Time‐series transcriptome analysis of peripheral blood mononuclear cells obtained from individuals who received the SARS‐CoV‐2 mRNA vaccine
Messenger ribonucleic acid (mRNA) vaccination against coronavirus disease 2019 (COVID‐19) is an effective prevention strategy, despite a limited understanding of the molecular mechanisms underlying the host immune system and individual heterogeneity of the variable effects of mRNA vaccination. We as...
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| Veröffentlicht in: | Journal of medical virology 2023-06, Vol.95 (6), p.e28884-n/a |
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| creator | Watanabe, Yoshiyuki Yamamoto, Hiroyuki Matsuba, Ikuro Watanabe, Karin Kunishima, Tomoyuki Takechi, Yukako Takuma, Tetsuo Araki, Yasushi Hirotsu, Nobuo Sakai, Hiroyuki Oikawa, Ritsuko Danno, Hiroki Fukuda, Masakazu Sugino, Ryuichi Futagami, Seiji Wada, Kota Itoh, Fumio Tateishi, Keisuke Oda, Ichiro Hatori, Yutaka Degawa, Hisakazu |
| description | Messenger ribonucleic acid (mRNA) vaccination against coronavirus disease 2019 (COVID‐19) is an effective prevention strategy, despite a limited understanding of the molecular mechanisms underlying the host immune system and individual heterogeneity of the variable effects of mRNA vaccination. We assessed the time‐series changes in the comprehensive gene expression profiles of 200 vaccinated healthcare workers by performing bulk transcriptome and bioinformatics analyses, including dimensionality reduction utilizing the uniform manifold approximation and projection (UMAP) technique. For these analyses, blood samples, including peripheral blood mononuclear cells (PBMCs), were collected from 214 vaccine recipients before vaccination (T1) and on Days 22 (T2, after second dose), 90, 180 (T3, before a booster dose), and 360 (T4, after a booster dose) after receiving the first dose of BNT162b2 vaccine (UMIN000043851). UMAP successfully visualized the main cluster of gene expression at each time point in PBMC samples (T1–T4). Through differentially expressed gene (DEG) analysis, we identified genes that showed fluctuating expression levels and gradual increases in expression levels from T1 to T4, as well as genes with increased expression levels at T4 alone. We also succeeded in dividing these cases into five types based on the changes in gene expression levels. High‐throughput and temporal bulk RNA‐based transcriptome analysis is a useful approach for inclusive, diverse, and cost‐effective large‐scale clinical studies. |
| doi_str_mv | 10.1002/jmv.28884 |
| format | Article |
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We assessed the time‐series changes in the comprehensive gene expression profiles of 200 vaccinated healthcare workers by performing bulk transcriptome and bioinformatics analyses, including dimensionality reduction utilizing the uniform manifold approximation and projection (UMAP) technique. For these analyses, blood samples, including peripheral blood mononuclear cells (PBMCs), were collected from 214 vaccine recipients before vaccination (T1) and on Days 22 (T2, after second dose), 90, 180 (T3, before a booster dose), and 360 (T4, after a booster dose) after receiving the first dose of BNT162b2 vaccine (UMIN000043851). UMAP successfully visualized the main cluster of gene expression at each time point in PBMC samples (T1–T4). Through differentially expressed gene (DEG) analysis, we identified genes that showed fluctuating expression levels and gradual increases in expression levels from T1 to T4, as well as genes with increased expression levels at T4 alone. We also succeeded in dividing these cases into five types based on the changes in gene expression levels. High‐throughput and temporal bulk RNA‐based transcriptome analysis is a useful approach for inclusive, diverse, and cost‐effective large‐scale clinical studies.