Amino Acid Sequences in V3 Loop Conformation

1. Amino Acid Sequences in V3 Loop Conformation Alex Cardenas, Bobby Arnold and Zeb Russo Loyola Marymount University Department of Biology BIO 398 11/02/11

2. Outline • CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). • Differing CD4 T cell count is correlated with conservation in amino acid sequences. • Usage of Star Biochem to Answer our Question • BioWorkbench used to create multiple sequence alignments • Results obtained from multiple sequence were observed and analyzed. • Discussion and thoughts of our findings are shared.

3. Outline • CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). • Differing CD4 T cell count is correlated with conservation in amino acid sequences. • Usage of Star Biochem to Answer our Question • BioWorkbench used to create multiple sequence alignments • Results obtained from multiple sequence were observed and analyzed. • Discussion and thoughts of our findings are shared.

4. CD4 T cell count is an trait in developing AIDs ( >200 safe, <200 AIDs) • Observations led us to conclude that CD4 T cell counts were crucial in developing AIDs. • Rapid Progressors – 1, 3, 4, 10, 11, 15. • Controls • Moderate progressor – 6. • Non Progessor – 13.

5. Outline • CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). • Differing CD4 T cell count is correlated with conservation in amino acid sequences. • Usage of Star Biochem to Answer our Question • BioWorkbench used to create multiple sequence alignments • Results obtained from multiple sequence were observed and analyzed. • Discussion and thoughts of our findings are shared.

6. CD4 T cell count and relationship to V3 loop amino acid sequence • We were interested in the amino acid sequences of AIDs subjects. • Our question • Is there a conserved region in the V3 loop sequence that led the subject to develop AIDs?

7. Outline • CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). • Differing CD4 T cell count is correlated with conservation in amino acid sequences. • Usage of Star Biochem to Answer our Question • BioWorkbench used to create multiple sequence alignments • Results obtained from multiple sequence were observed and analyzed. • Discussion and thoughts of our findings are shared.

8. Usage of Star Biochem and to Answer our Question • Star Biochem was used to determine the location and structure of the V3 loop region • ProtParam was considered as a tool to use, however all the variable regions were determined to be coils so therefore was not necessary

9. gp120 Secondary Structure Using Star Biochem Kwong et. al V3 region

10. V3 region Secondary Structure Stanfield et. al 1F58 313-325 2F58 315-324 1NAK 312-323

11. Stanfield et. al 2ndary Structure not useful in our examination of Markham data • Stanfield data composed of 12-14 amino acid chains • Markham data composed of 94-95 amino acid chains

12. Outline • CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). • Differing CD4 T cell count is correlated with conservation in amino acid sequences. • Usage of Star Biochem to Answer our Question • BioWorkbench used to create multiple sequence alignments • Results obtained from multiple sequence were observed and analyzed. • Discussion and thoughts of our findings are shared.

13. BioWorkbench used to create multiple sequence alignments • Each rapid progressor subject had a clone randomly chosen from their first and last visit • Control subjects had one clone randomly selected from last visit. • Each rapid progressor had their two sequences aligned • Control subjects were aligned with each other • All rapid progressor last visits were aligned with and without a control sequence

14. ClustalW Multiple Sequence Alignment Results Single Subject Alignments Multiple Subject Alignments

15. Outline • CD4 T cell count is a trait in developing AIDs ( >200 safe, <200 AIDs). • Differing CD4 T cell count is correlated with conservation in amino acid sequences. • Usage of Star Biochem to Answer our Question • BioWorkbench used to create multiple sequence alignments • Results obtained from multiple sequence were observed and analyzed. • Discussion and thoughts of our findings are shared.

16. PubMed Research Article • “Identification and structural characterization of novel genetic elements in the HIV-1 V3 loop regulation coreceptor usage” by Svicher et al. • Studied the interaction between HIV-1gp120 and CCR5 N terminus

17. HIV-1 entry involves gp120, CD4 recepor and CCR5 or CXCR4 • V3 loop is determining factor in coreceptor usage • Composed of 3 regions • Base • Stem • Hairpin crown • V3 positions 11, 24 and 25 bind with different coreceptors but mechanism that differentiates is not know

19. V3 base interaction with CCR5 critical for infection • Increased affinity of V3 for CCR5 • Study population: 323 blood samples from 323 HIV-1 patients • V3 mutation was tested for along with wild-type residues to see different coreceptor usage • Structural analysis taken from Huang et al. CCR5 model with HIV-1 gp120

20. V3 sequence determines coreceptor usage • 19 V3 sequence positions that correlated to CCR5 of CXCR4 usage • Of 29 mutations, 6 are at the V3 positions of 11, 24 and 25 select for CCR5 • 23 other mutations at 15 V3 positions select for CXCR4 • Mutations for CCR5 not seen in viruses that select for CXCR4 and vice versa • Less that 1% of mutation in each case