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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases</title><author><name sortKey="Terry, Frances E" sort="Terry, Frances E" uniqKey="Terry F" first="Frances E" last="Terry">Frances E. Terry</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Moise, Leonard" sort="Moise, Leonard" uniqKey="Moise L" first="Leonard" last="Moise">Leonard Moise</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Martin, Rebecca F" sort="Martin, Rebecca F" uniqKey="Martin R" first="Rebecca F" last="Martin">Rebecca F. Martin</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Torres, Melissa" sort="Torres, Melissa" uniqKey="Torres M" first="Melissa" last="Torres">Melissa Torres</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Pilotte, Nils" sort="Pilotte, Nils" uniqKey="Pilotte N" first="Nils" last="Pilotte">Nils Pilotte</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Williams, Steven A" sort="Williams, Steven A" uniqKey="Williams S" first="Steven A" last="Williams">Steven A. Williams</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="De Groot, Anne S" sort="De Groot, Anne S" uniqKey="De Groot A" first="Anne S" last="De Groot">Anne S. De Groot</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25193104</idno><idno type="pmc">4743591</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4743591</idno><idno type="RBID">PMC:4743591</idno><idno type="doi">10.1586/14760584.2015.955478</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">001323</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001323</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases</title><author><name sortKey="Terry, Frances E" sort="Terry, Frances E" uniqKey="Terry F" first="Frances E" last="Terry">Frances E. Terry</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Moise, Leonard" sort="Moise, Leonard" uniqKey="Moise L" first="Leonard" last="Moise">Leonard Moise</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Martin, Rebecca F" sort="Martin, Rebecca F" uniqKey="Martin R" first="Rebecca F" last="Martin">Rebecca F. Martin</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Torres, Melissa" sort="Torres, Melissa" uniqKey="Torres M" first="Melissa" last="Torres">Melissa Torres</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Pilotte, Nils" sort="Pilotte, Nils" uniqKey="Pilotte N" first="Nils" last="Pilotte">Nils Pilotte</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="Williams, Steven A" sort="Williams, Steven A" uniqKey="Williams S" first="Steven A" last="Williams">Steven A. Williams</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author><author><name sortKey="De Groot, Anne S" sort="De Groot, Anne S" uniqKey="De Groot A" first="Anne S" last="De Groot">Anne S. De Groot</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author></analytic><series><title level="j">Expert Review of Vaccines</title><idno type="ISSN">1476-0584</idno><idno type="eISSN">1744-8395</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Vaccines have been invaluable for global health, saving lives and reducing healthcare costs, while also raising the quality of human life. However, newly emerging infectious diseases (EID) and more well-established tropical disease pathogens present complex challenges to vaccine developers; in particular, neglected tropical diseases, which are most prevalent among the world’s poorest, include many pathogens with large sizes, multistage life cycles and a variety of nonhuman vectors. EID such as MERS-CoV and H7N9 are highly pathogenic for humans. For many of these pathogens, while their genomes are available, immune correlates of protection are currently unknown. These complexities make developing vaccines for EID and neglected tropical diseases all the more difficult. In this review, we describe the implementation of an immunoinformatics-driven approach to systematically search for key determinants of immunity in newly available genome sequence data and design vaccines. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Expert Rev Vaccines</journal-id><journal-id journal-id-type="iso-abbrev">Expert Rev Vaccines</journal-id><journal-id journal-id-type="publisher-id">IERV</journal-id><journal-id journal-id-type="publisher-id">ierv20</journal-id><journal-title-group><journal-title>Expert Review of Vaccines</journal-title></journal-title-group><issn pub-type="ppub">1476-0584</issn><issn pub-type="epub">1744-8395</issn><publisher><publisher-name>Informa Healthcare</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25193104</article-id><article-id pub-id-type="pmc">4743591</article-id><article-id pub-id-type="doi">10.1586/14760584.2015.955478</article-id><article-id pub-id-type="publisher-id">955478</article-id><article-categories><subj-group subj-group-type="heading"><subject>Review</subject></subj-group></article-categories><title-group><article-title>Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases</article-title><alt-title alt-title-type="running-title">NTD/EID: time for T?</alt-title><alt-title alt-title-type="running-authors">Terry, Moise, Martin <italic>et al</italic>.</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Terry</surname><given-names>Frances E</given-names></name><xref ref-type="aff" rid="end-a1"></xref></contrib><contrib contrib-type="author"><name><surname>Moise</surname><given-names>Leonard</given-names></name><xref ref-type="aff" rid="end-a1"></xref><xref ref-type="aff" rid="end-a2"></xref></contrib><contrib contrib-type="author"><name><surname>Martin</surname><given-names>Rebecca F</given-names></name><xref ref-type="aff" rid="end-a1"></xref></contrib><contrib contrib-type="author"><name><surname>Torres</surname><given-names>Melissa</given-names></name><xref ref-type="aff" rid="end-a3"></xref></contrib><contrib contrib-type="author"><name><surname>Pilotte</surname><given-names>Nils</given-names></name><xref ref-type="aff" rid="end-a3"></xref></contrib><contrib contrib-type="author"><name><surname>Williams</surname><given-names>Steven A</given-names></name><xref ref-type="aff" rid="end-a3"></xref></contrib><contrib contrib-type="author"><name><surname>De Groot</surname><given-names>Anne S</given-names></name><xref ref-type="corresp" rid="COR0001"></xref><xref ref-type="aff" rid="end-a1"></xref><xref ref-type="aff" rid="end-a2"></xref></contrib><aff id="end-a1"><sup>1</sup><institution>EpiVax, Inc.,</institution><addr-line>146 Clifford St, Providence, RI 02903,</addr-line><country>USA</country></aff><aff id="end-a2"><sup>2</sup><institution>Institute for Immunology and Informatics, University of Rhode Island,</institution><addr-line>80 Washington St, Providence, RI 02903,</addr-line><country>USA</country></aff><aff id="end-a3"><sup>3</sup><institution>Smith College,</institution><addr-line>Northampton, MA,</addr-line><country>USA</country></aff></contrib-group><author-notes><corresp id="COR0001">*Author for correspondence: <phone>+1 401 272 2123</phone><fax>+1 401 272 7562</fax><email xlink:href="dr.annie.degroot@gmail.com">dr.annie.degroot@gmail.com</email></corresp></author-notes><pub-date pub-type="collection"><year>2015</year></pub-date><pub-date pub-type="epub"><day>05</day><month>9</month><year>2014</year></pub-date><volume>14</volume><issue>1</issue><fpage seq="4">21</fpage><lpage>35</lpage><permissions><copyright-statement>Informa UK, Ltd.</copyright-statement><copyright-year>2015</copyright-year><copyright-holder>Informa UK, Ltd.</copyright-holder><license><license-p>This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="IERV_14_955478.pdf"></self-uri><abstract><p>Vaccines have been invaluable for global health, saving lives and reducing healthcare costs, while also raising the quality of human life. However, newly emerging infectious diseases (EID) and more well-established tropical disease pathogens present complex challenges to vaccine developers; in particular, neglected tropical diseases, which are most prevalent among the world’s poorest, include many pathogens with large sizes, multistage life cycles and a variety of nonhuman vectors. EID such as MERS-CoV and H7N9 are highly pathogenic for humans. For many of these pathogens, while their genomes are available, immune correlates of protection are currently unknown. These complexities make developing vaccines for EID and neglected tropical diseases all the more difficult. In this review, we describe the implementation of an immunoinformatics-driven approach to systematically search for key determinants of immunity in newly available genome sequence data and design vaccines. This approach holds promise for the development of 21st century vaccines, improving human health everywhere.</p></abstract><kwd-group kwd-group-type="author"><title>Keywords</title><kwd><italic>Brugia malayi</italic></kwd><kwd>computational vaccinology</kwd><kwd>EID</kwd><kwd>EpiMatrix</kwd><kwd>filarial</kwd><kwd>immunogenicity</kwd><kwd>immunoinformatics</kwd><kwd>immunomics</kwd><kwd>JanusMatrix</kwd><kwd>MHC</kwd><kwd>NTD</kwd><kwd>T cell</kwd><kwd>TCR</kwd><kwd>tregitope</kwd></kwd-group><counts><fig-count count="5"></fig-count><table-count count="2"></table-count><equation-count count="0"></equation-count><ref-count count="96"></ref-count><page-count count="15"></page-count></counts></article-meta></front><body><sec id="S0001"><title>Neglected tropical & emerging infectious diseases: new challenges</title><p>Climate change and international travel have had a dramatic impact on the geographic distribution of pathogens infecting humans and animals. Old world pathogens such as Dengue and Chikungunya virus, previously restricted to the Middle East, Africa and Asia have now appeared in the Americas <xref rid="CIT0001" ref-type="bibr">[1]</xref>. Newer pathogens such as Middle East Respiratory Syndrome coronavirus (MERS-CoV), an entirely new coronavirus affecting humans, have been spreading beyond the region of the world from which they derive their names <xref rid="CIT0002" ref-type="bibr">[2]</xref>. Meanwhile, human populations in developing areas of the world continue to be threatened by neglected tropical diseases (NTD). More than two billion people – nearly 30% of the world’s population – suffer from one or more NTD <xref rid="CIT0003" ref-type="bibr">[3]</xref>, which include leishmaniasis, lymphatic filariasis, onchocerciasis, schistosomiasis and soil-transmitted helminthiasis, among others <xref rid="T1" ref-type="table">(Table 1)</xref>. In addition to climate change and airline travel, economic conditions leading to transmigration contribute to the spread of NTD. Recent examples include the reemergence of Leishmania in Spain <xref rid="CIT0004" ref-type="bibr">[4]</xref> and Chagas disease in Texas <xref rid="CIT0005" ref-type="bibr">[5]</xref>.</p><table-wrap id="T1" orientation="portrait" position="float"><label>Table 1.</label><caption><p><bold>The neglected tropical diseases.</bold></p></caption><pmc-comment>OASIS TABLE HERE</pmc-comment>
