Dataset Information

Comprehensive proteomic quantification of bladder stone progression in a cystinuric mouse model using data-independent acquisitions.

Publications

Comprehensive proteomic quantification of bladder stone progression in a cystinuric mouse model using data-independent acquisitions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets