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    <ns1:identifier>o:7145</ns1:identifier>
    <ns1:title language="sr">Selekcija specifičnih antigena virusa humane imunodeficijencije i virusa hepatitisa C za prognostičke i dijagnostičke ELISA testove korišćenjem bioinformatičkih metoda  </ns1:title>
    <ns2:subtitle language="sr">doktorska disertacija</ns2:subtitle>
    <ns2:alt_title language="en">Selection of specific antigens of human immunodeficiency virus and hepatitis C for prognostic and diagnostic elisa by using bioinformatics methods </ns2:alt_title>
    <ns1:language>sr</ns1:language>
    <ns1:description language="sr">Uvod
Postojanje specifičnih i osetljivih, a samim tim, i pouzdanih imunohemijsk
ih 
testova za detekciju brojnih virusa, između ostalog i virusa hepatitisa C (HCV) i 
virusa 
humane  imunodeficijencije  (HIV),  je  od  velikog  značaja  u  savremenoj  dijagnostici. 
Sličnosti velikog broja proteina virusa i humanih proteina je ozbiljan izazov pril
ikom 
odabira specifičnog antigena za ELISA test, zbog potencijalnog unakrsnog imunskog 
prepoznavanja. Danas se sistemskim računarskim analizama proteinskih sekvenci mogu 
utvrditi  karakteristične  sličnosti  i  razlike  na  nivou  celokupnih  virusnih  i  humanih 
pr
oteoma. Cilj ovog rada je bio da se iz naučne literature identifikuju sve sekvence 
humanih antigena koji su potencijalne mete urođenog
humoralnog imunskog odgovora i 
da se na osnovu integrisanih bioinformatičkih analiza definišu specifični antigeni za: 1
.
anti
-
HCV ELISA skrining testove i 2. ELISA testove kojima bi se određivala antitela 
koja su karakteristična za neprogresivnu HIV
-
1 infekciju. 
Metode
Studija  informacionih  karakteristika  proteinskih  sekvenci  virusnih  i  humanih 
antigena  je  izvršena  bioinfo
rmatičkim  programskim  paketom  zasnovanim  na  metodi 
informacionih spektara. Za upoređivanje sekvenci i ispitivanje homologije korišćeni su 
programi za lokalno i globalno poravnavanje sekvenci. Biološki klasteri u proteinskim 
skupovima identifikovani su prog
ramima kojima se analizira obogaćenje pojmovima iz 
baze podataka GENE ONTOLOGY. 
In house
testovima ispitana je specifičnost ELISA 
antigena  čija  je  sekvenca  definisana  na  osnovu  informacija  dobijenih  integrisanim 
bioinformatičkim analizama Rezultati 
Naprav
ljena  je  kompilacija  hu
manih 
antigena  koji  su  reaktivni  sa  prirodnim 
autoantitelima
i identifikovane su dominantne zajedničke informacione karakteristike do 
sada poznatih sopstvenih humanih antigena. Poklapanje ovih karakteristika sa virusnim 
antigenima je
preduslov nespecifičnog imunskog prepoznavanja. Ispitali smo koji HCV 
proteini imaju značajne informacione sličnosti sa humanim autoantigenima i pokazali da su na osnovu karakteristika proteinskih sekvenci HCV
antigeni NS4 i NS5 nespecifični, 
što je u sag
lasnosti sa zaključcima iz kliničkih studija. Primenom metode informacionih 
spektara  dizajniran  je  peptidni  antigen  za  ELISA  test  za  koji  je  eksperimentalno 
pokazano  da  vezuje  antitela  iz  seruma  HIV
-
1  pozitivnih  pacijenata,  a  koja  su  markeri 
neprogresivne 
HIV
-
1  infekcije.  Takođe,  prisustvo  ovih  antitela  pokazano  je  i  u 
serumima  novorođenih  beba,  što  ukazuje  na  činjenicu  da  su  ona  deo  urođenog 
humoralnog imunskog odgovora, što je u saglasnosti sa litreraturnim podacima.