<table frame="hsides" rules="groups"><colgroup><col width="10*" align="left"></col><col width="10*" align="left"></col><col width="10*" align="left"></col><col width="10*" align="left"></col></colgroup><thead valign="top"><tr><th align="left"><bold>Disease</bold></th><th align="left"><bold>Primary causative agents</bold></th><th align="left"><bold>Primary vector(s)</bold></th><th align="left"><bold>DALYs × 1000 </bold><break></break><bold>(95% UI)</bold></th></tr></thead><tbody valign="top"><tr><td align="left">Buruli ulcer</td><td align="left"><italic>Mycobacterium ulcerans</italic></td><td align="left">Aquatic arthropods</td><td align="left">ND</td></tr><tr><td align="left">Dracunculiasis</td><td align="left"><italic>Dracunculus medinensis</italic></td><td align="left">Cyclops water fleas</td><td align="left">ND</td></tr><tr><td align="left">Human African trypanosomiasis</td><td align="left"><italic>Trypanosoma brucei</italic></td><td align="left">Tsetse flies</td><td align="left">560 <break></break>(76–1766)</td></tr><tr><td align="left">Leishmaniasis</td><td align="left"><italic>Leishmania</italic> spp.</td><td align="left">Phlebotomine sand flies</td><td align="left">3317 <break></break>(2180–4890)</td></tr><tr><td align="left">Leprosy</td><td align="left"><italic>Mycobacterium leprae, Mycobacterium lepromatosis</italic></td><td align="left">–</td><td align="left">6 <break></break>(3–11)</td></tr><tr><td align="left">Lymphatic filariasis</td><td align="left"><italic>Wuchereria bancrofti,</italic><break></break><italic>Brugia malayi, Loa loa</italic></td><td align="left">Mosquitoes</td><td align="left">2774 <break></break>(1807–4000)</td></tr><tr><td align="left">Schistosomiasis</td><td align="left"><italic>Shistosoma</italic> spp.</td><td align="left">Freshwater snails</td><td align="left">3309 <break></break>(1705–6260)</td></tr><tr><td align="left">Soil-transmitted helminthiasis</td><td align="left"><italic>Ancylostoma duodenale, Necator americanus, Ascaris lumbricoides, Trichuris trichiura</italic></td><td align="left">Fecal-oral transmission</td><td align="left">ND</td></tr><tr><td align="left">Trachoma</td><td align="left"><italic>Chlamydia trachomatis</italic></td><td align="left">Fomites, houseflies, others</td><td align="left">5 <break></break>(4–6)</td></tr><tr><td align="left">Yaws</td><td align="left"><italic>Treponema pallidum pertenue</italic></td><td align="left">Direct contact</td><td align="left">ND</td></tr><tr><td align="left">Chagas disease</td><td align="left"><italic>Trypanosoma cruzi</italic></td><td align="left">Triatomine bugs</td><td align="left">546 <break></break>(271–1054)</td></tr><tr><td align="left">Chikungunya</td><td align="left">Chikungunya virus</td><td align="left">Aedes mosquitoes</td><td align="left">ND</td></tr><tr><td align="left">Dengue and severe dengue</td><td align="left"><italic>Dengue virus</italic></td><td align="left">Aedes mosquitoes</td><td align="left">825 <break></break>(344–1412)</td></tr><tr><td align="left">Echinococcosis</td><td align="left"><italic>Echinococcus granulosus</italic></td><td align="left">Fecal-oral transmission</td><td align="left">144 <break></break>(69–286)</td></tr><tr><td align="left">Foodborne trematodiases</td><td align="left"><italic>Clonorchis sinensis, Opisthorchis</italic> spp., <italic>Fasciola</italic> spp., <italic>Paragonimus</italic> spp.</td><td align="left">Oral transmission</td><td align="left">1875 <break></break>(708–4837)</td></tr><tr><td align="left">Onchocerciasis</td><td align="left"><italic>Onchocerca volvulus</italic></td><td align="left">Black flies</td><td align="left">494 <break></break>(360–656)</td></tr><tr><td align="left">Taeniasis/cysticercosis</td><td align="left"><italic>Taenia solium</italic></td><td align="left">Oral transmission</td><td align="left">503 <break></break>(379–663) <break></break>(cysticercosis only)</td></tr></tbody></table><table-wrap-foot><fn><p>Affecting more than two billion people, the neglected tropical diseases shown here lack effective vaccines.</p><p>Computational vaccinology tools represent an accelerated, cost-sparing approach to vaccine design for these challenging targets. DALYs reflect global sum of years of life lost and years lived with disability as of 2010, presented with 95% UI.</p><p>DALYs: Disability-adjusted life year; ND: No data; UI: Uncertainty interval.</p><p>Data taken from <xref rid="CIT0096" ref-type="bibr">[96]</xref>.</p></fn></table-wrap-foot></table-wrap><p>Even while NTD expand their reach, vaccine development for these diseases lags behind. In contrast, vaccines for important emerging infectious diseases (EID, <xref rid="T2" ref-type="table">Table 2</xref>) are being developed, to a certain extent, by large biotechnology companies, particularly when these companies receive guaranteed purchase agreements or other incentives to accelerate vaccine development. Examples include the development of a vaccine for H7N9 (an emerging avian influenza) by Novartis and Novavax in 2013 <xref rid="CIT0006" ref-type="bibr">[6,7]</xref> and work toward the development of a new MERS-CoV vaccine in 2014 <xref rid="CIT0008" ref-type="bibr">[8]</xref>. However, the standard approach to develop new vaccines for emerging (and reemerging) infectious disease threats, which is to implement previously existing vaccine design methodologies such as cloning and expressing the dominant surface antigen <xref rid="CIT0009" ref-type="bibr">[9]</xref>, frequently results in the development of vaccines that are only effective when given with strong adjuvants <xref rid="CIT0010" ref-type="bibr">[10]</xref>. This approach is particularly unlikely to work for pathogens that have complex lifecycles (such as parasites) or are highly mutable (such as RNA viruses). This article will discuss new, computational approaches that may accelerate and improve the design of vaccines for NTD and EID.</p><table-wrap id="T2" orientation="portrait" position="float"><label>Table 2.</label><caption><p><bold>Emerging infectious diseases (as of 2014).</bold></p></caption><pmc-comment>OASIS TABLE HERE</pmc-comment>
<table frame="hsides" rules="groups"><colgroup><col width="10*" align="left"></col><col width="10*" align="left"></col><col width="10*" align="left"></col><col width="10*" align="left"></col></colgroup><thead valign="top"><tr><th align="left"><bold>Disease</bold></th><th align="left"><bold>Primary causative agents</bold></th><th align="left"><bold>Primary vector(s)</bold></th><th align="left"><bold>DALYs × 1000 </bold><break></break><bold>(95% UI)</bold></th></tr></thead><tbody valign="top"><tr><td align="left">Anthrax</td><td align="left"><italic>Bacillus anthracis</italic></td><td align="left">Animal remains</td><td align="left">ND</td></tr><tr><td align="left">Infections caused by microbes that have developed antimicrobial resistance</td><td align="left"><italic>Mycobacterium tuberculosis</italic><break></break><italic>Staphylococcus aureus</italic><break></break><italic>Enterococcus</italic> spp. <break></break><italic>Neisseria</italic> spp. <break></break><italic>Gonorrhoeae</italic> spp.</td><td align="left">Various</td><td align="left">ND</td></tr><tr><td align="left">Botulism</td><td align="left"><italic>Clostridium botulinum</italic> toxin</td><td align="left">Oral transmission</td><td align="left">ND</td></tr><tr><td align="left">Campylobacteriosis</td><td align="left"><italic>Campylobacter</italic> spp.