Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera
Identifieur interne : 001054 ( Pmc/Corpus ); précédent : 001053; suivant : 001055Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera
Auteurs : Yurij Ionov ; Artem S. RogovskyySource :
- PLoS ONE [ 1932-6203 ] ; 2020.
Abstract
Detection of protection-associated epitopes via reverse vaccinology is the first step for development of subunit vaccines against microbial pathogens. Mapping subunit vaccine targets requires high throughput methods, which would allow delineation of epitopes recognized by protective antibodies on a large scale. Phage displayed random peptide library coupled to Next Generation Sequencing (PDRPL/NGS) is the universal platform that enables high-yield identification of peptides that mimic epitopes (mimotopes). Despite being unsurpassed as a tool for discovery of polyclonal serum mimotopes, the PDRPL/NGS is far inferior as a quantitative method of immune response. Difficult-to-control fluctuations in amounts of antibody-bound phages after rounds of selection and amplification diminish the quantitative capacity of the PDRPL/NGS. In an attempt to improve the accuracy of the PDRPL/NGS method, we compared the discriminating capacity of two approaches for PDRPL/NGS data analysis. The whole-unique-sequence-based analysis (WUSA) involved generation of 7-mer peptide profiles and comparison of the numbers of sequencing reads for unique peptide sequences between serum samples. The motif-based analysis (MA) included identification of 4-mer consensus motifs unifying unique 7-mer sequences and comparison of motifs between serum samples. The motif comparison was based not on the numbers of sequencing reads, but on the numbers of distinct 7-mers constituting the motifs. Our PDRPL/NGS datasets generated from biopanning of protective and non-protective anti-
Url:
DOI: 10.1371/journal.pone.0226378
PubMed: 31940357
PubMed Central: 6961823
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PMC:6961823Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-<italic>Borrelia burgdorferi</italic> immune sera</title><author><name sortKey="Ionov, Yurij" sort="Ionov, Yurij" uniqKey="Ionov Y" first="Yurij" last="Ionov">Yurij Ionov</name><affiliation><nlm:aff id="aff001"><addr-line>Department of Cancer Genetics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Rogovskyy, Artem S" sort="Rogovskyy, Artem S" uniqKey="Rogovskyy A" first="Artem S." last="Rogovskyy">Artem S. Rogovskyy</name><affiliation><nlm:aff id="aff002"><addr-line>Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, United States of America</addr-line></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31940357</idno><idno type="pmc">6961823</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961823</idno><idno type="RBID">PMC:6961823</idno><idno type="doi">10.1371/journal.pone.0226378</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">001054</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001054</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-<italic>Borrelia burgdorferi</italic> immune sera</title><author><name sortKey="Ionov, Yurij" sort="Ionov, Yurij" uniqKey="Ionov Y" first="Yurij" last="Ionov">Yurij Ionov</name><affiliation><nlm:aff id="aff001"><addr-line>Department of Cancer Genetics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America</addr-line></nlm:aff></affiliation></author><author><name sortKey="Rogovskyy, Artem S" sort="Rogovskyy, Artem S" uniqKey="Rogovskyy A" first="Artem S." last="Rogovskyy">Artem S. Rogovskyy</name><affiliation><nlm:aff id="aff002"><addr-line>Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, United States of America</addr-line></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Detection of protection-associated epitopes via reverse vaccinology is the first step for development of subunit vaccines against microbial pathogens. Mapping subunit vaccine targets requires high throughput methods, which would allow delineation of epitopes recognized by protective antibodies on a large scale. Phage displayed random peptide library coupled to Next Generation Sequencing (PDRPL/NGS) is the universal platform that enables high-yield identification of peptides that mimic epitopes (mimotopes). Despite being unsurpassed as a tool for discovery of polyclonal serum mimotopes, the PDRPL/NGS is far inferior as a quantitative method of immune response. Difficult-to-control fluctuations in amounts of antibody-bound phages after rounds of selection and amplification diminish the quantitative capacity of the PDRPL/NGS. In an attempt to improve the accuracy of the PDRPL/NGS method, we compared the discriminating capacity of two approaches for PDRPL/NGS data analysis. The whole-unique-sequence-based analysis (WUSA) involved generation of 7-mer peptide profiles and comparison of the numbers of sequencing reads for unique peptide sequences between serum samples. The motif-based analysis (MA) included identification of 4-mer consensus motifs unifying unique 7-mer sequences and comparison of motifs between serum samples. The motif comparison was based not on the numbers of sequencing reads, but on the numbers of distinct 7-mers constituting the motifs. Our PDRPL/NGS datasets generated from biopanning of protective and non-protective anti-<italic>Borrelia burgdorferi</italic> sera of New Zealand rabbits were used to contrast the two approaches. As a result, the principle component analyses (PCA) showed that the discriminating powers of the WUSA and MA were similar. In contrast, the unsupervised hierarchical clustering obtained via the MA classified the preimmune, non-protective, and protective sera better than the WUSA-based clustering. Also, a total number of discriminating motifs was higher than that of discriminating 7-mers. In sum, our results indicate that MA approach improves the accuracy and quantitative capacity of the PDRPL/NGS method.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Pizza, M" uniqKey="Pizza M">M Pizza</name></author><author><name sortKey="Scarlato, V" uniqKey="Scarlato V">V Scarlato</name></author><author><name sortKey="Masignani, V" uniqKey="Masignani V">V Masignani</name></author><author><name sortKey="Giuliani, Mm" uniqKey="Giuliani M">MM Giuliani</name></author><author><name sortKey="Arico, B" uniqKey="Arico B">B Arico</name></author><author><name sortKey="Comanducci, M" uniqKey="Comanducci M">M Comanducci</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Moriel, Dg" uniqKey="Moriel D">DG Moriel</name></author><author><name sortKey="Bertoldi, I" uniqKey="Bertoldi I">I Bertoldi</name></author><author><name sortKey="Spagnuolo, A" uniqKey="Spagnuolo A">A Spagnuolo</name></author><author><name 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sortKey="Stafford, P" uniqKey="Stafford P">P Stafford</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bastas, G" uniqKey="Bastas G">G Bastas</name></author><author><name sortKey="Sompuram, Sr" uniqKey="Sompuram S">SR Sompuram</name></author><author><name sortKey="Pierce, B" uniqKey="Pierce B">B Pierce</name></author><author><name sortKey="Vani, K" uniqKey="Vani K">K Vani</name></author><author><name sortKey="Bogen, Sa" uniqKey="Bogen S">SA Bogen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ionov, Y" uniqKey="Ionov Y">Y Ionov</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Paull, Ml" uniqKey="Paull M">ML Paull</name></author><author><name sortKey="Johnston, T" uniqKey="Johnston T">T Johnston</name></author><author><name sortKey="Ibsen, Kn" uniqKey="Ibsen K">KN Ibsen</name></author><author><name sortKey="Bozekowski, Jd" uniqKey="Bozekowski J">JD Bozekowski</name></author><author><name sortKey="Daugherty, Ps" uniqKey="Daugherty P">PS Daugherty</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Elias, Af" uniqKey="Elias A">AF Elias</name></author><author><name sortKey="Stewart, Pe" uniqKey="Stewart P">PE Stewart</name></author><author><name sortKey="Grimm, D" uniqKey="Grimm D">D Grimm</name></author><author><name sortKey="Caimano, Mj" uniqKey="Caimano M">MJ Caimano</name></author><author><name sortKey="Eggers, Ch" uniqKey="Eggers C">CH Eggers</name></author><author><name sortKey="Tilly, K" uniqKey="Tilly K">K Tilly</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, CA USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">31940357</article-id><article-id pub-id-type="pmc">6961823</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0226378</article-id><article-id pub-id-type="publisher-id">PONE-D-19-21468</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Database and Informatics Methods</subject><subj-group><subject>Bioinformatics</subject><subj-group><subject>Sequence Analysis</subject><subj-group><subject>Sequence Motif Analysis</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Physiology</subject><subj-group><subject>Immune Physiology</subject><subj-group><subject>Antibodies</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Physiology</subject><subj-group><subject>Immune Physiology</subject><subj-group><subject>Antibodies</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Immunology</subject><subj-group><subject>Immune System Proteins</subject><subj-group><subject>Antibodies</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Immunology</subject><subj-group><subject>Immune System Proteins</subject><subj-group><subject>Antibodies</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Biochemistry</subject><subj-group><subject>Proteins</subject><subj-group><subject>Immune System Proteins</subject><subj-group><subject>Antibodies</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Molecular Biology</subject><subj-group><subject>Molecular Biology Techniques</subject><subj-group><subject>Molecular Biology Assays and Analysis Techniques</subject><subj-group><subject>Molecular Biology Display Techniques</subject><subj-group><subject>Phage Display</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Molecular Biology Techniques</subject><subj-group><subject>Molecular Biology Assays and Analysis Techniques</subject><subj-group><subject>Molecular Biology Display Techniques</subject><subj-group><subject>Phage Display</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Mathematical and Statistical Techniques</subject><subj-group><subject>Statistical Methods</subject><subj-group><subject>Multivariate Analysis</subject><subj-group><subject>Principal Component Analysis</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Physical Sciences</subject><subj-group><subject>Mathematics</subject><subj-group><subject>Statistics</subject><subj-group><subject>Statistical Methods</subject><subj-group><subject>Multivariate Analysis</subject><subj-group><subject>Principal Component Analysis</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Organisms</subject><subj-group><subject>Eukaryota</subject><subj-group><subject>Animals</subject><subj-group><subject>Vertebrates</subject><subj-group><subject>Amniotes</subject><subj-group><subject>Mammals</subject><subj-group><subject>Leporids</subject><subj-group><subject>Rabbits</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Animal Studies</subject><subj-group><subject>Experimental Organism Systems</subject><subj-group><subject>Animal Models</subject><subj-group><subject>Rabbits</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Organisms</subject><subj-group><subject>Bacteria</subject><subj-group><subject>Borrelia</subject><subj-group><subject>Borrelia Burgdorferi</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Microbiology</subject><subj-group><subject>Medical Microbiology</subject><subj-group><subject>Microbial Pathogens</subject><subj-group><subject>Bacterial Pathogens</subject><subj-group><subject>Borrelia</subject><subj-group><subject>Borrelia Burgdorferi</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Pathology and Laboratory Medicine</subject><subj-group><subject>Pathogens</subject><subj-group><subject>Microbial Pathogens</subject><subj-group><subject>Bacterial Pathogens</subject><subj-group><subject>Borrelia</subject><subj-group><subject>Borrelia Burgdorferi</subject></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Research and Analysis Methods</subject><subj-group><subject>Database and Informatics Methods</subject><subj-group><subject>Bioinformatics</subject><subj-group><subject>Sequence Analysis</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Biology and Life Sciences</subject><subj-group><subject>Immunology</subject><subj-group><subject>Immune Response</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3"><subject>Medicine and Health Sciences</subject><subj-group><subject>Immunology</subject><subj-group><subject>Immune Response</subject></subj-group></subj-group></subj-group></article-categories><title-group><article-title>Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-<italic>Borrelia burgdorferi</italic> immune sera</article-title><alt-title alt-title-type="running-head">Motif-based and unique sequence-based analyses of phage display library datasets</alt-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0002-7174-386X</contrib-id><name><surname>Ionov</surname><given-names>Yurij</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Data curation</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Methodology</role><role content-type="http://credit.casrai.org/">Writing – original draft</role><xref ref-type="aff" rid="aff001"><sup>1</sup></xref><xref ref-type="corresp" rid="cor001">*</xref></contrib><contrib contrib-type="author"><name><surname>Rogovskyy</surname><given-names>Artem S.