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<?covid-19-tdm?>
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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Cell. Infect. Microbiol.</journal-id>
<journal-title>Frontiers in Cellular and Infection Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Cell. Infect. Microbiol.</abbrev-journal-title>
<issn pub-type="epub">2235-2988</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fcimb.2023.1211348</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Cellular and Infection Microbiology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>COVID-19 alters human microbiomes: a meta-analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Reuben</surname>
<given-names>Rine Christopher</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="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/517842"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Beugnon</surname>
<given-names>R&#xe9;my</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/826153"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Jurburg</surname>
<given-names>Stephanie D.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/424126"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>German Centre of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig</institution>, <addr-line>Leipzig</addr-line>, <country>Germany</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Institute of Biology, Leipzig University</institution>, <addr-line>Leipzig</addr-line>, <country>Germany</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Leipzig Institute for Meteorology, Universit&#xe4;t Leipzig</institution>, <addr-line>Leipzig</addr-line>, <country>Germany</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>CEFE, Universit&#xe9; de Montpellier, CNRS, EPHE, IRD</institution>, <addr-line>Montpellier</addr-line>, <country>France</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ</institution>, <addr-line>Leipzig</addr-line>, <country>Germany</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Piyush Baindara, University of Missouri, United States</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Projoyita Samanta, All India Institute of Medical Sciences, India; Phoolwanti Rani, San Diego Biomedical Research Institute, United States; Dinata Roy, Mizoram University, India</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Rine Christopher Reuben, <email xlink:href="mailto:reubenrine@yahoo.com">reubenrine@yahoo.com</email>; Stephanie D. Jurburg, <email xlink:href="mailto:s.d.jurburg@gmail.com">s.d.jurburg@gmail.com</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>02</day>
<month>08</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>13</volume>
<elocation-id>1211348</elocation-id>
<history>
<date date-type="received">
<day>24</day>
<month>04</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>23</day>
<month>06</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Reuben, Beugnon and Jurburg</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Reuben, Beugnon and Jurburg</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>Introduction</title>
<p>Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has infected a substantial portion of the world&#x2019;s population, and novel consequences of COVID-19 on the human body are continuously being uncovered. The human microbiome plays an essential role in host health and well-being, and multiple studies targeting specific populations have reported altered microbiomes in patients infected with SARS-CoV-2. Given the global scale and massive incidence of COVID on the global population, determining whether the effects of COVID-19 on the human microbiome are consistent and generalizable across populations is essential.</p>
</sec>
<sec>
<title>Methods</title>
<p>We performed a synthesis of human microbiome responses to COVID-19. We collected 16S rRNA gene amplicon sequence data from 11 studies sampling the oral and nasopharyngeal or gut microbiome of COVID-19-infected and uninfected subjects. Our synthesis included 1,159 respiratory (oral and nasopharyngeal) microbiome samples and 267 gut microbiome samples from patients in 11 cities across four countries.</p>
</sec>
<sec>
<title>Results</title>
<p>Our reanalyses revealed communitywide alterations in the respiratory and gut microbiomes across human populations. We found significant overall reductions in the gut microbial diversity of COVID-19-infected patients, but not in the respiratory microbiome. Furthermore, we found more consistent community shifts in the gut microbiomes of infected patients than in the respiratory microbiomes, although the microbiomes in both sites exhibited higher host-to-host variation in infected patients. In respiratory microbiomes, COVID-19 infection resulted in an increase in the relative abundance of potentially pathogenic bacteria, including <italic>Mycoplasma</italic>.</p>
</sec>
<sec>
<title>Discussion</title>
<p>Our findings shed light on the impact of COVID-19 on the human-associated microbiome across populations, and highlight the need for further research into the relationship between long-term effects of COVID-19 and altered microbiota.</p>
</sec>
</abstract>
<kwd-group>
<kwd>COVID-19</kwd>
<kwd>gut microbiome</kwd>
<kwd>host health</kwd>
<kwd>SARS-C0V-2</kwd>
<kwd>infection</kwd>
<kwd>human microbiome</kwd>
</kwd-group>
<counts>
<fig-count count="5"/>
<table-count count="0"/>
<equation-count count="0"/>
<ref-count count="102"/>
<page-count count="10"/>
