A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes
The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We used an informatics-based approach to develop a transcriptional signature of β-cell GA stress usin...
Gespeichert in:
| Veröffentlicht in: | Diabetes (New York, N.Y.) N.Y.), 2020-11, Vol.69 (11), p.2364-2376 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2376 |
|---|---|
| container_issue | 11 |
| container_start_page | 2364 |
| container_title | Diabetes (New York, N.Y.) |
| container_volume | 69 |
| creator | Bone, Robert N Oyebamiji, Olufunmilola Talware, Sayali Selvaraj, Sharmila Krishnan, Preethi Syed, Farooq Wu, Huanmei Evans-Molina, Carmella |
| description | The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We used an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray data sets generated using human islets from donors with diabetes and islets where type 1 (T1D) and type 2 (T2D) diabetes had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. In parallel, we generated an RNA-sequencing data set from human islets treated with brefeldin A (BFA), a known GA stress inducer. Overlapping the T1D and T2D groups with the BFA data set, we identified 120 and 204 differentially expressed genes, respectively. In both the T1D and T2D models, pathway analyses revealed that the top pathways were associated with GA integrity, organization, and trafficking. Quantitative RT-PCR was used to validate a common signature of GA stress that included
,
,
, and
Taken together, these data indicate that GA-associated genes are dysregulated in diabetes and identify putative markers of β-cell GA stress. |
| doi_str_mv | 10.2337/db20-0636 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7576569</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2436397222</sourcerecordid><originalsourceid>FETCH-LOGICAL-c333t-232d552d1ca7b202feb11d29a361c3809a38a0f085c741e118fb6d602494b3a33</originalsourceid><addsrcrecordid>eNpdkc1KxDAQx4Moun4cfAEJeNFDNcm0SXMRlvUTFjysC95C2qZrlm5Tk1bwtXwQn8ksfqAyh5lhfvyZmT9Ch5ScMQBxXhWMJIQD30AjKkEmwMTjJhoRQllChRQ7aDeEJSGEx9hGO8ByFjs5QvMxnrhVN_S6t67VDR53nXe6fMK18_jS1La17QJrPLOLVveDN9jV-P0tmZimwTeuWVg8670JAdsWX1pdmN6EfbRV6yaYg6-8h-bXVw-T22R6f3M3GU-TEgD6hAGrsoxVtNQinsBqU1BaMamB0xJyEotck5rkWSlSaijN64JXnLBUpgVogD108anbDcXKVKVpe68b1Xm70v5VOW3V30lrn9TCvSiRCZ5xGQVOvgS8ex5M6NXKhjKeplvjhqBYChykYIxF9PgfunSDjy9bU5lMIROCROr0kyq9C8Gb-mcZStTaLLU2S63NiuzR7-1_yG934AOJE46T</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2459435770</pqid></control><display><type>article</type><title>A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes</title><source>MEDLINE</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Bone, Robert N ; Oyebamiji, Olufunmilola ; Talware, Sayali ; Selvaraj, Sharmila ; Krishnan, Preethi ; Syed, Farooq ; Wu, Huanmei ; Evans-Molina, Carmella</creator><creatorcontrib>Bone, Robert N ; Oyebamiji, Olufunmilola ; Talware, Sayali ; Selvaraj, Sharmila ; Krishnan, Preethi ; Syed, Farooq ; Wu, Huanmei ; Evans-Molina, Carmella</creatorcontrib><description>The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We used an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray data sets generated using human islets from donors with diabetes and islets where type 1 (T1D) and type 2 (T2D) diabetes had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. In parallel, we generated an RNA-sequencing data set from human islets treated with brefeldin A (BFA), a known GA stress inducer. Overlapping the T1D and T2D groups with the BFA data set, we identified 120 and 204 differentially expressed genes, respectively. In both the T1D and T2D models, pathway analyses revealed that the top pathways were associated with GA integrity, organization, and trafficking. Quantitative RT-PCR was used to validate a common signature of GA stress that included
,
,
, and
Taken together, these data indicate that GA-associated genes are dysregulated in diabetes and identify putative markers of β-cell GA stress.</description><identifier>ISSN: 0012-1797</identifier><identifier>EISSN: 1939-327X</identifier><identifier>DOI: 10.2337/db20-0636</identifier><identifier>PMID: 32820009</identifier><language>eng</language><publisher>United States: American Diabetes Association</publisher><subject>Activating transcription factor 3 ; Beta cells ; Brefeldin A ; Computer applications ; Computer Simulation ; Diabetes ; Diabetes mellitus (insulin dependent) ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 1 - metabolism ; Diabetes Mellitus, Type 2 - metabolism ; DNA microarrays ; Gene Expression Regulation - physiology ; Golgi Apparatus - physiology ; Golgi cells ; Health informatics ; Humans ; Inflammation ; Informatics ; Insulin ; Islet Studies ; Islets of Langerhans - metabolism ; Models, Biological ; Polymerase chain reaction ; Protein Array Analysis ; Ribonucleic acid ; RNA ; Stress, Physiological ; Transcription</subject><ispartof>Diabetes (New York, N.Y.), 2020-11, Vol.69 (11), p.2364-2376</ispartof><rights>2020 by the American Diabetes Association.