</td><td align="left">Fecal-oral transmission</td><td align="left">7541 <break></break>(5687–9374)</td></tr><tr><td align="left">MERS, SARS</td><td align="left">Coronaviruses</td><td align="left">–</td><td align="left"> </td></tr><tr><td align="left">Dengue fever</td><td align="left">DEN-1, DEN-2, DEN-3, DEN-4 virus</td><td align="left">Aedes mosquitoes</td><td align="left">825 <break></break>(344–1412)</td></tr><tr><td align="left">Ehrlichiosis</td><td align="left"><italic>Ehrlichia</italic> bacterium</td><td align="left">Blacklegged ticks</td><td align="left">ND</td></tr><tr><td align="left">Enteropathogenic <italic>Eschericia coli</italic> (EPEC) <break></break>Enterotoxigenic <italic>E. coli</italic> (ETEC)</td><td align="left"><italic>E. coli</italic></td><td align="left">Fecal-oral transmission, other</td><td align="left">EPEC: 7542 <break></break>(5686–9524) <break></break>ETEC: 6894 <break></break>(5619–8286)</td></tr><tr><td align="left">Flu (influenza)</td><td align="left">Avian/porcine influenza strains</td><td align="left">Birds, pigs</td><td align="left">19,244 <break></break>(16,906–21,451)</td></tr><tr><td align="left">Group A streptococcal infections</td><td align="left"><italic>Streptococcus pyogenes</italic></td><td align="left">Direct contact, oral transmission</td><td align="left">ND</td></tr><tr><td align="left">Hepatitis</td><td align="left">Hepatitis A, B, C, D or E virus</td><td align="left">Various</td><td align="left">13,258 <break></break>(11,364–15,855)</td></tr><tr><td align="left">Lyme disease</td><td align="left"><italic>Borrelia burgdorferi</italic></td><td align="left">Blacklegged ticks</td><td align="left">ND</td></tr><tr><td align="left">Plague</td><td align="left"><italic>Yersinia pestis</italic></td><td align="left">Infected animals, rodent fleas</td><td align="left">ND</td></tr><tr><td align="left">Prion diseases</td><td align="left">Abnormal prions</td><td align="left">Sporadically or inherited mutation of prion gene</td><td align="left">ND</td></tr><tr><td align="left">Salmonellosis</td><td align="left"><italic>Salmonella</italic> spp.</td><td align="left">Undercooked or raw meat, reptiles</td><td align="left">4847 <break></break>(3819–5949)</td></tr><tr><td align="left">Shigellosis</td><td align="left"><italic>Shigella</italic> spp.</td><td align="left">Fomites, houseflies, other</td><td align="left">7052 <break></break>(5676–8466)</td></tr><tr><td align="left">Tularemia</td><td align="left"><italic>Francisella tularensis</italic></td><td align="left">Ticks, deer flies, infected animals</td><td align="left">ND</td></tr><tr><td align="left">West Nile virus</td><td align="left">Flavivirus</td><td align="left">Mosquitoes</td><td align="left">ND</td></tr></tbody></table><table-wrap-foot><fn><p>Most emerging/reemerging infectious diseases shown here lack effective vaccines.</p><p>Computational vaccinology tools represent an accelerated, cost-sparing approach to vaccine design for these challenging targets. DALYs reflect global sum of years of life lost and years lived with disability as of 2010, presented with 95% UI.</p><p>DALYs: Disability-adjusted life year; ND: No data; UI: Uncertainty interval.</p><p>Data taken from <xref rid="CIT0096" ref-type="bibr">[96]</xref>.</p></fn></table-wrap-foot></table-wrap><p>Truly effective vaccines do not exist for the majority of NTD. Although vaccines are in development for several NTD pathogens <xref rid="CIT0011" ref-type="bibr">[11]</xref>, lack of financial incentive to invest in research and development programs for diseases concentrated in low-income countries has mired the progress of vaccine efforts among large pharmaceutical companies <xref rid="CIT0003" ref-type="bibr">[3]</xref>. Preventative chemotherapy mass drug administration (MDA) programs employing donated or extremely low-cost generic drugs are currently in progress to control lymphatic filariasis, onchocerciasis, leprosy, trachoma and helminthiases in many areas of the world <xref rid="CIT0012" ref-type="bibr">[12]</xref>. A complication inherent in this strategy is that because the regions affected by different NTD overlap, coinfections can be difficult to manage with antiparasitic agents <xref rid="CIT0013" ref-type="bibr">[13]</xref>. The success of these long-term efforts and off-target effects of mass-eradication campaigns at the individual and population levels remain to be determined <xref rid="CIT0014" ref-type="bibr">[14]</xref>. As has been observed for polio, geopolitical upheaval may hamper global efforts to eradicate NTD. Thus, effective NTD vaccines are still needed <xref rid="CIT0015" ref-type="bibr">[15]</xref>. New cost–effective design methodologies can help to bridge the gap between the great need for these vaccines and return on investment for pharmaceutical companies.</p><p>Fortunately, genomes for many newly emerging pathogens and neglected tropical disease-associated pathogens are becoming available due to research efforts worldwide <xref rid="CIT0016" ref-type="bibr">[16]</xref>. The availability of these genomes now makes it possible to apply computational vaccinology tools to these diseases of global health importance.</p></sec><sec id="S0002"><title>Basic principles of vaccinology</title><p>Host immune response to pathogens is mediated by the innate and adaptive arms of the immune system. Innate immune cells such as macrophages, neutrophils and natural killer cells are responsible for the first line of defense, while adaptive immunity provides a more targeted response to pathogens that establishes immune memory for more rapid responses upon repeated exposures. B cells produce antibodies which are capable of recognizing and neutralizing pathogenic antigens. T cells support antibody production, activation and memory development, and are capable of lysing infected cells. The potent response from T and B cells, however, comes at a cost. Whereas innate immune cells can respond within 24–72 h, the primary adaptive response normally takes 7–14 days to mature. Secondary adaptive responses driven by immune memory are much faster and much stronger. This is the principle behind vaccination: pre-exposure to pathogen-derived antigens can induce pathogen-specific immune memory. The discovery of critical antigens that drive protective memory is facilitated by new computational tools.</p><p>Indeed, the general principle that immune cells develop memory to specific pathogen components – has driven the development of genome-derived vaccines over the past two decades. Since T cells play a critical role in adaptive immunity and the development of immune memory required for an efficacious vaccine, computational tools have been used to search for small linear peptides (T-cell epitopes) derived from protein antigens that drive class I and class II T-cell responses. These peptides are displayed on the surface of APC by multiple alleles of the MHC. As human beings express multiple alleles of class I and class II MHC molecules, called human leukocyte antigens (HLA), computational vaccinologists now search for T-cell epitopes that can bind to the most common HLA alleles in the human population, reasoning that broad HLA coverage will contribute to the development of effective genome-derived vaccines. Computational tools can also be used to select epitope-rich surface proteins that are better immunogens to drive B-cell response. Fortunately, while B cells and antibodies generally recognize surface proteins, T cells recognize epitopes derived from a broader range of proteins, giving the computational vaccinologist many possible sources (internal and external proteins as well as secreted proteins) for the selection of T-cell epitopes for vaccines.</p><p>Exposure to a given pathogen generates memory T-cell clones capable of rapid and efficient response upon subsequent reinfection <xref rid="CIT0017" ref-type="bibr">[17]</xref>. This response may include T cell help for induction of higher antibody titers, T-cell-mediated lysis of infected cells and the expression of cytokines to coordinate other cell-mediated immune processes such as activation of APC. Breadth of T-cell response (responding to many different epitopes) appears to be correlated with protection from severe disease for many pathogens that affect humans. More specifically, for HIV, HBV, HCV, lymphocytic choriomeningitis virus and malaria, protection from disease has been correlated with broad T-cell epitope response to both ‘immunodominant’ and subdominant T-cell epitopes <xref rid="CIT0018" ref-type="bibr">[18–22]</xref>. These discoveries have contributed to the concept that vaccines can be made directly from genomes by selecting sets of epitopes that will stimulate immune responses and protect against diseases.</p><p>Based on the observation that broad T-cell response may be protective, computational vaccinologists have worked to define collections of T-cell epitopes that can recreate the requisite features of this response. T-cell-driven, epitope-based strategies for developing vaccines against EID and NTD are currently the focus of several NTD research laboratories. Proof of principle exists for a number of disease models: cellular immunity elicited by epitope immunization provided complete protection against respiratory syncytial virus challenge, partial protection of BALB/c mice against sporozoite challenge, elimination of malaria-infected hepatocytes <italic>in vitro</italic>, partial protection of BALB/c and CBA against encephalitis following intracerebral challenge with a lethal dose of measles virus, complete protection from intraperitoneal HSV challenge, protection against infection with malaria or influenza A virus and full protection of sheep against bovine leukemia virus (these examples are reviewed in <xref rid="CIT0023" ref-type="bibr">[23]</xref>). We have demonstrated complete protection against lethal vaccinia challenge <xref rid="CIT0024" ref-type="bibr">[24]</xref> and successful clearance of a chronic bacterial infection (<italic>Helicobacter pylori</italic>) following T-cell epitope-driven vaccination <xref rid="CIT0025" ref-type="bibr">[25]</xref>. In earlier studies, we achieved partial protection against an aerosolized bacterial pathogen (Tularemia <xref rid="CIT0026" ref-type="bibr">[26]</xref>) using a vaccine that contained only 14 epitopes. While mice are not humans, growing evidence that T-cell epitope-driven vaccines can be effective in humans has led to the establishment of a number of biotech startups and venture-backed companies focused entirely on T-cell epitope-based vaccines.