<word-count count="4271"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Intestinal Microbiome</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>The emergence and rapid spread of the novel beta-coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), caused severe and unprecedented public health and socioeconomic challenges globally (<xref ref-type="bibr" rid="B88">Waters et al., 2022</xref>). Primarily, COVID-19 presents as a multifaceted and multi-organ infection with variable severity. Symptoms range from acute respiratory distress syndrome to pneumonia and include non-specific flu-like symptoms, gastrointestinal symptoms, myocardial dysfunction, multiple organ failure, and death (<xref ref-type="bibr" rid="B42">Kumar Singh et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B8">Baud et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B63">Onder et&#xa0;al., 2020</xref>). In most cases, SARS-CoV-2-infected persons are either asymptomatic or show mild symptoms. However, approximately 5% of those infected, usually the elderly and/or individuals with comorbidities, develop a severe form of the disease, resulting in intensive medical care and death (<xref ref-type="bibr" rid="B30">Grasselli et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B90">Wiersinga et&#xa0;al., 2020</xref>). As of April 2023, there have been 762,791,152 confirmed COVID-19 cases with 6,897,025 mortalities worldwide (<xref ref-type="bibr" rid="B89">WHO Coronavirus (COVID-19) Dashboard</xref>).</p>
<p>Among the many long-term effects associated with COVID-19 infection, numerous studies have reported altered microbiota in COVID-19 patients. The human microbiome plays a vital role in host health (<xref ref-type="bibr" rid="B43">Kumpitsch et&#xa0;al., 2019</xref>) and has been suggested to act as an additional organ (<xref ref-type="bibr" rid="B7">Baquero and Nombela, 2012</xref>). These microbial communities maintain host homeostasis through complex and essential interactions, which result in improved immunomodulation, metabolism, organ functions, mucosal barrier integrity, and structural protection against intruding pathogens (<xref ref-type="bibr" rid="B40">Jandhyala et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B43">Kumpitsch et&#xa0;al., 2019</xref>). Specific microbial communities are associated with different human tissues (<xref ref-type="bibr" rid="B39">Human Microbiome Project Consortium, 2012</xref>; <xref ref-type="bibr" rid="B65">Pflughoeft and Versalovic, 2012</xref>).</p>
<p>Perturbations such as COVID-19 can result in microbiome dysbiosis in human microbiomes, in which the composition and diversity of beneficial and/or commensal microorganisms are altered, promoting the growth or opportunistic pathogens (<xref ref-type="bibr" rid="B37">Hoque et&#xa0;al., 2021b</xref>; <xref ref-type="bibr" rid="B72">Ren et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B81">Sun et&#xa0;al., 2022</xref>). In particular, the observation of increased host-to-host variability in the microbiomes associated with unhealthy hosts has been dubbed the Anna Karenina Principle (AKP), derived from the opening line of Tolstoy&#x2019;s <italic>Anna Karenina</italic>: &#x201c;All happy families are all alike; each unhappy family is unhappy in its own way&#x201d;.</p>
<p>Different human diseases including obesity, psoriasis, arthritis, inflammatory bowel disease (IBD), influenza, HBV, and HIV have been reported to significantly alter human microbiomes (<xref ref-type="bibr" rid="B46">Ling et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B47">Lu et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B26">Gilbert et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B28">Gon&#xe7;alves et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B97">Yun et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B76">Sencio et&#xa0;al., 2021</xref>). Similarly, several reports have demonstrated changes in the microbiomes (intestinal, nasopharyngeal, and oral) of COVID-19 patients during active infection and convalescent state, and these are usually characterized by the depletion of beneficial commensal microbes and a higher abundance of opportunistic pathogens (<xref ref-type="bibr" rid="B102">Zuo et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B101">Zuo et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B36">Hoque et&#xa0;al., 2021a</xref>; <xref ref-type="bibr" rid="B37">Hoque et&#xa0;al., 2021b</xref>; <xref ref-type="bibr" rid="B41">Jochems et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B72">Ren et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B94">Xu et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B96">Yeoh et&#xa0;al., 2021</xref>). The composition and diversity of the gut, nasal, or oral microbiome of COVID-19 patients are now widely believed to be predictive of COVID-19 prognosis, progression, and severity (<xref ref-type="bibr" rid="B54">Mathieu et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B92">Wypych et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B16">Chen et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B34">He et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B37">Hoque et&#xa0;al., 2021b</xref>). Moreover, the abundance of specific microbes within human microbiomes are now identified as biomarkers to distinguish COVID-19-infected individuals from healthy persons (<xref ref-type="bibr" rid="B29">Gou et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B102">Zuo et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B83">Tian et&#xa0;al., 2021</xref>).</p>
