Proteomic analysis of descending thoracic aorta identifies unique and universal signatures of aneurysm and dissection

Saddic, Louis A.; Orosco, Amanda; Guo, Dongchuan; Milewicz, Dianna M.; Troxlair, Dana; Heide, Richard Vander; Herrington, David M.; Wang, Yue; Azizzadeh, Ali; Parker, Sarah J.

doi:10.1016/j.jvssci.2022.01.001

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…We also observed several proteins whose differential abundance was similar between plasma and our previously published proteomic analysis of tissue samples comparing descending disease and control 10 . Among these, the monocyte marker CD14 and fibrinogens were uniformly upregulated whereas the protease inhibitor SERPINA5 (PAI-3), transthyrein (TTR), and Apoliprotein A4 (APOA4) were all prominently down regulated in both plasma and tissue from descending aortic disease patients, though APOA4 was even more substantially downregulated in dissection relative to aneurysm plasma and tissue.…”

Section: Discussionsupporting

confidence: 81%

“…By this time, many aneurysm patients may have very similar overall proinflammatory plasma proteome relative to aortic dissection patients. That being said, in our previous work we were able to differentiate proteomic signatures of late-stage aneurysm versus dissection tissues from these patients 10 . Interestingly, for proteins seen in both our previous tissue comparison and this plasma proteomic analysis, the direction of abundance differences was consistent between tissue and plasma, though not statistically significant.…”

Section: Discussionmentioning

confidence: 88%

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Differentiation between Descending Thoracic Aortic Diseases using Machine Learning and Plasma Proteomic Signatures

Momenzadeh

Kreimer

Guo

et al. 2023

Preprint

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Descending thoracic aortic aneurysms may go undetected until severe and catastrophic complications such as rupture or dissection occur. Few clinical indicators exist to screen for aneurysms or predict 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 controls, as well as aneurysm from descending aortic dissections. Of the 1,468 peptides and 195 proteins quantified, 853 peptides and 99 proteins were significantly different between disease and controls. Using machine learning to classify disease from control, the highest precision-recall area under the curve (PR AUC) was achieved using significantly different proteins (PR AUC 0.99), followed by significant peptides (PR AUC 0.96). Despite no statistically significant protein signatures between aneurysm and dissection, use of all proteins was able to modestly classify between the disease states (PR AUC 0.77). To address correlation between proteins, a disease versus control classifier was optimized using only seven unique protein clusters, which achieved comparable performance to models trained on all/significant proteins (PR AUC 0.90). Model interpretation with permutation importance revealed that proteins differentiating disease and control function in coagulation, protein-lipid complex remodeling, and acute inflammatory response.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 88%

Differentiation between Descending Thoracic Aortic Diseases using Machine Learning and Plasma Proteomic Signatures

Momenzadeh

Kreimer

Guo

et al. 2023

Preprint

Self Cite

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“…Several identified DEGs have been implicated in prior genetic and molecular studies. These include HDAC9 (histone deactylase 9), 15,51 ACTN4 , (alpha-actinin-4) 20,52,53 PLN 54 , CALM1 , 55 TGFB2 , 56,57 TGFBR3 , 58 KLF4 (Krüppel-like factor 4), 59 LTBP1 , 60,61 LTBP4 , 62 CCL2 , 63,64 POSTN , 52,65 CDH11 , 66 and LMO7 . 67 (Figure 4B) The VSMC2 cluster was notable for having the largest number of unique DEGs (131; Figure 4B and 4D).…”