</p><sec id="S0003"><title>T-cell epitopes as ‘payload’</title><p>As described in the following sections, computational tools are being used to identify proteins or antigens of interest directly from the genomes of pathogens. In theory, a minimal set of antigens or epitopes that induce a competent immune response to a pathogen can be discovered using the new tools. Adjuvant triggers innate immunity, which is an essential component of the protective immune response, directing it toward inflammation rather than tolerance. When combined with the minimum antigenic components that comprise the ‘payload’ of a genome-derived vaccine, delivered in the right vehicle, may trigger protective immune response. The fundamental principle of the genome-derived epitope-driven vaccine approach is illustrated Tthus:</p><disp-formula id="M0001"><alternatives><graphic xlink:href="IERV_A_955478_M0001.jpg" position="float" orientation="portrait"></graphic><mml:math id="d1e863"><mml:mrow><mml:mi>I</mml:mi><mml:mi>m</mml:mi><mml:mi>m</mml:mi><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mtext> </mml:mtext><mml:mi>P</mml:mi><mml:mi>a</mml:mi><mml:mi>y</mml:mi><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>a</mml:mi><mml:mi>d</mml:mi><mml:mo>+</mml:mo><mml:mi>A</mml:mi><mml:mi>d</mml:mi><mml:mi>j</mml:mi><mml:mi>u</mml:mi><mml:mi>v</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mi>D</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>i</mml:mi><mml:mi>v</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>y</mml:mi><mml:mtext> </mml:mtext><mml:mi>v</mml:mi><mml:mi>e</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mo>=</mml:mo><mml:mi>V</mml:mi><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:math></alternatives></disp-formula><p>The importance of epitopes as key determinants of protective immune responses is reflected by the flurry of immunoinformatics activity over the past decades. A number of T-cell epitope mapping tools have been developed to accelerate the identification of these critical components of the immune response. Using methods such as frequency analysis, support vector machines, hidden Markov models and neural networks, researchers have developed highly accurate tools for modeling the MHC-peptide interface and predicting T-cell epitopes. Available tools have been comprehensively reviewed: see Brusic <italic>et al.</italic><xref rid="CIT0027" ref-type="bibr">[27]</xref>, De Groot and Martin <xref rid="CIT0028" ref-type="bibr">[28]</xref> and Sette <italic>et al.</italic><xref rid="CIT0029" ref-type="bibr">[29]</xref>. The concept was at first called ‘vaccinomics’ by Brusic and Petrovsky in 2002 <xref rid="CIT0030" ref-type="bibr">[30]</xref>, then ‘reverse vaccinology’ by Rappuoli in 2003 <xref rid="CIT0031" ref-type="bibr">[31]</xref> and, more recently, ‘immunome-derived’ or ‘genome-derived vaccine design’ by Pederson <xref rid="CIT0032" ref-type="bibr">[32]</xref>; De Groot and Martin <xref rid="CIT0033" ref-type="bibr">[33]</xref>; and Doytchinova, Taylor and Flower <xref rid="CIT0034" ref-type="bibr">[34]</xref>.</p></sec><sec id="S0004"><title>B-cell epitopes: T-cell epitope content as a proxy for B-cell response?</title><p>Computational vaccinologists have been unable to successfully develop accurate tools for B-cell epitope prediction, even though one of the most commonly measured outcomes of vaccination and accepted determinant of protection is antibody generation <xref rid="CIT0035" ref-type="bibr">[35,36]</xref>. Thus, current computational vaccinology approaches to vaccine development must take B-cell response into consideration and develop approaches that include means of stimulating effective humoral immunity where it is required for protection against challenge. Given T-cell dependence for essential features of an effective antibody response, including B-cell affinity maturation, class switch recombination, plasma cell differentiation and memory B-cell differentiation <xref rid="CIT0037" ref-type="bibr">[37]</xref>, T-cell epitope analysis and quantification have been used by our group as a proxy for identifying good B-cell immunogens, linking <italic>in silico</italic> sequence analysis to desired putative B-cell responses <xref rid="CIT0028" ref-type="bibr">[28]</xref>.</p></sec></sec><sec id="S0005"><title>The iVAX approach to design genome-derived epitope-driven vaccines</title><p>De Groot and colleagues have integrated epitope-mapping tools with a wider array of vaccine design algorithms into the web-based iVAX toolkit, which will be described in some detail in the following sections. The tools were initially used by EpiVax and collaborators <xref rid="CIT0023" ref-type="bibr">[23,38–42]</xref>, and then expanded and refined for projects that have been in progress at the Institute of Immunology and Informatics (iCubed) <xref rid="CIT0043" ref-type="bibr">[43–45]</xref>. The iVAX toolkit is currently in use for NTD research at the iCubed and with academic collaborators under an agreement established between EpiVax and URI in 2009. iVAX tools are being used to evaluate the protective potential of existing NTD and EID vaccines <xref rid="CIT0046" ref-type="bibr">[46,47]</xref>, to predict immune response to newly emerging pathogens <xref rid="CIT0009" ref-type="bibr">[9,10]</xref> and to design novel NTD vaccines composed of T-cell epitopes (for Chagas disease, <italic>Brugia malayi</italic> and several different species of Leishmania <xref rid="CIT0048" ref-type="bibr">[48]</xref>). In the following few sections, we describe the iVAX approach to design genome-derived epitope-driven vaccines for NTD and EID.</p><sec id="S0006"><title>Selecting targets</title><p>One of the first questions facing computational vaccinologists is how to prioritize their search for antigenic proteins and epitope subunits. The entire set of proteins derived from a pathogen’s genome is an unlikely point of departure for epitope mapping, since many of these proteins may not be part of the ‘core genome’ for a set of bacterial or viral strains of the same pathogen. Others may be proteins that serve as ‘housekeeping’ genes that are also well conserved in harmless commensal organisms. On the other hand, proteins that are highly conserved across variant strains, that are pathogen-specific and those that are upregulated during interactions with the host, particularly those that are secreted by a pathogen (presumably in an attempt to alter the host environment), are excellent targets for vaccine development.</p><p>In addition to targeting upregulated, secreted and pathogen-specific antigens, other means of selecting antigens for epitope screening include identifying proteins that are more common in virulent as compared to avirulent strains, selection of genes differentially expressed in immunopathogenesis, prioritizing proteins exposed on the surface of the pathogen and focusing on proteins that are expressed early in the course of natural infection. The Expert Protein Analysis System (ExPASy) proteomics server of the Swiss Institute of Bioinformatics offers a wide variety of proteomics tools that can be used for this purpose, including tools related to protein identification and characterization. Our groups have adapted an approach first described by Gennaro <italic>et al.</italic> for <italic>Mycobacterium tuberculosis</italic> (Mtb) <xref rid="CIT0049" ref-type="bibr">[49]</xref>, employing a series of ExPASy tools (SignalP, TMPred and Prosite Scan <xref rid="CIT0050" ref-type="bibr">[50]</xref>) to triage pathogen genomes, reducing the number of potential targets from thousands of proteins to several dozen candidate antigens. In our first test of this approach, we found that a subset of epitopes derived from the Mtb genome elicited IFN-γ response from Mtb-exposed human samples, and prototype epitope-based TB vaccines were shown to be robustly immunogenic in murine studies <xref rid="CIT0051" ref-type="bibr">[51]</xref>.</p></sec><sec id="S0007"><title>Immunogenicity scale: ranking antigens</title><p>Once a set of antigens is selected using the methods described above, tools available through the iVAX toolkit can be used to rank proteins for relative immunogenicity, based on their putative T-cell epitope content. For this purpose, iVAX calculates an ‘immunogenicity score,’ which represents the deviation between the number of putative epitopes found in a given protein and the number we would expect to find in a randomly generated protein sequence, taking care to adjust for protein size. In this context, an immunogenicity score of zero reflects random or ‘average’ putative epitope content; positive scores reflect more epitope content than expected, while negative scores reflect less epitope content than expected <xref rid="CIT0052" ref-type="bibr">[52]</xref>. Large numbers of protein sequences derived directly from the genome of selected pathogens can be ordered by potential class I (CTL), class II (T helper) or both class I and class II epitope content and placed on an immunogenicity scale <xref ref-type="fig" rid="F0001">(Figure 1)</xref>. This tool allows researchers to quickly rank a given set of proteins both in relative (i.e., relative to each other) and absolute (i.e., relative to a panel of known immunogens and nonimmunogenic proteins) terms <xref rid="CIT0053" ref-type="bibr">[53]</xref>. In our experience, epitope-rich proteins are good vaccine targets and elicit strong antibody responses – thus, as previously stated, T-cell epitope content is a useful proxy for overall immunogenic potential.