<p>Extensive interactions exist between the host immune system and microbiome. These interactions seem to induce immune responses to diseases, and in turn, the immune system affects the composition and diversity of the microbiome (<xref ref-type="bibr" rid="B75">Round and Mazmanian, 2009</xref>; <xref ref-type="bibr" rid="B53">Mangalmurti and Hunter, 2020</xref>; <xref ref-type="bibr" rid="B6">Attaway et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B81">Sun et&#xa0;al., 2022</xref>). COVID-19 has been reported to induce aberrant immune responses that not only elevate inflammatory markers including tumor necrosis factor-&#x3b1;, interleukin (IL)-10, and C-reactive protein, but also affect gut microbiome composition with a decreased population of beneficial bacteria especially bifidobacteria, <italic>Eubacterium rectale</italic>, and <italic>Faecalibacterium prausnitzii</italic> (<xref ref-type="bibr" rid="B53">Mangalmurti and Hunter, 2020</xref>; <xref ref-type="bibr" rid="B6">Attaway et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B96">Yeoh et&#xa0;al., 2021</xref>). Understanding microbiome changes across multiple microbiome compartments can shed light on the level and mechanisms of microbiome perturbation/dysbiosis associated with COVID-19, and may in turn aid the development of effective strategies for COVID-19 diagnosis, long-term management, and prevention.</p>
<p>All humans are susceptible to SARS-CoV-2 infection. Nevertheless, the human microbiome slightly differs across age, ethnicity, sex, race, and even geography (<xref ref-type="bibr" rid="B39">Human Microbiome Project Consortium, 2012</xref>; <xref ref-type="bibr" rid="B10">Brooks et&#xa0;al., 2018</xref>). Consequently, determining the effects of COVID-19 infections on the human microbiome requires assessing changes in the microbiomes of a wide range of patients across geographic regions. To this end, we performed a synthesis of human microbiome responses to COVID-19. We collected 16S rRNA gene amplicon sequence data from 11 studies sampling the oral and nasopharyngeal, or gut microbiome of COVID-19-infected and uninfected subjects. Our synthesis included 1,159 respiratory (oral and nasopharyngeal) microbiome samples and 267 gut microbiome samples and spanned four countries. We hypothesized that (1) because of stronger immune responses, infected patients would have a lower microbiome richness across microbiome compartments; (2) the microbiomes of infected patients would be more variable from host to host than that of healthy individuals, in line with the AKP; (3) as SARS-COV-2 is primarily a respiratory disease, the oral and nasopharyngeal microbiomes would be more strongly affected than the gut microbiomes; and (4) COVID-19 infection would result in consistent shifts in microbiome composition in both compartments.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Literature search, selection criteria, and data extraction</title>
<p>This study was conducted through a robust literature search and selection using the RepOrting standards for Systematic Evidence Syntheses (ROSES) guidelines (<xref ref-type="bibr" rid="B31">Haddaway et&#xa0;al., 2018</xref>) (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). In January 2022, we performed a keyword search on the Web of Science database (<ext-link ext-link-type="uri" xlink:href="http://www.webofscience.com">www.webofscience.com</ext-link>) to identify and select relevant published articles. We used the terms &#x201c;COVID-19&#x201d; OR &#x201c;SARS-CoV-2&#x201d; OR &#x201c;severe acute respiratory syndrome coronavirus 2&#x201d; OR &#x201c;coronavirus disease 2019&#x201d; OR &#x201c;nCoV&#x201d; OR &#x201c;novel coronavirus&#x201d; AND &#x201c;microbiome&#x201d; OR &#x201c;microbiota&#x201d; OR &#x201c;microflora&#x201d; or &#x201c;flora&#x2019;&#x2019; OR &#x201c;biome&#x201d;. Furthermore, additional studies were included from other sources including PubMed and Google Scholar. We included published articles that performed 16S rRNA gene or transcript amplicon sequencing from gut/stool, nasopharyngeal, and oral samples collected from COVID-19+ individuals. We only included articles that were published in the English language. The authors independently reviewed the titles and abstracts of all the selected studies. Other COVID-19-related publications outside the scope of this study, as well as related commentaries, editorials, reviews, systematic reviews, and meta-analyses, were excluded.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>ROSES flowchart illustrating the systematic search, identification, screening, and final selection of articles.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-13-1211348-g001.tif"/>
</fig>
<p>For each selected study, we extracted information about the NCBI accession numbers to the 16S rRNA gene/transcript sequences, author names, patient characteristics, sample types, sample size, the 16S rRNA gene region sequenced, and the sequencing platform used. Studies were grouped based on the systems examined: gut microbiome, or oral, nasopharyngeal, and upper respiratory tract (URT) microbiomes (heretofore oral/URT).</p>
</sec>
<sec id="s2_2">
<title>Bioinformatics and statistical analyses</title>
<p>All sequences were downloaded from NCBI as.fastq files and sequence data were processed using R (v 4.1) software (<xref ref-type="bibr" rid="B11">Bunn, 2008</xref>) and the <italic>dada2</italic> (<xref ref-type="bibr" rid="B12">Callahan et&#xa0;al., 2016</xref>) package. Following preliminary assessments of quality, we downloaded data for 1,588 samples from 11 studies. First, forward reads from each study were inspected to determine the optimal processing parameters using the <italic>plotQualityProfile</italic> function (detailed for each study in <xref ref-type="supplementary-material" rid="SM3">
