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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2024.1411771</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Immunology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Causality between herpes virus infections and allograft dysfunction after tissue and organ transplantation: a two-sample bidirectional Mendelian randomization study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Qiu</surname>
<given-names>Xiaojuan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2705275"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/software/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ma</surname>
<given-names>Tianjiao</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhao</surname>
<given-names>Shishun</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/supervision/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zheng</surname>
<given-names>Zongyu</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Department of Urology, The First Hospital of Jilin University</institution>, <addr-line>Changchun</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>College of Mathematics, Jilin University</institution>, <addr-line>Changchun</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Department of Rheumatology and Immunology, China-Japan Union Hospital of the Jilin University</institution>, <addr-line>Changchun</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Sarah Julia Reiling, McGill University Health Centre, Canada</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Nilesh Chitnis, Baylor College of Medicine, United States</p>
<p>Chief Ben-Eghan, University of Cambridge, United Kingdom</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Zongyu Zheng, <email xlink:href="mailto:zhengzongyu@jlu.edu.cn">zhengzongyu@jlu.edu.cn</email>
</p>
</fn>
<fn fn-type="other" id="fn003">
<p>&#x2020;These authors share first authorship</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>08</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1411771</elocation-id>
<history>
<date date-type="received">
<day>03</day>
<month>04</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>26</day>
<month>07</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Qiu, Ma, Zhao and Zheng</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Qiu, Ma, Zhao and Zheng</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Observational studies have suggested that herpes virus infections increase the risk of allograft dysfunction after tissue and organ transplantation, but it is still unclear whether this association is causal. The aim of this study was to assess the causal relationship between four herpes virus infections and allograft dysfunction.</p>
</sec>
<sec>
<title>Methods</title>
<p>We used two-sample bidirectional Mendelian randomization (MR) to investigate the causality between four herpes virus infections &#x2014; cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes simplex virus (HSV) and varicella zoster virus (VZV) &#x2014; and allograft dysfunction after tissue and organ transplantation. Based on summary data extracted from genome-wide association studies (GWAS), we chose eligible single nucleotide polymorphisms (SNPs) as instrumental variables. The Inverse variance weighted (IVW) method was used as the main analysis method, supplemented by Weighted median and MR-Egger analyses. The MR-PRESSO test, MR-Egger intercept test, heterogeneity test, leave-one-out analysis and funnel plot were used to analyze the sensitivity of MR results.</p>
</sec>
<sec>
<title>Results</title>
<p>We found EBV early antigen-D (EA-D) antibody levels and shingles were the only two variables associated with an increased risk of allograft dysfunction. No evidence of allograft dysfunction increasing the risk of the four herpes virus infections was observed. Sensitivity analyses confirmed the robustness of our results.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>Our results suggest that EBV and VZV are involved in graft rejection or dysfunction. However, the relationship between CMV and HSV infections and allograft dysfunction remains unclear and requires further clarification.</p>
</sec>
</abstract>
<kwd-group>
<kwd>Mendelian randomization</kwd>
<kwd>tissue and organ transplantation</kwd>
<kwd>allograft dysfunction</kwd>
<kwd>herpes virus infection</kwd>
<kwd>antibody</kwd>
</kwd-group>
<counts>
<fig-count count="9"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="67"/>
<page-count count="12"/>
<word-count count="4437"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Alloimmunity and Transplantation</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Solid organ transplantation (SOT) has been an established and practical definitive treatment option for patients with end-organ dysfunction, and has transformed the survival and quality of life of patients with end-organ dysfunction (<xref ref-type="bibr" rid="B1">1</xref>). However, allograft dysfunction can affect the survival of grafts and SOT recipients. In this study, allograft dysfunction was defined as failure and rejection of transplanted organs and tissues due to external causes. Although there are many external factors that can cause allograft dysfunction, infectious diseases after SOT are a significant cause of chronic allograft dysfunction and allograft Survival (<xref ref-type="bibr" rid="B2">2</xref>).</p>