</p><fig fig-type="figure" id="F0001" orientation="portrait" position="float"><label>Figure 1.</label><caption><p><bold>Lymphatic filariasis antigens on the EpiMatrix protein immunogenicity scale.</bold> All other factors being equal, the more HLA ligands (i.e., putative T-cell epitopes) contained in a given protein, the more likely that protein is to induce an immune response. To capture this concept, the EpiMatrix immunogenicity scale presents proteins by the EpiMatrix protein score, and compares them to other known immunogens. The EpiMatrix protein score is the difference between the number of predicted T-cell epitopes expected in a protein of a given size and the number of putative epitopes predicted by the EpiMatrix. The EpiMatrix protein scores are ‘normalized’ and can be plotted on a standardized scale. ‘Average’ proteins score near zero. Protein scores above zero indicate the presence of excess MHC ligands and denote a higher potential for immunogenicity, while scores below zero indicate the presence of fewer potential MHC ligands than expected and a lower potential for immunogenicity. The EpiMatrix protein score is correlated with observed immunogenicity <italic>in vitro</italic> and <italic>in vivo</italic>. As shown here, proteins scoring above +20, such as <italic>Brugia malayi</italic> antigen Juv-p120, are considered to have a significant immunogenic potential. Proteins scoring below –20, such as TPX-2 above, are less likely to be immunogenic <italic>in vivo</italic>.</p></caption><graphic xlink:href="IERV_A_955478_F0001_OC"></graphic></fig><p>Antigen selection is particularly complicated when targeting parasitic organisms due to their comparatively massive genomes and multistaged life cycles, and ranking of these antigens may assist with the selection of better targets. For example, in <xref ref-type="fig" rid="F0001">Figure 1</xref>, we show two candidate antigens derived from <italic>B. malayi</italic>, a causative agent of lymphatic filariasis, whose life cycle is divided into multiple larval stages including a microfilarial stage <xref rid="CIT0054" ref-type="bibr">[54]</xref>. In this case, TPX-2, a protein that has been identified as a potential vaccine target <xref rid="CIT0055" ref-type="bibr">[55]</xref>, is shown to contain minimal T-cell epitope content, with an immunogenicity score of –27.61, and thus it may be less successful as a vaccine candidate. In contrast, Juv-p120, a <italic>B. malayi</italic> ortholog of a <italic>Litomosoides sigmodontis</italic> antigen implicated in conferring protection against microfilarial infection <xref rid="CIT0056" ref-type="bibr">[56]</xref> carries substantially more T-cell epitope content, scoring +94.14 on the immunogenicity scale, in the same range as other well-known immunogens. Furthermore, as is illustrated here in the case of <italic>B. malayi</italic>, we frequently find evidence that pathogens appear to reduce T-cell epitope content in key proteins to avoid human immune responses. Epitope deletion is an established means of immune evasion in HIV and HCV <xref rid="CIT0057" ref-type="bibr">[57,58]</xref>; thus, the mechanism may also be relevant in the context of infections that are associated with chronic infection caused by filaria, leishmania and other chronic NTD, particularly in stages associated with chronic parasitism and parasite persistence in the face of immune pressure. We will discuss additional means of immune evasion that can be uncovered by computational tools below.</p></sec><sec id="S0008"><title>EpiMatrix: T-cell epitope mapping of selected antigens</title><p>T-cell epitopes are short linear peptides that can bind to MHC molecules and engage T cells through their receptors (TCR), activating specific populations of CD8+ and/or CD4+ lymphocytes. These epitopes are key to forming the immunological synapse between antigen-presenting cells and T cells. Because TCRs are produced in a myriad of possible conformations (much like antibodies, to which they are related), MHC binding is the dominant event in immune recognition. In other words, most MHC ligands are also T-cell epitopes, and T-cell epitopes are by definition, MHC ligands. The MHC-peptide interaction is well characterized <xref rid="CIT0059" ref-type="bibr">[59,60]</xref>. Based on these characterizations, pattern-matching algorithms such as EpiMatrix have been developed to screen protein sequences for peptides that will bind MHC.</p><p>The human MHC molecules, or HLA, are among the most variable proteins in the human genome. This variation ensures that the surveillance capabilities of the human immune system are both broad and deeply redundant, making immune escape through mutation more difficult for pathogenic organisms. Fortunately, some alleles are much more common than others in the human population and the binding repertoire of many alleles significantly overlap.</p><p>By focusing on alleles that are both common (in the human population) and significantly different from each other (representative of human diversity), HLA alleles can be grouped into ‘supertypes,’ which can reduce the search space to a manageable number of evaluations. Six of these class I super-type alleles that ‘cover’ the genetic backgrounds of most humans worldwide have been used to define CTL epitopes: A*0101, A*0201, A*0301, A*2402, B*0702 and B*4403 <xref rid="CIT0061" ref-type="bibr">[61]</xref>. For class II T helper epitopes, mapping for a panel of eight common alleles: DRB1*0101, *0301, *0401, *0701, *0801, *1101, *1301 and *1501, gives broad T helper epitope coverage <xref rid="CIT0062" ref-type="bibr">[62]</xref>. The concept of supertype alleles is generally accepted and widely applied to vaccine design in the field of computational vaccinology <xref rid="CIT0061" ref-type="bibr">[61,62]</xref>.</p><p>Using the set of selected protein antigens as a starting point, iVAX uses EpiMatrix to parse each into overlapping 9-mer frames where each 9-mer overlaps the last by eight amino acids. Each 9-mer is then scored for predicted binding affinity to a panel of class I or class II HLA alleles. The EpiMatrix algorithm compares the amino acid sequence of each given 9-mer peptide to the coefficients contained in stored probability matrices and produces a raw score. In order to compare potential epitopes across multiple HLA alleles, EpiMatrix raw scores are converted to a normalized ‘Z’ scale. Peptides scoring above 1.64 on the EpiMatrix ‘Z’ scale (typically the top 5% of any given sample) are likely to be MHC ligands <xref rid="CIT0063" ref-type="bibr">[63]</xref>. Evidence from animal studies suggests that the number of epitopes required for full protection is a small and definable subset (∼50) <xref rid="CIT0064" ref-type="bibr">[64,65]</xref>; thus, epitope-driven vaccines developed by our group generally contain a payload of 50–100 epitopes that provide broad coverage of human genetic backgrounds. With a combination of promiscuous class II epitopes and class I supertype epitopes, it is possible to attain >99% coverage of the HLA of most human populations <xref rid="CIT0061" ref-type="bibr">[61,62]</xref>.</p></sec><sec id="S0009"><title>Eliminating regulatory or suppressor epitopes using JanusMatrix</title><p>A recent development in vaccine design includes the consideration of epitopes that induce regulatory or suppressive immune responses <xref rid="CIT0066" ref-type="bibr">[66]</xref>. Our group has been investigating epitope cross-conservation with the human genome and its association with diminished or regulatory immune responses. Using a recently developed tool called JanusMatrix we first determined that published effector T-cell epitopes can be distinguished from reported regulatory T-cell epitopes on the basis of TCR-specific cross-reactive potential with the human genome and human microbiome <xref rid="CIT0067" ref-type="bibr">[67]</xref>. JanusMatrix differs from whole-sequence alignment tools such as BLAST <xref rid="CIT0068" ref-type="bibr">[68]</xref> in its basis upon T-cell receptor homology. Pathogenic peptides whose TCR-facing residues are identical to the epitopes contained in multiple self may be recognized by T cells specific to those human proteins. Of course, even though the MHC-facing residues may differ, these peptides must still have the capacity to bind to the same MHC as the pathogen sequence, provided that binding is preserved. Taking this into account, JanusMatrix compares the TCR-facing contour of pathogen ligands to other genomes of interest, identifying matches therein that are predicted to bind the same MHC. TCR-homologous epitopes shared between pathogens and humans, or pathogens and other microbes, can be uncovered with remarkable speed using the JanusMatrix tool.