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Label-free quantification proteomics of human aortic tissues by LC-MSMS

ABSTRACT: We profiled the proteome of human aotic tissues to idntified the different expressed proteins of thoracic aortic dissection

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Vessel, Aorta Smooth Muscle Tissue, Blood Vessel Endothelial Cell

DISEASE(S): Aortic Dissection

SUBMITTER: Zhijue Xu

LAB HEAD: Xinwu Lu

PROVIDER: PXD029051 | Pride | 2022-02-15

REPOSITORIES: Pride

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			Action	DRS
	20210206-YP202101406-DDA-120min-11-dui.raw	Raw
	20210206-YP202101406-DDA-120min-13-nei.raw	Raw
	20210206-YP202101406-DDA-120min-14-nei.raw	Raw
	20210206-YP202101406-DDA-120min-16-nei.raw	Raw
	20210206-YP202101406-DDA-120min-17-nei.raw	Raw

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1 - 5 of 26

Publications

Potential Clinical Value of Biomarker-Guided Emergency Triage for Thoracic Aortic Dissection.

Qiu Peng P Yang Meng M Pu Hongji H Hou Jingli J Chen Xu X Wu Zhaoyu Z Huang Qun Q Huang Siyi S Fu Yan Y Wen Zi'ang Z Zhang Chengxin C Zha Binshan B Yang Yang Y Xu Zhijue Z Chen Fuxiang F Lu Xinwu X

Frontiers in cardiovascular medicine 20220112

<b>Aim:</b> Thoracic aortic dissection (TAD) is a high-risk vascular disease. The mortality rate of untreated TADs in 24 h was as high as 50%. Thus, rapid diagnosis of TAD in the emergency department would get patients to the right treatments to save their lives. <b>Methods:</b> We profiled the proteome of aortic tissues from TAD patients using a label-free quantification proteomics method. The differentially expressed proteins were screened and subjected to bioinformatics analysis. Candidate bi ...[more]

PMID: 35096998

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Expression data from control, aortic aneurysm, and aortic dissection cells

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Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

Project description:Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict their risk of dissection or rupture. This study generated a plasma proteomic dataset from 150 patients with descending thoracic aortic disease and 52 controls to identify proteomic signatures capable of differentiating descending thoracic aortic disease from non-disease controls, as well as between cases with aneurysm versus descending ‘type B’ dissection. Of the 1,468 peptides and 195 proteins quantified across all samples, 853 peptides and 99 proteins were quantitatively different between disease and control patients (BH adjusted p-value < 0.01 from t-tests). Supervised machine learning (ML) methods were used to classify disease from control and aneurysm from descending dissection cases. The highest precision-recall area under the curve (PR AUC) was achieved on the held-out test set using significantly different proteins between disease and control patients (PR AUC 0.99), followed by input of significant peptides (PR AUC 0.96). Despite no statistically significant proteins between aneurysm and dissection cases, use of all proteins was able to modestly classify between the two disease states (PR AUC 0.77). To overcome correlation in the proteins and enable biological pathway analysis, a disease versus control classifier was optimized using only seven unique protein clusters, which achieved comparable performance to models trained on all/significant proteins (accuracy 0.88, F1-score 0.78, PR AUC 0.90). Model interpretation with permutation importance revealed that proteins in the most important clusters for differentiating disease and control function in coagulation, protein-lipid complex remodeling, and acute inflammatory response.

2024-06-16 | PXD041337 | Pride

Gene expression in aortic aneurysms associated with tricuspid and bicuspid valves

Project description:Patients with bicuspid aortic valve (BAV) have increased risk of thoracic ascending aortic aneurysm (AscAA) and dissection compared to those with a normal tricuspid aortic valve (TAV). The present study was undertaken to evaluate whether differences in gene expression exist in aortas from BAV and TAV patients with AscAA. Keywords: disease state analysis

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Integrated Multi-Omics Profiling Reveals Neutrophil Extracellular Traps Promote Aortic Dissection

Project description:Adverse aortic remodeling increases the risk of aorta-related adverse events (AAEs) after thoracic endovascular aortic repair (TEVAR) and affects the overall prognosis of aortic dissection (AD). It is imperative to delve into the exploration of prognostic indicators to We performed single-cell transcriptomic and proteomic profiling of aortic lesions and peripheral blood samples, respectively, from patients with AD and healthy subjects. We then integrated single-cell RNA to identify phenotype-relevant subpopulations.

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Dissecting aortic aneurysm in Marfan syndrome is associated with losartan-sensitive transcriptomic modulation of aortic cells

Project description:To improve our limited understanding of the pathogenesis of thoracic aortic aneurysm (TAA) leading to acute aortic dissection, we used single-cell RNA sequencing to profile disease-relevant transcriptomic changes of aortic cell populations in a well-characterized mouse model of the most commonly diagnosed form of Marfan syndrome (MFS). As result,MFSmod were identified only in the aorta of Fbn1mgR/mgR mice. In situ hybridizations of diagnostic transcripts located MFSmod cells to the intima of Fbn1mgR/mgR aortas. Consistent with angiotensin II type I receptor (At1r) contribution to TAA development, MFSmod cells were absent in the aorta of Fbn1mgR/mgR mice treated with the At1r antagonist losartan. Altogether, our findings indicate that a discrete dynamic alteration of aortic cell identity is associated with dissecting TAA in MFS mice and increased risk of aortic dissection in MFS patients.

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Understanding the role of TGFbeta signaling on aortic aneurysm and rupture using Marfan syndrome mouse models

Project description:Although abnormal TGFbeta signaling is observed in several heritable forms of thoracic aortic aneurysms and dissections including Marfan syndrome, the precise role of TGFbeta signaling in aortic disease progression is still disputed. Using a mouse genetic approach and quantitative isobaric labeling proteomics, we sought to investigate the role of TGFbeta signaling in molecular pathways of pathogenesis associated with development of aortic aneurysm and aortic rupture. This study reports an isoform-specific effect of TGFbeta in MFS aortic disease and the effects of deleting the first hybrid domain of fibrillin-1 on TGFbeta signaling. Distinct molecular differences in mouse models of aneurysm (Fbn_GT-8_plus), of aneurysm and rupture (Fbn1_GT-8_H1delta), and of microdissection (Fbn1_H1delta_plus) were identified, which associated with TGFbeta signaling and extracellular matrix composition, possibly contributing to the development of dissection and rupture. These findings offer new insights into the pathophysiological mechanisms that potentially drive initiation of aortic dissection and could pave the way for development of new treatment targets of aortic disease.

2023-10-09 | PXD036222 | Pride

17 -estradiol impedes aortic root dilation and rupture in male Marfan mice

Project description:Thoracic aortic aneurysms have a higher prevalence in male patients compared to female patients. Marfan syndrome causes a hereditary form of TAA with dilation of the aortic root. Male patients with Marfan syndrome are more likely than women to have aortic dilation and dissection and mouse models of Marfan syndrome demonstrate larger aortic roots in males compared to females even after adjustment for body size. Similar sex disparities are present in patients and models of abdominal aortic aneurysms where estrogen has been demonstrated to attenuate aneurysm formation perhaps through anti-inflammatory mechanisms. In this study we demonstrate the effects of estrogen on aortic dilation and rupture in a Marfan mouse model and we investigate if these effects operate through suppression of complement components of the immune system.

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Exaggerated autophagy as molecular characterization of Stanford type A aortic dissection: A transcriptome analysis of human ascending aortic tissues

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Circular RNA expression profile in human thoracic aortic dissection

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