</description><identifier>ISSN: 0146-6615</identifier><identifier>EISSN: 1096-9071</identifier><identifier>DOI: 10.1002/jmv.28884</identifier><identifier>PMID: 37342886</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Antibodies, Viral ; Bioinformatics ; Blood ; BNT162 Vaccine ; bulk RNA‐based transcriptome analysis ; Coronaviruses ; Cost analysis ; COVID-19 ; COVID-19 - prevention & control ; COVID-19 Vaccines ; Gene expression ; Gene Expression Profiling ; Genes ; Heterogeneity ; Humans ; Immune system ; Leukocytes, Mononuclear ; Medical personnel ; Molecular modelling ; mRNA vaccination ; mRNA Vaccines ; PBMC ; Peripheral blood mononuclear cells ; Ribonucleic acid ; RNA ; RNA, Messenger - genetics ; SARS-CoV-2 - genetics ; Severe acute respiratory syndrome coronavirus 2 ; Transcriptome ; Transcriptomes ; UMAP ; Vaccination ; Vaccines ; Viral diseases ; Virology</subject><ispartof>Journal of medical virology, 2023-06, Vol.95 (6), p.e28884-n/a</ispartof><rights>2023 Wiley Periodicals LLC.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3534-8c667edda63bcc1809c9807bb96e30768a991bbd93e881951c34856e6f17becc3</citedby><cites>FETCH-LOGICAL-c3534-8c667edda63bcc1809c9807bb96e30768a991bbd93e881951c34856e6f17becc3</cites><orcidid>0000-0001-9037-3596 ; 0000-0002-0954-6806</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmv.28884$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmv.28884$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37342886$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Watanabe, Yoshiyuki</creatorcontrib><creatorcontrib>Yamamoto, Hiroyuki</creatorcontrib><creatorcontrib>Matsuba, Ikuro</creatorcontrib><creatorcontrib>Watanabe, Karin</creatorcontrib><creatorcontrib>Kunishima, Tomoyuki</creatorcontrib><creatorcontrib>Takechi, Yukako</creatorcontrib><creatorcontrib>Takuma, Tetsuo</creatorcontrib><creatorcontrib>Araki, Yasushi</creatorcontrib><creatorcontrib>Hirotsu, Nobuo</creatorcontrib><creatorcontrib>Sakai, Hiroyuki</creatorcontrib><creatorcontrib>Oikawa, Ritsuko</creatorcontrib><creatorcontrib>Danno, Hiroki</creatorcontrib><creatorcontrib>Fukuda, Masakazu</creatorcontrib><creatorcontrib>Sugino, Ryuichi</creatorcontrib><creatorcontrib>Futagami, Seiji</creatorcontrib><creatorcontrib>Wada, Kota</creatorcontrib><creatorcontrib>Itoh, Fumio</creatorcontrib><creatorcontrib>Tateishi, Keisuke</creatorcontrib><creatorcontrib>Oda, Ichiro</creatorcontrib><creatorcontrib>Hatori, Yutaka</creatorcontrib><creatorcontrib>Degawa, Hisakazu</creatorcontrib><title>Time‐series transcriptome analysis of peripheral blood mononuclear cells obtained from individuals who received the SARS‐CoV‐2 mRNA vaccine</title><title>Journal of medical virology</title><addtitle>J Med Virol</addtitle><description>Messenger ribonucleic acid (mRNA) vaccination against coronavirus disease 2019 (COVID‐19) is an effective prevention strategy, despite a limited understanding of the molecular mechanisms underlying the host immune system and individual heterogeneity of the variable effects of mRNA vaccination. We assessed the time‐series changes in the comprehensive gene expression profiles of 200 vaccinated healthcare workers by performing bulk transcriptome and bioinformatics analyses, including dimensionality reduction utilizing the uniform manifold approximation and projection (UMAP) technique. For these analyses, blood samples, including peripheral blood mononuclear cells (PBMCs), were collected from 214 vaccine recipients before vaccination (T1) and on Days 22 (T2, after second dose), 90, 180 (T3, before a booster dose), and 360 (T4, after a booster dose) after receiving the first dose of BNT162b2 vaccine (UMIN000043851). UMAP successfully visualized the main cluster of gene expression at each time point in PBMC samples (T1–T4). Through differentially expressed gene (DEG) analysis, we identified genes that showed fluctuating expression levels and gradual increases in expression levels from T1 to T4, as well as genes with increased expression levels at T4 alone. We also succeeded in dividing these cases into five types based on the changes in gene expression levels. High‐throughput and temporal bulk RNA‐based transcriptome analysis is a useful approach for inclusive, diverse, and cost‐effective large‐scale clinical studies.