<bold>Supplementary Table&#xa0;1</bold>
</xref>), and trimmed to 100 base pairs with the <italic>filterAndTrim</italic> function, with <italic>maxEE</italic> = 2 and <italic>truncQ</italic> = 2. Reads were assigned a taxonomy using SILVA V.132 (<xref ref-type="bibr" rid="B67">Quast et&#xa0;al., 2013</xref>). The proportion of reads lost at each processing step for each study is shown in <xref ref-type="supplementary-material" rid="SM2">
<bold>Supplementary Figure S1</bold>
</xref>. According to available metadata, technical controls (e.g., blanks, mock communities) were removed prior to further processing.</p>
<p>Statistical analyses were performed with the <italic>phyloseq</italic> (<xref ref-type="bibr" rid="B56">McMurdie and Holmes, 2013</xref>), <italic>vegan</italic> (<xref ref-type="bibr" rid="B62">Oksanen et al., 2022</xref>), <italic>Maaslin2</italic> (<xref ref-type="bibr" rid="B51">Mallick et&#xa0;al., 2021</xref>), and <italic>lmerTest</italic> (<xref ref-type="bibr" rid="B44">Kuznetsova et&#xa0;al., 2017</xref>) packages. Prior to analyses, all samples were standardized to 2,000 reads per sample using the <italic>rarefy_even_depth</italic> function, which led to a loss of 63 samples. To assess the effects of COVID-19 infection on different compartments of microbial diversity (i.e., rare and dominant), we calculated Hill numbers (richness, effective Shannon diversity, and inverse Simpson diversity, or <italic>q</italic> = 0, <italic>q</italic> = 1, and <italic>q</italic> = 2, respectively; <xref ref-type="bibr" rid="B14">Chao et&#xa0;al., 2014</xref>). Richness is more heavily affected by the diversity of rare taxa, while inverse Simpson diversity is more affected by the diversity of dominant taxa. To determine the contribution of COVID-19 infection to oral/URT and gut microbiomes, we performed a distance-based variance partitioning analysis using the <italic>varpart</italic> function of <italic>vegan</italic>, with Bray&#x2013;Curtis dissimilarities. The Bray&#x2013;Curtis dissimilarities between samples taken from the same region among COVID-19-infected and uninfected patients were used to measure microbiome variance. Unless otherwise noted, diversity measures are presented as mean &#xb1; standard deviation.</p>
<p>To test the effect of COVID-19 infection on microbiome diversity (i.e., Hill numbers, H1) and variability (i.e., Bray&#x2013;Curtis dissimilarities, H2), we used linear mixed effect models, with the study as a random effect, and COVID-19 infection as a fixed effect using the lmer function from the <italic>lmerTest</italic> package (<xref ref-type="bibr" rid="B44">Kuznetsova et&#xa0;al., 2017</xref>). To compare the effect of SARS-CoV-2 infection on gut and oral/URT microbiomes (H3), we used linear mixed effect models, with the study as a random effect, and the interaction between COVID-19 status and the sampled region (i.e., gut <italic>vs.</italic> oral/URT) as a fixed effect. In addition, a contrast analysis was performed using the emmeans function from the <italic>emmeans</italic> package (<xref ref-type="bibr" rid="B45">Lenth et al., 2023</xref>) to quantify the effect of COVID-19 infection within sampled regions. Model assumptions and performances were tested using the performance package (<xref ref-type="bibr" rid="B48">L&#xfc;decke et&#xa0;al., 2021</xref>); all model outputs and performances are found in the <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Materials</bold>
</xref>.</p>
<p>To identify bacterial genera that were consistently under- or overrepresented in SARS-CoV-2-infected patients, we used microbiome-oriented linear models (<italic>MaAsLin2</italic> package; <xref ref-type="bibr" rid="B51">Mallick et&#xa0;al., 2021</xref>) for gut and oral/URT samples separately, with the study and sample type as random effects, SARS-CoV-2 infection as a fixed effect, and a prevalence threshold of 0.2.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<p>In total, we collected 1,426 high-quality, processed samples from the USA (Chicago, Jackson, Nashville, New York City, Philadelphia, and San Diego), Spain (Alicante), France (Paris), and China (Guangdong, Shanghai, and Wuhan) (<xref ref-type="supplementary-material" rid="SM2">
<bold>Supplementary Figure S2</bold>
</xref>), represented by 29,491 amplicon sequence variants, or ASVs (<xref ref-type="supplementary-material" rid="SM3">
<bold>Supplementary Table&#xa0;1</bold>
</xref>). Of the 11 studies surveyed, 8 included COVID-19-infected patients and controls (<xref ref-type="bibr" rid="B21">Engen et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B57">Merenstein et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B59">Minich et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B61">Newsome et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B77">Shilts et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B78">Smith et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B85">Ventero et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B91">Wu et&#xa0;al., 2021</xref>), and 2 of these (<xref ref-type="bibr" rid="B61">Newsome et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B91">Wu et&#xa0;al., 2021</xref>) also included samples of recovered patients. Three of the studies only sampled infected patients (<xref ref-type="bibr" rid="B77">Shilts et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B94">Xu et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B86">Ventero et&#xa0;al., 2022</xref>), and one sampled infected and recovered patients (<xref ref-type="bibr" rid="B83">Tian et&#xa0;al., 2021</xref>).</p>