</rights><rights>Copyright American Diabetes Association Nov 1, 2020</rights><rights>2020 by the American Diabetes Association 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c333t-232d552d1ca7b202feb11d29a361c3809a38a0f085c741e118fb6d602494b3a33</citedby><cites>FETCH-LOGICAL-c333t-232d552d1ca7b202feb11d29a361c3809a38a0f085c741e118fb6d602494b3a33</cites><orcidid>0000-0001-7764-8663</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576569/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576569/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32820009$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bone, Robert N</creatorcontrib><creatorcontrib>Oyebamiji, Olufunmilola</creatorcontrib><creatorcontrib>Talware, Sayali</creatorcontrib><creatorcontrib>Selvaraj, Sharmila</creatorcontrib><creatorcontrib>Krishnan, Preethi</creatorcontrib><creatorcontrib>Syed, Farooq</creatorcontrib><creatorcontrib>Wu, Huanmei</creatorcontrib><creatorcontrib>Evans-Molina, Carmella</creatorcontrib><title>A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes</title><title>Diabetes (New York, N.Y.)</title><addtitle>Diabetes</addtitle><description>The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We used an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray data sets generated using human islets from donors with diabetes and islets where type 1 (T1D) and type 2 (T2D) diabetes had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. In parallel, we generated an RNA-sequencing data set from human islets treated with brefeldin A (BFA), a known GA stress inducer. Overlapping the T1D and T2D groups with the BFA data set, we identified 120 and 204 differentially expressed genes, respectively. In both the T1D and T2D models, pathway analyses revealed that the top pathways were associated with GA integrity, organization, and trafficking. Quantitative RT-PCR was used to validate a common signature of GA stress that included
,
,
, and
Taken together, these data indicate that GA-associated genes are dysregulated in diabetes and identify putative markers of β-cell GA stress.</description><subject>Activating transcription factor 3</subject><subject>Beta cells</subject><subject>Brefeldin A</subject><subject>Computer applications</subject><subject>Computer Simulation</subject><subject>Diabetes</subject><subject>Diabetes mellitus (insulin dependent)</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 1 - metabolism</subject><subject>Diabetes Mellitus, Type 2 - metabolism</subject><subject>DNA microarrays</subject><subject>Gene Expression Regulation - physiology</subject><subject>Golgi Apparatus - physiology</subject><subject>Golgi cells</subject><subject>Health informatics</subject><subject>Humans</subject><subject>Inflammation</subject><subject>Informatics</subject><subject>Insulin</subject><subject>Islet Studies</subject><subject>Islets of Langerhans - metabolism</subject><subject>Models, Biological</subject><subject>Polymerase chain reaction</subject><subject>Protein Array Analysis</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>Stress, Physiological</subject><subject>Transcription</subject><issn>0012-1797</issn><issn>1939-327X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkc1KxDAQx4Moun4cfAEJeNFDNcm0SXMRlvUTFjysC95C2qZrlm5Tk1bwtXwQn8ksfqAyh5lhfvyZmT9Ch5ScMQBxXhWMJIQD30AjKkEmwMTjJhoRQllChRQ7aDeEJSGEx9hGO8ByFjs5QvMxnrhVN_S6t67VDR53nXe6fMK18_jS1La17QJrPLOLVveDN9jV-P0tmZimwTeuWVg8670JAdsWX1pdmN6EfbRV6yaYg6-8h-bXVw-T22R6f3M3GU-TEgD6hAGrsoxVtNQinsBqU1BaMamB0xJyEotck5rkWSlSaijN64JXnLBUpgVogD108anbDcXKVKVpe68b1Xm70v5VOW3V30lrn9TCvSiRCZ5xGQVOvgS8ex5M6NXKhjKeplvjhqBYChykYIxF9PgfunSDjy9bU5lMIROCROr0kyq9C8Gb-mcZStTaLLU2S63NiuzR7-1_yG934AOJE46T</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Bone, Robert N</creator><creator>Oyebamiji, Olufunmilola</creator><creator>Talware, Sayali</creator><creator>Selvaraj, Sharmila</creator><creator>Krishnan, Preethi</creator><creator>Syed, Farooq</creator><creator>Wu, Huanmei</creator><creator>Evans-Molina, Carmella</creator><general>American Diabetes Association</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>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-7764-8663</orcidid></search><sort><creationdate>20201101</creationdate><title>A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes</title><author>Bone, Robert N ; Oyebamiji, Olufunmilola ; Talware, Sayali ; Selvaraj, Sharmila ; Krishnan, Preethi ; Syed, Farooq ; Wu, Huanmei ; Evans-Molina, Carmella</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-232d552d1ca7b202feb11d29a361c3809a38a0f085c741e118fb6d602494b3a33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Activating transcription factor 3</topic><topic>Beta cells</topic><topic>Brefeldin A</topic><topic>Computer applications</topic><topic>Computer Simulation</topic><topic>Diabetes</topic><topic>Diabetes mellitus (insulin dependent)</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diabetes Mellitus, Type 1 - metabolism</topic><topic>Diabetes Mellitus, Type 2 - metabolism</topic><topic>DNA microarrays</topic><topic>Gene Expression Regulation - physiology</topic><topic>Golgi Apparatus - physiology</topic><topic>Golgi cells</topic><topic>Health informatics</topic><topic>Humans</topic><topic>Inflammation</topic><topic>Informatics</topic><topic>Insulin</topic><topic>Islet Studies</topic><topic>Islets of Langerhans - metabolism</topic><topic>Models, Biological</topic><topic>Polymerase chain reaction</topic><topic>Protein Array Analysis</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Stress, Physiological</topic><topic>Transcription</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bone, Robert N</creatorcontrib><creatorcontrib>Oyebamiji, Olufunmilola</creatorcontrib><creatorcontrib>Talware, Sayali</creatorcontrib><creatorcontrib>Selvaraj, Sharmila</creatorcontrib><creatorcontrib>Krishnan, Preethi</creatorcontrib><creatorcontrib>Syed, Farooq</creatorcontrib><creatorcontrib>Wu, Huanmei</creatorcontrib><creatorcontrib>Evans-Molina, Carmella</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Diabetes (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bone, Robert N</au><au>Oyebamiji, Olufunmilola</au><au>Talware, Sayali</au><au>Selvaraj, Sharmila</au><au>Krishnan, Preethi</au><au>Syed, Farooq</au><au>Wu, Huanmei</au><au>Evans-Molina, Carmella</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes</atitle><jtitle>Diabetes (New York, N.Y.)</jtitle><addtitle>Diabetes</addtitle><date>2020-11-01</date><risdate>2020</risdate><volume>69</volume><issue>11</issue><spage>2364</spage><epage>2376</epage><pages>2364-2376</pages><issn>0012-1797</issn><eissn>1939-327X</eissn><abstract>The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We used an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray data sets generated using human islets from donors with diabetes and islets where type 1 (T1D) and type 2 (T2D) diabetes had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. In parallel, we generated an RNA-sequencing data set from human islets treated with brefeldin A (BFA), a known GA stress inducer. Overlapping the T1D and T2D groups with the BFA data set, we identified 120 and 204 differentially expressed genes, respectively. In both the T1D and T2D models, pathway analyses revealed that the top pathways were associated with GA integrity, organization, and trafficking. Quantitative RT-PCR was used to validate a common signature of GA stress that included
,
,
, and
Taken together, these data indicate that GA-associated genes are dysregulated in diabetes and identify putative markers of β-cell GA stress.</abstract><cop>United States</cop><pub>American Diabetes Association</pub><pmid>32820009</pmid><doi>10.2337/db20-0636</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-7764-8663</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0012-1797 |
| ispartof | Diabetes (New York, N.Y.), 2020-11, Vol.69 (11), p.2364-2376 |
| issn | 0012-1797 1939-327X |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7576569 |
| source | MEDLINE; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Activating transcription factor 3 Beta cells Brefeldin A Computer applications Computer Simulation Diabetes Diabetes mellitus (insulin dependent) Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 1 - metabolism Diabetes Mellitus, Type 2 - metabolism DNA microarrays Gene Expression Regulation - physiology Golgi Apparatus - physiology Golgi cells Health informatics Humans Inflammation Informatics Insulin Islet Studies Islets of Langerhans - metabolism Models, Biological Polymerase chain reaction Protein Array Analysis Ribonucleic acid RNA Stress, Physiological Transcription |
| title | A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T21%3A19%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Computational%20Approach%20for%20Defining%20a%20Signature%20of%20%CE%B2-Cell%20Golgi%20Stress%20in%20Diabetes&rft.jtitle=Diabetes%20(New%20York,%20N.Y.)&rft.au=Bone,%20Robert%20N&rft.date=2020-11-01&rft.volume=69&rft.issue=11&rft.spage=2364&rft.epage=2376&rft.pages=2364-2376&rft.issn=0012-1797&rft.eissn=1939-327X&rft_id=info:doi/10.2337/db20-0636&rft_dat=%3Cproquest_pubme%3E2436397222%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2459435770&rft_id=info:pmid/32820009&rfr_iscdi=true |