</description><subject>Antibodies, Viral</subject><subject>Bioinformatics</subject><subject>Blood</subject><subject>BNT162 Vaccine</subject><subject>bulk RNA‐based transcriptome analysis</subject><subject>Coronaviruses</subject><subject>Cost analysis</subject><subject>COVID-19</subject><subject>COVID-19 - prevention & control</subject><subject>COVID-19 Vaccines</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Genes</subject><subject>Heterogeneity</subject><subject>Humans</subject><subject>Immune system</subject><subject>Leukocytes, Mononuclear</subject><subject>Medical personnel</subject><subject>Molecular modelling</subject><subject>mRNA vaccination</subject><subject>mRNA Vaccines</subject><subject>PBMC</subject><subject>Peripheral blood mononuclear cells</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA, Messenger - genetics</subject><subject>SARS-CoV-2 - genetics</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Transcriptome</subject><subject>Transcriptomes</subject><subject>UMAP</subject><subject>Vaccination</subject><subject>Vaccines</subject><subject>Viral diseases</subject><subject>Virology</subject><issn>0146-6615</issn><issn>1096-9071</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10U9PHCEYBnDS1OhqPfQLNCS96GEUhlkGjpuNf2q0TdR6nQDzTpbNMExhZs3e_Aj6Ff0kZV3bQ5Ne4MDvfQg8CH2m5IQSkp8u3eokF0IUH9CEEskzSUr6EU0ILXjGOZ3uof0Yl4QQIfN8F-2xkhVpgE_Qy7118Pr0HCFYiHgIqosm2H7wDrDqVLuONmLf4D6BfgFBtVi33tfY-c53o2lBBWygbZPSg7Id1LgJ3mHb1XZl61Glk8eFxwEM2FU6HRaA72a3d-nWuX9Ia47d7fcZXilj0vgntNOkGTh83w_Qz_Oz-_lldv3j4tt8dp0ZNmVFJgznJdS14kwbQwWRRgpSai05MFJyoaSkWteSgRBUTqlhhZhy4A0tNRjDDtDRNrcP_tcIcaicjZuHqA78GKtc5KIsC8F5ol__oUs_hvQ5G8UopUXOWFLHW2WCjzFAU_XBOhXWFSXVpqcq9VS99ZTsl_fEUTuo_8o_xSRwugWPtoX1_5Oqq5uHbeRv0cGhGA</recordid><startdate>202306</startdate><enddate>202306</enddate><creator>Watanabe, Yoshiyuki</creator><creator>Yamamoto, Hiroyuki</creator><creator>Matsuba, Ikuro</creator><creator>Watanabe, Karin</creator><creator>Kunishima, Tomoyuki</creator><creator>Takechi, Yukako</creator><creator>Takuma, Tetsuo</creator><creator>Araki, Yasushi</creator><creator>Hirotsu, Nobuo</creator><creator>Sakai, Hiroyuki</creator><creator>Oikawa, Ritsuko</creator><creator>Danno, Hiroki</creator><creator>Fukuda, Masakazu</creator><creator>Sugino, Ryuichi</creator><creator>Futagami, Seiji</creator><creator>Wada, Kota</creator><creator>Itoh, Fumio</creator><creator>Tateishi, Keisuke</creator><creator>Oda, Ichiro</creator><creator>Hatori, Yutaka</creator><creator>Degawa, Hisakazu</creator><general>Wiley Subscription Services, 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>7QL</scope><scope>7TK</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9037-3596</orcidid><orcidid>https://orcid.org/0000-0002-0954-6806</orcidid></search><sort><creationdate>202306</creationdate><title>Time‐series transcriptome analysis of peripheral blood mononuclear cells obtained from individuals who received the SARS‐CoV‐2 mRNA vaccine</title><author>Watanabe, Yoshiyuki ; Yamamoto, Hiroyuki ; Matsuba, Ikuro ; Watanabe, Karin ; Kunishima, Tomoyuki ; Takechi, Yukako ; Takuma, Tetsuo ; Araki, Yasushi ; Hirotsu, Nobuo ; Sakai, Hiroyuki ; Oikawa, Ritsuko ; Danno, Hiroki ; Fukuda, Masakazu ; Sugino, Ryuichi ; Futagami, Seiji ; Wada, Kota ; Itoh, Fumio ; Tateishi, Keisuke ; Oda, Ichiro ; Hatori, Yutaka ; Degawa, Hisakazu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3534-8c667edda63bcc1809c9807bb96e30768a991bbd93e881951c34856e6f17becc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Antibodies, Viral</topic><topic>Bioinformatics</topic><topic>Blood</topic><topic>BNT162 