How Does Antiretroviral Therapy Affect HIV Mutation and Vice Versa?

1. How Does Antiretroviral Therapy Affect HIV Mutation and Vice Versa? Arlin Toro Devin Iimoto

3. Purpose • To use a case or scenario to motivate student learning about topics in biology and biochemistry courses

4. Courses For Which This is an Appropriate Module • Lower level biology courses such as Introduction to Biology or a Basic Cell Biology course • Upper level biology courses such as Biochemistry, Molecular Biology, Microbiology, or Virology

5. The Case • Undergraduate students who are shadowing a physician working with HIV infected patients • Physician decides to determine the amino acid and nucleotide sequences in HIV-1 protease and reverse transcriptase before proscribing medication

6. Database for Exercise • The HIV Reverse Transcriptase and Protease Sequence Database • Case study used from the database Cabana, Clotet, and Martinez. Emergence and genetic evolution of HIV-1 variants with mutations conferring resistance to multiple reverse transcriptase and protease inhibitors. J. Med. Virol. 59: 480-490 (1999).

7. Topics for Student Learning • DNA and protein: structure and function • Enzymes • Evolution • Bioinformatics • AIDS – HIV structure and replication and treatments

8. Learning Process • Students will be asked the following • What do you already know about antiretroviral therapy and HIV mutation? • What questions do you have? • What do you need to know to understand how antiretroviral therapy and HIV are linked together?

9. Background Information – All course levels • General AIDS information • HIV structure • HIV replication • Immune system function

10. Topics For Lower Division Courses • DNA replication • Types of mutations • Impacts of those mutations • Epidemiology • Amino acids nomenclature and structure • Dendogram interpretation • Biology workbench • Clustal W

11. Exercises for Lower Division Courses • Questions • Are the sequences different? • What kind of mutations did you think have occurred? • How many nucleotides or regions have changed? • In the regions where you can see changes, based on the most common nucleotide observed, which of the three sequences changes most? Do this is related to the time from which the sample was obtained? • What did you observe about the mutation rate in HIV in this patient? • From what you learn from your evolution class, what does this rate compare to the evolution rate for most of organisms?

12. How many amino acids would be different in each sequence? • Select a region where you can see a change. Compare the structure of the most frequent amino acid with the one that’s different. • Based on the side chains of the amino acids, did the substitution could lead to a different protein structure? Check on the other amino acids substitutions to address this question. • Do you think the mutations in the virus infecting patient a, are enough to enable virus resistance to the drug that targets the viral protease.

14. Topics for Upper Division Courses • Michaelis-Menten kinetics • Michaelis-Menten inhibitors • Enzyme Mechanism • HIV-1 protease structure and function

15. Bioinformatics Exercise for Upper Division Courses • Questions - Is there greater amino acid sequence variation in HIV-1 protease between patients or between time visits for an individual patient? - What amino acids are more likely to mutate, and what type of amino acids do they mutate to? What does this tell you about viral mutation and drug resistance?

16. - In which protein domains do these mutations cluster? What does that tell you about viral mutation and drug resistance? - What are some possible structural impacts of these mutations? - How much uncertainty is there in predicting protein secondary structure from primary structure?

17. 10 20 30 40 50 60 ....:....x....:....x....:....x....:....x....:....x....:....x PQITLWQRPLVTIRIGGQLKEALLDTGADDTVLEDMDLPGRWKPKMIGGIGRFIKVRQYD Cab-b_1990-05 CCCCCCCEEEEEEEEECCCCCCCCCCCCCCCHHHHCCCCCCCCCCCCCCCCCCCCEHHEC BPS CCEEEEHHCEEEEEECCCHHHHHHCCCCCCEEHHHHCCCCCCCCCHECCECEEEEEEHEC D_R CCCCCCCCCEEEEEECCCHHHHHHCCCCCHHHHHHCCCCCCCEEEEECCCCCCEEECCCC DSC CCCCCCCCCEEEEEECCHHHHHHHHCCCCCEEEECCCCCCCCCCCCEEECCEEEEECCCC GGR CHHEEHHCCEEEEEHCCCCHHHHHHHCCCCHHHHHHHCCCCCCCCCCCCCEEEEEECCCC GOR CCCEEECCCCCCEECCCCCHHHHHHCCCCCCCCCCCCCCCCCCCCCCCCCCCCHHHHCCC H_K CCCCCCCCCCHHHHHHHHHHCCCCCCHHHHHHHHHCCCCCCHHHHHHHHHHHHHHHHCCC K_S CCCCCCCCCEEEEEECCCHHHHHHCCCCCCCHHHHCCCCCCCCCCCCCCCCCCEEECCCC JOI 70 80 90 100 110 120 ....:....x....:....x....:....x....:....x....:....x....:....x QIPIEICGHKAIGTVLVGPTPINIIGRNLLTQIGCTLNF Cab-b_1990-05 CCCCCCCCCCCEEEEECEEEECCCEEEECCEEEEECCCC BPS CECEEEECCCHEEEEEECCCCEEEECECHEEEEEEEECC D_R CCEEEEECCCCCEEEEECCCCCEEECCCCCCCCCCCCCC DSC EECEEECCCCCCEEEECCCCCCEEECCEEEEEECCEEEC GGR CCCHEEECCCCCEEEEECCCCCEEEECEEEECEEEEECC GOR CCCCCCCCCCCCEEEECCCCCCCEECCCCCCCCCCCCCC H_K CHHHHHHHHHHHHHHHHHCCCCHHHHHHHHHHHHHCCCC K_S CCCCEECCCCCCEEEECCCCCCEEECCCCCECECCCCCC JOI

18. Future directions • EVOLVE • model the mutation rate • STELLA • Enzyme kinetics • HIV Prevalence rates • EPIDEMIOLOGY • Predictions on spread of HIV

19. Biology Workbench Programs • TACG • Six Frame • Traducción de la actividad para estudiantes y facultad hispano parlantes