</given-names></name><role content-type="http://credit.casrai.org/">Conceptualization</role><role content-type="http://credit.casrai.org/">Data curation</role><role content-type="http://credit.casrai.org/">Formal analysis</role><role content-type="http://credit.casrai.org/">Funding acquisition</role><role content-type="http://credit.casrai.org/">Investigation</role><role content-type="http://credit.casrai.org/">Project administration</role><role content-type="http://credit.casrai.org/">Resources</role><role content-type="http://credit.casrai.org/">Supervision</role><role content-type="http://credit.casrai.org/">Validation</role><role content-type="http://credit.casrai.org/">Visualization</role><role content-type="http://credit.casrai.org/">Writing – review & editing</role><xref ref-type="aff" rid="aff002"><sup>2</sup></xref><xref ref-type="corresp" rid="cor001">*</xref></contrib></contrib-group><aff id="aff001"><label>1</label><addr-line>Department of Cancer Genetics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America</addr-line></aff><aff id="aff002"><label>2</label><addr-line>Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, United States of America</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Mantis</surname><given-names>Nicholas J</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>New York State Department of Health, UNITED STATES</addr-line></aff><author-notes><fn fn-type="COI-statement" id="coi001"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><corresp id="cor001">* E-mail: <email>Yurij.Ionov@RoswellPark.org</email> (YI); <email>arogovskyy@tamu.edu</email> (ASR)</corresp></author-notes><pub-date pub-type="epub"><day>15</day><month>1</month><year>2020</year></pub-date><pub-date pub-type="collection"><year>2020</year></pub-date><volume>15</volume><issue>1</issue><elocation-id>e0226378</elocation-id><history><date date-type="received"><day>30</day><month>7</month><year>2019</year></date><date date-type="accepted"><day>25</day><month>11</month><year>2019</year></date></history><permissions><copyright-statement>© 2020 Ionov, Rogovskyy</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Ionov, Rogovskyy</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="pone.0226378.pdf"></self-uri><abstract><p>Detection of protection-associated epitopes via reverse vaccinology is the first step for development of subunit vaccines against microbial pathogens. Mapping subunit vaccine targets requires high throughput methods, which would allow delineation of epitopes recognized by protective antibodies on a large scale. Phage displayed random peptide library coupled to Next Generation Sequencing (PDRPL/NGS) is the universal platform that enables high-yield identification of peptides that mimic epitopes (mimotopes). Despite being unsurpassed as a tool for discovery of polyclonal serum mimotopes, the PDRPL/NGS is far inferior as a quantitative method of immune response. Difficult-to-control fluctuations in amounts of antibody-bound phages after rounds of selection and amplification diminish the quantitative capacity of the PDRPL/NGS. In an attempt to improve the accuracy of the PDRPL/NGS method, we compared the discriminating capacity of two approaches for PDRPL/NGS data analysis. The whole-unique-sequence-based analysis (WUSA) involved generation of 7-mer peptide profiles and comparison of the numbers of sequencing reads for unique peptide sequences between serum samples. The motif-based analysis (MA) included identification of 4-mer consensus motifs unifying unique 7-mer sequences and comparison of motifs between serum samples. The motif comparison was based not on the numbers of sequencing reads, but on the numbers of distinct 7-mers constituting the motifs. Our PDRPL/NGS datasets generated from biopanning of protective and non-protective anti-<italic>Borrelia burgdorferi</italic> sera of New Zealand rabbits were used to contrast the two approaches. As a result, the principle component analyses (PCA) showed that the discriminating powers of the WUSA and MA were similar. In contrast, the unsupervised hierarchical clustering obtained via the MA classified the preimmune, non-protective, and protective sera better than the WUSA-based clustering. Also, a total number of discriminating motifs was higher than that of discriminating 7-mers. In sum, our results indicate that MA approach improves the accuracy and quantitative capacity of the PDRPL/NGS method.</p></abstract><funding-group><award-group id="award001"><funding-source><institution-wrap><institution-id institution-id-type="funder-id">http://dx.doi.org/10.13039/100000002</institution-id><institution>National Institutes of Health</institution></institution-wrap></funding-source><award-id>R03AI135159-02</award-id><principal-award-recipient><name><surname>Rogovskyy</surname><given-names>Artem S.</given-names></name></principal-award-recipient></award-group><funding-statement>The authors received no specific funding for this work.</funding-statement></funding-group><counts><fig-count count="5"></fig-count><table-count count="0"></table-count><page-count count="17"></page-count></counts><custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name><meta-value>All relevant data are within the paper and its Supporting Information files.</meta-value></custom-meta></custom-meta-group></article-meta><notes><title>Data Availability</title><p>All relevant data are within the paper and its Supporting Information files.</p></notes></front><body><sec sec-type="intro" id="sec001"><title>Introduction</title><p>Identification of protection-associated (PA) antigens and/or their epitopes via reverse vaccinology is one of the very first steps for development of vaccines against human pathogens [<xref rid="pone.0226378.ref001" ref-type="bibr">1</xref>–<xref rid="pone.0226378.ref009" ref-type="bibr">9</xref>]. To select the most promising vaccine candidate(s) initially requires mapping multiple PA targets, which then can be individually tested for their protective efficacy via animal immunization assays [<xref rid="pone.0226378.ref010" ref-type="bibr">10</xref>]. To map numerous PA epitopes, high throughput methods, which would allow PA targets to be specifically recognized by protective serum antibodies on a large scale, are needed.</p><p>The high-density peptide microarray is a traditional method for mapping linear epitopes of antibodies developed against protein antigens [<xref rid="pone.0226378.ref011" ref-type="bibr">11</xref>–<xref rid="pone.0226378.ref013" ref-type="bibr">13</xref>]. The microarray contains overlapping peptides that cover the entire length of a target protein. However, this relatively straightforward method is not optimal for analyzing repertoires of antibodies developed against intact bacterial pathogens because these microorganisms are represented by an enormously complex mixture of antigens. Generation and application of customized microarrays with peptides that would encompass an entire proteome of a virus or bacterium is highly cost-prohibitive and time-consuming.</p><p>An alternative to customized platforms is the random peptide array, where 10<sup>4</sup> 17-mer random peptide sequences selected by a random number generator are printed on glass slides [<xref rid="pone.0226378.ref014" ref-type="bibr">14</xref>]. When a serum sample is applied to the surface of this peptide array, distinct binding patterns of the unique molecular recognition elements associated with pathogen-specific antibodies are generated. The random peptide array is highly reproducible and, hence, allows changes in the antibody repertoires that are characteristic for different sates of a disease to be detected [<xref rid="pone.0226378.ref015" ref-type="bibr">15</xref>, <xref rid="pone.0226378.ref016" ref-type="bibr">16</xref>]. However, the disadvantage is that densely surface-immobilized ligands can amplify non-specific interactions of low affinity, which reduces the overall capacity of the random peptide array for identifying disease-specific interactions. The other drawback is that the total number of peptides that can be attached to a single 25×75-mm glass is limited. Despite previous studies successfully used 10<sup>4</sup> random peptides to characterize different immune responses [<xref rid="pone.0226378.ref017" ref-type="bibr">17</xref>], the assay with such a low number of ligands can still miss important antibody reactivities.</p><p>Phage displayed random peptide library (referred to here as PDRPL) is an inexpensive alternative to the high-density peptide array. The first application of PDRPL demonstrated the feasibility of affinity selection for identifying a peptide mimetic, which represented cognate epitopes of monoclonal antibodies [<xref rid="pone.0226378.ref018" ref-type="bibr">18</xref>]. Since then, PDRPL has been extensively applied to characterize specificity of polyclonal sera from cancer or infectious disease patients [<xref rid="pone.0226378.ref019" ref-type="bibr">19</xref>–<xref rid="pone.0226378.ref024" ref-type="bibr">24</xref>]. Similar to the random peptide array, PDRPL is also a universal platform, yet with a much higher capacity to represent sequences of a complete proteome of any microorganism. For example, a commercially available Ph.D.-7 library of random heptapeptides (New England Biolabs, USA) has a complexity on the order of 10<sup>9</sup> independent clones and thus can represent any unique 7-mer peptide sequence (referred to here as 7-mer sequence). According to the product specifications, the phage concentration of the Ph.D.-7 library is 10<sup>13</sup> phage particles per mL. The latter means that 10 μl of the library used for a biopanning experiment contains 10<sup>11</sup> phage particles and each 7-mer is thus represented on average by almost 80 times (which is calculated by dividing 10<sup>11</sup> by 20<sup>7</sup>). In addition to linear epitopes, phage displayed peptides may also represent both linear and conformational epitope mimetics of protein and carbohydrate antigens also known as mimotopes [<xref rid="pone.0226378.ref025" ref-type="bibr">25</xref>].</p><p>Until a recent advent of Next Generation Sequencing technology (referred to here as NGS), the main disadvantage of traditional PDRPL was the necessity to individually sequence phage clones. The NGS has transformed the phage display into a high throughput method, which, to date, allows the diversity of antibody specificities to be thoroughly examined [<xref rid="pone.0226378.ref026" ref-type="bibr">26</xref>]. By using the NGS, hundred thousands of affinity-selected peptide sequences can be obtained in a single sequencing run [<xref rid="pone.0226378.ref027" ref-type="bibr">27</xref>]. As a result, the PDRPL coupled with the NGS (referred to here as PDRPL/NGS) has been extensively utilized to analyze repertoires of serum antibodies [<xref rid="pone.0226378.ref028" ref-type="bibr">28</xref>–<xref rid="pone.0226378.ref030" ref-type="bibr">30</xref>].</p><p>In our recent studies [<xref rid="pone.0226378.ref010" ref-type="bibr">10</xref>, <xref rid="pone.0226378.ref031" ref-type="bibr">31</xref>, <xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>], we successfully applied the PDRPL/NGS to define repertoires of antibodies developed in mice and rabbits that had an active infection with <italic>Borrelia burgdorferi</italic>, the bacterial agent of Lyme disease (LD) [<xref rid="pone.0226378.ref033" ref-type="bibr">33</xref>–<xref rid="pone.0226378.ref041" ref-type="bibr">41</xref>]. LD pathogen has the capacity to establish persistent infection in mice, the primary natural mammalian reservoir of <italic>B</italic>. <italic>burgdorferi</italic> in the United States [<xref rid="pone.0226378.ref042" ref-type="bibr">42</xref>–<xref rid="pone.0226378.ref047" ref-type="bibr">47</xref>], and human patients [<xref rid="pone.0226378.ref048" ref-type="bibr">48</xref>–<xref rid="pone.0226378.ref053" ref-type="bibr">53</xref>], despite very strong anti-<italic>B</italic>. <italic>burgdorferi</italic> antibody responses [<xref rid="pone.0226378.ref054" ref-type="bibr">54</xref>–<xref rid="pone.0226378.ref060" ref-type="bibr">60</xref>]. In immunocompetent mouse models, the ability of <italic>B</italic>. <italic>burgdorferi</italic> to successfully evade otherwise potent antibodies is mainly attributed to the highly efficacious VlsE-encoding system, whose genetic removal results in rapid clearance of LD pathogen by host antibodies [<xref rid="pone.0226378.ref054" ref-type="bibr">54</xref>, <xref rid="pone.0226378.ref059" ref-type="bibr">59</xref>, <xref rid="pone.0226378.ref061" ref-type="bibr">61</xref>–<xref rid="pone.0226378.ref069" ref-type="bibr">69</xref>]. In contrast to experimental mouse or any other animal LD models [<xref rid="pone.0226378.ref070" ref-type="bibr">70</xref>–<xref rid="pone.0226378.ref086" ref-type="bibr">86</xref>], however, New Zealand White (NZW) rabbits develop a protective antibody-mediated response, which effectively clears <italic>B</italic>. <italic>burgdorferi</italic> within 3–8 weeks, despite the antigenically varying surface protein, VlsE [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>, <xref rid="pone.0226378.ref087" ref-type="bibr">87</xref>, <xref rid="pone.0226378.ref088" ref-type="bibr">88</xref>]. Our recent study has examined repertoires of antibodies in sera collected from <italic>B</italic>. <italic>burgdorferi</italic>-infected NZW rabbits at day 14 and 28 postinfection, the time points at which the rabbit antibodies were shown to be non-protective or protective against the LD pathogen in mice, respectively [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>].</p><p>In the present work, we used our previously generated PDRPL/NGS data to improve on the PDRPL/NGS analysis in discriminating between the protective and non-protective anti-<italic>B</italic>. <italic>burgdorferi</italic> sera of NZW rabbits [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>]. Specifically, in order to extract more information on any qualitative and quantitative alterations in anti-<italic>B</italic>. <italic>burgdorferi</italic> antibody specificities, we have compared two distinct modes of analyses of the PDRPL/NGS data. The whole-unique-sequence-based analysis (referred to here as WUSA) involved 7-mer sequences and compared the number of sequencing reads for each unique peptide between the protective and non-protective sera after the normalization for the total number of all the sequences. In contrast, the alternative motif-based (referred to here as MA) method included identification and comparison of four-amino-acid-long consensus motifs (referred to here as 4-mer motifs), each representing a cluster of related 7-mer sequences that differed by only few amino acids.