<p>Herpes virus is a common opportunistic virus after transplantation. These DNA viruses are divided into four subfamilies based on their physicochemical properties: (i) &#x3b1; herpes viruses such as herpes simplex virus (HSV) or varicella zoster virus (VZV), (ii) &#x3b2; herpes viruses such as cytomegalovirus (CMV), (iii) &#x3b3; herpes viruses such as Epstein-Barr virus (EBV), and (iv) unclassified herpes viruses (<xref ref-type="bibr" rid="B3">3</xref>). In Europe, the infection rate of herpes viruses in the general population is as high as 95% for HSV and VZV, 90% for EBV, and 60% for CMV (<xref ref-type="bibr" rid="B4">4</xref>), with prevalence rate increasing with age (<xref ref-type="bibr" rid="B4">4</xref>). Due to the administration of immunosuppressants, organ transplant recipients generally have weakened immunity. Consequently, the incidence of postoperative secondary herpes virus infection is significantly higher, increasing the risk of disease and mortality among this population (<xref ref-type="bibr" rid="B5">5</xref>&#x2013;<xref ref-type="bibr" rid="B9">9</xref>).</p>
<p>Previous studies have shown that CMV is the primary cause of infectious diseases within the first year following solid organ transplantation (SOT), and CMV is also considered a risk factor for allograft dysfunction and rejection (<xref ref-type="bibr" rid="B10">10</xref>). Similarly, post-transplant lymphoproliferative disorders resulting from EBV infection are considered as one of the most severe complications of organ transplantation, often occurring in the early post-transplant period (<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B12">12</xref>). The mortality rate among transplant recipients suffering from post-transplant lymphoproliferative disorders has been reported to be as high as 60% (<xref ref-type="bibr" rid="B13">13</xref>). Furthermore, up to 70% of SOT recipients may develop VZV or HSV infections if preventive measures are not taken, some of which can be life-threatening and pose a risk to the transplanted organ (<xref ref-type="bibr" rid="B14">14</xref>). VZV and two HSV have also been reported to establish a lifelong latency period in the ganglia of SOT patients after the initial primary infection (<xref ref-type="bibr" rid="B14">14</xref>). Therefore, after tissue and organ transplantation, the use of antiviral drugs or the addition of immunoglobulin to suppress herpes virus infection has become a widespread consensus (<xref ref-type="bibr" rid="B15">15</xref>&#x2013;<xref ref-type="bibr" rid="B17">17</xref>).</p>
<p>While there is scientific evidence supporting that CMV, EBV, VZV and HSV increase the risk of rejection or death after tissue and organ transplants (<xref ref-type="bibr" rid="B10">10</xref>&#x2013;<xref ref-type="bibr" rid="B14">14</xref>), there is currently no direct evidence of a causal relationship. In fact, many of the observational studies performed in this field presented numerous shortcomings, such as residual and unmeasured confounding, detection bias, and reverse causality (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B19">19</xref>). In recent years, Mendelian randomization (MR) has emerged as a powerful technique for inferencing causality based on genome-wide association studies (GWAS) (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>).</p>
<p>MR uses genetic variation as an instrumental variable (IV) to infer whether a risk factor has a causal effect on outcomes (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B21">21</xref>). In MR studies, genetic variation follows the principle of assigning random alleles to offspring, similar to randomized controlled trials (<xref ref-type="bibr" rid="B22">22</xref>). This approach effectively mitigates the confounding factors and reverse causality that are often encountered in observational studies (<xref ref-type="bibr" rid="B23">23</xref>). MR has been widely applied in herpes virus research. For instance, MR studies have shown that there is no causal relationship between herpes virus infection and pulmonary fibrosis (<xref ref-type="bibr" rid="B24">24</xref>), that CMV infection dose not significantly increase the risk of autism spectrum disorder (<xref ref-type="bibr" rid="B25">25</xref>), or that there is a causal relationship between EBV infection and Alzheimer&#x2019;s disease (<xref ref-type="bibr" rid="B26">26</xref>). Recent MR studies have also shown that lipids may trigger causal pathological processes that lead to allograft dysfunction after organ and tissue transplantation (<xref ref-type="bibr" rid="B27">27</xref>). However, to our knowledge, there are no studies investigating a potential causal relationship between herpes virus infections and tissue and organ transplant dysfunction.</p>
<p>Herein, we used a two-sample bidirectional MR to assess the causal relationship between four herpes virus (CMV, EBV, HSV, VZV) infectious diseases, associated antibody and immunoglobulin G (IgG) levels, and allograft dysfunction after tissue and organ transplantation.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Study design</title>
<p>MR Studies need to meet the following assumptions: First, IVs should be closely related to exposure; Second, IVs are not associated with any possible confounders; Third, IVs can only affect the outcome through exposure (<xref ref-type="bibr" rid="B20">20</xref>). When an IV can affect the outcome through a path other than genetic variant-expose-outcome, we consider the IV to have horizontal pleiotropy. The data in this study came from publicly available GWAS databases (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>; <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;1</bold>
