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					                                                                                                                         Summary of presentation
       The use of HIV-1 gene sequence data to detect ARV
        drug resistance mutations and RegaDB database.


                                   Tulio de Oliveira                                                         1) HIV-1 Resistance algorithms introduction
                 Africa Centre for Health and Population Studies,                                               and practical usage in clinical setting.
            Nelson R Mandela School of Medicine, UKZN, South Africa.

                                                                                                             2) RegaDB - HIV Data Management and
                                 FIRST and SATuRN collaborator
                                                                                                                Analysis Environment.

                                                                                                             3) Resistance Cases (x3).
                                       www.bioafrica.net
                                      www.africacentre.com




   1) Gene sequencing in the clinic: HIV resistance
                                                                                                       Gene sequencing in the clinic: HIV resistance testing
                      testing
                                                                                                           Many ‘resistance mutations’ have been defined for HIV - these are changes in viral
                                                                                                           protein sequence that are associated with decreased susceptibility to specific drugs
                                      2. Population sequencing of          3. Enter mutations into
1. Monitor viral load and CD4            PR and RT genes of the          algorithm to select optimal
                                         dominant HIV strain in            new treatment regimen         Mutations in the reverse transcriptase gene associated with resistance to nucleoside and nucleotide reverse transcriptase inhibitors
                                                 plasma




- rise in viral load indicates        - compare to reference sequence    - change regimen based on
resistance may be emerging            to identify resistance mutations   algorithm results




                                                                                                       IAS-USA 2008




                                                                                                                      Drug Resistance Interpretation Algorithms:


                                                                                                             Public algorithms:
                                                                                                             • ANRS - French AIDS Research Agency
                                                                                                             • HIVDB - Stanford HIV database
                                                                                                             • REGA - REGA Institute

                                                                                                             Private algorithms:
                                                                                                             HIVSeq, TrueGene, Therapy Edge and others.




                                                                                                                                                                                                                                                1
                                                                                                 Genotyping interpretation
                     Matrix “score” for mutations




                                                                            Stanford HIVdb drug interpretation algorithm.




REGAdb Interpretation Algorithms (ANRS, HIVDB, REGA)
                                                                           2) RegaDB

                                                                               – HIV Data and Analysis Management environment
                                                                               – To be used by
                                                                                      • Clinicians
                                                                                      • Researchers


                                                                           • Two main usage scenarios:
                                                                               – On a collaboration server (e.g. SATuRN and IRD-France).
                                                                               – Easy access with Web browser
                                                                               – Runs locally
                                                                                      • On a server in your institute
                                                                                      • On your laptop




                                                                                                     Developed by: REGA Institute, Leuven and Mybiodata




     RegaDB Objective                                                                                Data Entities
 •   A free and open-source database system
                                                                                                                       Event

 •   Facilitate research collaborations
      – Easily interchange data, keeping track of data origin                              Therapy                                   Attribute
      – Create collaborative databases keeping track of dataset versions
      – Easily publish data sets to Stanford HIVDB                                                                    Patient

 •   Empower clinicians with analysis tools                                                Dataset                                 Viral Isolate
      – Patient history graph
      – Sequence analyses
                                                                                                                        Test
            • HIV typing and subtyping
            • Genotypic analyses for resistance interpretation
            • Third party analyses as “web services”
      – Resistance reports




                                                                                                                                                          2
                                   Attributes                                                                   Viral Isolates
     • Annotate Patient with information                                                                               Viral
                                                                                                                     Isolates
          – Adapts to “your (research) interest”
          – Typically clinical and epidemiological information                                                     Nucleotide
                                                                                                                   Sequence

     • Examples                                                                           A
                                                                                          N                        Amino Acid
          – Gender                                                                        A                        Sequence                 Information
                                                                                          L
                                                                                          Y                                                 automatically
          – HIV transmission risk group                                                   S          Mutation                   Insertion
                                                                                                                                            inferred
                                                                                          E
          – Most of information collected in Collaborative projects                       S
                                                                                                                     Protein

     • Attributes may be grouped (like “SATuRN or SPREAD”)‫‏‬




                      Analysis Services                                                          Synchronize auxiliary data
     • Built-in services                                                                 • Auto-update services for
          – REGA Subtype tool                                                                 – Drug definitions
          – Stanford ASI genotypic resistance algorithms                                      – New (versions of) resistance algorithms
              • ANRS, HIVDB, REGA                                                             – Test types / Tests
          – Resistance report generation                                                      – Attribute types / Attribute groups
              • Using your own template documents



     • Integrate with external analysis services
          – Using “web services”
          – e.g. connect to geno2pheno or Stanford




      Collaborative SATuRN version (currently 1.187 patients with genotype
                                   information).
Users:
•      Stellenbosh Univ. (Dr. Gert Van Zyl),
•      Pretoria Univ. (Prof. Sharon Cassol and Theresa Russouw),
•      UFS (Dr. Cloete van Vurren, Dewald Stein),
•      NICD (Prof. Lynn Morris),
•      Stanford-UZ (Prof. David Katzenstein),
•      AC-UKZN (Dr. Tulio de Oliveira).
•      MRC (Dr. Chris Seebregts)


Policy:
1.     No private patient information or public identification number is added to the
       database.
2.     Data is owned by the researchers.
3.     Database is password protected and datasets and dataset access is exclusive
       to the researcher. Researcher can give access to part of the data to its
       collaborators.
4.     After publication researchers cachoose to deposit the data in a collaborative
       area “public area” of the database where other participants of the network have
       access to the data.
5.     Publication of manuscripts from “public” area will include as authors up to two
       members of the group that deposited data.




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Questions?




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