</p></sec><sec sec-type="results" id="sec002"><title>Results</title><p>Our previous study has demonstrated that by day 28 postinfection, NZW rabbits developed a repertoire of protective antibodies against <italic>B</italic>. <italic>burgdorferi</italic> infection [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>]. Specifically, it was shown that, when passively transferred to naïve mice, the rabbit antibodies prevented infection by highly immune-evasive <italic>B</italic>. <italic>burgdorferi</italic>. Moreover, in mice with ongoing infection, the rabbit antibodies were able to significantly reduce LD-induced inflammation in joints [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>], the avascular collagenous tissues to which antibody access is somewhat limited [<xref rid="pone.0226378.ref089" ref-type="bibr">89</xref>]. Our follow-up study defined specificities of day-14 (non-protective) and day-28 (protective) rabbit antibodies via the PDRPL/NGS and then directly compared the two repertoires. As a result, specificities of the rabbit protective antibodies and their respective targets were identified [<xref rid="pone.0226378.ref009" ref-type="bibr">9</xref>].</p><p>In the present study, we have compared two distinct approaches, the WUSA and MA, for the analysis of the PDRPL/NGS data [<xref rid="pone.0226378.ref009" ref-type="bibr">9</xref>]. Both approaches directly compared mimotope sequences between the day-14 (non-protective) and day-28 (protective) immune serum samples from 3 NZW rabbits (animals P, Y, and Z). The preimmune sera were pooled from the 3 rabbits prior to their challenge with wild-type <italic>B</italic>. <italic>burgdorferi</italic> B31-A3 strain and served as a background control. To obtain mimotope sequences, 1 pooled preimmune, 3 non-protective, and 3 protective serum samples (referred to here as PI, NP, and PR samples, respectively) were analyzed via commercially available Ph.D.-7 library of random heptapeptides followed by sequencing via Illumina 2500 sequencing platform [<xref rid="pone.0226378.ref009" ref-type="bibr">9</xref>]. One PR sample was analyzed in duplicate (samples P28a and P28b). For both methods, 1,000 most abundant 7-mer sequences generated from the PDRPL/NGS analysis of NP (P14, Y14, and Z14), PR (P28a, P28b, Y28, and Z28), and PI samples were selected.</p><sec id="sec003"><title>Analyzing the protective and non-protective rabbit serum samples via the MA approach</title><p>In the MA approach, we first identified mimotope motifs that represented clusters of 7-mer sequences. Within each cluster, the respective 7-mers differed by few amino acids outside their 4-mer motif. Motifs, in turn, could have conservative amino acid substitutions. To generate 50 4-mer motifs for each sample, a total of 8 mimotope sequence datasets (P14, Y14, Z14, P28a, P28b, Y28, Z28, and PI) were separately processed via MEME software tool (<ext-link ext-link-type="uri" xlink:href="http://meme-suite.org/tools/meme">http://meme-suite.org/tools/meme</ext-link>) [<xref rid="pone.0226378.ref090" ref-type="bibr">90</xref>]. Each dataset consisted of 1,000 7-mers and each motif was derived from at least 3 distinct sequence variants. To make the 7-mer sequences compatible with the MEME tool (the minimum length is 8 letters), the letter X was added to each 7-mer. Prior to running the program, we chose that each motif has the minimum sites of 3 (to unify at least 3 unique sequences) and the width of 4 amino acids. The MEME output exemplified in <xref ref-type="fig" rid="pone.0226378.g001">Fig 1</xref> shows two abundant motifs, QKPL and KIGD, which unified 9 and 46 distinct 7-mer sequences, respectively, for sample Z28. As a result of this <italic>in silico</italic> analysis, we found that some motifs were shared by the 8 datasets. After the redundant motifs were removed, there was a total of 330 distinct motifs for the 8 samples. <xref ref-type="supplementary-material" rid="pone.0226378.s001">S1 Table</xref> provides a complete list of these motifs and the respective numbers of the 7-mer sequences that constituted each motif for each serum sample. After performing the paired two-tailed <italic>t</italic>-test (leaving out one of the two technical replicates for one protective sample), we identified 9 most significant motifs that discriminated between the NP and PR samples (<italic>p</italic><0.1). Furthermore, out of these 9 motifs, 4 motifs were most significant (<italic>p</italic><0.05) and were derived from a most frequently represented 7-mer for the 4 PR samples, GLLQKPL. <xref ref-type="fig" rid="pone.0226378.g002">Fig 2A</xref> shows the distribution of 10 selected motifs that were most significant for the 8 samples. Since the <italic>t</italic>-test may be not the best statistics to identify discriminating motifs, a different method was also tested. For that, the minimum number of motif variants for the PR samples was compared with the maximum number of motif variants for the NP samples. As a result, we identified a total of 10 motifs, whose minimum numbers for the PR samples were higher than the maximum numbers for the NP samples. Interestinlgy, 7 of these 10 motifs were identical to the most statistically significant motifs identified via the <italic>t</italic>-test (<xref ref-type="supplementary-material" rid="pone.0226378.s001">S1 Table</xref>, Sheet 2).</p><fig id="pone.0226378.g001" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0226378.g001</object-id><label>Fig 1</label><caption><title>Examples of MEME-generated 4-mer motifs unifying the unique 7-mer peptide sequences.</title></caption><graphic xlink:href="pone.0226378.g001"></graphic></fig><fig id="pone.0226378.g002" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0226378.g002</object-id><label>Fig 2</label><caption><title>Frequency distribution across rabbit serum samples for the discriminating 4-mer motifs (A) and unique 7-mer peptides (B) with the lowest <italic>p-</italic>values according to the <italic>t-</italic>test.</title></caption><graphic xlink:href="pone.0226378.g002"></graphic></fig><p>We then performed Principal Component Analysis (PCA) on the 330 motifs by using the web tool, BETA, which was developed to visualize clustering of multivariate data (<ext-link ext-link-type="uri" xlink:href="https://biit.cs.ut.ee/clustvis/">https://biit.cs.ut.ee/clustvis/</ext-link>) [<xref rid="pone.0226378.ref091" ref-type="bibr">91</xref>]. Since the first principle component, which determines the direction of highest variability in the data, captured only 18% of the variability, the plot showed a high degree of data randomness (<xref ref-type="fig" rid="pone.0226378.g003">Fig 3A</xref>). Despite the observed randomness, however, the PCA analysis still separated the 4 PR samples from all the 3 NP and 1 PI samples (<xref ref-type="fig" rid="pone.0226378.g003">Fig 3A</xref>). The heatmap generated by the unsupervised hierarchical clustering via the ClustVis also demonstrated a high degree of variability between the 8 samples (<xref ref-type="fig" rid="pone.0226378.g003">Fig 3B</xref>). However, similar to the PCA plot, the respective heatmap showed that the PI sample was well segregated from the 4 PR samples; and yet neighbored with a cluster of the 3 NP samples (<xref ref-type="fig" rid="pone.0226378.g003">Fig 3B</xref>). Consistently, the duplicate samples, P28a and P28b, were grouped together despite a high degree of variability within each technical replicate (<xref ref-type="fig" rid="pone.0226378.g003">Fig 3B</xref>). Of note, the proximity of PCA values for samples Z14 and Z28 and their respective heatmap patterns suggested that NZW rabbit Z had developed a protective antibody repertoire later compared to animals P and Y. Indeed, in contrast to NZW rabbits P and Y, skin biopsies of animal Z sampled at day 28 postinfection were only weakly culture-positive for <italic>B</italic>. <italic>burgdorferi</italic>, whose very low spirochetal numbers and impaired (slow) motility indicated a relatively late clearance [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>].</p><fig id="pone.0226378.g003" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0226378.g003</object-id><label>Fig 3</label><caption><title>Principal Component Analysis (PCA) plot (A) and heatmap (B) generated, via the ClustVis, by the motif-based analysis (MA).</title><p>PDRPL/NGS data were produced by biopanning of preimmune (PI), non-protective (P14, Y14, and Z14), and protective (P28a, P28b, Y28, and Z28) rabbit serum samples. Blue lines drawn across the PCA plot (A) separate the 3 groups of serum samples.</p></caption><graphic xlink:href="pone.0226378.g003"></graphic></fig></sec><sec id="sec004"><title>Analyzing the protective and non-protective rabbit serum samples via the WUSA approach</title><p>To compare the discriminating capacity of the MA with that of the more convential WUSA method, the same 8 mimotope sequence datasets (P14, Y14, Z14, P28a, P28b, Y28, Z28, and PI) were analyzed via the WUSA. First, we calculated the number of sequencing reads for each 7-mer sequence, which reflected the relative titers of the respective mimotope-recognizing antibodies (1S Table). Thus, the numbers of sequencing reads were used to directly compare levels of antibodies of distinct specificities within and between the serum samples. Importantly, the numbers of 7-mers for each sample were normalized to have totals of sequencing reads equal across the 8 samples. This normalization was necessary to eliminate any biases that are associated with potential differences in amplification efficiency between the PCR reactions performed prior to multiplexing a phage DNA library.</p><p>For each dataset, we selected the top 50 most abundant sequences to match with the number of motifs (n = 50) used for the MA method. After eliminating the redundant sequences, there was a total of 172 distinct 7-mers for the 8 datasets. <xref ref-type="supplementary-material" rid="pone.0226378.s002">S2 Table</xref> lists the unique 7-mer sequences and the respective numbers of their sequencing reads. After performing the paired two-tailed <italic>t</italic>-test, we identified 11 most significant 7-mers that discriminated between the NP and PR samples (<italic>p</italic><0.1). <xref ref-type="fig" rid="pone.0226378.g002">Fig 2B</xref> shows the 10 peptide sequences that had the lowest <italic>p</italic>-values. Then, the minimum numbers of sequencing reads for the PR samples were compared with the respective maximum values for the NP samples. Consequenlty, only 3 out of 11 peptides (<italic>α</italic> = 0.1) had the higher numbers of their sequencing reads for the PR samples compared to the NP samples. Out of these 3 peptides, GLLQKPL was most discriminating as it had the highest positive difference in the number of its sequencing reads between the PR and NP samples (<xref ref-type="fig" rid="pone.0226378.g002">Fig 2B</xref>). Consistently, the PCA analysis showed a high degree of data randomness with the first principle component capturing only 21% of the variability. Despite this randomness, the PI, NP, and PR samples were still segregated by the PCA plots (<xref ref-type="fig" rid="pone.0226378.g004">Fig 4A</xref>). Interestingly, the respective heatmap did not discriminate between the PI and NP samples (<xref ref-type="fig" rid="pone.0226378.g004">Fig 4B</xref>). Consistent with the MA method results, the duplicate samples, P28a and P28b, were also clustered together; although this time this overlap was slightly higher (<xref ref-type="fig" rid="pone.0226378.g004">Fig 4B</xref>).</p><fig id="pone.0226378.g004" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0226378.g004</object-id><label>Fig 4</label><caption><title>Principal Component Analysis plot (A) and heatmap (B) generated, via the ClustVis, by the whole-unique-sequence-based analysis (WUSA).</title><p>PDRPL/NGS data were produced by biopanning of preimmune (PI), non-protective (P14, Y14, and Z14), and protective (P28a, P28b, Y28, and Z28) rabbit serum samples. Blue lines drawn across the PCA plot (A) separate the 3 groups of serum samples.</p></caption><graphic xlink:href="pone.0226378.g004"></graphic></fig></sec><sec id="sec005"><title>Improving on the MA and WUSA methods</title><p>The high degree of data randomness shown by both WUSA and MA methods may be due to a high level of noise inherently associated with the PDRPL/NGS. In addition, the observed randomness could indicate that only a small fraction of antibody repertoires of infected NZW rabbits is related to an anti-<italic>B</italic>. <italic>burgdorferi</italic> immune response; whereas the majority of rabbit antibody repertoires may simply reflect the previous exposure to unrelated antigens. To increase the signal to noise ratio, this time we generated the PCA plots and respective heatmaps only for the first 25 motifs and the first 25 7-mer sequences as ranked by the <italic>t</italic>-test. The PCA plots showed that the overall separation between the PR and NP samples were noticeably improved by both WUSA and MA methods (<xref ref-type="fig" rid="pone.0226378.g005">Fig 5A and 5C</xref>). Overall, the heatmap generated via the MA method reflected the immunological status (protective vs. non-protective responses) more accurately than the WUSA-based heatmap (<xref ref-type="fig" rid="pone.0226378.g005">Fig 5B and 5D</xref>). Curiously, the arrangement of the 8 samples from left to right showed the progression of protective immune response against the LD pathogen. Furthermore, sample Z28 from the NZW rabbit that had developed its protective antibodies later than the other animals was placed on the border between the PR and NP samples and was adjacent to sample Z14 derived from the same rabbit (<xref ref-type="fig" rid="pone.0226378.g005">Fig 5B</xref>). The replicates, P28a and P28b, were clustered together and the PI sample was distantly positioned from the 4 PR samples (<xref ref-type="fig" rid="pone.0226378.g005">Fig 5B</xref>). On the contrary, the heatmap generated via the WUSA, placed the PI sample between the PR and NP samples; and yet the technical replicates, P28a and P28b, were consitently grouped together (<xref ref-type="fig" rid="pone.0226378.g005">Fig 5D</xref>).