EGAS00001000091-sc-20110715 - samples
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ABSTRACT:
PROVIDER: EGAD00001000029 | EGA |
REPOSITORIES: EGA
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Project description:Loss of NBEAL2 function leads to grey platelet syndrome (GPS), a bleeding disorder characterized by macro-thrombocytopenia and α-granule-deficient platelets. A proportion of patients with GPS develop autoimmunity through an unknown mechanism, which might be related to the proteins NBEAL2 interacts with, specifically in immune cells. Here we show a comprehensive interactome of NBEAL2 in primary T cells, based on mass spectrometry identification of altogether 76 protein association partners. These include LRBA, a member of the same BEACH domain family as NBEAL2, recessive mutations of which cause autoimmunity and lymphocytic infiltration through defective CTLA-4 trafficking
2023-05-12 | PXD042180 | JPOST Repository
Project description:Variants in NBEAL2 are causal of Grey Platelet Syndrome (GPS), a rare bleeding disorder characterized by absence of alpha- and specific- granules in platelets and neutrophils, respectively. The role of the scaffolding multidomain NBEAL2 protein in cell biology and granule homeostasis is unknown. We have performed proteomics to identify NBEAL2’s binding partners followed by different layers of validation including biochemical, cellular and functional analysis in vitro and in vivo.
2018-02-14 | PXD006091 | Pride
Project description:Loss of NBEAL2 function leads to grey platelet syndrome (GPS), a bleeding disorder characterized by macro-thrombocytopenia and α-granule-deficient platelets. A proportion of patients with GPS develop autoimmunity through an unknown mechanism, which might be related to the proteins NBEAL2 interacts with, specifically in immune cells. Here we show a comprehensive interactome of NBEAL2 in primary T cells, based on mass spectrometry identification of altogether 74 protein association partners. These include LRBA, a member of the same BEACH domain family as NBEAL2, recessive mutations of which cause autoimmunity and lymphocytic infiltration through defective CTLA-4 trafficking. Investigating the potential association between NBEAL2 and CTLA-4 signalling that is suggested by the mass spectrometry results, we confirm by co-immunoprecipitation that CTLA-4 and NBEAL2 interact with each-other. Interestingly, NBEAL2 deficiency leads to low CTLA-4 expression in patient-derived effector T cells, while their regulatory T cells appear unaffected. Knocking- down NBEAL2 in healthy primary T cells recapitulates the low CTLA-4 expression observed in T cells of GPS patients. Our results thus show that NBEAL2 is involved in the regulation of CTLA-4 expression in conventional T cells and provide a rationale for considering CTLA-4- immunoglobulin therapy in patients with GPS and autoimmune disease.
2023-05-12 | GSE196606 | GEO
Project description:Gray platelet syndrome (GPS) is a rare inherited bleeding disorder characterized by absence of platelet ɑ-granules and pathogenic variants in NBEAL2. To discern the spectrum of pathological features, we obtained genotype and phenotype data from 47 GPS patients and performed RNA sequencing and protein mass spectrometry on blood cells and plasma in a subset of these patients. We identified 37 novel GPS-causing variants in NBEAL2. There were widespread differences in the transcriptome and proteome of GPS platelets, neutrophils, monocytes, and CD4-lymphocytes. Proteins less abundant in these cells were enriched for constituents of granules, supporting a role for Nbeal2 in the function of these organelles across a wide range of blood cells. Finally, we show that the plasma proteome of GPS patients has increased levels of proteins associated with inflammation and immune response, 27% of which are synthesized outside of the hematopoietic system, predominantly in the liver.
2020-07-28 | PXD017227 | Pride
Project description:As part of the Bloodomics collaboration we have several categories of pedigrees with diseases/syndromes relevant to cardiovascular diseases (CVD). One such pedigree, Grey Platelet Syndrome (GPS) is a rare congenital bleeding disorder caused by a reduction or absence of alpha granules in platelets. Exome sequencing has been performed as part of a discovery program to ascertain potential causative variants of the clinical phenotype.
| EGAS00001000091 | EGA
Project description:Gray Platelet Syndrome (GPS) is an autosomal recessive bleeding disorder characterized by a lack of α-granules in platelets and progressive myelofibrosis. Rare loss of function variants in NBEAL2, a member of the family of BEACH genes, are causal of GPS. The gene is involved in fusion, fission and trafficking of vesicles and granules. Whether NBEAL2 controls the ontogeny of granules of myeloid cells remains disputed. We found that neutrophils obtained from the peripheral blood from GPS patients have a normal distribution of azurophilic granules, but show a deficiency of specific granules, as confirmed by immuno-electron microscopy and mass spectrometry proteomics analyses. In cultures from peripheral CD34+ hematopoietic stem cells (HSCs) into mature neutrophils, the time dynamics showed concordance of NBEAL2 and specific granule protein expression at transcriptional and protein level, which were discordant in neutrophils obtained GPS-HSCs. This is indicative of normal granulopoiesis in GPS and identifies NBEAL2 as an important regulator of granule release (similar to platelets) which is suggested to occur upon egress into the blood stream. Patient neutrophil functions, including production of reactive oxygen species, chemotaxis and killing of bacteria and fungi were intact. Since GPS patients do not excessively suffer from infections, the consequence of the reduced specific granule content and lack of NET formation for innate immunity remains to be explored.
2021-01-28 | PXD017784 | Pride
Project description:Gray Platelet Syndrome (GPS) is a rare recessive bleeding disorder resulting from biallelic variants in NBEAL2. As part of a comprehensive evaluation of the phenotype and genotype in 47 patients with GPS, four different blood cell-types (platelets, neutrophils, monocytes, and CD4-lymphocytes) were evaluated using bulk RNA-seq in five patients and five controls. These data are deposited in this archive in FASTQ format.
| EGAD00001005950 | EGA
Project description:The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at https://github.com/InfectionMedicineProteomics/gps
2023-07-20 | PXD038394 | Pride
Project description:The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18\% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74\%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377\% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87\%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at (\url{https://github.com/InfectionMedicineProteomics/gps})
2023-07-20 | PXD038377 | Pride
Project description:Gray Platelet Syndrome (GPS) is a rare recessive bleeding disorder resulting from biallelic variants in NBEAL2. As part of a comprehensive evaluation of the phenotype and genotype in 47 patients with GPS, four different blood cell-types (platelets, neutrophils, monocytes, and CD4-lymphocytes) were evaluated using bulk RNA-seq in five patients and five controls. These data are deposited in this archive in FASTQ format.
| EGAS00001004216 | EGA