Vaccine</topic><topic>bulk RNA‐based transcriptome analysis</topic><topic>Coronaviruses</topic><topic>Cost analysis</topic><topic>COVID-19</topic><topic>COVID-19 - 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Academic</collection><jtitle>Journal of medical virology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Watanabe, Yoshiyuki</au><au>Yamamoto, Hiroyuki</au><au>Matsuba, Ikuro</au><au>Watanabe, Karin</au><au>Kunishima, Tomoyuki</au><au>Takechi, Yukako</au><au>Takuma, Tetsuo</au><au>Araki, Yasushi</au><au>Hirotsu, Nobuo</au><au>Sakai, Hiroyuki</au><au>Oikawa, Ritsuko</au><au>Danno, Hiroki</au><au>Fukuda, Masakazu</au><au>Sugino, Ryuichi</au><au>Futagami, Seiji</au><au>Wada, Kota</au><au>Itoh, Fumio</au><au>Tateishi, Keisuke</au><au>Oda, Ichiro</au><au>Hatori, Yutaka</au><au>Degawa, Hisakazu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Time‐series transcriptome analysis of peripheral blood mononuclear cells obtained from individuals who received the SARS‐CoV‐2 mRNA vaccine</atitle><jtitle>Journal of medical virology</jtitle><addtitle>J Med Virol</addtitle><date>2023-06</date><risdate>2023</risdate><volume>95</volume><issue>6</issue><spage>e28884</spage><epage>n/a</epage><pages>e28884-n/a</pages><issn>0146-6615</issn><eissn>1096-9071</eissn><abstract>Messenger ribonucleic acid (mRNA) vaccination against coronavirus disease 2019 (COVID‐19) is an effective prevention strategy, despite a limited understanding of the molecular mechanisms underlying the host immune system and individual heterogeneity of the variable effects of mRNA vaccination. We assessed the time‐series changes in the comprehensive gene expression profiles of 200 vaccinated healthcare workers by performing bulk transcriptome and bioinformatics analyses, including dimensionality reduction utilizing the uniform manifold approximation and projection (UMAP) technique. For these analyses, blood samples, including peripheral blood mononuclear cells (PBMCs), were collected from 214 vaccine recipients before vaccination (T1) and on Days 22 (T2, after second dose), 90, 180 (T3, before a booster dose), and 360 (T4, after a booster dose) after receiving the first dose of BNT162b2 vaccine (UMIN000043851). UMAP successfully visualized the main cluster of gene expression at each time point in PBMC samples (T1–T4). Through differentially expressed gene (DEG) analysis, we identified genes that showed fluctuating expression levels and gradual increases in expression levels from T1 to T4, as well as genes with increased expression levels at T4 alone. We also succeeded in dividing these cases into five types based on the changes in gene expression levels. High‐throughput and temporal bulk RNA‐based transcriptome analysis is a useful approach for inclusive, diverse, and cost‐effective large‐scale clinical studies.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>37342886</pmid><doi>10.1002/jmv.28884</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-9037-3596</orcidid><orcidid>https://orcid.org/0000-0002-0954-6806</orcidid></addata></record> |
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| subjects | Antibodies, Viral Bioinformatics Blood BNT162 Vaccine bulk RNA‐based transcriptome analysis Coronaviruses Cost analysis COVID-19 COVID-19 - prevention & control COVID-19 Vaccines Gene expression Gene Expression Profiling Genes Heterogeneity Humans Immune system Leukocytes, Mononuclear Medical personnel Molecular modelling mRNA vaccination mRNA Vaccines PBMC Peripheral blood mononuclear cells Ribonucleic acid RNA RNA, Messenger - genetics SARS-CoV-2 - genetics Severe acute respiratory syndrome coronavirus 2 Transcriptome Transcriptomes UMAP Vaccination Vaccines Viral diseases Virology |
| title | Time‐series transcriptome analysis of peripheral blood mononuclear cells obtained from individuals who received the SARS‐CoV‐2 mRNA vaccine |
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