</p><p>Exploring further, we have uncovered a high degree of host (human) homology in viruses that tend to establish chronic infections in humans such as EBV and CMV <xref rid="CIT0069" ref-type="bibr">[69]</xref>. Furthermore, ‘commensal’ viruses can be shown to contain significantly more human genome-homologous epitopes relative to those causing acute infection (e.g., Ebola, Marburg) <xref rid="CIT0069" ref-type="bibr">[69]</xref>. The limited clinical efficacy of some vaccines against selected microbial pathogens may, in fact, have been due to their extensive cross-conservation with the human genome <xref rid="CIT0010" ref-type="bibr">[10]</xref>. The JanusMatrix tool is currently being used by our team and collaborators to identify significant homology between candidate payload epitopes and proteins contained within the human genome and the human microbiome. Using the tool, we find that not only viruses but also bacteria that establish chronic infections in humans ‘deimmunize’ (remove T-cell epitopes) and ‘tolerize’ (modify epitopes to be more cross-reactive to human T-cell epitopes). Comprehensive studies of NTD genomes (and stage-by-stage analysis of parasite antigens) will be performed using the JanusMatrix tool in the near future.</p><p>It follows that careful selection of T-cell epitopes, and redesign of whole antigens, to avoid the inclusion of T-cell epitopes that may be highly cross-reactive with the human genome could improve the efficacy of whole-antigen and epitope-based vaccines. JanusMatrix complements recent research <xref rid="CIT0070" ref-type="bibr">[70]</xref> on the development of adaptive immunity and supports the hypothesis that adaptive T-cell responses are reinforced by cross-reactivity with the human microbiome <xref rid="CIT0071" ref-type="bibr">[71–73]</xref>.</p><p>Cytoscape is an online tool that is usually used by bioinformaticians to illustrate the relatedness between proteins, for example, all of the intracellular proteins that might be involved in the stimulation of a cell through toll-like receptors. We have repurposed Cytoscape to describe the relationship between epitopes across proteins in groups of sequences (the human genome, the human microbiome, pathogen genomes <xref rid="CIT0067" ref-type="bibr">[67]</xref>). Using Cytoscape <xref rid="CIT0074" ref-type="bibr">[74]</xref>, the results of JanusMatrix analysis (e.g., comparing a pathogen epitope to the human genome) can be visualized as networks where each epitope derived from a pathogen is linked to its TCR-matched counterparts in the search database, which themselves are linked to their source proteins. For example, an influenza T-cell epitope previously identified by Mark Davis and colleagues <xref rid="CIT0070" ref-type="bibr">[70]</xref> that stimulates T cells in subjects never exposed to influenza can be shown to have an extensive network of cross-reactive TCR-facing epitopes in the human microbiome. In contrast, an epitope from vaccinia virus synthesized and tested by Larry Stern’s group is shown to have extensive cross-reactivity with the human genome by JanusMatrix. This epitope was nonimmunogenic <italic>in vitro</italic> (by IFN-γ ELISpot) even though it was shown to bind to the correct class II MHC <xref rid="CIT0075" ref-type="bibr">[75]</xref>. This epitope fits the emerging <italic>in silico</italic> definition of a Treg epitope.</p><p>Due to their commensal nature and need to avoid human immune responses over many years of coexistence, it is even more likely for selected human parasites to share putative T-cell epitope content with their human hosts. In <xref ref-type="fig" rid="F0002">Figure 2</xref>, we offer two example peptides from <italic>B. malayi</italic> antigens TPX-2 and Juv-p120, compared to published Treg epitopes from human immunoglobulin (Tregitopes) and effector epitopes, the CEFT pool (a set of peptides used as ‘control positive’ peptides in ELISpots <xref rid="CIT0067" ref-type="bibr">[67]</xref>). The potential cross-reactivity network differential is evident between the TPX-2 epitope, with many related epitopes derived from human sequences, and the Juv-p120 sequence, whose related human epitopes are few. This finding underscores the importance of validating the response phenotype of T cells stimulated by epitopes identified <italic>in silico</italic> prior to their inclusion in vaccine constructs, and also illustrates the importance of this type of analysis for the selection of candidate epitopes for NTD.</p><fig fig-type="figure" id="F0002" orientation="portrait" position="float"><label>Figure 2.</label><caption><p><bold>JanusMatrix analysis.</bold> This tool considers identity of TCR-facing residues to target proteins or genomes independently from residues that contribute to MHC binding. Peptides that have similar TCR-facing residues and are presented in the context of the same HLA can be identified. Extensive homology is easy to identify using the Cytoscape network visualization tool. The extent of the network can be used to distinguish potential regulatory T-cell epitopes <bold>(A)</bold> from potential effector T-cell epitopes <bold>(B)</bold>. A published Treg epitope example is shown in <bold>(C)</bold>, and several published Teff epitope examples are shown in <bold>(D)</bold>. For these illustrations, yellow hexagons identify the source antigens, turquoise diamonds identify the source T-cell epitope clusters, gray squares indicate the source 9-mers, dark blue triangles indicate matched human 9-mers and light blue circles indicate human antigens in which matched 9-mers are found.</p></caption><graphic xlink:href="IERV_A_955478_F0002_OC"></graphic><p><fn><p>Data for C, D taken from <xref rid="CIT0067" ref-type="bibr">[67]</xref>.</p></fn></p></fig></sec><sec id="S0010"><title>ClustiMer: finding promiscuous T-cell epitopes</title><p>Promiscuous HLA binding potential is a feature of class II-restricted T-cell epitopes particularly exploitable for vaccine design purposes. It has been shown that putative epitopes for HLA class II are not often distributed evenly across protein sequences, but instead tend to cluster in specific regions, where it is not uncommon to observe several reactive 9-mer frames in close proximity <xref rid="CIT0076" ref-type="bibr">[76]</xref>. These ‘clusters’ of unusually high predicted epitope density can be identified <italic>in silico</italic> using the ClustiMer algorithm. In general, T-cell epitope clusters identified by the ClustiMer algorithm tend to be promiscuous MHC binders and are frequently T-cell epitopes <xref rid="CIT0052" ref-type="bibr">[52]</xref>. Due to overlapping peptide-binding preferences among HLA-DR alleles, it is also possible to identify single 9-mers capable of binding four or more HLA alleles <xref rid="CIT0076" ref-type="bibr">[76]</xref>. These sequences have been dubbed ‘EpiBars’ due to their horizontal, band-like signature in readout from EpiMatrix <xref ref-type="fig" rid="F0003">(Figure 3)</xref>. T-cell epitope clusters can be very powerful, and EpiBars may be a characteristic feature of highly immunogenic, promiscuous class II epitopes. These compact, highly reactive peptides are relatively easy to deliver and show great promise as vaccine components when cross-reactivity with the human genome is limited (see above). We have used these clusters extensively in our own work <xref rid="CIT0024" ref-type="bibr">[24,43,51]</xref>.</p><fig fig-type="figure" id="F0003" orientation="portrait" position="float"><label>Figure 3.</label><caption><p><bold>Example of an EpiBar: EpiMatrix analysis of candidate lymphatic filariasis epitope.</bold> In addition to providing an overall immunogenicity score, EpiMatrix can be used to analyze epitopes at the local level. A <italic>Brugia malayi</italic> Juv-p120 peptide is shown above, parsed into 9-mer frames and analyzed for predicted immunogenicity. EpiMatrix assessments above 1.64 constitute the top 5% of predicted HLA binders and are shaded medium blue, while scores above 2.32 fall in the top 1% and are shaded dark blue. This Juv-p120 peptide registers significant scores for all eight alleles in EpiMatrix in a single 9-mer frame, and based on the EpiMatrix method, has a cluster score of 16.81 (reflecting the number of predicted binders per amino acid length). Cluster scores higher than 10 are considered to be significant based on retrospective and prospective studies carried out by the EpiVax group. The band-like pattern illustrated in frame 35 is called an EpiBar and is characteristic of promiscuous epitopes.</p></caption><graphic xlink:href="IERV_A_955478_F0003_OC"></graphic></fig></sec><sec id="S0011"><title>Class I epitope selection</title><p>Promiscuous T-cell epitopes also exist, to a certain degree, for class I alleles; however, this is much less common than for class II. Some laboratories have demonstrated cross-presentation of peptides within HLA ‘superfamilies,’ such as the A3 superfamily: A3, A11, A31, A33 and A68 <xref rid="CIT0077" ref-type="bibr">[77]</xref>. Cross-MHC binding and presentation to T cells has been confirmed in HIV vaccine studies <xref rid="CIT0078" ref-type="bibr">[78]</xref>. However, we have found that weighting toward the selection of highly promiscuous class I epitopes may lead to identification of candidate epitopes that have lower binding affinities overall. Higher binding affinities appear to be a critical aspect of CTL epitope efficacy <xref rid="CIT0079" ref-type="bibr">[79]</xref>, thus our group prefers to select a small set of the best-scoring putative epitopes for each of the six class I HLA superfamilies from a given protein or set of conserved peptides <xref ref-type="fig" rid="F0004">(Figure 4)</xref>.</p><fig fig-type="figure" id="F0004" orientation="portrait" position="float"><label>Figure 4.</label><caption><p><bold>Class I epitope ‘staircase’ ranking.