</p><fig id="pone.0226378.g005" orientation="portrait" position="float"><object-id pub-id-type="doi">10.1371/journal.pone.0226378.g005</object-id><label>Fig 5</label><caption><title>ClustVis-generated Principal Component Analysis plots (A and C) and heatmaps (B and D) produced for the first 25 most statistically significant 4-mer motifs (A and B) and unique 7-mer sequences (C and D).</title><p>PDRPL/NGS data were generated by biopanning of preimmune (PI), non-protective (P14, Y14, and Z14), and protective (P28a, P28b, Y28, and Z28) rabbit serum samples.</p></caption><graphic xlink:href="pone.0226378.g005"></graphic></fig></sec></sec><sec sec-type="conclusions" id="sec006"><title>Discussion</title><p>Reverse vaccinology is a powerful vaccine development approach, which involves initial identification of vaccine targets via genome-based computational analysis [<xref rid="pone.0226378.ref092" ref-type="bibr">92</xref>]. In our recent studies, we utilized a subtractive reverse vaccinology to delineate putative surface epitopes of <italic>B</italic>. <italic>burgdorferi</italic> associated with antibody-mediated protection against the LD pathogen [<xref rid="pone.0226378.ref010" ref-type="bibr">10</xref>, <xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>, <xref rid="pone.0226378.ref093" ref-type="bibr">93</xref>]. By using the two LD animal models, protective and non-protective immune sera from C3H mice and NZW rabbits were analyzed via the PDRPL/NGS and compared within each animal species [<xref rid="pone.0226378.ref010" ref-type="bibr">10</xref>, <xref rid="pone.0226378.ref093" ref-type="bibr">93</xref>]. To further evaluate the utility of the PDRPL/NGS for quantitative analysis of immune responses, in the present study, we conveniently made use of the PDRPL/NGS datasets previously generated from the NZW rabbit model [<xref rid="pone.0226378.ref093" ref-type="bibr">93</xref>].</p><p>Although the PDRPL/NGS is a very sensitive high throughput method for identifying disease-specific mimotopes recognized by serum antibodies [<xref rid="pone.0226378.ref094" ref-type="bibr">94</xref>]; its reproducibility and accuracy are low [<xref rid="pone.0226378.ref030" ref-type="bibr">30</xref>] compared to the random peptide array [<xref rid="pone.0226378.ref014" ref-type="bibr">14</xref>, <xref rid="pone.0226378.ref015" ref-type="bibr">15</xref>, <xref rid="pone.0226378.ref095" ref-type="bibr">95</xref>]. There are at least two probable causes for the low reproducibility. First, the binding of low adundant antibodies to their cognate targets is a stochastic process, since, during the first round of selection, these targets are present in very low numbers. Second, there is a loss of relevant sequences over secondary rounds of selection and propagation of phages [<xref rid="pone.0226378.ref030" ref-type="bibr">30</xref>]. Importantly, these multiple rounds of selection and amplification are required for making the ratio of a target to its cognate antibody large enough to reach the positive correlation between the number of sequencing reads for a given unique peptide and a titer of its specific antibody. However, because of these required secondary rounds, the phage amplification becomes highly sensitive to fluctuations in the number of bacterial cells successfully infected by competing phage particles. As a result, the numbers of sequencing reads in technical replicates may show a large variation, which in turn reduces the overall accuracy of the quantitative analysis of the PDRPL/NGS data.</p><p>To improve the reproducibility of the PDRPL/NGS method, in the present study, we evaluated the feasibility of using 4-mer motifs as potential biomarkers of the protective anti-<italic>B</italic>. <italic>burgdorderi</italic> immune response. Given that most of the binding energy is typically derived from 4–6 amino acids [<xref rid="pone.0226378.ref017" ref-type="bibr">17</xref>], the specificity of antigen-antibody interaction is defined by only a few anchor residues within an epitope [<xref rid="pone.0226378.ref029" ref-type="bibr">29</xref>, <xref rid="pone.0226378.ref096" ref-type="bibr">96</xref>–<xref rid="pone.0226378.ref098" ref-type="bibr">98</xref>]. Previoulsy, we [<xref rid="pone.0226378.ref027" ref-type="bibr">27</xref>] and others [<xref rid="pone.0226378.ref034" ref-type="bibr">34</xref>] demonstrated that the four-amino-acid consensus is a minimum motif length, which is sufficient to define an antibody specificity. Thus, the high copy number of shorter targets in the initial selection makes the process of target recognition more deterministic. While the unique 7-mer sequence is represented on average by almost 80 copies, the tetramer is represented by a much greater number of copies: 10<sup>11</sup>/20<sup>4</sup> = 625,000. Moreover, it was previously noted that the size of a peptide family (a total number of sequence-related peptides) sharing a given motif positively correlated with a number of sequencing reads for the most represented sequence of that family and the titer of serum antibodies recognizing that motif [<xref rid="pone.0226378.ref029" ref-type="bibr">29</xref>]. Importantly, this positive correlation was experimentally confirmed via ELISA by utilizing immobilized motif-containing peptides [<xref rid="pone.0226378.ref029" ref-type="bibr">29</xref>]. Thus, considering the numbers of distinct motif-constituting sequences transforms qualitative peptide differences into a quantitative parameter, which more accurately reflects titers of target-specific antibodies.</p><p>The present study showed that, in general, the MA method discriminated between the protective and non-protective serum samples as equally well as the WUSA with a couple of noteworthy differences. First, although the number of the discriminating 7-mer sequences identified by the <italic>t</italic>-test (<italic>α</italic> = 0.1) is a little higher than that of discriminating motifs (11 vs. 9), only 3 out of 11 whole sequence peptides were represented by higher numbers of their respective sequencing reads for the PR samples compared to the NP samples. In contrast, 7 out of 9 motifs well discriminated the PR samples (<italic>p</italic> = 0.024619 by Chi-Square test). Second, the unsupervised hierarchical clustering obtained via the MA method arranged the samples in the order that more accurately reflected the development of the protective anti-<italic>B</italic>. <italic>burgdorferi</italic> immune response compared to the WUSA-based clustering. The observed outcome difference can be attributed to the fact that the MA method is more quantitative and, hence, more disciminating by nature compared to the WUSA approach. For example, when group A and group B sera (3 samples in each group) are compared and each of the 3 samples of group A has only 1 unique 7-mer; and together these 7-mers form a motif, than it is only the motif (rather than the 7-mers) that will disciriminate between the two groups. Also, the MA method tends to level off high fluctuations in numbers of sequencing reads for 7-mer sequences. For example, despite the GLLEKLH peptide was consistently detected and absent in all the PR and NP samples, respectively; the high variance in its sequencing reads prevented this 7-mer from distinguishing between the two serum groups. To contrast, the GLLE motif shared by this and other 7-mers of the 4 PR samples was well discriminating. Therefore, the MA approach allows each of motif-constituting 7-mers to quantitatively contribute to the overall discriminatory power of the respective motif regardless of high variations in its sequencing reads. Given that a total number of a given unique peptide positively correlates with the number of its respective sequencing reads [<xref rid="pone.0226378.ref029" ref-type="bibr">29</xref>]; motifs, in general, are likely to more accurately reflect both quantitative and qualitative alterations in antibody repertoires.</p></sec><sec sec-type="materials|methods" id="sec007"><title>Methods</title><sec id="sec008"><title>Bacteria and rabbit infection</title><p>In the prior study [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>], <italic>B</italic>. <italic>burgdorferi</italic> B31-A3 strain [<xref rid="pone.0226378.ref099" ref-type="bibr">99</xref>] was utilized to infect New Zealand White (NZW) rabbits. Spirochetes were cultivated in liquid Barbour-Stoenner-Kelly medium supplemented with 6% rabbit serum (Gemini Bio-Products, USA; referred to here as BSK-II) and incubated at 35°C under 2% CO<sub>2</sub>. For our prior study [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>], a total of 3 female NZW rabbits of 12–14 weeks of age (rabbits P, Y, and Z; 3.0–3.5 kg by weight each) were purchased from Charles River Laboratories (USA). The rabbits were singly housed in an animal BSL-2 facility at Texas A&M University. The animals were daily subjected to visual welfare-related assessment for the entire duration of their stay. In order to obtain preimmune sera, the 3 rabbits were bled via the marginal ear vein after their acclimation. Each animal was then individually inoculated intradermally at six sites along the spine with <italic>B</italic>. <italic>burgdorferi</italic> B31-A3 strain at 10<sup>6</sup> cells per each inoculation site as described [<xref rid="pone.0226378.ref088" ref-type="bibr">88</xref>]. The infection of each animal was verified by culture-positive skin biopsies taken around inoculation sites (3 mm in diameter) at weeks 1, 2, and 3 postinfection. Skin tissues collected at week 4 postinfection were also collected and showed that all the skin biopsies were culture-negative with an exception being NZW rabbit Z, whose skin culture was weakly positive. In addition to very low numbers of spirochetes in the NZW rabbit Z skin culture, spirochetes exhibited no or very little (impaired) motility, which indicated the final stage of spirochetal clearance [<xref rid="pone.0226378.ref032" ref-type="bibr">32</xref>]. The blood samples were placed at 4°C until next day. Serum samples were then generated from each blood sample via centrifugation at 5,000 x g for 10 minutes. The serum samples were stored at -80°C. To prevent bacterial and fungal contamination during skin biopsy culture, BSK-II was supplemented with 0.02 mg ml<sup>-1</sup> phosphomycin, 0.05 mg ml<sup>-1</sup> rifampicin, and 2.5 mg ml<sup>-1</sup> amphotericin B. All the cultures were weekly examined for 6 weeks via dark-field microscopy for the presence of viable spirochetes. At week 4 postinfection, the NZW 3 rabbits were humanely sacrificed by applying isoflurane.</p></sec><sec id="sec009"><title>Phage display (Ph.D.) library</title><p>Twenty μl of each rabbit serum sample and 10 μl of random peptide library Ph.D.-7 (NEB, MA, USA) were diluted in 200 μl of TRIS Buffered Saline buffer with 0.1% Tween 20 (referred to here as TBST) and 1% bovine serum albumin and incubated at 25°C for 18 hr [<xref rid="pone.0226378.ref029" ref-type="bibr">29</xref>]. Antibody-bound phages were isolated by utilizing protein G-agarose beads (Santa Cruz Biotechnology, Inc., TX, USA) to the phage antibody mixture for 1 hr. The bead mixture was transferred to a 96-well MultiScreen-Mesh filter plate (EMD Millipore, MA, USA) with a 20-μm-pore-size nylon mesh at the bottom. To remove unbound phages, vacuum was used to the exterior of the nylon mesh. The beads were washed 4 times with 100 μl of TBST buffer for each well. Antibody-bound phages were eluted with 100 μl of 100 mM Tris-glycine buffer (pH 2.2) and subsequently neutralized by adding 20 μl of 1 M Tris buffer (pH 9.1). The solution was then used for amplification of eluted phages by infecting bacteria per the manufacturer’s instructions. Amplified phages were subjected to the next round of biopanning, after which antibody-bound phages were isolated by using protein G-agarose beads. After washing the beads in the 96-well MultiScreen-Mesh filter plate, DNA was isolated by phenol-chloroform extraction and ethanol precipitation. Lastly, 21-nucleotide-long DNA fragments that encoded random peptides were amplified by PCR utilizing the following forward and reverse primers, respectively: <monospace>5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGAT CT(INDEX)TGGTACCTTTCTATTCTCACTCT-3′ and 5′-CAAGCAGAAGAGGGCATACGAGCTCTTCCGATCTAACAGTTTCGGCCGAACCTCCACC-3′</monospace>. The INDEX of each forward primer was defined by the unique six-nucleotide long sequence. Thus, for each serum, a forward primer with the unique INDEX was used [<xref rid="pone.0226378.ref029" ref-type="bibr">29</xref>]. After purification, generated PCR-amplified DNA library was multiplexed and sequenced by 50 single read cycles via Illumina HiSeq 2500 platform at University of Buffalo Genomics and Bioinformatics Core, New York State Center of Excellence Bioinformatics & Life Sciences, Buffalo, New York.</p></sec><sec id="sec010"><title>DNA reads analysis</title><p>The sequencing resulted in approximately 1.79 x10<sup>8</sup> DNA reads. Reads were de-multiplexed based on the unique bar codes. Each read was tagged by the unique INDEX and contained a 21- nucleotide-long sequence, which encoded a given peptide. The 21-nucleotide sequences were extracted between positions 30 and 50 and translated into 7-mers in the first frame. The mean number of peptides that had no stop codon per serum was 5.9 x 10<sup>6</sup>, of which approximately 2.1 x 10<sup>5</sup> peptides per sample were non-redundant. The numbers of peptides were normalized in order that the total number of peptides to be equal between samples and the first 1,000 most abundant unique peptide sequences were selected for each serum samples. For each serum sample, <xref ref-type="supplementary-material" rid="pone.0226378.s003">S3 Table</xref> shows the first 1,000 unique peptides with the corresponding normalized number of sequencing reads for each unique peptide sequence.