NR4A3 and CCL20 Clusters Dominate the Dynamic Gene Network of Peripheral CD146+ Blood Cells in the Early Stage of Acute Myocardial Infarction in Human.

NR4A3 and CCL20 Clusters Dominate the Dynamic Gene Network of Peripheral CD146+ Blood Cells in the Early Stage of Acute Myocardial Infarction in Human. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research NR4A3 and CCL20 Clusters Dominate the Dynamic Gene Network of Peripheral CD146 + Blood Cells in the Early Stage of Acute Myocardial Infarction in Human. Yanhui Wang, Chenxin Li, Jessica M Stephenson, Sean P Marrelli, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-133154/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: CD146 is a tight junction associated molecule involved in maintaining endothelial barrier and balancing immune-inflammation response in cardiovascular disease. Notably, the peripheral CD146 + cells significantly upsurge under vessel dyshomeostasis like acute myocardial injury (AMI), appearing to be promising therapeutic targets. In this study, in a new view of gene correlation, we aim at deciphering the underlying complex mechanism of CD146 + cells in the development of AMI. Methods: Transcription dataset GSE 66360 of CD146 + blood cells from clinical subjects were downloaded from NCBI. Pearson networks were constructed and the clustering coefficients were calculated to disclose the differential connectivity genes (DCGs). Analysis of gene connectivity and gene expression was performed to reveal the hub genes and hub genes clusters followed by gene enrichment analysis. Results and Conclusions: Among the total 23520 genes, 27 genes out of 126 differential expression genes are identified as DCGs. Those DCGs normally stay in the peripheral of networks while transfer to the functional central position under AMI situation. Moreover, it is revealed that DCGs spontaneously crowd together into two functional models, CCL20 cluster and NR4A3 cluster, influencing the CD146-mediated signaling pathways during the pathology of AMI for the first time. Medical Genetics acute myocardial infarction (AMI) CD146 Pearson network clustering coefficient differential connectivity genes (DCGs) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Tables Due to technical limitations, tables docx is only available as a download in the Supplemental Files section. Supplementary Files Tables.docx supplementaryfigures.pdf supplementarytables.pdf Cite Share Download PDF Status: Under Review Version 1 posted Review # 2 received at journal 28 Apr, 2021 Editorial decision: Major revision 28 Apr, 2021 Review # 1 received at journal 16 Apr, 2021 Reviewer # 2 agreed at journal 07 Apr, 2021 Reviewer # 1 agreed at journal 02 Apr, 2021 Reviewers invited by journal 17 Jan, 2021 Editor assigned by journal 18 Dec, 2020 Submission checks completed at journal 18 Dec, 2020 Editor invited by journal 18 Dec, 2020 First submitted to journal 17 Dec, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-133154","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":6843037,"identity":"3efe2907-841f-4407-8397-e0f5258bbc97","order_by":0,"name":"Yanhui Wang","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Wang","suffix":""},{"id":6843038,"identity":"2768cc53-39f6-4016-8c94-a8bbf3a346f1","order_by":1,"name":"Chenxin Li","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chenxin","middleName":"","lastName":"Li","suffix":""},{"id":6843039,"identity":"0e41235e-c957-4155-a3f4-25243c7cc379","order_by":2,"name":"Jessica M Stephenson","email":"","orcid":"","institution":"University of Texas Health Science Center at Houston","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"M","lastName":"Stephenson","suffix":""},{"id":6843040,"identity":"3b2f1ea8-42ae-46df-a529-870040364662","order_by":3,"name":"Sean P Marrelli","email":"","orcid":"","institution":"University of Texas health science center at houston","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sean","middleName":"P","lastName":"Marrelli","suffix":""},{"id":6843041,"identity":"115d220c-9d71-4cac-92ab-f2997bef9cc3","order_by":4,"name":"Yanming Kou","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanming","middleName":"","lastName":"Kou","suffix":""},{"id":6843042,"identity":"a01a9641-312f-44dc-a48c-68d5e94a2e00","order_by":5,"name":"Dazhi Meng","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dazhi","middleName":"","lastName":"Meng","suffix":""},{"id":6843043,"identity":"91e00cfc-10fb-482c-b3d0-b059c29149bc","order_by":6,"name":"Ting Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACZgglx8DAAxNKwK+DB6rFmIENqOUAUVqgdGID0Vrs2XkPv2BsO5y+4X7vAeaPOXYM/Ow5BgQcxpdmAdSSu+EYXwLDwW3JDJI9bwhp4TEzYNwG0sJjANRygMHgBkFbIFrSDWBa7InQYvwAqCUBrsVAgpCWwzxmDIn/0g1nHssxOHB2WzKPxJlnBXi1sPefMf7w4Yy1PN/hM4YPKrfZyfG3J2/AqwUI2CQSoKwDDEhpAB9g/kCMqlEwCkbBKBjBAAC8NUJeWL7gXQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8152-1112","institution":"University of Texas Health Science Center at Houston","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2020-12-21 11:43:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-133154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-133154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4551028,"identity":"b35f9e7e-25d5-47ec-b861-27eb350a8ade","added_by":"auto","created_at":"2020-12-28 18:38:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":476049,"visible":true,"origin":"","legend":"Flow chart for study design. DEGs, differential expression genes; DCGs, differential\nconnectivity genes.