Gene expression signatures in conjunctival fornix aspirates of patients with dry eye disease associated with Meibomian gland dysfunction. A proof-of-concept study
Meibomian gland dysfunction (MGD) is one of the most common conditions in ophthalmic practice and the most frequent cause of evaporative dry eye disease (DED). However, the immune mechanisms leading to this pathology are not fully understood and the diagnostic tests available are limited. Here, we u...
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| Veröffentlicht in: | The ocular surface 2023-10, Vol.30, p.42-50 |
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| creator | Vergés, Carlos Giménez-Capitán, Ana Ribas, Verónica Salgado-Borges, José March de Ribot, Francesc Mayo-de-las-Casas, Clara Armiger-Borras, Noelia Pedraz, Carlos Molina-Vila, Miguel Ángel |
| description | Meibomian gland dysfunction (MGD) is one of the most common conditions in ophthalmic practice and the most frequent cause of evaporative dry eye disease (DED). However, the immune mechanisms leading to this pathology are not fully understood and the diagnostic tests available are limited. Here, we used the nCounter technology to analyze immune gene expression in DED-MGD that can be used for developing diagnostic signatures for DED.
Conjunctival cell samples were obtained by aspiration from patients with DED-MGD (n = 27) and asymptomatic controls (n = 22). RNA was purified, converted to cDNA, preamplified and analyzed using the Gene Expression Human Immune V2 panel (NanoString), which includes 579 target and 15 housekeeping genes. A machine learning (ML) algorithm was applied to design a signature associated with DED-MGD.
Forty-five immune genes were found upregulated in DED-MGD vs. controls, involved in eight signaling pathways, IFN I/II, MHC class I/II, immunometabolism, B cell receptor, T Cell receptor, and T helper-17 (Th-17) differentiation. Additionally, statistically significant correlations were found between 31 genes and clinical characteristics of the disease such as lid margin or tear osmolarity (Pearson's r |
| doi_str_mv | 10.1016/j.jtos.2023.07.010 |
| format | Article |
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Conjunctival cell samples were obtained by aspiration from patients with DED-MGD (n = 27) and asymptomatic controls (n = 22). RNA was purified, converted to cDNA, preamplified and analyzed using the Gene Expression Human Immune V2 panel (NanoString), which includes 579 target and 15 housekeeping genes. A machine learning (ML) algorithm was applied to design a signature associated with DED-MGD.
Forty-five immune genes were found upregulated in DED-MGD vs. controls, involved in eight signaling pathways, IFN I/II, MHC class I/II, immunometabolism, B cell receptor, T Cell receptor, and T helper-17 (Th-17) differentiation. Additionally, statistically significant correlations were found between 31 genes and clinical characteristics of the disease such as lid margin or tear osmolarity (Pearson's r < 0.05). ML analysis using a recursive feature elimination (RFE) algorithm selected a 4-gene mRNA signature that discriminated DED-MGD from control samples with an area under the ROC curve (AUC ROC) of 0.86 and an accuracy of 77.5%.
Multiplexed mRNA analysis of conjunctival cells can be used to analyze immune gene expression patterns in patients with DED-MGD and to generate diagnostic signatures.</description><identifier>ISSN: 1542-0124</identifier><identifier>EISSN: 1937-5913</identifier><identifier>DOI: 10.1016/j.jtos.2023.07.010</identifier><identifier>PMID: 37524297</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Dry eye disease ; Dry Eye Syndromes - diagnosis ; Dry Eye Syndromes - genetics ; Gene expression ; Humans ; Machine learning ; Meibomian gland dysfunction ; Meibomian Gland Dysfunction - metabolism ; Meibomian Glands - metabolism ; nCounter ; RNA, Messenger ; Tears ; Tears - metabolism ; Transcriptome</subject><ispartof>The ocular surface, 2023-10, Vol.30, p.42-50</ispartof><rights>2023</rights><rights>Copyright © 2023. Published by Elsevier Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-a8e1c9ba9bab0c2c88606ded055b1b03415ee8a7de5dc940375037ba82aa77da3</citedby><cites>FETCH-LOGICAL-c356t-a8e1c9ba9bab0c2c88606ded055b1b03415ee8a7de5dc940375037ba82aa77da3</cites><orcidid>0000-0003-2575-1225</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37524297$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Vergés, Carlos</creatorcontrib><creatorcontrib>Giménez-Capitán, Ana</creatorcontrib><creatorcontrib>Ribas, Verónica</creatorcontrib><creatorcontrib>Salgado-Borges, José</creatorcontrib><creatorcontrib>March de Ribot, Francesc</creatorcontrib><creatorcontrib>Mayo-de-las-Casas, Clara</creatorcontrib><creatorcontrib>Armiger-Borras, Noelia</creatorcontrib><creatorcontrib>Pedraz, Carlos</creatorcontrib><creatorcontrib>Molina-Vila, Miguel Ángel</creatorcontrib><title>Gene expression signatures in conjunctival fornix aspirates of patients with dry eye disease associated with Meibomian gland dysfunction. A proof-of-concept study</title><title>The ocular surface</title><addtitle>Ocul Surf</addtitle><description>Meibomian gland dysfunction (MGD) is one of the most common conditions in ophthalmic practice and the most frequent cause of evaporative dry eye disease (DED). However, the immune mechanisms leading to this pathology are not fully understood and the diagnostic tests available are limited. Here, we used the nCounter technology to analyze immune gene expression in DED-MGD that can be used for developing diagnostic signatures for DED.