<p>On average, gut microbiome samples were more diverse (136 &#xb1; 54 ASVs) than oral/URT samples (80 &#xb1; 79 ASVs). Within studies, COVID-19 infection caused minor, but consistent decreases in gut microbial richness (Hill <italic>q</italic> = 0, estimate &#xb1; SE = 22.46 &#xb1; 6.22, <italic>p</italic> &lt; 0.001), but not in oral/URT richness (<italic>p</italic> = 0.55, <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>; <xref ref-type="supplementary-material" rid="SM2">
<bold>Supplementary Figure S3</bold>
</xref>). This decrease was also significant for <italic>q</italic> = 1 and <italic>q</italic> = 2 (<italic>q</italic> = 1: 8.20 &#xb1; 2.50, <italic>p</italic> = 0.001; <italic>q</italic> = 2: 3.71 &#xb1; 1.38, <italic>p</italic> = 0.008, <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>), highlighting richness losses in the dominant portion of the community.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>The effect of COVID-19 infection on the alpha diversity of the gut and oral/URT microbiomes. Hill richness (<bold>A</bold>: <italic>q</italic> = 0), effective Shannon (<bold>B</bold>: <italic>q</italic> = 1) and Inverse Simpson (<bold>C</bold>: <italic>q</italic> = 2) diversity indices were calculated to assess the impact of increasingly dominant portions of the community. Points are colored by studies, and the average across studies is shown with a black point. Significant differences between infected and non-infected patients across studies are indicated with asterisks where significant (***<italic>p</italic> &lt; 0.001; ** <italic>p</italic> &lt; 0.01) and with &#x201c;n.s.&#x201d; otherwise.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-13-1211348-g002.tif"/>
</fig>
<p>SARS-CoV-2 infection led to changes in the microbiome composition, significantly explaining 2% of the variation in the microbial community, although these changes were study-dependent (<italic>p</italic> &lt; 0.001, <xref ref-type="supplementary-material" rid="SM2">
<bold>Supplementary Figure S4</bold>
</xref>). Notably, of the 14.9% of the variance in community composition explained by each study, 7.3% could be ascribed to the participant&#x2019;s country of origin (<xref ref-type="supplementary-material" rid="SM2">
<bold>Supplementary Figure S4</bold>
</xref>). In line with the AKP, patients infected with COVID-19 had a higher host-to-host variance in both the oral/URT and gut microbiome than uninfected patients (an increase of 0.06 &#xb1; 0.003 in Bray&#x2013;Curtis dissimilarity relative to uninfected patients, <italic>p</italic> &lt; 0.001, <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>COVID-19 increases host-to-host variability across microbiomes. Community distances were measured as the Bray&#x2013;Curtis distance between individuals in the same study, from the same body site, and with the same infection status. Points are colored by studies, and the average across studies is shown with a black point. Significant differences between infected and non-infected patients across studies are indicated with asterisks where significant (***<italic>p</italic> &lt; 0.001).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-13-1211348-g003.tif"/>
</fig>
<p>In general, the gut microbiomes of infected and non-infected patients had more consistent shifts across studies for the gut than for the oral/URT microbiomes. We identified 51 dominant genera (38.3% &#xb1; 22.2% of the community) in the gut microbiome, whose relative abundances consistently and significantly differed between infected and non-infected patients across studies (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref>). These taxa belonged predominantly to Firmicutes, with the most consistent decreases in <italic>Fusicatenibacter, Lachnospiraceae NK4A316 group, Lachnoclostridum, Blautia</italic>, and <italic>Roseburia</italic> and the most consistent increases in <italic>Finegoldia, Porphyromonas, Anaerococcus</italic>, and <italic>Peptoniphilus</italic> for COVID-19-infected patients, relative to uninfected patients. In contrast, we only found 16 genera (23.8% &#xb1; 25.9% of the community on average) that consistently differed between the oral/URT microbiome of COVID-19-infected and uninfected patients (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>). Of these, <italic>Enterococcus, Pseudomonas, unclassified Enterobacteriaceae</italic>, and <italic>Solobacterium</italic> were lower in uninfected patients, whereas <italic>Prevotella, Mycoplasma, Veillonella, Cutibacterium, Atopobium</italic>, and <italic>Megasphaera</italic> were consistently and significantly more abundant in COVID-19-infected patients.</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Altered relative abundances of bacterial genera in the gut microbiome of SARS-CoV-2-infected individuals. Bacterial genera that exhibit significantly different (<italic>p</italic> &lt; 0.01) relative abundances between SARS-CoV- 2-infected and uninfected individuals in the gut microbiome were selected using the MaAsLin2 approach, which included random effects for sample type and study. Only significantly different genera are displayed, relative abundances are colored by quantiles, and genera are grouped according to Ward&#x2019;s clustering method. Phylum membership is displayed on the left bar. These 51 genera make up 38.3% &#xb1; 22.2% of the community, on average across all samples.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-13-1211348-g004.tif"/>