</p></sec><sec id="sec011"><title>Generating motifs using MEME software</title><p>One thousand most abundant unique peptide sequences were uploaded to MEME software available online (<ext-link ext-link-type="uri" xlink:href="http://meme-suite.org/tools/meme">http://meme-suite.org/tools/meme</ext-link>). The parameters of running the algorithm were selected to generate 50 motifs. To make the 7-mer sequences compatible with the MEME tool requirements (the minimum length is 8 letters), the letter X was added to each 7-mer. Prior to running the program, we chose that each motif has the minimum sites of 3 (to unify at least 3 unique sequences) and the width of 4 amino acids exactly.</p></sec></sec><sec id="sec012"><title>Declarations</title><sec id="sec013"><title>Ethics approval and consent to participate</title><p>NZW rabbits were maintained at Texas A&M University in an animal facility accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care International. All the experimental practices, which involved animals, had been approved by the Institutional Animal Care and Use Committee of Texas A&M University (IACUC 2017–0390) and were carried out in accordance with Public Health Service Policy on Humane Care and Use of Laboratory Animals (2002), Guide for the Care and Use of Agricultural Animals in Research and Teaching (2010), and Guide for the Care and Use of Laboratory Animals (2011).</p></sec></sec><sec sec-type="supplementary-material" id="sec014"><title>Supporting information</title><supplementary-material content-type="local-data" id="pone.0226378.s001"><label>S1 Table</label><caption><p><bold>Sheet 1.</bold> Frequency distribution for the discriminating 4-mer motifs with <italic>p</italic>-values calculated by the <italic>t</italic>-test to distinguish between the non-protective (P14, Y14, and Z14) and protective (P28, Y28, and Z28) rabbit serum samples. <bold>Sheet 2.</bold> Frequency distribution for the discriminating 4-mer motifs that were ranked by the difference between the minimum values of the protective serum samples (P28, Y28, and Z28) and the maximum values of the non-protective serum samples (P14, Y14, and Z14). Highlighted in yellow are statistically significant motifs.</p><p>(XLSX)</p></caption><media xlink:href="pone.0226378.s001.xlsx"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pone.0226378.s002"><label>S2 Table</label><caption><p><bold>Sheet 1</bold>. Frequency distribution for the discriminating unique 7-mer sequences with <italic>p</italic>-values calculated via the <italic>t</italic>-test to distinguish between the non-protective (P14, Y14, and Z14) and protective (P28, Y28, and Z28) rabbit serum samples. <bold>Sheet 2.</bold> Frequency distribution for the discriminating unique 7-mer sequences that were ranked by the difference between the minimum values of the protective serum samples (P28, Y28, and Z28) and the maximum values of the non-protective serum samples (P14, Y14, and Z14).</p><p>(XLSX)</p></caption><media xlink:href="pone.0226378.s002.xlsx"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type="local-data" id="pone.0226378.s003"><label>S3 Table</label><caption><title>The first one thousand of the most abundant 7-mer peptides with the respective numbers of their sequencing reads for each rabbit serum sample.</title><p>PI denotes pooled preimmune serum sample. P14, Y14, and Z14 are non-protective serum samples. P281, P28b, Y28, and Z28 are protective serum samples. 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Please do not edit.]</p><p>Reviewers' comments:</p><p>Reviewer's Responses to Questions</p><p><bold>Comments to the Author</bold></p><p>1. Is the manuscript technically sound, and do the data support the conclusions?</p><p>The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. </p><p>Reviewer #1: Partly</p><p>Reviewer #2: Yes</p><p>Reviewer #3: Partly</p><p>**********</p><p>2. Has the statistical analysis been performed appropriately and rigorously? </p><p>Reviewer #1: No</p><p>Reviewer #2: Yes</p><p>Reviewer #3: Yes</p><p>**********</p><p>3. Have the authors made all data underlying the findings in their manuscript fully available?</p><p>The <ext-link ext-link-type="uri" xlink:href="http://www.plosone.org/static/policies.action#sharing">PLOS Data policy</ext-link> requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.</p><p>Reviewer #1: Yes</p><p>Reviewer #2: Yes</p><p>Reviewer #3: Yes</p><p>**********</p><p>4. Is the manuscript presented in an intelligible fashion and written in standard English?</p><p>PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.</p><p>Reviewer #1: Yes</p><p>Reviewer #2: Yes</p><p>Reviewer #3: Yes</p><p>**********</p><p>5. Review Comments to the Author</p><p>Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)</p><p>Reviewer #1: This manuscript by Ionov and Rogovskyy uses a phage display system, coupled with Next Generation sequencing, to identify epitopes or mimotopes of polyclonal serum antibodies against the spirochete Borrelia burgdorferi. They obtain sequencing counts of full-length peptides, or motifs found within them, and compare the results between protective and non-protective serum of New Zealand rabbits. They determine that both methods (full length and motif) provide discriminating power between protective and non-protective serum, but that the motif-based method has some advantages, which they discuss. While the manuscript is interesting and could provide new methods of Lyme diagnosis and/or post vaccine protection determination, there are several issues that should be addressed before acceptance.</p><p>Major Issues</p><p>1) The math in the paragraph between lines 91 and 107 should be checked, and also described in more detail. In some cases the math appears wrong, and in other cases it’s unclear where a particular number comes from. For example:</p><p>a. On line 95, it states that in 10 uL of library, each 7-mer is represented by 10^11 phages, and is thus represented 70 times. This is likely supposed to say that in 10 uL there are 10^11 phages, and since there are 20^7 (not 7^20 as stated) possible 7-mers, that averages to ~78 of each 7-mer in the sample. The authors may also want to state that the library documentation states that the phage concentration is 10^13 phages per mL, which is where the 10^11 phages in 10 uL number comes from.</p><p>b. The next sentence makes a point that there are 6.4x10^7 possible 6-mer sequences, and thus the library is big enough to represent them all. But the previous sentence has already stated that the library is big enough to not only contain all possible 7-mer sequences, but to do so ~80 times each.</p><p>c. On line 102, where does the “2” in “2x10^11” come from? Previously it was established that the 10 uL sample of library contains 10^11 phages, not 2x10^11.</p><p>d. Please also explain where the rest of the numbers in this paragraph (32,000, 4x10^11, 1.6x10^5) come from, or how they are calculated.</p><p>2) On lines 137-140, it states that you are identifying motifs of 4 amino acids, and clustering peptides that contain that motif that differ by “only few” amino acids. How can the 4-mer motif still be considered intact if a “few” amino acids are different? Is this instead referring to the other 3 amino acids in the 7-mer peptide that aren’t part of the motif? Either way, please clarify.</p><p>3) On line 198, Figure 1 is referenced, and the text says that it shows the most abundant motif for sample Z28. Figure 1 shows the motif QKPL, but in Supplementary Table 2, the most abundant motif for sample Z28 appears to be KIGD. QKPL is not even the most abundant motif in sample Z28 among the motifs deemed to be statistically significant.</p><p>4) I’m not a statistician, but I think there are a few issues with the way the statistics were performed on the sample sets. First, on line 205 it says that significance testing was done between the “three non-protective and four protective sera.” Indeed, looking at the provided supplemental tables in Excel, that is how the calculation was done. However, there were not four independent protective sera, only three. One of the protective serum samples, P28, was analyzed in duplicate. I don’t believe you can use BOTH of those data sets that came out of the P28 sample together in the t-test calculation, because they are not independent data sets. Second, it states on lines 203-204 that a “two sample two tailed t-test (assuming unequal variances)” was used. However, since these sets consist of serum samples that were taken from the same set of three animals at two different time points, shouldn’t the test have been a paired t-test? P14 and P28 are paired, as are Y14/Y28 and Z14/Z28. And finally, why was p<0.1 chosen as the cutoff for significance, when generally p<0.05 is used? Again, I don’t have a strong statistical background, so please do explain if I’m wrong about these issues.</p><p>5) On lines 233-238, there seems to be a contradiction. It first states that you identified 13 distinct 7-mers that could discriminate between protective and non-protective serum, and you note that these 13 are identified by having p<0.1 (not 0.01), meaning that they are significant. But it then says that out of these 13 peptides, only GLLQKPL was significantly different between protective and non-protective serum.</p><p>6) Please explain Figures 3 and 4 in clearer terms. In the text, the only thing it says about Figures 3A and 4A is that a tool was used to separate the eight sera samples by principal component analysis. However, it does not explain what the two principal components are, or what the graph axes mean (what are the percentages?). Similarly, Figures 3B and 4B don’t really explain how to interpret them. For instance, in both heat maps the duplicate samples (P28a and P28b) do not match very well, with red and blue colors not matching up at all. I would think that these samples should look almost identical, right? It also says that the preimmune serum is well segregated from the other samples, but it doesn’t look to me any more different from the other samples than P28a looks from P28b.</p><p>Minor Issues</p><p>1) Comparing lines 128-129 with lines 165-166, you have two different things that are abbreviated as “PI” (post-infection and preimmune). One is labeled as lower case pi, while the other is uppercase PI, but I do think the letters should be different between the two abbreviations as well.</p><p>2) On line 165, one of the day-14 samples is listed as Z28, instead of Z14.</p><p>3) On line 235, p<0.01 should be p<0.1.</p><p>4) In the methods on line 321, it says that skin tissues that were collected at week 4 post infection show all culture-negative biopsies, but earlier, on line 221, it states that the day 28 skin biopsy of animal Z was still culture-positive.</p><p>5) The Ethics paragraph starting on line 383 is largely identical to the paragraph starting on line 404.</p><p>Reviewer #2: This manuscript describes the use of two methods employing phage displayed random peptide libraries coupled to Next Gen Sequencing (PDRPL/NGS) to examine antibody target epitopes in the context of Borrelia burgdorferi (Bb) infection. The authors use motif-based and unique sequence-based analyses of phage display library datasets to map the protective and non-protective antibody target epitopes in rabbits infected with Bb.</p><p>1. The study is based on the premise that New Zealand rabbits can clear the Bb infection solely because of their protective antibodies (as opposed to humans). However, not enough evidence is provided to directly support this premise. For example, is it clear that the rabbit antibodies against the identified epitopes are more protective than human antibodies against Bb?</p><p>2. The manuscript does not seem to link the identified epitopes to any parent proteins in borrelia. This would be important and helpful information to include and discuss.</p><p>3. The introduction discusses the advantages of PDRPL/NGS, but doesn’t really mention the potential disadvantages and biases of the PDRPL/NGS approach in epitope mapping.</p><p>4. The are some inaccurate statements concerning Lyme disease in the manuscript. For example, that “LD human patients, who have not been timely treated with antimicrobials [46-51], remain infected with the spirochete for life despite very strong anti-B. burgdorferi antibody responses”. In fact, the majority of Lyme patients’ infection resolves spontaneously even without antibiotics (refer to papers from A. Steere and others from the 80s and 90s). The paper can benefit greatly from further careful review to make sure the text is accurate and well-referenced with regard to statements about Lyme disease.</p><p>5. The manuscript can also benefit from further editing for typos, grammar, and structure. There are many long and convoluted sentences that seriously reduce the readability of the paper.</p><p>Reviewer #3: The submitted manuscript "Comparison of motif-based and unique sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera" is very interesting both from the theoretical and practical point of view. I recommend proceeding with publication process after minor revision.</p><p>There is an only minor correction which should be addressed</p><p>Minor revisions, recommendations</p><p>Line 75: protective epitopes – authors should stick to protection-associated epitopes. Protective epitope should be isolated and proved in vivo.</p><p>Line 154-155: Short notice describing both approaches in a few words should be included. There is mention about “the first approach” the second one follows much later but the text in between concerns both of them.</p><p>Line 400: TBST is Tris-buffered with a tween. TBST is well-known abbreviations; moreover, it is explained in Material and Methods. There is no need to include it into the list.