Peptide Composition of Stroke Causing Emboli Correlate with Serum Markers of Atherosclerosis and Inflammation

Rao, Neal; Capri, Joseph; Cohn, Whitaker; Abdaljaleel, Maram; Restrepo, Lucas; Gornbein, Jeffrey; Yong, William H.; Liebeskind, David S; Whitelegge, Julian P.

doi:10.3389/fneur.2017.00427

Cited by 24 publications

(27 citation statements)

References 8 publications

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“…We are aware that an efficient transition from SVM proteome analysis to clinical laboratory tools (e.g., Elisa) could be challenging and time consuming. (12,27). The latter will be possible only when definitive molecular candidate(s) will be confirmed in larger populations with results replicated across stroke centers.…”

Section: Study Limitations and Prospectivesmentioning

confidence: 99%

Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study

et al. 2020

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Objective: Mechanical retrieval of thrombotic material from acute ischemic stroke patients provides a unique entry point for translational research investigations. Here, we resolved the proteomes of cardioembolic and atherothrombotic cerebrovascular human thrombi and applied an artificial intelligence routine to examine protein signatures between the two selected groups. Methods: We specifically used n = 32 cardioembolic and n = 28 atherothrombotic diagnosed thrombi from patients suffering from acute stroke and treated by mechanical thrombectomy. Thrombi proteins were successfully separated by gel-electrophoresis. For each thrombi, peptide samples were analyzed by nano-flow liquid chromatography coupled to tandem mass spectrometry (nano-LC-MS/MS) to obtain specific proteomes. Relative protein quantification was performed using a label-free LFQ algorithm and all dataset were analyzed using a support-vector-machine (SVM) learning method. Data are available via ProteomeXchange with identifier PXD020398. Clinical data were also analyzed using SVM, alone or in combination with the proteomes. Results: A total of 2,455 proteins were identified by nano-LC-MS/MS in the samples analyzed, with 438 proteins constantly detected in all samples. SVM analysis of LFQ proteomic data delivered combinations of three proteins achieving a maximum of 88.3% for correct classification of the cardioembolic and atherothrombotic samples in our cohort. The coagulation factor XIII appeared in all of the SVM protein trios, associating with cardioembolic thrombi. A combined SVM analysis of the LFQ proteome and clinical data did not deliver a better discriminatory score as compared to the proteome only. Conclusion: Our results advance the portrayal of the human cerebrovascular thrombi proteome. The exploratory SVM analysis outlined sets of proteins for a proof-of-principle characterization of our cohort cardioembolic and atherothrombotic samples. The integrated analysis proposed herein could be further developed Dargazanli et al. Proteomic Composition of Intracranial Thrombi and retested on a larger patients population to better understand stroke origin and the associated cerebrovascular pathophysiology.

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Section: Study Limitations and Prospectivesmentioning

confidence: 99%

Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study

et al. 2020

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“…34,38,39,43,45,49 Thrombus composition analysis unveiling pathogenic feature: bacterial signatures, endothelial cells, calcifications, vascular wall components, valve embolisms. 30,45,48,49,50,52,53,56 Correlation studies between thrombus composition analysis, etiology and imaging to predict thrombus stroke etiology even when the thrombus is not extracted. 100 Assessing thrombus age.…”

Section: Thrombus Analysis For Secondary Stroke Preventionmentioning

confidence: 99%

“…Besides histological feature analysis, pilot studies have shown the feasibility of proteomic analysis by mass spectrometry on thrombi, 52,53 as well as studies on RNA sequencing (RNA Sequencing Analysis in Large Vessel Occlusion Stroke DATA Bank, RNASA-LVOSB, NCT03490552) might prospect additional information to differentiate thrombi from patients with varying clinical characteristics. Even bacterial DNA retrieval, as found in coronary thrombi of patients with myocardial infarction undergoing primary percutaneous coronary intervention 41 or peripheral thrombi, 54 might be valuable, although up to now no published study on cerebral thrombi has explored the diagnostic role of bacterial signatures in collected thrombi.…”

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confidence: 99%

Insights from thrombi retrieved in stroke due to large vessel occlusion

Bacigaluppi¹,

Semerano²,

Gullotta³

et al. 2019

J Cereb Blood Flow Metab

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The recent advances of endovascular procedures to treat stroke due to large cerebral vessel occlusion have made it possible to analyze the retrieved thrombus material. Analysis of cerebral thrombi is emerging as a relevant opportunity to complement the diagnostic workup of etiology, to develop new lytic approaches and to optimize the acute treatment of stroke due to large vessel occlusion. Nonetheless, retrieved thrombi are frequently discarded since their informative potential is often neglected and standards are missing. This review provides an overview of the current knowledge and expanding research relating to thrombus composition analysis in large vessel occlusions. We first discuss the heterogeneity of thrombogenic factors that underlie the thrombotic formation in stroke and its implications to identify stroke etiology and thrombus age. Further, we show that understanding structural characteristics of thrombus is pivotal for the development of new-targeted lytic therapies as well as to improve, through thrombus modeling, the development of thrombectomy devices. Finally, we discuss the on-going attempts to identify a signature of thrombus composition indirectly through imaging and peripheral blood biomarkers, which might in future assist treatment decision-making as well as secondary prevention. Thrombus analysis might contribute to the advancement and optimization of personalized stroke treatments.

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“…and LDL-C may be correlated with in ammation [24]. The high LDL-C/HDL-C ratio may be due to an increase in in ammatory components, a decrease in the anti-in ammatory and antioxidative components re ected in the denominator, or both.…”

Section: Discussionmentioning

confidence: 99%

LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

Zhang

Meng

Shang

et al. 2020

Preprint

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Background: This study explored the relationships between the low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients.Methods: NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS occurred. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS.Results: The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile ( ≥2.73) was 16.23-fold that of the lowest quartile ( < 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients.Conclusions: LDL-C/HDL-C >1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

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Peptide Composition of Stroke Causing Emboli Correlate with Serum Markers of Atherosclerosis and Inflammation

Cited by 24 publications

References 8 publications

Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study

Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study

Insights from thrombi retrieved in stroke due to large vessel occlusion

LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

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