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/21f6bd6870d844cb88d1d0f7.jpg"},{"id":4551104,"identity":"c6246ada-e7d9-4f69-aa9d-76bcb4b24c67","added_by":"auto","created_at":"2020-12-28 18:41:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1716077,"visible":true,"origin":"","legend":"Gene expression profiles of DEGs. 126 genes show significant differential\nexpressions between the AMI and the control groups in the discovery cohort (A) and validation\ncohort (B), thus define as DEGs. DEGs, differential expression genes.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/34e4c64eed278f1ec39144cb.jpg"},{"id":4551034,"identity":"35186a10-50c7-449d-82dc-7497123ea184","added_by":"auto","created_at":"2020-12-28 18:38:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2661418,"visible":true,"origin":"","legend":"Assessment of DEGs’ networks. Networks in the control and AMI groups are\nindependent and separable according to the average clustering coefficients of DEGs (A).\nNumber of clusters within DEGs’ networks progressively decline when thresholds increase\nfrom 0.1 to 0.9 (B). The AMI group has a lower decline slope. The gene networks of DEGs in\nthe AMI group has more complex connection compare to that in the control group (C).\nNetworks are present under threshold 0.5 and 0.7. Darker line represents connections under\nthreshold 0.7; lighter line represents connections under threshold 0.5. DEGs, differential\nexpression genes.","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/cdc7ede97caf1b07ed214297.jpg"},{"id":4551032,"identity":"bc062011-42e9-44cd-a7cf-8b099822a7ac","added_by":"auto","created_at":"2020-12-28 18:38:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3278578,"visible":true,"origin":"","legend":"Identification of DCGs. Genes that clustering coefficient increased over 0.1 in the\nAMI group, in discovery cohort and validation cohort, are revealed as DCGs (A). Gene\nexpression profile of DCGs shows stable increase in AMI group in two cohorts (B). The\nconnection among DCGs in the AMI group are denser (C) and the average degrees of DCGs\nin AMI group are higher (D) compare to the control group in two cohorts. Networks are\npresented under threshold 0.5 and 0.7. Darker line represents connections under threshold 0.7;\nlighter line represents connections under threshold 0.5. Degree are presented as mean ± SEM.\nDCGs, differential connectivity genes.","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/e6470dd7e560dd427c1f90aa.jpg"},{"id":4551036,"identity":"5c9e5a46-2f5a-4146-8fb1-1730ad629d72","added_by":"auto","created_at":"2020-12-28 18:38:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":270922,"visible":true,"origin":"","legend":"Visualization of DCGs in DEGs’ networks. The networks of DEGs in discovery\ncohort (A) and in the validation cohort (B) indicate that the DCGs participate in distinctive\nways in the control group and in the AMI group. DCGs switch to central functional position of\nnetworks and participate in more intricate connections under AMI situation. Yellow nodes\nindicate the DCGs. Red gene names indicate the hub genes. DEGs, differential expression\ngenes; DCGs, differential connectivity genes.","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/236b5e38d6e48a99df2be02d.jpg"},{"id":4551106,"identity":"8babb77f-2f88-40e3-af1b-c49e25d7c8d9","added_by":"auto","created_at":"2020-12-28 18:41:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1406423,"visible":true,"origin":"","legend":"Analysis of gene connection and expression of DCGs in discovery cohort. The\nanalysis of clustering coefficient and gene expression revealed CCL20 and NR4A3 as hub genes\n(A). The CCL20 is a chemoattractant while NR4A3 is a nuclear factor receptor (B). Subgraphs\nof CCL20 and NR4A3 substantiate their important roles in AMI development (C). Networks\nare presented under threshold 0.5 and 0.7. Darker line represents connections under threshold\n0.7; lighter line represents connections under threshold 0.5. DCGs, differential connectivity\ngenes; CC, clustering coefficient; GeExp, gene expression.","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/2f785bbbbd5a5d5c7dabd0cd.jpg"},{"id":4551215,"identity":"df2c2353-b8b7-44c1-a330-257a77101098","added_by":"auto","created_at":"2020-12-28 18:44:25","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":998832,"visible":true,"origin":"","legend":"CCL20 cluster and NR4A3 cluster formation in early-stage AMI. CCL20 and NR4A3\nstay in the peripheral position of DCGs’ network under normal state (A). However, they shift\nto the primary position of DCGs’ network dominating two functional clusters under AMI\nstimulation (B). DCGs, differential connectivity genes.","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/9aaf4d4a6eaa4b716a083219.jpg"},{"id":13568966,"identity":"60829d10-33d2-4205-969c-ae2a01afa5f2","added_by":"auto","created_at":"2021-09-17 