</p><p>The discussion should be rephrased more extensively</p><p>In discussion data obtained should be compared to other publications. It is not a summary of the results. There are some ideas what might be discussed or authors can find better topics:</p><p>The methods used can be compared to other available data or previously published methods. The strengths and weaknesses might be described. This would also be a good place to try to find out the identity of protein containing the epitope GLLQkpl if possible and to describe properties essential for “protectiveness” – physiological function, expression pattern, accessibility for antibodies and so on. Or it would be possible to highlight other epitopes revealed by analysis even with lower significance especially when they might be mapped to known surface proteins.</p><p>**********</p><p>6. PLOS authors have the option to publish the peer review history of their article (<ext-link ext-link-type="uri" xlink:href="https://journals.plos.org/plosone/s/editorial-and-peer-review-process#loc-peer-review-history">what does this mean?</ext-link>). If published, this will include your full peer review and any attached files.</p><p>If you choose “no”, your identity will remain anonymous but your review may still be made public.</p><p><bold>Do you want your identity to be public for this peer review?</bold> For information about this choice, including consent withdrawal, please see our <ext-link ext-link-type="uri" xlink:href="https://www.plos.org/privacy-policy">Privacy Policy</ext-link>.</p><p>Reviewer #1: No</p><p>Reviewer #2: No</p><p>Reviewer #3: No</p><p>[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]</p><p>While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, <ext-link ext-link-type="uri" xlink:href="https://pacev2.apexcovantage.com/">https://pacev2.apexcovantage.com/</ext-link>. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at <email>figures@plos.org</email>. Please note that Supporting Information files do not need this step.</p></body></sub-article><sub-article id="pone.0226378.r002" article-type="author-comment"><front-stub><article-id pub-id-type="doi">10.1371/journal.pone.0226378.r002</article-id><title-group><article-title>Author response to Decision Letter 0</article-title></title-group><related-article id="rel-obj002" ext-link-type="doi" xlink:href="10.1371/journal.pone.0226378" related-article-type="editor-report"></related-article><custom-meta-group><custom-meta><meta-name>Submission Version</meta-name><meta-value>1</meta-value></custom-meta></custom-meta-group></front-stub><body><p><named-content content-type="author-response-date">11 Oct 2019</named-content></p><p>Response to the reviewers</p><p>PONE-D-19-21468</p><p>Comparison of motif-based and unique sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera</p><p>PLOS ONE</p><p>Dear Nicholas J Mantis,</p><p>First we want express our gratitude to the very helpful criticism and commentaries of the reviewers that enabled us to essentially improve our manuscript. Below are our responses indicated in bold to each point raised by the three reviewers. </p><p>Please do not hesitate to contact me directly, if any more questions arise.</p><p>Sincerely,</p><p>Yurij Ionov, PhD</p><p>Journal Requirements:</p><p>When submitting your revision, we need you to address these additional requirements.</p><p>1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at</p><p><ext-link ext-link-type="uri" xlink:href="http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf">http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf</ext-link> and <ext-link ext-link-type="uri" xlink:href="http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf">http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf</ext-link></p><p>2. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information:<ext-link ext-link-type="uri" xlink:href="http://journals.plos.org/plosone/s/supporting-information">http://journals.plos.org/plosone/s/supporting-information</ext-link>.</p><p>The captions for the Supporting Information files have been included at the end of the manuscript and the citations have been updated. </p><p>Responses to Reviewer #1: </p><p>This manuscript by Ionov and Rogovskyy uses a phage display system, coupled with Next Generation sequencing, to identify epitopes or mimotopes of polyclonal serum antibodies against the spirochete Borrelia burgdorferi. They obtain sequencing counts of full-length peptides, or motifs found within them, and compare the results between protective and non-protective serum of New Zealand rabbits. They determine that both methods (full length and motif) provide discriminating power between protective and non-protective serum, but that the motif-based method has some advantages, which they discuss. While the manuscript is interesting and could provide new methods of Lyme diagnosis and/or post vaccine protection determination, there are several issues that should be addressed before acceptance.</p><p>Major Issues</p><p>1) The math in the paragraph between lines 91 and 107 should be checked, and also described in more detail. In some cases the math appears wrong, and in other cases it’s unclear where a particular number comes from. For example:</p><p>a. On line 95, it states that in 10 uL of library, each 7-mer is represented by 10^11 phages, and is thus represented 70 times. This is likely supposed to say that in 10 uL there are 10^11 phages, and since there are 20^7 (not 7^20 as stated) possible 7-mers, that averages to ~78 of each 7-mer in the sample. The authors may also want to state that the library documentation states that the phage concentration is 10^13 phages per mL, which is where the 10^11 phages in 10 uL number comes from. </p><p>The math in the indicated part of the text was corrected and described in more details to clarify the undertaken calculations. </p><p>b. The next sentence makes a point that there are 6.4x10^7 possible 6-mer sequences, and thus the library is big enough to represent them all. But the previous sentence has already stated that the library is big enough to not only contain all possible 7-mer sequences, but to do so ~80 times each.</p><p>The text was corrected to eliminate the possible redundancies. </p><p>c. On line 102, where does the “2” in “2x10^11” come from? Previously it was established that the 10 uL sample of library contains 10^11 phages, not 2x10^11.</p><p>The “2” comes from the fact that each 7-mer contains two distinct 6-mers starting from the first and the second position of the 7-mer. </p><p>d. Please also explain where the rest of the numbers in this paragraph (32,000, 4x10^11, 1.6x10^5) come from, or how they are calculated</p><p>All calculations were corrected and explained in the revised text..</p><p>2) On lines 137-140, it states that you are identifying motifs of 4 amino acids, and clustering peptides that contain that motif that differ by “only few” amino acids. How can the 4-mer motif still be considered intact if a “few” amino acids are different? Is this instead referring to the other 3 amino acids in the 7-mer peptide that aren’t part of the motif? Either way, please clarify.</p><p>We included the clarification in the text why we used the phrase “few amino acids” but not exactly the 3 amino acids. This is because each letter in the motif can be the combination of distinct letters as can be seen in the Figure1. The numbers for the motifs were generated by MEME software and not by our counting of particular tetramers in all samples.</p><p>3) On line 198, Figure 1 is referenced, and the text says that it shows the most abundant motif for sample Z28. Figure 1 shows the motif QKPL, but in Supplementary Table 2, the most abundant motif for sample Z28 appears to be KIGD. QKPL is not even the most abundant motif in sample Z28 among the motifs deemed to be statistically significant.</p><p>We corrected this error in the revised text.</p><p>4) I’m not a statistician, but I think there are a few issues with the way the statistics were performed on the sample sets. First, on line 205 it says that significance testing was done between the “three non-protective and four protective sera.” Indeed, looking at the provided supplemental tables in Excel, that is how the calculation was done. However, there were not four independent protective sera, only three. One of the protective serum samples, P28, was analyzed in duplicate. I don’t believe you can use BOTH of those data sets that came out of the P28 sample together in the t-test calculation, because they are not independent data sets. Second, it states on lines 203-204 that a “two sample two tailed t-test (assuming unequal variances)” was used. However, since these sets consist of serum samples that were taken from the same set of three animals at two different time points, shouldn’t the test have been a paired t-test? P14 and P28 are paired, as are Y14/Y28 and Z14/Z28. And finally, why was p<0.1 chosen as the cutoff for significance, when generally p<0.05 is used? Again, I don’t have a strong statistical background, so please do explain if I’m wrong about these issues.</p><p>Although the reviewer modestly claims that he is not an expert in statistics his comments were quite enlightening for us. We corrected our calculations and used statistics according to his recommendations. For example we used paired t-test instead oftwo-tailed t-test with unequal variances, and we removed one replica from duplicate protective sample to recalculate the p-values. Although p< 0.05 is generally used the significance level P<0.1 is also acceptable. From Google: “The significance level is denoted by α and is the probability of rejecting the null hypothesis, if it is true. Typical values for α are 0.01, 0.05 and 0.1. It is a value that we select based on the certainty we need.”</p><p>5) On lines 233-238, there seems to be a contradiction. It first states that you identified 13 distinct 7-mers that could discriminate between protective and non-protective serum, and you note that these 13 are identified by having p<0.1 (not 0.01), meaning that they are significant. But it then says that out of these 13 peptides, only GLLQKPL was significantly different between protective and non-protective serum.</p><p>We corrected this contradiction in the text that was the consequence of the poor wording. We really had in mind that this peptide was visually distinct between the protective and non-protective sera on the figure. </p><p>6) Please explain Figures 3 and 4 in clearer terms. In the text, the only thing it says about Figures 3A and 4A is that a tool was used to separate the eight sera samples by principal component analysis. However, it does not explain what the two principal components are, or what the graph axes mean (what are the percentages?). Similarly, Figures 3B and 4B don’t really explain how to interpret them. For instance, in both heat maps the duplicate samples (P28a and P28b) do not match very well, with red and blue colors not matching up at all. I would think that these samples should look almost identical, right? It also says that the preimmune serum is well segregated from the other samples, but it doesn’t look to me any more different from the other samples than P28a looks from P28b.</p><p> We provided better explanations of the graphs and plots in the revised text. Poor matching of the blue and red is caused by inherent high noise of the method, however, despite the high noise the math clusters the samples correctly in the motif based approach. </p><p>Minor Issues</p><p>1) Comparing lines 128-129 with lines 165-166, you have two different things that are abbreviated as “PI” (post-infection and preimmune). One is labeled as lower case pi, while the other is uppercase PI, but I do think the letters should be different between the two abbreviations as well.</p><p>We removed “pi” as abbreviation for “postinfection” for the better clarity. </p><p>2) On line 165, one of the day-14 samples is listed as Z28, instead of Z14.</p><p>This has been corrected. Thank you for catching it.</p><p>3) On line 235, p<0.01 should be p<0.1.</p><p>We corrected the p values accordingly.</p><p>4) In the methods on line 321, it says that skin tissues that were collected at week 4 post infection show all culture-negative biopsies, but earlier, on line 221, it states that the day 28 skin biopsy of animal Z was still culture-positive.</p><p>Thank you for pointing this out. This has been corrected and clarified accordingly. Please see the text. </p><p>5) The Ethics paragraph starting on line 383 is largely identical to the paragraph starting on line 404.</p><p>The Ethics paragraph starting on line 383 was removed. Thank you for pointing this out.</p><p>Reviewer #2: </p><p>This manuscript describes the use of two methods employing phage displayed random peptide libraries coupled to Next Gen Sequencing (PDRPL/NGS) to examine antibody target epitopes in the context of Borrelia burgdorferi (Bb) infection. The authors use motif-based and unique sequence-based analyses of phage display library datasets to map the protective and non-protective antibody target epitopes in rabbits infected with Bb.</p><p>1. The study is based on the premise that New Zealand rabbits can clear the Bb infection solely because of their protective antibodies (as opposed to humans). However, not enough evidence is provided to directly support this premise. For example, is it clear that the rabbit antibodies against the identified epitopes are more protective than human antibodies against Bb?</p><p>We agree with the reviewer and now we are contrasting the ability of NZW rabbit immune sera (not human sera) to prevent B. burgdorferi infection to anti-Borrelia mouse sera. Please see the respective change in the text.</p><p>2. The manuscript does not seem to link the identified epitopes to any parent proteins in borrelia. This would be important and helpful information to include and discuss.