</bold> In the process of generating a selection of predicted high-affinity class I epitopes for inclusion in T-cell-driven vaccines, parsed 9-mers from any antigen are ranked by potential to bind supertype HLA alleles and collated in a ‘staircase’ report. In this example, the top five highest-scoring peptides from a given antigen are shown. In general, prioritizing class I epitopes by score for each of the individual alleles is preferred to define epitopes that bind across alleles.</p></caption><graphic xlink:href="IERV_A_955478_F0004_OC"></graphic></fig></sec><sec id="S0012"><title>EpiAssembler: generating immunogenic consensus sequences</title><p>Selecting epitopes that are broadly reactive across circulating strains can enhance broad applicability of new vaccines. The problem of pathogen variability significantly complicates the selection of epitopes for vaccine design. To address this problem, EpiVax has developed EpiAssembler <xref rid="CIT0080" ref-type="bibr">[80]</xref> to identify sets of overlapping, conserved and immunogenic epitopes and to assemble them into extended immunogenic consensus sequences (ICS, <xref ref-type="fig" rid="F0005">Figure 5</xref>).</p><fig fig-type="figure" id="F0005" orientation="portrait" position="float"><label>Figure 5.</label><caption><p><bold>EpiAssembler construction of immunogenic consensus sequences.</bold> This figure illustrates the process of assembling highly conserved T-cell epitopes into a single molecule. First, a highly conserved, promiscuous epitope is identified to form the 9-mer core of the ICS peptide (red bar). Overlapping conserved epitopes (pink, orange, green and blue bars) are then added to the N- and C-termini of the peptide until a suitable length is reached for binding in the class II HLA binding groove. This economical approach allows for targeting of multiple strains of a given pathogen using a single peptide, as illustrated by the blended bar at the bottom of the figure.</p></caption><graphic xlink:href="IERV_A_955478_F0005_OC"></graphic><p><fn><p>ICS: Immunogenic consensus sequences.</p></fn></p></fig><p>The theory behind developing ICS is that processing and presentation of these sequences would allow for presentation of the highly conserved class II-restricted epitopes contained in the ICS in the context of more than one MHC. The resulting peptide is not a ‘pseudo-sequence’ as such, since each constituent epitope occurs in its corresponding position in the native protein; adjacent epitopes may be similarly conserved but not in the same variant of the pathogen. The ICS approach has been useful for identifying highly immunogenic epitopes for HIV vaccine design <xref rid="CIT0038" ref-type="bibr">[38]</xref>. Using HIV as an example, while the full composite ICS peptides happen to be exactly conserved in a few individual strains of HIV, each peptide represents a significant percentage of circulating strains because every constituent overlapping epitope is conserved in a large number (range 893–2254) of individual HIV-1 strains <xref rid="CIT0038" ref-type="bibr">[38]</xref>.</p><p>By extending the approach described above, it is possible to develop completely synthetic antigens whose sequences are optimized for T helper potential. With an eye to structural considerations, even recombinant protein-only vaccines could be optimized in this way, enabling primary cognate T help to be maximized and B-cell memory to be elicited. An ideal vaccine might include whole proteins in addition to some epitopes; some or all of these antigens could be optimized using the ICS approach. Linking ICS epitopes to a carrier protein (such as a surface protein target of B-cell response) would further maximize primary cognate T help, since B cells that capture the recombinant proteins would be able to process and present T helper epitopes derived from more variable proteins.</p><p>As compared with ICS, randomly selected counterparts, on average, contain half as many binding motifs and cover one-third fewer isolates <xref rid="CIT0040" ref-type="bibr">[40]</xref>. To develop vaccines of equivalent antigenic ‘payload’ using conventional methods would be prohibitively expensive, as it would require use of multiple variants of each antigen. We believe that this and similar approaches that harness conserved T help have tremendous potential and deserves careful consideration in vaccine design.</p></sec><sec id="S0013"><title>From genomes to vaccines: epitope annotation, validation & down-selection</title><p>After generating a preliminary list of candidate vaccine components, the next step in the iVAX approach is to review the putative epitopes produced by the EpiMatrix system, adding qualitative and quantitative annotations wherever possible, leading to an investigator-driven down-selection process. Putative epitopes derived from known antigens or from proteins overexpressed during early stages of infection or proteins known to be exposed to immune surveillance as reported in the literature may be prioritized. Furthermore, putative epitopes with the <italic>in silico</italic> profile of potential regulatory T-cell epitopes (based on JanusMatrix analysis) are removed from further consideration.</p></sec><sec id="S0014"><title>Individualized T-cell epitope measure</title><p>Algorithms can also be helpful to interpret vaccine component responses in preclinical and clinical studies. In studies of immune response to therapeutic proteins and vaccines, the authors have observed that subject-to-subject variation in T-cell response closely relates to subject HLA type and the number of motifs or peptides that match the subject’s HLA haplotype. To describe this relationship, EpiVax researchers have developed a metric that may be useful in clinical assessment of immune response to vaccines, called the ‘individualized T-cell epitope measure’ or iTEM. For a given T-cell epitope, an individual’s iTEM score can be calculated by weighting and summing the epitope’s EpiMatrix Z-scores for each HLA allele in a given subject’s haplotype. This calculated score allows for individualized immunogenic potential to be predicted based on the number of putative epitopes contained in a protein and a given individual’s HLA haplotype. Using this score, it is possible to analyze the contribution of haplotype to the corresponding T-cell response. In prospective and retrospective evaluations, significant correlations were found between the IFN-γ response to a given antigen and the iTEM scores for individual subjects <xref rid="CIT0042" ref-type="bibr">[42]</xref>. In addition, correlations between the iTEM score and patient HLA have been observed for antibody titers <xref rid="CIT0040" ref-type="bibr">[40,81,82]</xref>, reflecting the importance of HLA-restricted T-cell responses to the genesis of a robust antidrug antibody response.</p></sec><sec id="S0015"><title>Iterative combinatorial analysis for vaccine design</title><p>A number of methods for enhancing epitope-based vaccines have been described and implemented <xref rid="CIT0083" ref-type="bibr">[83,84]</xref>. One approach is to align the individual epitopes in a protein or DNA vaccine construct as a ‘string of beads’ without any intervening sequences or spacers between the payload epitopes <xref rid="CIT0085" ref-type="bibr">[85]</xref>. However, the lack of spacers between the payload epitopes has raised concern that these sequences may contain junctional epitopes. VaccineCAD, an algorithm that iteratively analyzes epitope assemblies and minimizes the potential for junctional immunogenicity in any string-of-beads construct, has been developed to address this concern <xref rid="CIT0040" ref-type="bibr">[40]</xref>. Peptide sequences contained in the junctional regions between the target epitopes are evaluated for potential immunogenicity. The highest scoring junction is identified and the algorithm optimizes the order of epitopes by evaluating potential alternative sequences. The process is repeated until no additional reductions in junctional immunogenicity can be achieved or until all junctional immunogenic potential has been eliminated. When the potential for junctional immunogenicity cannot be sufficiently reduced, a cleavage promoting spacer sequence, typically ‘AAY’ for class I restricted constructs <xref rid="CIT0086" ref-type="bibr">[86]</xref> or a binding inhibiting ‘breaker’ sequence such as ‘GPGPG’ for class II restricted constructs <xref rid="CIT0087" ref-type="bibr">[87]</xref> is placed between the two offending epitopes. The ability to minimize junctional immunogenicity while simultaneously minimizing the presence of transmembrane domains or highly hydrophobic peptide segments which may be difficult to express would be a logical extension of this tool’s capabilities.</p></sec><sec id="S0016"><title>Successful vaccine design</title><p>The integration of computational tools for epitope discovery has enabled the development of genome-derived vaccines <xref rid="CIT0041" ref-type="bibr">[41,45,44]</xref>. Compared to conventional strategies, this approach has the potential to create more effective and safer next-generation vaccines, as carefully selected epitopes focus immune responses on the minimal, essential pathogen-specific antigenic elements; epitopes directed against conserved ‘self’ (host) antigens are eliminated. This approach is also well suited for highly variable pathogens, as selection of epitopes that are conserved across multiple strains or subtypes enables the development of a broadly applicable, multipathogen vaccine. The genome-derived vaccine strategy has been applied by our team to a wide range of pathogens, including <italic>F. tularensis</italic>, variola, HIV, Mtb, <italic>H. pylori</italic> and influenza. These studies demonstrate that immunoinformatic-predicted epitopes are immunoreactive in vaccinees and survivors of infection, and stimulate <italic>de novo</italic>, protective immune responses <italic>in vivo</italic> in HLA transgenic mice (e.g., <xref rid="CIT0024" ref-type="bibr">[24–26,51]</xref>).