</xref>). All consortiums initially involved in the GWAS studies completed the participants&#x2019; ethical approval and written informed consent. <xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref> summarizes the flow chart of a two-sample bidirectional MR Design.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Brief description of datasets utilized in the Mendelian randomization study.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Phenotype</th>
<th valign="middle" align="center">GWAS ID</th>
<th valign="middle" align="center">Source</th>
<th valign="middle" align="center">Sample size<break/>(Cases\Controls)</th>
<th valign="middle" align="center">Population</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Mononucleosis</td>
<td valign="middle" align="center">mononucleosis</td>
<td valign="middle" rowspan="4" align="center">23andMe cohort</td>
<td valign="middle" align="center">17457\68446</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">Cold scores</td>
<td valign="middle" align="center">cold scores</td>
<td valign="middle" align="center">25108\63332</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">Chickenpox</td>
<td valign="middle" align="center">chickenpox</td>
<td valign="middle" align="center">107769\15982</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">Shingles</td>
<td valign="middle" align="center">shingles</td>
<td valign="middle" align="center">16711\118152</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">CMV pp28 antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006894</td>
<td valign="middle" rowspan="10" align="center">UK Biobank cohort</td>
<td valign="middle" align="center">5,087</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">CMV pp52 antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006895</td>
<td valign="middle" align="center">5,681</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">CMV pp150 antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006896</td>
<td valign="middle" align="center">5,136</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">EBV EA-D antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006898</td>
<td valign="middle" align="center">7,763</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">EBV EBNA-1 antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006899</td>
<td valign="middle" align="center">7,972</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">EBV VCA p18 antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006900</td>
<td valign="middle" align="center">8,518</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">EBV ZEBRA antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006901</td>
<td valign="middle" align="center">8,191</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">HSV-1 mgG-1 antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006918</td>
<td valign="middle" align="center">6,199</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">HSV-2 mgG-1 antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006920</td>
<td valign="middle" align="center">1,382</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">VZV glycoproteins E and I antibody levels</td>
<td valign="middle" align="center">ebi-a-GCST90006929</td>
<td valign="middle" align="center">7,595</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">Anti-CMV IgG levels</td>
<td valign="middle" align="center">ieu-b-4900</td>
<td valign="middle" rowspan="3" align="center">IEU OPEN GWAS</td>
<td valign="middle" align="center">5,010</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">Anti-EBV IgG levels</td>
<td valign="middle" align="center">ieu-b-4901</td>
<td valign="middle" align="center">5,010</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">Anti-HSV-1 IgG levels</td>
<td valign="middle" align="center">ieu-b-4906</td>
<td valign="middle" align="center">683</td>
<td valign="middle" align="center">European</td>
</tr>
<tr>
<td valign="middle" align="center">Failure and rejection of transplanted organs and tissues</td>
<td valign="middle" align="center">FAILU_REJEC_TP_ ORGANS_TISSU</td>
<td valign="middle" align="center">FinnGen cohort</td>
<td valign="middle" align="center">209\278724</td>
<td valign="middle" align="center">European</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>EBV, Epstein-Barr virus; CMV, cytomegalovirus; HSV, herpes simplex; VZV, Varicella zoster virus; EA, EBV early antigen; EBNA-1, EBV nuclear antigen-1; VCA, viral capsid antigen; IgG, immunoglobulin G.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>The flow chart of MR Design. In the forward MR analysis, exposures (herpes virus infections) are shown in red, and outcome (allograft dysfunction after organ and tissue transplantation) is shown in blue; In the reverse MR Analysis, exposure (allograft dysfunction) is shown in blue, and outcomes (herpes virus infections) are shown in red. Abbreviation: GWAS, genome-wide association study; MR-PRESSO, MR pleiotropy residual sum and outliers; MR, Mendelian randomization; SNP, single nucleotide polymorphisms.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>GWAS data collection</title>