03:38:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1065714,"visible":true,"origin":"","legend":"","description":"","filename":"MRmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1_covered.pdf"},{"id":4551408,"identity":"fc262d61-d130-46e8-ac3a-2ea73d7f31de","added_by":"auto","created_at":"2020-12-28 18:47:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":292786,"visible":true,"origin":"","legend":"","description":"","filename":"MRmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1_stamped.pdf"},{"id":4551105,"identity":"599622fb-1b4d-483a-b588-bdd1ee7ce049","added_by":"auto","created_at":"2020-12-28 18:41:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48991,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/555e7fbaa82ce08f33e62522.docx"},{"id":4551109,"identity":"6df8dd32-d36c-4382-af3e-1ea328bd10f9","added_by":"auto","created_at":"2020-12-28 18:41:26","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21490856,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/1a0fd10f1e992116cc7bf27e.pdf"},{"id":4551214,"identity":"ec9d65fa-6320-459f-9a92-13872bea26cb","added_by":"auto","created_at":"2020-12-28 18:44:25","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":174338,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-133154/v1/f29c6b5bbc223fbf86aa7082.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cem\u003eNR4A3 \u003c/em\u003eand \u003cem\u003eCCL20 \u003c/em\u003eClusters Dominate the Dynamic Gene Network of Peripheral CD146\u003csup\u003e+\u003c/sup\u003e Blood Cells in the Early Stage of Acute Myocardial Infarction in Human.\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"\u003cp\u003eThis preprint is available for \u003ca href='/article/rs-133154/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003eDue to technical limitations, tables docx is only available as a download in the Supplemental Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"acute myocardial infarction (AMI), CD146, Pearson network, clustering coefficient, differential connectivity genes (DCGs)","lastPublishedDoi":"10.21203/rs.3.rs-133154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-133154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e CD146 is a tight junction associated molecule involved in maintaining endothelial barrier and balancing immune-inflammation response in cardiovascular disease. Notably, the peripheral CD146\u003csup\u003e+\u003c/sup\u003e cells significantly upsurge under vessel dyshomeostasis like acute myocardial injury (AMI), appearing to be promising therapeutic targets. In this study, in a new view of gene correlation, we aim at deciphering the underlying complex mechanism of CD146\u003csup\u003e+\u003c/sup\u003e cells in the development of AMI. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Transcription dataset GSE 66360 of CD146\u003csup\u003e+\u003c/sup\u003e blood cells from clinical subjects were downloaded from NCBI. Pearson networks were constructed and the clustering coefficients were calculated to disclose the differential connectivity genes (DCGs). Analysis of gene connectivity and gene expression was performed to reveal the hub genes and hub genes clusters followed by gene enrichment analysis. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults and Conclusions:\u003c/strong\u003e Among the total 23520 genes, 27 genes out of 126 differential expression genes are identified as DCGs. Those DCGs normally stay in the peripheral of networks while transfer to the functional central position under AMI situation. Moreover, it is revealed that DCGs spontaneously crowd together into two functional models, \u003cem\u003eCCL20 \u003c/em\u003ecluster and \u003cem\u003eNR4A3 \u003c/em\u003ecluster, influencing the CD146-mediated signaling pathways during the pathology of AMI for the first time.\u003c/p\u003e","manuscriptTitle":"NR4A3 and CCL20 Clusters Dominate the Dynamic Gene Network of Peripheral CD146+ Blood Cells in the Early Stage of Acute Myocardial Infarction in Human.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-28 18:38:23","doi":"10.21203/rs.3.rs-133154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-04-29T00:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"decision","content":"Major revision","date":"2021-04-29T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-04-17T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2021-04-08T00:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-04-03T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-01-18T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-12-19T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-12-18T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-12-18T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-12-18T00:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9fd03af7-f2e0-4b66-80a9-e42c410a913e","owner":[],"postedDate":"December 28th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":1612494,"name":"Medical Genetics"}],"tags":[],"updatedAt":"2021-09-26T16:52:46+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-28 18:38:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-133154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-133154","identity":"rs-133154","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}