Zaključak
Sistemske  analize  virusnog  i
humanog proteoma i primene bioinformatičkih 
metoda su od izuzetnog značaja za selekciju i dizajniranje antigena u cilju poboljšanja 
karakteristika ili pravljenja novih imunohemijskih testova.</ns1:description>
    <ns1:description language="en">Introduction An outbreak of specific and sensitive, and therefore reliable immunochemical tests for detecting numerous viruses, hepatitis C virus (HCV) and human immunodeficiency virus (HIV) included, is essential for modern diagnostics. Similarity of great number of virus proteins and human proteins is a serious challenge when selecting a specific antigen for ELISA test, due to potential cross-reactivity immune recognition. Nowadays, by using systematic computer-based analyses of protein sequences characteristic similarities and differences can be established on the level of overall virus and human proteomes. This paper is aimed at identifying all the sequences of human antigens from scientific literature which are potential targets of innate humoral immune response and at defining based on integrated bioinformatics analysis, specific antigens for: 1. anti-HCV ELISA screening tests and 2. ELISA tests to determine antibodies characteristic for non-progressive HIV-1 infection.
Methods The study of informational features of protein sequences of virus and human antigens was performed by bioinformatics program package based on information-spectrum method. Programs for local and global sequence aligning were used to compare sequences and examine homology. Biological clusters in protein sets were identified by programs of enrichment analysis of notions from GENE ONTOLOGY database. In house tests examined specificity of ELISA antigen whose sequence was defined based on information obtained by integrated bioinformatics analyses.
Results A compilation of human antigens reactive with natural auto antibodies was made and dominant common information characteristics of own human antigens were identified. Coincidence of these features with the virus antigens is a prerequisite for nonspecific immune recognition. We have examined which HCV proteins have significant informatics similarities with human autoantigens and showed that based on the features of protein sequences, HCV antigens NS4 and NS5 are nonspecific, which is in compliance with the clinical studies conclusions. By applying the information-
spectrum method, a peptide antigen was designed for ELISA test, experimentally proven to connect antibodies from the serum of HIV-1 positive patients, that are markers of non-progressive HIV-1 infection. Additionally, the presence of those antibodies was proven in the sera of the newborn, which indicates to the fact that they make part of the innate humoral immune response, in compliance with literature data.
Conclusion Systematic analyses of virus and human proteome and application of bioinformatics methods are crucial for antigens selection and design in order to improve characteristics or create new immunochemical tests.</ns1:description>
    <ns1:description language="sr">Medicinska biohemija / Medical biochemistry
Datum odbrane: 25.06.2013.</ns1:description>
    <ns1:keyword language="sr">ELISA, prirodna autoantitela, virus humane imunodeficijencije, virus hepatitisa C, bioinformatika</ns1:keyword>
    <ns1:keyword language="en">ELISA, Natural autoantibodies, Human immunodeficiency virus, Hepatitis C virus, Bioinformatics</ns1:keyword>
    <ns2:irdata>yes</ns2:irdata>
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      <ns2:identifier>44740879</ns2:identifier>
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    <ns2:peer_reviewed>no</ns2:peer_reviewed>
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      <ns1:ext_role>mentor</ns1:ext_role>
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        <ns3:firstname> Ana I. 1979-</ns3:firstname>
        <ns3:lastname>Đorđević-Vujičić</ns3:lastname>
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      <ns1:date>2013</ns1:date>
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        <ns3:firstname> Vesna, 1956- </ns3:firstname>
        <ns3:lastname>Spasojević-Kalimanovska</ns3:lastname>
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        <ns3:firstname> Nevena</ns3:firstname>
        <ns3:lastname>Veljković</ns3:lastname>
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      <ns1:date>2013</ns1:date>
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        <ns3:firstname> Zorica, 1965- </ns3:firstname>
        <ns3:lastname>Stojić-Vukanić</ns3:lastname>
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      <ns1:date>2013</ns1:date>
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    <ns1:location>http://phaidrabg.bg.ac.rs/o:7145</ns1:location>
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  <ns1:rights>
    <ns1:cost>no</ns1:cost>
    <ns1:copyright>yes</ns1:copyright>
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    <ns7:description language="sr">Medicinska biohemija / Medical biochemistry
Datum odbrane: 25.06.2013.</ns7:description>
    <ns7:keyword language="sr" seq="0">ELISA, prirodna autoantitela, virus humane imunodeficijencije, virus hepatitisa C, bioinformatika</ns7:keyword>
    <ns7:keyword language="en" seq="1">ELISA, Natural autoantibodies, Human immunodeficiency virus, Hepatitis C virus, Bioinformatics</ns7:keyword>
    <ns7:keyword language="sr" seq="2">577.27 : 616-072 (043.3)</ns7:keyword>
    <ns7:keyword language="sr" seq="3">HIV virus</ns7:keyword>
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