<p>Genomic data associated with herpesvirus infectious diseases was extracted from a previous GWAS study (<xref ref-type="bibr" rid="B28">28</xref>), that used the summary data of 23andMe cohort (only the top 8,000 SNPs are listed). Only participants of European ancestry &gt;97% were included in the analysis (<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B28">28</xref>), and a rigorous self-report questionnaire on infection history was used to determine the phenotype. Specifically, we selected mononucleosis (17,457 cases and 68,446 controls) and cold sores (25,108 cases and 63,332 controls) caused by EBV and HSV, and chickenpox (107,769 cases and 15,982 controls) and shingles (16,711 cases and 118,152 controls) caused by HSV (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). Since only the first 8,000 SNPS with the lowest <italic>p</italic>-value in the 23andMe cohort were available, the data were not used as exposure data for the reverse MR Study of allograft dysfunction and herpes virus infection. We obtained GWAS summary data related to herpesvirus-associated IgG levels from the IEU Open GWAS project (<xref ref-type="bibr" rid="B29">29</xref>, <xref ref-type="bibr" rid="B30">30</xref>). We selected the GWAS summary data sets ieu-b-4900 (n = 5,010) for the study of anti-CMV IgG levels, ieu-b-4901 (n = 5,010) for investigating anti-EBV IgG levels and ieu-b-4906 (n = 683) for anti-HSV-1 IgG levels (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). GWAS summary data on herpesvirus-associated antibody levels was collected from the UK Biobank cohort (<xref ref-type="bibr" rid="B31">31</xref>). We selected genomic data regarding antibody levels against CMV pp28 (n = 5,087), CMV pp52 (n = 5,681), CMV pp150 (n = 5,136), EBV early antigen-D (EA-D, n = 7,763), EBV nuclear antigen-1 (EBNA-1, n = 7,972), EBV viral capsid antigen (VCA) p18 (n = 8,518), EBV ZEBRA (n = 8,191), HSV-1 mgG-1 (n = 6,199), HSV-2 mgG-1 (n = 1,382), and VZV glycoprotein E and I (n = 7,595). We selected GWAS summary data for failure and rejection of transplanted organs and tissues that was described as injury, poisoning and certain other consequences of external causes (FAILU_REJEC_TRANSPLANTED_ORGANS_TISSU, 209 cases, 278,724 controls) from the FinnGen cohort (<xref ref-type="bibr" rid="B32">32</xref>).</p>
<p>The study used the large publicly available GWAS databases, which have received approval from their relevant ethical review board and participants.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Instrumental variable identification</title>
<p>Consistent with previous studies (<xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B33">33</xref>), to obtain a sufficient number of single nucleotide polymorphisms (SNPs), we chose a relatively loose threshold (<italic>p</italic>&lt;5&#xd7;10<sup>-5</sup>) for analysis. To ensure the selection of independent SNPs and minimize the influence of linkage disequilibrium (LD) on the results, SNPs were selected at a threshold of LD <italic>r</italic>
<sup>2</sup>&gt;0.001 and a distance of 10,000 kb (<xref ref-type="bibr" rid="B34">34</xref>). The strength of the correlation between the instrumental variable and the exposure factor was assessed by the F-statistic. To mitigate the bias caused by weak instrumental variables, we only consider SNPs with F-statistics &gt;10 (<xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B36">36</xref>). We excluded SNPs with a minor allele frequency (MAF) of less than 0.01 because the effects of these SNPs were observed not to be stable (<xref ref-type="bibr" rid="B24">24</xref>), and deleted palindromic sequences with minor allele frequency (MAF&gt;0.42) to prevent chain ambiguity errors (<xref ref-type="bibr" rid="B37">37</xref>). In addition, since a pleiotropic effect between lipids and allograft dysfunction was observed in the original GWAS study (<xref ref-type="bibr" rid="B27">27</xref>), We searched the PhenoScanner website (<xref ref-type="bibr" rid="B38">38</xref>&#x2013;<xref ref-type="bibr" rid="B40">40</xref>) to exclude SNPs associated with blood lipids (high-density lipoprotein, low-density lipoprotein, cholesterol, and triglycerides) in the relationship between herpes virus and allograft dysfunction. These SNPs were genome-wide significant <italic>(p&lt;</italic>5&#xd7;10<sup>-5</sup>) and known as confounding factors (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;2</bold>
</xref>) (<xref ref-type="bibr" rid="B24">24</xref>).</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Statistical analysis</title>
<p>We conducted a two-sample bidirectional Mendelian randomization study using the &#x201c;TwoSampleMR&#x201d; package (version 0.5.8) (<xref ref-type="bibr" rid="B41">41</xref>) in R software (version 4.2.1) (<xref ref-type="bibr" rid="B42">42</xref>) to investigate the relationship between four herpes viruses and allograft dysfunction after tissue and organ transplantation.</p>
<p>We mainly used Inverse variance weighting (IVW), the weighted median and MR-Egger method to carry out MR analysis to obtain the odds ratio (OR) estimates and <italic>p</italic>-values of causal effect, in which IVW method was used as the main method. When <italic>p</italic>&lt; 0.05, the causal relationship between exposure and outcome was considered significant. In fixed effects meta-analyses, SNP-exposure coefficients and SNP-outcome coefficients were combined using IVW methods to give an overall estimate of causal effects (<xref ref-type="bibr" rid="B43">43</xref>). This is equivalent to a weighted regression of the SNP-outcome coefficient to the SNP-exposure coefficient with a zero intercept. The causal estimate for the IVW analysis represents a causal increase in outcome per unit change in exposure. The method assumes that all variables are valid IVs based on the MR assumption (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>) and have no horizontal pleiotropy. To account for potential violations of the assumptions underlying the IVW MR analysis, we compared the IVW results with the Weighted median and MR-Egger methods, known to be more robust for horizontal pleiotropy, albeit at the cost of reduced statistical power (<xref