Conjunctival cell samples were obtained by aspiration from patients with DED-MGD (n = 27) and asymptomatic controls (n = 22). RNA was purified, converted to cDNA, preamplified and analyzed using the Gene Expression Human Immune V2 panel (NanoString), which includes 579 target and 15 housekeeping genes. A machine learning (ML) algorithm was applied to design a signature associated with DED-MGD.
Forty-five immune genes were found upregulated in DED-MGD vs. controls, involved in eight signaling pathways, IFN I/II, MHC class I/II, immunometabolism, B cell receptor, T Cell receptor, and T helper-17 (Th-17) differentiation. Additionally, statistically significant correlations were found between 31 genes and clinical characteristics of the disease such as lid margin or tear osmolarity (Pearson's r < 0.05). ML analysis using a recursive feature elimination (RFE) algorithm selected a 4-gene mRNA signature that discriminated DED-MGD from control samples with an area under the ROC curve (AUC ROC) of 0.86 and an accuracy of 77.5%.
Multiplexed mRNA analysis of conjunctival cells can be used to analyze immune gene expression patterns in patients with DED-MGD and to generate diagnostic signatures.</description><subject>Dry eye disease</subject><subject>Dry Eye Syndromes - diagnosis</subject><subject>Dry Eye Syndromes - genetics</subject><subject>Gene expression</subject><subject>Humans</subject><subject>Machine learning</subject><subject>Meibomian gland dysfunction</subject><subject>Meibomian Gland Dysfunction - metabolism</subject><subject>Meibomian Glands - metabolism</subject><subject>nCounter</subject><subject>RNA, Messenger</subject><subject>Tears</subject><subject>Tears - metabolism</subject><subject>Transcriptome</subject><issn>1542-0124</issn><issn>1937-5913</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kcFu1DAQhi0EoqXwAhyQj1wSbCeOE4lLVUFBasWlPVuOPSmOdu3gcdrmdXhSvGzhWGksj-Vvfo_nJ-Q9ZzVnvPs013OOWAsmmpqpmnH2gpzyoVGVHHjzsuSyFRXjoj0hbxBnxpquY-I1OWmUFK0Y1Cn5fQkBKDwuCRB9DBT9XTB5LUfqA7UxzGuw2d-bHZ1iCv6RGlx8MrkAcaKLyR5CRvrg80_q0kZhA-o8gkEoKEbrC-uO99fgx7j3JtC7nQmOug2nv_Ix1PScLinGqSpRnrWwZIp5ddtb8moyO4R3T_sZuf365ebiW3X14_L7xflVZRvZ5cr0wO0wmhIjs8L2fcc6B45JOfKRNS2XAL1RDqSzQ8vKDMoaTS-MUcqZ5ox8POqWNn6tgFnvPVrYlU4hrqhF37adGgYpCiqOqE0RMcGkl-T3Jm2aM33wRs_64I0-eKOZ0sWbUvThSX8d9-D-l_wzowCfjwCUX957SBptGa4F5xPYrF30z-n_Ad5bpU8</recordid><startdate>202310</startdate><enddate>202310</enddate><creator>Vergés, Carlos</creator><creator>Giménez-Capitán, Ana</creator><creator>Ribas, Verónica</creator><creator>Salgado-Borges, José</creator><creator>March de Ribot, Francesc</creator><creator>Mayo-de-las-Casas, Clara</creator><creator>Armiger-Borras, Noelia</creator><creator>Pedraz, Carlos</creator><creator>Molina-Vila, Miguel Ángel</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2575-1225</orcidid></search><sort><creationdate>202310</creationdate><title>Gene expression signatures in conjunctival fornix aspirates of patients with dry eye disease associated with Meibomian gland dysfunction. A proof-of-concept study</title><author>Vergés, Carlos ; Giménez-Capitán, Ana ; Ribas, Verónica ; Salgado-Borges, José ; March de Ribot, Francesc ; Mayo-de-las-Casas, Clara ; Armiger-Borras, Noelia ; Pedraz, Carlos ; Molina-Vila, Miguel Ángel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-a8e1c9ba9bab0c2c88606ded055b1b03415ee8a7de5dc940375037ba82aa77da3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Dry eye disease</topic><topic>Dry Eye Syndromes - diagnosis</topic><topic>Dry Eye Syndromes - genetics</topic><topic>Gene expression</topic><topic>Humans</topic><topic>Machine learning</topic><topic>Meibomian gland dysfunction</topic><topic>Meibomian Gland Dysfunction - metabolism</topic><topic>Meibomian Glands - metabolism</topic><topic>nCounter</topic><topic>RNA, Messenger</topic><topic>Tears</topic><topic>Tears - metabolism</topic><topic>Transcriptome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vergés, Carlos</creatorcontrib><creatorcontrib>Giménez-Capitán, Ana</creatorcontrib><creatorcontrib>Ribas, Verónica</creatorcontrib><creatorcontrib>Salgado-Borges, José</creatorcontrib><creatorcontrib>March de Ribot, Francesc</creatorcontrib><creatorcontrib>Mayo-de-las-Casas, Clara</creatorcontrib><creatorcontrib>Armiger-Borras, Noelia</creatorcontrib><creatorcontrib>Pedraz, Carlos</creatorcontrib><creatorcontrib>Molina-Vila, Miguel Ángel</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>The ocular surface</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vergés, Carlos</au><au>Giménez-Capitán, Ana</au><au>Ribas, Verónica</au><au>Salgado-Borges, José</au><au>March de Ribot, Francesc</au><au>Mayo-de-las-Casas, Clara</au><au>Armiger-Borras, Noelia</au><au>Pedraz, Carlos</au><au>Molina-Vila, Miguel Ángel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gene expression signatures in conjunctival fornix aspirates of patients with dry eye disease associated with Meibomian gland dysfunction. A proof-of-concept study</atitle><jtitle>The ocular surface</jtitle><addtitle>Ocul Surf</addtitle><date>2023-10</date><risdate>2023</risdate><volume>30</volume><spage>42</spage><epage>50</epage><pages>42-50</pages><issn>1542-0124</issn><eissn>1937-5913</eissn><abstract>Meibomian gland dysfunction (MGD) is one of the most common conditions in ophthalmic practice and the most frequent cause of evaporative dry eye disease (DED). However, the immune mechanisms leading to this pathology are not fully understood and the diagnostic tests available are limited. Here, we used the nCounter technology to analyze immune gene expression in DED-MGD that can be used for developing diagnostic signatures for DED.
Conjunctival cell samples were obtained by aspiration from patients with DED-MGD (n = 27) and asymptomatic controls (n = 22). RNA was purified, converted to cDNA, preamplified and analyzed using the Gene Expression Human Immune V2 panel (NanoString), which includes 579 target and 15 housekeeping genes. A machine learning (ML) algorithm was applied to design a signature associated with DED-MGD.
Forty-five immune genes were found upregulated in DED-MGD vs. controls, involved in eight signaling pathways, IFN I/II, MHC class I/II, immunometabolism, B cell receptor, T Cell receptor, and T helper-17 (Th-17) differentiation. Additionally, statistically significant correlations were found between 31 genes and clinical characteristics of the disease such as lid margin or tear osmolarity (Pearson's r < 0.05). ML analysis using a recursive feature elimination (RFE) algorithm selected a 4-gene mRNA signature that discriminated DED-MGD from control samples with an area under the ROC curve (AUC ROC) of 0.86 and an accuracy of 77.5%.
Multiplexed mRNA analysis of conjunctival cells can be used to analyze immune gene expression patterns in patients with DED-MGD and to generate diagnostic signatures.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>37524297</pmid><doi>10.1016/j.jtos.2023.07.010</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-2575-1225</orcidid></addata></record> |
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| ispartof | The ocular surface, 2023-10, Vol.30, p.42-50 |
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| source | MEDLINE; Alma/SFX Local Collection |
| subjects | Dry eye disease Dry Eye Syndromes - diagnosis Dry Eye Syndromes - genetics Gene expression Humans Machine learning Meibomian gland dysfunction Meibomian Gland Dysfunction - metabolism Meibomian Glands - metabolism nCounter RNA, Messenger Tears Tears - metabolism Transcriptome |
| title | Gene expression signatures in conjunctival fornix aspirates of patients with dry eye disease associated with Meibomian gland dysfunction. A proof-of-concept study |
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