</p><p>Epitope-driven vaccines offer distinct advantages over traditional subunit vaccines. Multiple epitopes derived from several antigens can be packaged together. Thus, a broad-based immune response directed against several different antigenic proteins can be elicited without manufacturing and administering the entire protein, much of which will be immunologically irrelevant. This may reduce formulation challenges, cost and safety risk. The use of epitopes also mitigates safety concerns arising from the use of intact recombinant proteins that may have undesired biological activity.</p><p>This review of vaccine design tools developed by the EpiVax team is by no means comprehensive, and has mainly focused on antigen selection and design. Topics not covered in this review include formulation of epitope-driven vaccines, route of delivery (mucosal, intradermal, etc.), adjuvanting, selection of delivery vehicles and preclinical and clinical testing. A major caveat concerning the use of the iVAX toolkit is that none of the vaccines designed using these tools have advanced to the clinic. Given the cycle of vaccine development, this is not surprising (it may take up to 20 years to develop a vaccine with full industry support). Retrospective and prospective studies have provided extensive validation of the tools described here <xref rid="CIT0010" ref-type="bibr">[10,28,46,47]</xref>. Nonetheless, algorithms developed and applied by this group to a wide range of pathogens have met with significant preclinical success and are currently in use for the development of vaccines against NTD parasites, EID viruses and bioterror pathogens.</p><p>Access to nearly all of the tools described in this article is freely available to trained users through the iVAX toolkit <xref rid="CIT0088" ref-type="bibr">[88]</xref>. The website was developed with funding from the National Institutes of Health in 2010. Access to the iVAX toolkit and training on the tools is available for interested researchers under collaborative agreements with the University of Rhode Island (primarily for NTD, but other arrangements are possible). Commercial users are directed to EpiVax <xref rid="CIT0089" ref-type="bibr">[89]</xref>, which provides a secure-access version of the iVAX website for commercial users.</p></sec></sec><sec id="S0017"><title>Expert commentary</title><p>In general, the field of vaccine research has been slow to adopt new vaccine design tools, and even fewer NTD researchers are familiar with the use of the tools, despite proof of principle for the genome-derived vaccine approach and the fact that it significantly reduces time and effort to make vaccines. For EID, ‘tried and true’ approaches often win out over newer approaches, even though traditional approaches have no greater likelihood of success. The MERS-CoV vaccine development programs that have been implemented illustrate this principle; despite the fact that the virus belongs to a family of coronaviruses that have a history of rapid evolution, vaccination approaches are once again targeting the ‘spike’ protein. A similar approach was used during the emergence of SARS and completely failed to protect against rapidly evolving SARS viruses in animal challenge models <xref rid="CIT0009" ref-type="bibr">[9,90]</xref>. Application of advanced immunoinformatics tools to NTD vaccines has also lagged for a number of reasons. NTD researchers do not use the tools because they lack access to and familiarity with them, and there are no widely publicized examples focusing on diseases that impact the developing world.</p><p>A series of technical challenges for NTD vaccines have been described recently, including antigen discovery, process development, preclinical development, clinical trials in resource-poor settings and the immune response to NTD infection, including what is commonly referred to as the IgE trap, through which certain individuals, perhaps especially those in endemic regions, may have elevated preexisting IgE antibodies for potential NTD vaccine antigens, leading to increased risk with vaccination <xref rid="CIT0091" ref-type="bibr">[91]</xref>. Computational vaccinology cannot currently address all of these challenges; however, the approach described here offers a unique opportunity to address certain hurdles early in the developmental process. Early in the pipeline, antigen discovery using T-cell epitope prediction and ranking, along with candidate epitope triage using cluster analysis and cross-reactivity prediction provide valuable leads. Selected peptide candidates can be screened <italic>ex vivo</italic> in order to verify the phenotype of the immune response prior to inclusion in a final vaccine product.</p><p>Finally, T-cell epitope-based strategies are exceptionally platform-flexible, adaptable to synthetic peptide formulations deliverable in saline, emulsion or microparticle, or encoding into plasmid vectors for DNA vaccination or recombinant protein production, thus allowing for novel distribution strategies necessary to reach the world’s poorest. This flexibility extends to the antigen discovery approach as well, in that many kinds of targets may be explored using immunoinformatics tools. A pertinent example for NTD and EID applies to vector-based targets. Vaccine components based upon the salivary proteins of arthropod vectors are already under investigation <xref rid="CIT0092" ref-type="bibr">[92]</xref>. However, vector salivary antigens also have known immunomodulatory properties allowing for extended host tolerance <xref rid="CIT0093" ref-type="bibr">[93,94]</xref>. The same discovery and evaluation strategy described for pathogenic antigens could be applied to such proteins, potentially providing a mechanism through which to stimulate robust immune response in the absence of the immunomodulatory properties of the complete salivary antigens.</p></sec><sec id="S0018"><title>Five-year view</title><p>The amount of data generated through new technologies, such as next-generation sequencing, continues to expand exponentially. By applying these technologies to the study of EID and NTD causative agents, and expanding our genomic knowledge of these organisms, the feasibility of using high-throughput, informatics-based tools for the identification of putative protein and peptide targets increases. Vaccine efficacy may also improve, as the selection of targets can be refined by comparing the antigen to other genome sequences, such as the human genome and the human microbiome. The <italic>in silico</italic>-based approach to vaccine design may also alleviate many of the funding-associated challenges common to traditional vaccine design, by reducing the number of assays that need to be performed to select vaccine targets. Reduced cost should allow for the reallocation of critical funding to the testing of <italic>in silico</italic>-predicted targets and constructs. And finally, improved safety, by eliminating human genome cross-conserved epitopes, may reduce unwanted adverse effects. Looking further into the future, we are confident that the evolution of the tools described here will eventually contribute to the development of personalized, on-demand vaccines <xref rid="CIT0095" ref-type="bibr">[95]</xref>.</p><p>Considering the importance of controlling infectious diseases to global economic stability, the integration of computational vaccinology tools and their application to the design of vaccines for NTD and EID is of paramount importance. Delay is no longer acceptable. Vaccine developers must implement computational vaccinology tools if they wish to contribute to improve world health in the 21st century.</p></sec></body><back><sec><title>Financial & competing interests disclosure</title><p>AS De Groot is CEO, CSO of EpiVax. FE Terry, L Moise and R Martin are employees of EpiVax. In addition, L Moise holds options in EpiVax and AS De Groot is a majority stockholder. This work was supported by National Institutes of Health Research Grant U19 AI082642. The American Recovery and Reinvestment Act funded the development of the iVAX website and the application of the suite of computational vaccinology tools to Neglected Tropical Diseases. The NIH/NIAID Filariasis Research Reagent Resource Center provided funding to M Torres, N Pilotte and SA Williams and provided all Brugia parasite materials. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.</p><p>No writing assistance was utilized in the production of this manuscript.</p></sec><sec id="S0019"><title></title><boxed-text position="float" orientation="portrait"><caption><title>Key issues</title></caption><list list-type="bullet"><list-item><p>New immunoinformatics tools have been developed that address critical problems in vaccine design.</p></list-item><list-item><p>These tools have been extensively validated in preclinical models.</p></list-item><list-item><p>The design of vaccines for neglected tropical diseases would benefit from expanded use of these tools.</p></list-item></list></boxed-text></sec><ref-list><title>References</title><p>Papers of special note have been highlighted as:</p><p>• of interest</p><p>•• of 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