ref-type="bibr" rid="B44">44</xref>). First, we employed the Weighted median MR method that allows 50% of the instrumental variables to be invalid (<xref ref-type="bibr" rid="B45">45</xref>). Secondly, we used MR-Egger regression based on the &#x201c;NO Measurement Error&#x201d; (NOME) assumption. This method allows all instrumental variables to be affected by horizontal pleiotropy, intercept represents the causal estimation deviation due to pleiotropy, and slope represents the causal estimation effect (<xref ref-type="bibr" rid="B46">46</xref>). Therefore, the MR-Egger regression intercept can assess the pleiotropy and provide an estimation effect that is not affected by pleiotropy. In addition to the MR-Egger regression intercept, MR pleiotropy residual sum and outliers (MR-PRESSO) tests are also used to detect outliers and horizontal pleiotropy (<xref ref-type="bibr" rid="B47">47</xref>). A <italic>p</italic>&gt; 0.05 indicated no significant horizontal pleiotropy.</p>
<p>Since the exposure and outcome of two-sample MR came from different samples, there could be different population heterogeneity. We used the Cochran&#x2019;s s Q statistic (IVW method) and Rucker&#x2019;s s Q statistic (MR-Egger method) for heterogeneity tests (<xref ref-type="bibr" rid="B47">47</xref>). A <italic>p</italic>&gt; 0.05 indicated no significant heterogeneity. The funnel plots were also used to assess for heterogeneity among individual genetic variants. When there was no heterogeneity, the funnel plot was symmetrical. In addition, a &#x201c;leave-one-out&#x201d; analysis was performed to examine whether the causal relationship between exposure and outcome was influenced by a single SNP by removing SNPs one by one to see whether the OR changes significantly (<xref ref-type="bibr" rid="B48">48</xref>). The MR results were visualized using forest plots and scatter plots (&#x201c;TwoSampleMR&#x201d; package). The forest plots present the estimated causal effect for each SNP. Each point in the scatter plots represents a SNP, showing how each genetic variation is associated with exposure and outcome.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<p>The results of MR-PRESSO, pleiotropy test and heterogeneity test are shown in <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;3</bold>
</xref>. Scatter plots, leave-one-out plots, forest plots and funnel plots of MR Analysis results are shown in <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Materials</bold>
</xref> (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figures&#xa0;1-30</bold>
</xref>).</p>
<sec id="s3_1">
<label>3.1</label>
<title>Effect of CMV infection on allograft dysfunction</title>
<p>IVW results did not support that antibody levels against CMV pp28 (OR = 0.847, 95% confidence interval (CI): 0.613-1.171, <italic>p</italic> = 0.316),CMV pp52 (OR = 0.883, 95% CI: 0.670-1.162, <italic>p</italic> = 0.372), CMV pp150 (OR = 1.190, 95% CI: 0.922-1.536, <italic>p</italic> = 0.181) and anti-CMV IgG (OR = 1.068, 95% CI: 0.843-1.352, <italic>p</italic> = 0.586) had effects on allograft dysfunction (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). Similarly, the results obtained using the Weighted median and MR-Egger methods did not support a causal relationship between CMV infection and allograft dysfunction either (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>The forest plot of the causal relationship between cytomegalovirus and allograft dysfunction. CMV, cytomegalovirus; nSNP, number of single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g002.tif"/>
</fig>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Effect of EBV infection on allograft dysfunction</title>
<p>The IVW analysis found a positive effect of EBV EA-D antibody levels on allograft dysfunction (OR = 1.405, 95% CI:1.036-1.905, <italic>p</italic> = 0.029). And the OR greater than 1 indicated that higher antibody levels would increase the risk of allograft dysfunction. There was no other evidence of a causal relationship between the other EBV antibody levels, mononucleosis and EBV IgG levels, and allograft dysfunction (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). However, the calculated <italic>p</italic>-value of Egger intercept for EBV EA-D antibody levels was 0.046, indicating that there is some evidence of directional horizontal pleiotropy in the MR analysis, and therefore a potential bias in the causal estimate derived from the MR analysis (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Under this circumstance, we used the MR-Egger method to provide a more reliable estimate (<xref ref-type="bibr" rid="B49">49</xref>, <xref ref-type="bibr" rid="B50">50</xref>), and it still indicated a causal relationship between EBV EA-D antibodies and allograft dysfunction (OR = 2.690, 95% CI: 1.339-5.404, <italic>p</italic> = 0.007). No heterogeneity was found with the Cochran&#x2019;s Q and Rucker&#x2019;s Q tests for EBV EA-D antibody levels (<italic>p</italic> = 0.533, <italic>p</italic> = 0.644) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Moreover, the leave-one-out plot of EBV EA-D antibody levels showed that the sequential removal of each SNP had little effect on the results, and no single SNP had a significant effect on the overall causal effect estimate. The funnel plot is essentially symmetrical, indicating the robustness of this result (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>The forest plot of the causal relationship between Epstein-Barr virus and allograft dysfunction. EBV, Epstein-Barr virus; nSNP, number of single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g003.tif"/>