</fig>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Altered relative abundances of bacterial genera in the oral and URT microbiome of SARS-CoV-2-infected individuals. Bacterial genera that exhibit significantly different (<italic>p</italic> &lt; 0.01) relative abundances between SARS-CoV-2-infected and uninfected individuals in the oral or URT microbiome were selected using the MaAsLin2 approach, which included random effects for sample type and study. The log<sub>2</sub> fold changes in taxon abundances for infected patients relative to non-infected patients are displayed. Only genera with significantly different (<italic>p</italic> &lt; 0.01) abundances between these two groups are displayed, and in total, they represent 23.8% &#xb1; 25.9% of the whole community, on average across all samples.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-13-1211348-g005.tif"/>
</fig>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>SARS-CoV-2 invades the human body mainly through the angiotensin-converting enzyme 2 (ACE2) and cofactor transmembrane serine protease 2 (TMPRSS2) receptors in the epithelial cells of the nasopharyngeal tract, and then gradually moves to initiate infection in the lungs, which gradually results in gastrointestinal involvement as well as affects other organs including the heart, kidneys, pancreas, eyes, and skin (<xref ref-type="bibr" rid="B25">Gavriatopoulou et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B36">Hoque et&#xa0;al., 2021a</xref>; <xref ref-type="bibr" rid="B70">Rahman et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B69">Rahman et&#xa0;al., 2021</xref>). Interestingly, high levels of both ACE2 and TMPRSS2 receptors are naturally expressed in multiple organs of the human respiratory and gastrointestinal tracts (<xref ref-type="bibr" rid="B64">Perlot &amp; Penninger, 2013</xref>; <xref ref-type="bibr" rid="B73">Roncon et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B93">Xiao et&#xa0;al., 2020</xref>), thus enabling SARS-CoV-2 to circulate and induce severe inflammation, immune imbalance, and microbiome dysbiosis within these systems (<xref ref-type="bibr" rid="B35">Hoffmann et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B87">Villapol, 2020</xref>). Moreover, recent insights into coronavirus biology and SARS-CoV-2-human interactions attribute COVID-19 pathophysiology to aberrant and aggressive immune responses in SARS-CoV-2 clearance (<xref ref-type="bibr" rid="B37">Hoque et&#xa0;al., 2021b</xref>; <xref ref-type="bibr" rid="B96">Yeoh et&#xa0;al., 2021</xref>). COVID-19 infection can therefore result in a wide variety of responses in the human-associated microbiome, which may be further modulated by the host&#x2019;s environment, such as diet and exposure to pollutants, which is population-specific. By simultaneously reanalyzing microbiome data from various microbiome compartments in infected and uninfected patients across the world, we sought to identify consistent, COVID-19 infection-specific changes in the human microbiome.</p>
<p>Consistent with our hypotheses, we found that SARS-CoV-2-infected individuals had a lower microbial diversity in the gut, but not in the upper respiratory tract. The reduction of gut microbial diversity in COVID-19 has been similarly reported (<xref ref-type="bibr" rid="B102">Zuo et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B55">Mazzarelli et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B83">Tian et&#xa0;al., 2021</xref>), regardless of antibiotic use (<xref ref-type="bibr" rid="B102">Zuo et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B55">Mazzarelli et&#xa0;al., 2021</xref>) and even several weeks after viral clearance (<xref ref-type="bibr" rid="B102">Zuo et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B83">Tian et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B94">Xu et&#xa0;al., 2021</xref>). The gut microbiome is relatively stable, and a major predictor of normal gut functioning, immunomodulation, and overall host health (<xref ref-type="bibr" rid="B74">Rooks and Garrett, 2016</xref>; <xref ref-type="bibr" rid="B83">Tian et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B94">Xu et&#xa0;al., 2021</xref>). Our study highlights SARS-CoV-2&#x2019;s ability to disrupt human gut microbiome eubiosis through the depletion of gut microbial diversity, which may contribute to disease severity and opportunistic infections. The consistent decrease in diversity found across Hill numbers for the gut microbiome richness highlights that richness loss occurs in dominant taxa. This may have major implications on the composition and diversity of the gut microbiome in COVID-19 infection resulting in dysbiosis, impaired immune functioning, pro-inflammatory conditions, etc.</p>
<p>Our study did not control for COVID-19 patients&#x2019; medication use (e.g., antibiotics and antivirals), age, genetic background, sex, or diet, which may also affect the gut microbial diversity and further confound COVID-19-associated gut microbial signatures. However, the consistent results we recorded across studies after controlling for study&#x2013;study particularities suggest that the decrease in gut microbial diversity is indeed due to SARS-CoV-2 infection. Intriguingly, the intestinal ACE2, which is the receptor of SARS-CoV-2, plays a vital role in maintaining the gut microbiome eubiosis (<xref ref-type="bibr" rid="B32">Hamming et&#xa0;al., 2004</xref>; <xref ref-type="bibr" rid="B33">Hashimoto et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B83">Tian et&#xa0;al., 2021</xref>), and the SARS-CoV-2 infection may downregulate the expression and availability of ACE2, which could disrupt gut homeostasis, adversely impacting microbial