التفاصيل البيبلوغرافية
| العنوان: |
Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort. |
| المؤلفون: |
Govender, Melanie A., Stoychev, Stoyan H., Brandenburg, Jean-Tristan, Ramsay, Michèle, Fabian, June, Govender, Ireshyn S. |
| المصدر: |
Clinical Proteomics; 2/24/2024, Vol. 21 Issue 1, p1-12, 12p |
| مصطلحات موضوعية: |
LIQUID chromatography-mass spectrometry, BLACK South Africans, ALBUMINURIA, PROTEOMICS, FALSE discovery rate |
| مصطلحات جغرافية: |
AFRICA |
| مستخلص: |
Background: Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition. Methods: The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform. Results: Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10− 45), innate immune system (q = 1.1 × 10− 32), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76–100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls. Conclusions: The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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