</fig>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>The pleiotropic and heterogeneous results of EBV EA-D antibody levels and allograft dysfunction.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="4" align="center">Exposure</th>
<th valign="middle" rowspan="4" align="center">Outcome</th>
<th valign="middle" colspan="2" align="center">MR-PRESSO</th>
<th valign="middle" align="center">Pleiotropy test</th>
<th valign="middle" colspan="2" align="center">Heterogeneity test</th>
</tr>
<tr>
<th valign="middle" rowspan="2" align="center">Distortion test</th>
<th valign="middle" rowspan="2" align="center">Global test</th>
<th valign="middle" rowspan="2" align="center">Egger intercept</th>
<th valign="middle" align="center">Cochran&#x2019;s Q test</th>
<th valign="middle" align="center">Rucker&#x2019;s Q test</th>
</tr>
<tr>
<th valign="middle" align="center">P-value</th>
<th valign="middle" align="center">P-value</th>
</tr>
<tr>
<th valign="middle" align="center">Outliers</th>
<th valign="middle" align="center">P-value</th>
<th valign="middle" align="center">P-value</th>
<th valign="middle" align="center">IVW</th>
<th valign="middle" align="center">MR-Egger</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">EBV EA-D</td>
<td valign="middle" align="center">FAILU_REJEC_TP_ORGANS_TISSU</td>
<td valign="middle" align="center">NA</td>
<td valign="middle" align="center">0.527</td>
<td valign="middle" align="center">0.046</td>
<td valign="middle" align="center">0.533</td>
<td valign="middle" align="center">0.644</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>The leave-one-out plot and funnel plot of EBV EA-D antibody levels and allograft dysfunction.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g004.tif"/>
</fig>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Effect of HSV infection on allograft dysfunction</title>
<p>The results obtained with the IVW method did not support that antibody levels targeting HSV-1 mgG-1 (OR = 0.971, 95% CI: 0.744-1.266, <italic>p</italic> = 0.826), HSV-2 mgG-1 (OR = 0.938, 95% CI: 0.826-1.066, <italic>p</italic> = 0.328) and Anti-HSV-1 IgG (OR = 1.025, 95% CI: 0.919-1.144, <italic>p</italic> = 0.651), nor cold scores (OR = 1.545, 95% CI: 0.902-2.649, <italic>p</italic> = 0.113) had effects on allograft dysfunction (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>). Likewise, the analyses performed using the Weighted median and MR-Egger methods did not support a causal relationship between HSV infection and allograft dysfunction either (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>).</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>The forest plot of the causal relationship between herpes simplex virus and allograft dysfunction. HSV, herpes simplex virus; nSNP, number of single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g005.tif"/>
</fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Effect of VZV infection on allograft dysfunction</title>
<p>According to the IVW analysis results, shingles was positively associated with allograft dysfunction (OR = 1.555, 95% CI: 1.008-2.401, <italic>p</italic> = 0.046). On the contrary, there was no evidence of a causal relationship between chickenpox (OR = 0.908, 95% CI: 0.614-1.341, <italic>p</italic> = 0.626) and VZV glycoprotein E and I antibody levels (OR = 1.187, 95% CI: 0.859-1.640, <italic>p</italic> = 0.298), and allograft dysfunction (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6</bold>
</xref>). The MR-Egger method for shingles also confirmed this conclusion (OR&#xa0;= 3.721, 95%CI: 1.420-9.745, <italic>p</italic> = 0.010). Additionally, neither Cochran&#x2019;s Q test nor Rucker&#x2019;s Q showed heterogeneity in shingles (<italic>p</italic>&#xa0;=&#xa0;0.792, <italic>p</italic> = 0.880) (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). In addition, no significant MR-Egger intercept was observed (<italic>p</italic> = 0.052), and the MR-PRESSO test was not significant (<italic>p</italic> = 0.807), indicating no horizontal pleiotropy (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). Furthermore, the leave-one-out analysis demonstrated the robustness of our MR Analysis, as it is not affected by any single SNP, and the funnel plot is nearly symmetrical (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7</bold>
</xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>The forest plot of the causal relationship between varicella zoster virus and allograft dysfunction. VZV, varicella zoster virus; nSNP, number of single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g006.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>The pleiotropic and heterogeneous results of shingles and allograft dysfunction.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="4" align="center">Exposure</th>
<th valign="middle" rowspan="4" align="center">Outcome</th>
<th valign="middle" colspan="2" align="center">MR-PRESSO</th>
<th valign="middle" align="center">Pleiotropy test</th>
<th valign="middle" colspan="2" align="center">Heterogeneity test</th>
</tr>
<tr>
<th valign="middle" rowspan="2" align="center">Distortion test</th>
<th valign="middle" rowspan="2" align="center">Global test</th>
<th valign="middle" rowspan="2" align="center">Egger intercept</th>
<th valign="middle" align="center">Cochran&#x2019;s Q test</th>
<th valign="middle" align="center">Rucker&#x2019;s Q test</th>
</tr>
<tr>
<th valign="middle" align="center">P-value</th>
<th valign="middle" align="center">P-value</th>
</tr>
<tr>
<th valign="middle" align="center">Outliers</th>
<th valign="middle" align="center">P-value</th>