diversity. Our inability to detect a significant effect on the microbial richness of the oral/URT microbiome may be attributed to the oral/URT microbiome being more dynamic, resilient, and transient than the gut microbiome due to frequent bidirectional air and mucus movement as well as its regular exposure to the environment (<xref ref-type="bibr" rid="B38">Huffnagle et&#xa0;al., 2017</xref>). Recent studies have found inconsistent effects of COVID-19 on the microbial diversity of the URT (<xref ref-type="bibr" rid="B18">De Maio et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B60">Mostafa et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B9">Braun et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B58">Miao et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B95">Yamamoto et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B99">Zhang et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B68">Rafiqul Islam et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B84">Uehara et&#xa0;al., 2022</xref>), and others have proposed that SARS-CoV-2 has weak effects on the URT microbiome (<xref ref-type="bibr" rid="B18">De Maio et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B9">Braun et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B95">Yamamoto et&#xa0;al., 2021</xref>) akin to acute respiratory virus infections in humans (<xref ref-type="bibr" rid="B71">Ramos-Sevillano et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B18">De Maio et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B95">Yamamoto et&#xa0;al., 2021</xref>).</p>
<p>Higher variability is generally associated with lower stability and predictability. In accordance with previous studies (<xref ref-type="bibr" rid="B50">Ma, 2020</xref>; <xref ref-type="bibr" rid="B4">Altabtbaei et&#xa0;al., 2021</xref>), we also found that host-to-host gut and URT microbiome variability was greater in SARS-CoV-2-infected patients than in non-infected controls, in line with the AKP (<xref ref-type="bibr" rid="B98">Zaneveld et&#xa0;al., 2017</xref>) and with a previous synthesis, which found that most human-associated diseases result in a higher microbiome variability across patients (<xref ref-type="bibr" rid="B50">Ma, 2020</xref>).</p>
<p>Notably, our study shows that COVID-19 infection resulted in an overall loss of beneficial bacteria, and worryingly, a consistent increase in pathogenic bacteria, particularly in the oral/URT microbiome. The increased relative abundances and colonization of opportunistic pathogens including <italic>Mycoplasma</italic>, <italic>Prevotella</italic>, <italic>Peptostreptococcus</italic>, <italic>Veillonella</italic>, <italic>Cutibacterium</italic>, and Saccharibacteria in the oral/URT recorded in our study may be associated with the early-stage SARS-CoV-2-induced inflammation, the loss of beneficial bacteria, and the increased exposure and receptiveness to allochthonous and indigenous microorganisms (<xref ref-type="bibr" rid="B52">Man et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B19">Dubourg et&#xa0;al., 2019</xref>).</p>
<p>In the gut microbiome, increased relative abundances of pathogenic bacteria including <italic>Campylobacter</italic>, <italic>Corynebacterium</italic>, <italic>Staphylococcus</italic>, <italic>Clostridium</italic>, <italic>Peptostreptococcus</italic>, <italic>Prevotella</italic>, <italic>Anaerococcus</italic>, <italic>Actinomyces</italic>, <italic>Porphyromonas</italic>, and <italic>Bacteroides</italic> were recorded in SARS-CoV-2 infection. Increasingly, emerging reports posit that alterations in the gut microbiome may facilitate blooms of both pathogenic and previously rare bacteria, which can further aggravate overall gut inflammation (<xref ref-type="bibr" rid="B55">Mazzarelli et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B83">Tian et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B94">Xu et&#xa0;al., 2021</xref>). The presence and increased abundance of common oral/URT commensals and pathogens in the gut (e.g., <italic>Corynebacterium</italic>, <italic>Peptostreptococcus</italic>, <italic>Porphyromonas</italic>, <italic>Prevotella</italic>, and <italic>Staphylococcus</italic>) may suggest a possible translocation of these organisms from the oral/URT to the gut. Previously, inflammation, disruption, and increased permeability of membrane mucosa were associated with COVID-19 (<xref ref-type="bibr" rid="B13">Cao and Li, 2020</xref>). Increased permeability of membrane mucosa facilitates the translocation of some oral/URT microbes as well as enriched opportunistic pathogens to the gut (<xref ref-type="bibr" rid="B52">Man et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B13">Cao and Li, 2020</xref>; <xref ref-type="bibr" rid="B27">Giron et&#xa0;al., 2021</xref>).</p>
<p>We also detected the loss of beneficial microbes <italic>Fusicatenibacter, Lachnospiraceae NK4A316 group, Lachnoclostridium, Blautia</italic>, and <italic>Roseburia</italic> in the gut. These beneficial bacteria often enhance and maintain the integrity and function of mucosal barriers, metabolism, and immunomodulation, and protect against pathogen invasion through several mechanisms including the secretion of short-chain fatty acids (SCFA) and antimicrobial peptides (<xref ref-type="bibr" rid="B24">Gallo and Hooper, 2012</xref>; <xref ref-type="bibr" rid="B2">Abt &amp; Pamer, 2014</xref>; <xref ref-type="bibr" rid="B100">Zhang et&#xa0;al., 2015</xref>). In line with our findings, SARS-CoV-2-associated microbiome perturbations were previously associated with a decline in SCFA (<xref ref-type="bibr" rid="B49">Lv et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B79">Sokol et&#xa0;al., 2021</xref>), thus promoting a systemic pro-inflammatory condition (<xref ref-type="bibr" rid="B66">Qin et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B22">Esquivel-Elizondo et&#xa0;al., 2017</xref>) and the severity of pulmonary viral infections such as COVID-19 (<xref ref-type="bibr" rid="B15">Chemudupati et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B23">Friedland and Haribabu, 2020</xref>; <xref ref-type="bibr" rid="B82">Tang et&#xa0;al., 2020</xref>). In the same vein, Lv and colleagues reported a pathogen-regulated feedback loop between the decline in SCFA production and SARS-CoV-2 infection (<xref ref-type="bibr" rid="B49">Lv et&#xa0;al., 2021</xref>).</p>