Section: Resultsmentioning

confidence: 99%

Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta

Chou

Chaffin

Simonson

et al. 2022

ATVB

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Background: Mural cells in ascending aortic aneurysms undergo phenotypic changes that promote extracellular matrix destruction and structural weakening. To explore this biology, we analyzed the transcriptional features of thoracic aortic tissue. Methods: Single-nuclear RNA sequencing was performed on 13 samples from human donors, 6 with thoracic aortic aneurysm, and 7 without aneurysm. Individual transcriptomes were then clustered based on transcriptional profiles. Clusters were used for between-disease differential gene expression analyses, subcluster analysis, and analyzed for intersection with genetic aortic trait data. Results: We sequenced 71 689 nuclei from human thoracic aortas and identified 14 clusters, aligning with 11 cell types, predominantly vascular smooth muscle cells (VSMCs) consistent with aortic histology. With unbiased methodology, we found 7 vascular smooth muscle cell and 6 fibroblast subclusters. Differentially expressed genes analysis revealed a vascular smooth muscle cell group accounting for the majority of differential gene expression. Fibroblast populations in aneurysm exhibit distinct behavior with almost complete disappearance of quiescent fibroblasts. Differentially expressed genes were used to prioritize genes at aortic diameter and distensibility genome-wide association study loci highlighting the genes JUN, LTBP4, and IL34 in fibroblasts, ENTPD1 , PDLIM5 , ACTN4 , and GLRX in vascular smooth muscle cells, as well as LRP1 in macrophage populations. Conclusions: Using nuclear RNA sequencing, we describe the cellular diversity of healthy and aneurysmal human ascending aorta. Sporadic aortic aneurysm is characterized by differential gene expression within known cellular classes rather than by the appearance of novel cellular forms. Single-nuclear RNA sequencing of aortic tissue can be used to prioritize genes at aortic trait loci.

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“…The specific serum proteome signature of acute aortic dissections has been in the focus of several published studies, e.g., in comparison to healthy controls (e.g., [ 9 , 10 ]), acute myocardial infarction (e.g., [ 11 ]), aortic aneurysms (e.g., [ 12 ]) or combinations thereof (e.g., [ 12 ]).…”

Section: Resultsmentioning

confidence: 99%

“…Proteomics using liquid chromatography–tandem mass spectrometry (LC-MS/MS) enables the explorative, quantitative profiling of 100 s of serum or plasma proteins from only a few microliters of liquid biopsy. Previous serum proteome studies in the field of aortic diseases largely focused on comparison of selected aortic diseases vs. a set of controls [ 9 , 10 , 11 , 12 ]. The present study focuses on aortic pathologies including true aneurysms chronic Type B dissections and acute aortic syndromes.…”

Section: Introductionmentioning

confidence: 99%

Acute, Chronic, and Treated Aortic Diseases Present Distinguishable Serum Proteome Fingerprints with Protein Profiles That Correlate with Disease Severity

et al. 2022

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Aortic diseases are a rare but potentially life-threatening condition. We present a serum proteomic study for a spectrum of aortic diseases including thoracic aortic aneurysms (n = 11), chronic dissections (n = 9), acute aortic dissections (n = 11), and surgically treated dissections (n = 19) as well as healthy controls (n = 10) and patients of coronary heart disease (n = 10) to represent non-aortic cardiovascular disease. In total, we identified and quantified 425 proteins across all 70 samples. The different aortic diseases represented distinguishable proteome profiles. We identified protein clusters that positively or negatively correlate with disease severity, including increase of cytosolic tissue leakage proteins and decrease of components of the coagulation and complement system. Further, we identified a serum proteome fingerprint of acute aortic dissections, consisting, among others, of enriched inflammatory markers such as C-reactive protein and members of the S100 protein family. The study underlines the applicability of serum proteomics for the investigation of aortic diseases and highlights the possibility to establish disease-specific prognostic markers.

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Proteomic analysis of descending thoracic aorta identifies unique and universal signatures of aneurysm and dissection

Cited by 11 publications

References 31 publications

Differentiation between Descending Thoracic Aortic Diseases using Machine Learning and Plasma Proteomic Signatures

Differentiation between Descending Thoracic Aortic Diseases using Machine Learning and Plasma Proteomic Signatures

Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta

Acute, Chronic, and Treated Aortic Diseases Present Distinguishable Serum Proteome Fingerprints with Protein Profiles That Correlate with Disease Severity

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