</p><p>We fully agree with the reviewer that it is a logical next step of the work. However, given 1) that the focus of this manuscript is solely to compare the two methods in identifying discriminatory epitopes between the protective and non-protective sera; 2) that we used only limited number of peptides (n=1,000) rather than the all available peptides for this comparative analysis; 3) that mapping of identified epitopes/motifs is an extensive analysis in itself (due to the fact that mapping has to be performed to the entire proteome of B. burgdorferi); and 4) that we plan to do this mapping study by using the methods described in this work and utilizing the full data sets for the next manuscript; we would like to refrain from discussing this aspect in the current manuscript. </p><p>In addition it is known that only about 10% of all antibodies recognize the linear epitopes that can be identified by sequence alignment. The majority of antibodies are against conformational epitopes or even against carbohydrate structures on the surface of cells and such identification is simply impossible. The identified peptides represent the mimotopes and if they are protective they can be used for vaccination without knowledge of their real target. </p><p>3. The introduction discusses the advantages of PDRPL/NGS, but doesn’t really mention the potential disadvantages and biases of the PDRPL/NGS approach in epitope mapping.</p><p>We expanded our discussion and included the comparing with the alternative method. </p><p>4. There are some inaccurate statements concerning Lyme disease in the manuscript. For example, that “LD human patients, who have not been timely treated with antimicrobials [46-51], remain infected with the spirochete for life despite very strong anti-B. burgdorferi antibody responses”. In fact, the majority of Lyme patients’ infection resolves spontaneously even without antibiotics (refer to papers from A. Steere and others from the 80s and 90s). The paper can benefit greatly from further careful review to make sure the text is accurate and well-referenced with regard to statements about Lyme disease.</p><p>Although there is some evidence that B. burgdorferi can still persist in humans with Lyme disease symptoms despite antibiotic treatment (e.g., Middelveen MJ, Sapi E, Burke J, Filush KR, Franco A, Fesler MC, Stricker RB: Persistent Borrelia Infection in Patients with Ongoing Symptoms of Lyme Disease. Healthcare (Basel) 2018, 6(2)), we still see with the reviewer argument and hence rephrased the respective statement in order to avoid this highly controversial topic among the readers. </p><p>5. The manuscript can also benefit from further editing for typos, grammar, and structure. There are many long and convoluted sentences that seriously reduce the readability of the paper.</p><p>We are sorry to hear that. Accordingly we worked on improving the readability of this manuscript. </p><p>Reviewer #3: </p><p>The submitted manuscript "Comparison of motif-based and unique sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera" is very interesting both from the theoretical and practical point of view. I recommend proceeding with publication process after minor revision.</p><p>There is an only minor correction which should be addressed</p><p>Minor revisions, recommendations</p><p>Line 75: protective epitopes – authors should stick to protection-associated epitopes. Protective epitope should be isolated and proved in vivo.</p><p>This has been corrected. Thank you for pointing this out.</p><p>Line 154-155: Short notice describing both approaches in a few words should be included. There is mention about “the first approach” the second one follows much later but the text in between concerns both of them.</p><p>We corrected the text according to this recommendation. </p><p>Line 400: TBST is Tris-buffered with a tween. TBST is well-known abbreviations; moreover, it is explained in Material and Methods. There is no need to include it into the list.</p><p>Per your request, the abbreviation “TBST” was removed from the list.</p><p>The discussion should be rephrased more extensively</p><p>In discussion data obtained should be compared to other publications. It is not a summary of the results. There are some ideas what might be discussed or authors can find better topics: The methods used can be compared to other available data or previously published methods. The strengths and weaknesses might be described. </p><p>We corrected the revised text according to this recommendation </p><p>This would also be a good place to try to find out the identity of protein containing the epitope GLLQkpl if possible and to describe properties essential for “protectiveness” – physiological function, expression pattern, accessibility for antibodies and so on. Or it would be possible to highlight other epitopes revealed by analysis even with lower significance especially when they might be mapped to known surface proteins.</p><p>We fully agree with the reviewer that it is a logical next step of the work. However, given 1) that the focus of this manuscript is solely to compare the two methods in identifying discriminatory epitopes between the protective and non-protective sera; 2) that we used only limited number of peptides (n=1,000) rather than the all available peptides for this comparative analysis; 3) that mapping of identified epitopes/motifs is an extensive analysis in itself (due to the fact that mapping has to be performed to the entire proteome of B. burgdorferi); and 4) that we plan to do this mapping study by using the methods described in this work and utilizing the full data sets for the next manuscript; we would like to refrain from discussing this aspect in the current manuscript. Besides, the epitope GLLQKPL may be the mimotope of the conformational epitope consisting of amino acids brought together on the surface of a protein by the pattern of protein folding</p><supplementary-material content-type="local-data" id="pone.0226378.s004"><label>Attachment</label><caption><p>Submitted filename: <named-content content-type="submitted-filename">Response to reviewers 09_27-2009.docx</named-content></p></caption><media xlink:href="pone.0226378.s004.docx"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></body></sub-article><sub-article id="pone.0226378.r003" article-type="editor-report"><front-stub><article-id pub-id-type="doi">10.1371/journal.pone.0226378.r003</article-id><title-group><article-title>Decision Letter 1</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Mantis</surname><given-names>Nicholas J</given-names></name><role>Academic Editor</role></contrib></contrib-group><permissions><copyright-statement>© 2020 Nicholas J Mantis</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Nicholas J Mantis</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><related-article id="rel-obj003" ext-link-type="doi" xlink:href="10.1371/journal.pone.0226378" related-article-type="reviewed-article"></related-article><custom-meta-group><custom-meta><meta-name>Submission Version</meta-name><meta-value>1</meta-value></custom-meta></custom-meta-group></front-stub><body><p><named-content content-type="letter-date">16 Oct 2019</named-content></p><p>PONE-D-19-21468R1</p><p>Comparison of motif-based and unique sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera</p><p>PLOS ONE</p><p>Dear Dr Ionov,</p><p>Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.</p><p>As you requested in an email to me, I am returning the manuscript to you for revisions to the Supplemental Tables before resubmission. </p><p>We would appreciate receiving your revised manuscript by Nov 30 2019 11:59PM. 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Please note that Supporting Information files do not need this step.</p></body></sub-article><sub-article id="pone.0226378.r004" article-type="author-comment"><front-stub><article-id pub-id-type="doi">10.1371/journal.pone.0226378.r004</article-id><title-group><article-title>Author response to Decision Letter 1</article-title></title-group><related-article id="rel-obj004" ext-link-type="doi" xlink:href="10.1371/journal.pone.0226378" related-article-type="editor-report"></related-article><custom-meta-group><custom-meta><meta-name>Submission Version</meta-name><meta-value>2</meta-value></custom-meta></custom-meta-group></front-stub><body><p><named-content content-type="author-response-date">16 Oct 2019</named-content></p><p>Response to the reviewers</p><p>PONE-D-19-21468</p><p>Comparison of motif-based and unique sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera</p><p>PLOS ONE</p><p>Dear Nicholas J Mantis,</p><p>First we want express our gratitude to the very helpful criticism and commentaries of the reviewers that enabled us to essentially improve our manuscript. Below are our responses indicated in bold to each point raised by the three reviewers. </p><p>Please do not hesitate to contact me directly, if any more questions arise.</p><p>Sincerely,</p><p>Yurij Ionov, PhD</p><p>Journal Requirements:</p><p>When submitting your revision, we need you to address these additional requirements.</p><p>1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at</p><p><ext-link ext-link-type="uri" xlink:href="http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf">http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf</ext-link> and <ext-link ext-link-type="uri" xlink:href="http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf">http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf</ext-link></p><p>2. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information:<ext-link ext-link-type="uri" xlink:href="http://journals.plos.org/plosone/s/supporting-information">http://journals.plos.org/plosone/s/supporting-information</ext-link>.</p><p>The captions for the Supporting Information files have been included at the end of the manuscript and the citations have been updated. </p><p> </p><p>Responses to Reviewer #1: </p><p>This manuscript by Ionov and Rogovskyy uses a phage display system, coupled with Next Generation sequencing, to identify epitopes or mimotopes of polyclonal serum antibodies against the spirochete Borrelia burgdorferi. They obtain sequencing counts of full-length peptides, or motifs found within them, and compare the results between protective and non-protective serum of New Zealand rabbits. They determine that both methods (full length and motif) provide discriminating power between protective and non-protective serum, but that the motif-based method has some advantages, which they discuss. While the manuscript is interesting and could provide new methods of Lyme diagnosis and/or post vaccine protection determination, there are several issues that should be addressed before acceptance.</p><p>Major Issues</p><p>1) The math in the paragraph between lines 91 and 107 should be checked, and also described in more detail. In some cases the math appears wrong, and in other cases it’s unclear where a particular number comes from. For example:</p><p>a. On line 95, it states that in 10 uL of library, each 7-mer is represented by 10^11 phages, and is thus represented 70 times. This is likely supposed to say that in 10 uL there are 10^11 phages, and since there are 20^7 (not 7^20 as stated) possible 7-mers, that averages to ~78 of each 7-mer in the sample. The authors may also want to state that the library documentation states that the phage concentration is 10^13 phages per mL, which is where the 10^11 phages in 10 uL number comes from. </p><p>The math in the indicated part of the text was corrected and described in more details to clarify the undertaken calculations. </p><p>b. The next sentence makes a point that there are 6.4x10^7 possible 6-mer sequences, and thus the library is big enough to represent them all. But the previous sentence has already stated that the library is big enough to not only contain all possible 7-mer sequences, but to do so ~80 times each.</p><p>The text was corrected to eliminate the possible redundancies. </p><p>c. On line 102, where does the “2” in “2x10^11” come from? Previously it was established that the 10 uL sample of library contains 10^11 phages, not 2x10^11.</p><p>The “2” comes from the fact that each 7-mer contains two distinct 6-mers starting from the first and the second position of the 7-mer. </p><p>d. Please also explain where the rest of the numbers in this paragraph (32,000, 4x10^11, 1.6x10^5) come from, or how they are calculated</p><p>All calculations were corrected and explained in the revised text.</p><p>2) On lines 137-140, it states that you are identifying motifs of 4 amino acids, and clustering peptides that contain that motif that differ by “only few” amino acids. How can the 4-mer motif still be considered intact if a “few” amino acids are different? Is this instead referring to the other 3 amino acids in the 7-mer peptide that aren’t part of the motif? Either way, please clarify.</p><p>We included the clarification in the text why we used the phrase “few amino acids” and not exactly the 3 amino acids. Each letter in the motif can be a combination of distinct letters as can be seen in Figure1. The numbers for the motifs were automatically generated by MEME software.</p><p>3) On line 198, Figure 1 is referenced, and the text says that it shows the most abundant motif for sample Z28. Figure 1 shows the motif QKPL, but in Supplementary Table 2, the most abundant motif for sample Z28 appears to be KIGD. QKPL is not even the most abundant motif in sample Z28 among the motifs deemed to be statistically significant.</p><p>We corrected this error in the revised text.</p><p>4) I’m not a statistician, but I think there are a few issues with the way the statistics were performed on the sample sets. First, on line 205 it says that significance testing was done between the “three non-protective and four protective sera.” Indeed, looking at the provided supplemental tables in Excel, that is how the calculation was done. However, there were not four independent protective sera, only three. One of the protective serum samples, P28, was analyzed in duplicate. I don’t believe you can use BOTH of those data sets that came out of the P28 sample together in the t-test calculation, because they are not independent data sets. Second, it states on lines 203-204 that a “two sample two tailed t-test (assuming unequal variances)” was used. However, since these sets consist of serum samples that were taken from the same set of three animals at two different time points, shouldn’t the test have been a paired t-test? P14 and P28 are paired, as are Y14/Y28 and Z14/Z28. And finally, why was p<0.1 chosen as the cutoff for significance, when generally p<0.05 is used? Again, I don’t have a strong statistical background, so please do explain if I’m wrong about these issues.