<th valign="middle" align="center">P-value</th>
<th valign="middle" align="center">IVW</th>
<th valign="middle" align="center">MR-Egger</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Shingles</td>
<td valign="middle" align="center">FAILU_REJEC_TP_ORGANS_TISSU</td>
<td valign="middle" align="center">NA</td>
<td valign="middle" align="center">0.807</td>
<td valign="middle" align="center">0.052</td>
<td valign="middle" align="center">0.792</td>
<td valign="middle" align="center">0.880</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>The leave-one-out plot and funnel plot of shingles and allograft dysfunction.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g007.tif"/>
</fig>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>Effect of allograft dysfunction on herpes virus infection</title>
<p>The IVW analysis results showed that there was no significant causal relationship between allograft dysfunction and the infection with any of the four tested herpes viruses (<xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8</bold>
</xref>). Similarly, neither the MR-Egger method nor the Weighted median method supported the conclusion that allograft dysfunction had a causal relationship with CMV, EBV, HSV or VZV. Although the MR-Egger analysis showed that allograft dysfunction may have an impact on the CMV pp52 antibody levels (OR = 0.937, 95% CI: 0.880-0.997, <italic>p</italic> = 0.050), the MR-Egger funnel plot (<xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9</bold>
</xref>) is not symmetrical. This indicates that this result is not robust, and therefore, the conclusion of a causal relationship between allograft dysfunction and CMVpp52 antibody levels is not supported.</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>The forest plot of the causal relationship between allograft dysfunction and herpes virus infections. CMV, cytomegalovirus; EBV, Epstein-Barr virus; HSV, herpes simplex virus; VZV, varicella zoster virus; nSNP, number of single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g008.tif"/>
</fig>
<fig id="f9" position="float">
<label>Figure&#xa0;9</label>
<caption>
<p>The funnel plot of allograft dysfunction and CMV pp52.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1411771-g009.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>To our knowledge, this study is the first to assess the causal relationship between CMV, EBV, HSV and VZV and allograft dysfunction, and vice versa. Our findings support that there is a significant causal association between EBVEA-D antibody levels and allograft dysfunction, as well as an association between shingles and allograft dysfunction. Patients with higher levels of EBV EA-D antibodies or shingles are more likely to be at high risk for allograft dysfunction. These findings are robust based on the sensitivity analyses, which demonstrated that the methodology used in this project is less susceptible to confounding and reverse causality bias than many previous traditional observational studies (<xref ref-type="bibr" rid="B51">51</xref>).</p>
<p>EBNA-1, ZEBRA, EA-D and VCA-p18 are the four EBV proteins targeted in serology assays. Different serological characteristics may be related to the incubation and clearance periods of EBV infection (<xref ref-type="bibr" rid="B52">52</xref>, <xref ref-type="bibr" rid="B53">53</xref>). For instance, IgM and IgG anti-EBV-CA (capsid antigen-CA) and anti-EA antibodies are produced during primary infection. In contrast, anti-EBNA-1 antibodies are detected during recovery and in advanced stages of primary EBV infection (<xref ref-type="bibr" rid="B54">54</xref>, <xref ref-type="bibr" rid="B55">55</xref>). Our study found a significant association between anti-EA-D antibody levels and allograft dysfunction, suggesting that initial infection with EBV may increase the risk of allograft dysfunction. This increased risk may be associated with post-transplant lymphoproliferative disorders (PTLD). A statistical study showed that 63.6% of organ transplant recipients with EBV viremia were likely to progress to PTLD (<xref ref-type="bibr" rid="B56">56</xref>). In kidney transplantation, one study illustrates the association between subclinical cytomegalovirus and/or EBV viremia and decreased kidney function in patients under 5 years of age (<xref ref-type="bibr" rid="B57">57</xref>). Whether it is the direct viral cytopathic effect, indirect inflammatory effect, or the combination of various mechanisms that lead to allograft injury is still a key question that needs further investigation.</p>
<p>Both shingles and chickenpox are caused by VZV (<xref ref-type="bibr" rid="B58">58</xref>). However, chickenpox is caused by a primary VZV infection, whereas shingles is caused by the reactivation of latent VZV within the dorsal root ganglion (<xref ref-type="bibr" rid="B14">14</xref>). Therefore, the effects of the two infectious diseases on allograft dysfunction may differ. Primary chickenpox is an uncommon complication post-solid-organ transplant (SOT), except among pediatric transplant patients and those seronegative for VZV (<xref ref-type="bibr" rid="B59">59</xref>). As the majority of SOT recipients are seropositive for VZV, shingles occurs frequently following SOT, particularly among older recipients (&#x2265;65 years of age) and those receiving more intensive immunosuppression (<xref ref-type="bibr" rid="B59">59</xref>). Previous studies have also shown a high incidence of shingles among organ transplant recipients (<xref ref-type="bibr" rid="B60">60</xref>&#x2013;<xref ref-type="bibr" rid="B62">62</xref>). A retrospective analysis showed that the incidence of shingles infection varied among different types of organ transplants: 17.1% in the heart, 14.0% in the lungs, 5.8% in the liver, and 9.2% in kidney transplant recipients (<xref ref-type="bibr" rid="B63">63</xref>). Our study further supports previous work and provides evidence that shingles is a risk factor for allograft dysfunction. Considering these results, we believe it is important monitor the zoster infection of organ transplant recipients promptly and take effective measures to prevent it.</p>
