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

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

3. 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

4. The Case • Undergraduate students 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 prescribing medication

5. 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).

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

7. 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?

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

9. 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

10. Exercises for Lower Division Courses • Questions • Are the sequences different? • What mutations occurred? • How many nucleotides or regions have changed? • In mutated regions of the protein/gene, which sequences changed most? • Which patient had the greatest number of mutations in the protein/nucleotide sequences over time? What did you observe about the mutation rate in the patient? • From what you have learned from your evolution class, how does this evolution rate compare to most organisms?

11. How many amino acids are different in each sequence? • Select a region where you can see a change. Compare the structure of the most frequently mutated amino acid before and after mutation. • Based on the side chains of the amino acids, could the substitution 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 viral resistance to the drug that targets the viral protease.

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

14. 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?

15. - 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?

16. 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

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

18. Biology Workbench Programs • TACG • Six Frame • Traducción de la actividad para estudiantes y facultad de habla hispana