</p><p>Although the reviewer modestly claims that he is not an expert in statistics his comments were quite enlightening for us. We corrected our calculations and used statistics according to his recommendations. For example, we used the paired t-test instead of the two-tailed t-test with unequal variances, and we removed one replicated sample of the protective sample to recalculate the p-values. Although p< 0.05 is generally used the significance level p<0.1 is also acceptable.</p><p>5) On lines 233-238, there seems to be a contradiction. It first states that you identified 13 distinct 7-mers that could discriminate between protective and non-protective serum, and you note that these 13 are identified by having p<0.1 (not 0.01), meaning that they are significant. But it then says that out of these 13 peptides, only GLLQKPL was significantly different between protective and non-protective serum.</p><p>We corrected this. What we had in mind that is this peptide was visually distinct between the protective and non-protective sera on the figure. </p><p>6) Please explain Figures 3 and 4 in clearer terms. In the text, the only thing it says about Figures 3A and 4A is that a tool was used to separate the eight sera samples by principal component analysis. However, it does not explain what the two principal components are, or what the graph axes mean (what are the percentages?). Similarly, Figures 3B and 4B don’t really explain how to interpret them. For instance, in both heat maps the duplicate samples (P28a and P28b) do not match very well, with red and blue colors not matching up at all. I would think that these samples should look almost identical, right? It also says that the preimmune serum is well segregated from the other samples, but it doesn’t look to me any more different from the other samples than P28a looks from P28b.</p><p>We provided better explanations of the graphs and plots in the revised text. Poor matching of the blue and red is caused by inherent high noise of the method, however, despite the high noise, the samples are correctly clustered in the motif-based approach. </p><p>Minor Issues</p><p>1) Comparing lines 128-129 with lines 165-166, you have two different things that are abbreviated as “PI” (post-infection and preimmune). One is labeled as lower case pi, while the other is uppercase PI, but I do think the letters should be different between the two abbreviations as well.</p><p>We removed “pi” as abbreviation for “postinfection” for the better clarity. </p><p>2) On line 165, one of the day-14 samples is listed as Z28, instead of Z14.</p><p>This has been corrected. Thank you for catching it.</p><p>3) On line 235, p<0.01 should be p<0.1.</p><p>We corrected the p-values accordingly.</p><p>4) In the methods on line 321, it says that skin tissues that were collected at week 4 post infection show all culture-negative biopsies, but earlier, on line 221, it states that the day 28 skin biopsy of animal Z was still culture-positive.</p><p>Thank you for pointing this out. This has been corrected and clarified accordingly. Please see the text. </p><p>5) The Ethics paragraph starting on line 383 is largely identical to the paragraph starting on line 404.</p><p>The Ethics paragraph starting on line 383 was removed. Thank you for pointing this out.</p><p>Responses to Reviewer #2: </p><p>This manuscript describes the use of two methods employing phage displayed random peptide libraries coupled to Next Gen Sequencing (PDRPL/NGS) to examine antibody target epitopes in the context of Borrelia burgdorferi (Bb) infection. The authors use motif-based and unique sequence-based analyses of phage display library datasets to map the protective and non-protective antibody target epitopes in rabbits infected with Bb.</p><p>1. The study is based on the premise that New Zealand rabbits can clear the Bb infection solely because of their protective antibodies (as opposed to humans). However, not enough evidence is provided to directly support this premise. For example, is it clear that the rabbit antibodies against the identified epitopes are more protective than human antibodies against Bb?</p><p>We agree with the reviewer and now we are contrasting the ability of NZW rabbit immune sera (not human sera) to prevent B. burgdorferi infection to anti-Borrelia mouse sera. Please see the respective change in the text.</p><p>2. The manuscript does not seem to link the identified epitopes to any parent proteins in borrelia. This would be important and helpful information to include and discuss.</p><p>We fully agree with the reviewer that it is a logical next step of the work. However, given 1) that the focus of this manuscript is solely to compare the two methods in identifying discriminatory epitopes between the protective and non-protective sera; 2) that we used only limited number of peptides (n=1,000) rather than the all available peptides for this comparative analysis; 3) that mapping of identified epitopes/motifs is an extensive analysis in itself (due to the fact that mapping has to be performed to the entire proteome of B. burgdorferi); and 4) that we plan to do this mapping study by using the methods described in this work and utilizing the full data sets for the next manuscript; we would like to refrain from discussing this aspect in the current manuscript. </p><p>Finally, it is known that only about 10% of all antibodies recognize the linear epitopes that can be identified by sequence alignment. The majority of antibodies are against conformational epitopes or even against carbohydrate structures on the surface of cells and such identification is simply impossible. The identified peptides represent the mimotopes and if they are protective they can be used for vaccination without knowledge of their real target. </p><p>3. The introduction discusses the advantages of PDRPL/NGS, but doesn’t really mention the potential disadvantages and biases of the PDRPL/NGS approach in epitope mapping.</p><p>We expanded our discussion and included the comparing with the alternative method. </p><p>4. There are some inaccurate statements concerning Lyme disease in the manuscript. For example, that “LD human patients, who have not been timely treated with antimicrobials [46-51], remain infected with the spirochete for life despite very strong anti-B. burgdorferi antibody responses”. In fact, the majority of Lyme patients’ infection resolves spontaneously even without antibiotics (refer to papers from A. Steere and others from the 80s and 90s). The paper can benefit greatly from further careful review to make sure the text is accurate and well-referenced with regard to statements about Lyme disease.</p><p>Although there is some evidence that B. burgdorferi can still persist in humans with Lyme disease symptoms even after antibiotic treatment (e.g., Middelveen MJ, Sapi E, Burke J, Filush KR, Franco A, Fesler MC, Stricker RB: Persistent Borrelia Infection in Patients with Ongoing Symptoms of Lyme Disease. Healthcare (Basel) 2018, 6(2)), we still see with the reviewer argument and hence rephrased the respective statement in order to avoid this highly controversial topic among the readers. </p><p>5. The manuscript can also benefit from further editing for typos, grammar, and structure. There are many long and convoluted sentences that seriously reduce the readability of the paper.</p><p>We are sorry to hear that. Accordingly we worked on improving the readability of this manuscript. </p><p>Responses to Reviewer #3: </p><p>The submitted manuscript "Comparison of motif-based and unique sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera" is very interesting both from the theoretical and practical point of view. I recommend proceeding with publication process after minor revision.</p><p>There is an only minor correction which should be addressed</p><p>Minor revisions, recommendations</p><p>Line 75: protective epitopes – authors should stick to protection-associated epitopes. Protective epitope should be isolated and proved in vivo.</p><p>This has been corrected. Thank you for pointing this out.</p><p>Line 154-155: Short notice describing both approaches in a few words should be included. There is mention about “the first approach” the second one follows much later but the text in between concerns both of them.</p><p>We corrected the text according to this recommendation. </p><p>Line 400: TBST is Tris-buffered with a tween. TBST is well-known abbreviations; moreover, it is explained in Material and Methods. There is no need to include it into the list.</p><p>Per your request, the abbreviation “TBST” was removed from the list.</p><p>The discussion should be rephrased more extensively</p><p>In discussion data obtained should be compared to other publications. It is not a summary of the results. There are some ideas what might be discussed or authors can find better topics: The methods used can be compared to other available data or previously published methods. The strengths and weaknesses might be described. </p><p>We corrected the revised text according to this recommendation </p><p>This would also be a good place to try to find out the identity of protein containing the epitope GLLQkpl if possible and to describe properties essential for “protectiveness” – physiological function, expression pattern, accessibility for antibodies and so on. Or it would be possible to highlight other epitopes revealed by analysis even with lower significance especially when they might be mapped to known surface proteins.</p><p>We fully agree with the reviewer that it is a logical next step of the work. However, given 1) that the focus of this manuscript is solely to compare the two methods in identifying discriminatory epitopes between the protective and non-protective sera; 2) that we used only limited number of peptides (n=1,000) rather than the all available peptides for this comparative analysis; 3) that mapping of identified epitopes/motifs is an extensive analysis in itself (due to the fact that mapping has to be performed to the entire proteome of B. burgdorferi); and 4) that we plan to do this mapping study by using the methods described in this work and utilizing the full data sets for the next manuscript; we would like to refrain from discussing this aspect in the current manuscript. Finally, the epitope GLLQKPL may be the mimotope of the conformational epitope consisting of amino acids brought together on the surface of a protein by the pattern of protein folding.</p><supplementary-material content-type="local-data" id="pone.0226378.s005"><label>Attachment</label><caption><p>Submitted filename: <named-content content-type="submitted-filename">Response to reviewers 09_27-2009.docx</named-content></p></caption><media xlink:href="pone.0226378.s005.docx"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></body></sub-article><sub-article id="pone.0226378.r005" article-type="aggregated-review-documents"><front-stub><article-id pub-id-type="doi">10.1371/journal.pone.0226378.r005</article-id><title-group><article-title>Decision Letter 2</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Mantis</surname><given-names>Nicholas J</given-names></name><role>Academic Editor</role></contrib></contrib-group><permissions><copyright-statement>© 2020 Nicholas J Mantis</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Nicholas J Mantis</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><related-article id="rel-obj005" ext-link-type="doi" xlink:href="10.1371/journal.pone.0226378" related-article-type="reviewed-article"></related-article><custom-meta-group><custom-meta><meta-name>Submission Version</meta-name><meta-value>2</meta-value></custom-meta></custom-meta-group></front-stub><body><p><named-content content-type="letter-date">26 Nov 2019</named-content></p><p>Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-Borrelia burgdorferi immune sera</p><p>PONE-D-19-21468R2</p><p>Dear Dr. Ionov,</p><p>We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.</p><p>Within one week, you will receive an e-mail containing information on the amendments required prior to publication. 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If published, this will include your full peer review and any attached files.</p><p>If you choose “no”, your identity will remain anonymous but your review may still be made public.</p><p><bold>Do you want your identity to be public for this peer review?</bold> For information about this choice, including consent withdrawal, please see our <ext-link ext-link-type="uri" xlink:href="https://www.plos.org/privacy-policy">Privacy Policy</ext-link>.</p><p>Reviewer #2: Yes: Armin Alaedini</p></body></sub-article><sub-article id="pone.0226378.r006" article-type="editor-report"><front-stub><article-id pub-id-type="doi">10.1371/journal.pone.0226378.r006</article-id><title-group><article-title>Acceptance letter</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Mantis</surname><given-names>Nicholas J</given-names></name><role>Academic Editor</role></contrib></contrib-group><permissions><copyright-statement>© 2020 Nicholas J Mantis</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Nicholas J Mantis</copyright-holder><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><related-article id="rel-obj006" ext-link-type="doi" xlink:href="10.1371/journal.pone.0226378" related-article-type="reviewed-article"></related-article></front-stub><body><p><named-content content-type="letter-date">3 Dec 2019</named-content></p><p>PONE-D-19-21468R2 </p><p>Comparison of motif-based and whole-unique-sequence-based analyses of phage display library datasets generated by biopanning of anti-<italic>Borrelia burgdorferi</italic> immune sera </p><p>Dear Dr. Ionov:</p><p>I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. 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