<p>Whether increased abundances of gut and oral/URT pathogenic and pro-inflammatory bacteria in SARS-CoV-2 infection actually play an active part in COVID-19 or mainly thrive opportunistically, exploiting the depletion of commensal bacteria, remains unknown. Nevertheless, our findings demonstrate that oral/URT and gut microbiomes are systematically perturbed by COVID-19, resulting in a lower microbial diversity, loss of beneficial microbes, and increased presence of pathogenic bacteria, which could trigger prolonged pro-inflammatory reactions, immunological changes, and secondary bacterial infections that could account for chronic COVID-19-associated symptoms, as well as prolonged sequelae. Understanding the dynamics of COVID-19-associated microbiome alterations may help identify microbiome-based strategies with potential applications in COVID-19 management and treatment. Furthermore, our findings highlight that non-invasive organ and/or system-based microbiome profiling may serve not only for COVID-19 diagnosis and prognosis but also, for the identification of individuals at risk of secondary infections, chronic disease, and/or degenerative inflammatory symptoms, including Kawasaki-like disease (KLD) and multisystem inflammation, as is the case with children and young adults (<xref ref-type="bibr" rid="B3">Akca et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B17">Cheung et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B80">Sokolovsky et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B20">Elouardi et&#xa0;al., 2022</xref>).</p>
<p>Finally, human microbiome composition and diversity are highly heterogeneous and largely driven by biogeographies, environments, ethnicity, and socioeconomic status (<xref ref-type="bibr" rid="B5">Amato et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B95">Yamamoto et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B96">Yeoh et&#xa0;al., 2021</xref>). While our study design highlights the importance of sampling across human populations to understand a disease, our study lacks samples from the Southern Hemisphere, in line with recent reports indicating very limited public human microbiome data from the Global South (<xref ref-type="bibr" rid="B1">Abdill et&#xa0;al., 2022</xref>). A global representation of data in human microbiome studies is critical to understanding global drivers and patterns of disease (in this case, COVID-19) to provide sustainable interventions to all populations without bias.</p>
</sec>
<sec id="s5" sec-type="data-availability">
<title>Data availability statement</title>
<p>INSDC accession numbers for each dataset reused in this study are included in <xref ref-type="supplementary-material" rid="SM3">
<bold>Supplementary Table&#xa0;1</bold>
</xref>. Code used for data analysis is available in <uri xlink:href="https://github.com/drcarrot/COVID">https://github.com/drcarrot/COVID</uri>, and detailed model descriptions are included in the <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>.</p>
</sec>
<sec id="s6" sec-type="author-contributions">
<title>Author contributions</title>
<p>RR conceptualized the study, carried out the literature search, reviewed and selected relevant literature, and wrote the article. RB performed the statistical analyses. SJ conceptualized the study, extracted and assembled the 16S rRNA data and metadata, organized the presentation of the results, and created all figures. All authors contributed substantially to subsequent revisions. All authors approved the submitted version.</p>
</sec>
</body>
<back>
<ack>
<title>Acknowledgments</title>
<p>RR was funded by the Alexander von Humboldt Foundation, Germany. RB acknowledges support by the Saxon State Ministry for Science, Culture and Tourism (SMWK), Germany &#x2013; [3-7304/35/6-2021/48880]. SJ acknowledges support of sDiv, the Synthesis Center of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (DFG&#x2013; FZT 118, 202548816). We acknowledge support by the iDiv Open Science Publication Fund as well as the Open Access Publishing Fund of Leipzig University, supported by the German Research Foundation within the program Open Access Publication Funding.</p>
</ack>
<sec id="s7" 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="s8" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s9" sec-type="supplementary-material">
<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/fcimb.2023.1211348/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fcimb.2023.1211348/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="DataSheet_1.pdf" id="SM1" mimetype="application/pdf"/>
<supplementary-material xlink:href="Table_1.docx" id="SM2" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
<supplementary-material xlink:href="Table_2.xlsx" id="SM3" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"/>
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