<p>Studies have shown that CMV is associated with increased mortality in patients following SOT (<xref ref-type="bibr" rid="B64">64</xref>). Helanter&#xe4; et&#xa0;al. showed that CMV infection significantly reduced renal graft survival and renal function (<xref ref-type="bibr" rid="B65">65</xref>). Our study did not detect any causal relationship between CMV and allograft dysfunction, possibly due to insufficient data. Hence, further studies are needed to explore the relationship between CMV virus and allograft dysfunction.</p>
<p>Previous studies have shown that to prevent rejection after allogeneic organ transplantation, long-term immunosuppressive therapy is usually given to SOT recipients. This therapy often results in immune cell damage and lowered immunity in SOT recipients, making them more susceptible to herpes virus reactivation (<xref ref-type="bibr" rid="B66">66</xref>, <xref ref-type="bibr" rid="B67">67</xref>). Therefore, the use of immunosuppressants or immune system conditions in SOT recipients is more likely than graft rejection or dysfunction to be associated causally with herpes virus infections. Further research is needed to confirm this conclusion.</p>
<p>There are some limitations to our study. First, the GWAS data used for the study may not have been comprehensive enough. The GWAS data we utilized came from populations of European descent, and as such, the applicability of our findings to other populations and regions remains to be determined. And the 23andMe database relies on self-reported questionnaires, so the dataset can only study symptomatic herpes virus infections. The datasets on antibody levels used in this study can provide a reference for asymptomatic virus herpes infections. Additionally, it was not possible to obtain data on all traits of herpes virus for MR analysis, such as anti-VZV IGg levels. Second, significant results were obtained only in IVW and MR-Egger. Therefore, further studies are needed to confirm and extend these findings, especially in larger clinical cohorts. Third, the lack of additional details regarding the failure and rejection of transplanted organs and tissues, such as transplant type, family medical history, genetic factors, age, sex, health awareness, other diseases, dietary habits, and the type and time of the rejection event, prevents us from conducting further stratified analysis. Hence, future studies should focus on collecting data from independent populations, obtain more SNPs, or expanding the sample size. Nevertheless, our work is the first to investigate the causal relationship between four herpes viruses and allograft dysfunction after tissue and organ transplantation using MR analyses, thus providing valuable insights into the field.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>Overall, our study is the first to confirm, through Mendelian randomization, that initial infection with EBV or shingles in SOT recipients increases the risk of allograft dysfunction after organ and tissue transplantation. In addition, these results suggest that EBV and VZV play a crucial role in the pathological processes affecting allograft failure and rejection. This study provides valuable insights into the prevention and treatment of allograft dysfunction after organ and tissue transplantation.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The study used the large publicly available GWAS databases, which have received approval from their relevant ethical review board and participants.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>XQ: Data curation, Software, Writing &#x2013; original draft. TM: Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. SZ: Methodology, Supervision, Writing &#x2013; review &amp; editing. ZZ: Conceptualization, Investigation, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Open subject of Key Laboratory of Organ Regeneration and Transplantation of the Ministry of Education (KFKTJC2205).</p>
</sec>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
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<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fimmu.2024.1411771/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fimmu.2024.1411771/full#supplementary-material</ext-link>
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<supplementary-material xlink:href="DataSheet1.pdf" id="SM1" mimetype="application/pdf"/>
</sec>
<fn-group>
<title>Abbreviations</title>
<fn fn-type="abbr" id="abbrev1">
<p>CI, confidence interval; CMV, cytomegalovirus; EA-D, EBV early antigen-D; EBNA-1, EBV nuclear antigen-1; EBV, Epstein-Barr virus; GWAS, genome-wide association studies; HSV, herpes simplex virus; IgG, immunoglobulin G; IV, instrumental variable; IVW, inverse variance weighted; LD, linkage disequilibrium; MAF, minor allele frequency; MR, Mendelian randomization; MR-PRESSO, MR pleiotropy residual sum and outliers; OR, odds ratio; SNPs, single nucleotide polymorphisms; SOT, solid organ transplantation; VCA, viral capsid antigen; VZV, varicella zoster virus.</p>
</fn>
</fn-group>
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