Deregulation in adult IgA vasculitis skin as the basis for the discovery of novel serum biomarkers
© 2024. The Author(s)..
INTRODUCTION: Immunoglobulin A vasculitis (IgAV) in adults has a variable disease course, with patients often developing gastrointestinal and renal involvement and thus contributing to higher mortality. Due to understudied molecular mechanisms in IgAV currently used biomarkers for IgAV visceral involvement are largely lacking. Our aim was to search for potential serum biomarkers based on the skin transcriptomic signature.
METHODS: RNA sequencing analysis was conducted on skin biopsies collected from 6 treatment-naïve patients (3 skin only and 3 renal involvement) and 3 healthy controls (HC) to get insight into deregulated processes at the transcriptomic level. 15 analytes were selected and measured based on the transcriptome analysis (adiponectin, lipopolysaccharide binding protein (LBP), matrix metalloproteinase-1 (MMP1), C-C motif chemokine ligand (CCL) 19, kallikrein-5, CCL3, leptin, C-X-C motif chemokine ligand (CXCL) 5, osteopontin, interleukin (IL)-15, CXCL10, angiopoietin-like 4 (ANGPTL4), SERPIN A12/vaspin, IL-18 and fatty acid-binding protein 4 (FABP4)) in sera of 59 IgAV and 22 HC. Machine learning was used to assess the ability of the analytes to predict IgAV and its organ involvement.
RESULTS: Based on the gene expression levels in the skin, we were able to differentiate between IgAV patients and HC using principal component analysis (PCA) and a sample-to-sample distance matrix. Differential expression analysis revealed 49 differentially expressed genes (DEGs) in all IgAV patient's vs. HC. Patients with renal involvement had more DEGs than patients with skin involvement only (507 vs. 46 DEGs) as compared to HC, suggesting different skin signatures. Major dysregulated processes in patients with renal involvement were lipid metabolism, acute inflammatory response, and extracellular matrix (ECM)-related processes. 11 of 15 analytes selected based on affected processes in IgAV skin (osteopontin, LBP, ANGPTL4, IL-15, FABP4, CCL19, kallikrein-5, CCL3, leptin, IL-18 and MMP1) were significantly higher (p-adj < 0.05) in IgAV serum as compared to HC. Prediction models utilizing measured analytes showed high potential for predicting adult IgAV.
CONCLUSION: Skin transcriptomic data revealed deregulations in lipid metabolism and acute inflammatory response, reflected also in serum analyte measurements. LBP, among others, could serve as a potential biomarker of renal complications, while adiponectin and CXCL10 could indicate gastrointestinal involvement.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
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| Enthalten in: |
Arthritis research & therapy - 26(2024), 1 vom: 12. Apr., Seite 85 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bajželj, Matija [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 15.04.2024 Date Revised 25.04.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s13075-024-03317-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370993713 |
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| 245 | 1 | 0 | |a Deregulation in adult IgA vasculitis skin as the basis for the discovery of novel serum biomarkers |
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| 500 | |a Date Revised 25.04.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a INTRODUCTION: Immunoglobulin A vasculitis (IgAV) in adults has a variable disease course, with patients often developing gastrointestinal and renal involvement and thus contributing to higher mortality. Due to understudied molecular mechanisms in IgAV currently used biomarkers for IgAV visceral involvement are largely lacking. Our aim was to search for potential serum biomarkers based on the skin transcriptomic signature | ||
| 520 | |a METHODS: RNA sequencing analysis was conducted on skin biopsies collected from 6 treatment-naïve patients (3 skin only and 3 renal involvement) and 3 healthy controls (HC) to get insight into deregulated processes at the transcriptomic level. 15 analytes were selected and measured based on the transcriptome analysis (adiponectin, lipopolysaccharide binding protein (LBP), matrix metalloproteinase-1 (MMP1), C-C motif chemokine ligand (CCL) 19, kallikrein-5, CCL3, leptin, C-X-C motif chemokine ligand (CXCL) 5, osteopontin, interleukin (IL)-15, CXCL10, angiopoietin-like 4 (ANGPTL4), SERPIN A12/vaspin, IL-18 and fatty acid-binding protein 4 (FABP4)) in sera of 59 IgAV and 22 HC. Machine learning was used to assess the ability of the analytes to predict IgAV and its organ involvement | ||
| 520 | |a RESULTS: Based on the gene expression levels in the skin, we were able to differentiate between IgAV patients and HC using principal component analysis (PCA) and a sample-to-sample distance matrix. Differential expression analysis revealed 49 differentially expressed genes (DEGs) in all IgAV patient's vs. HC. Patients with renal involvement had more DEGs than patients with skin involvement only (507 vs. 46 DEGs) as compared to HC, suggesting different skin signatures. Major dysregulated processes in patients with renal involvement were lipid metabolism, acute inflammatory response, and extracellular matrix (ECM)-related processes. 11 of 15 analytes selected based on affected processes in IgAV skin (osteopontin, LBP, ANGPTL4, IL-15, FABP4, CCL19, kallikrein-5, CCL3, leptin, IL-18 and MMP1) were significantly higher (p-adj < 0.05) in IgAV serum as compared to HC. Prediction models utilizing measured analytes showed high potential for predicting adult IgAV | ||
| 520 | |a CONCLUSION: Skin transcriptomic data revealed deregulations in lipid metabolism and acute inflammatory response, reflected also in serum analyte measurements. LBP, among others, could serve as a potential biomarker of renal complications, while adiponectin and CXCL10 could indicate gastrointestinal involvement | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Acute inflammatory response | |
| 650 | 4 | |a Adults | |
| 650 | 4 | |a IgA vasculitis | |
| 650 | 4 | |a Lipid metabolism | |
| 650 | 4 | |a Machine learning | |
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| 650 | 4 | |a Serum biomarkers | |
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| 700 | 1 | |a Hladnik, Matjaž |e verfasserin |4 aut | |
| 700 | 1 | |a Blagus, Rok |e verfasserin |4 aut | |
| 700 | 1 | |a Jurčić, Vesna |e verfasserin |4 aut | |
| 700 | 1 | |a Markež, Ana |e verfasserin |4 aut | |
| 700 | 1 | |a Toluay, Tanya Deniz |e verfasserin |4 aut | |
| 700 | 1 | |a Sodin-Šemrl, Snežna |e verfasserin |4 aut | |
| 700 | 1 | |a Hočevar, Alojzija |e verfasserin |4 aut | |
| 700 | 1 | |a Lakota, Katja |e verfasserin |4 aut | |
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