Angela A. Garibaldi Week 5

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Background

Looking at annual rate of CD4 T cell decline of subjects 7,6 (moderate progressors), subject 10 (Rapid progressor), and Subject 13 (Non progressor).

  • We noticed that subject 7 has a similar rate of decline to the Rapid Progressor (10).
  • We noticed that subject 6 has a similar rate of decline to Non Progressor (13)
  • We want to compare the divergence and diversity with a focus on divergence of 7 and 10, 6 and 13 to see if the sequences of these subjects are more similar to one another in regards to viral divergence and diversity in comparison with 6 and 7 to compare two currently labeled Moderate progressors. If the assumption is that by categorizing subjects, subjects within those categories are similar enough in HIV progression to compare to other distinct groups of different levels of HIV progression, then their viral patterns of diversity and divergence should also be similar to solidify that the subjects are experiencing the same step in the life cycle of the virus. Basically, we are questioning the methods of categorizing subjects' progressor status.
  • We chose Subject 10 as the Rapid progressor to compare subject 7 instead of subject 11 because subject 11, according to the BedRock data table, has no sequences for the last 5 visits. Sequences at these time points are crucial to our analysis because it is during the later time points that the CD4 counts decrease the most drastically, creating the rate of CD4 T cell decline that we believe would best characterize a subject's progressor status.
    • For subject 7, we chose all distinct clones for visits 3,4 and 5 for a total of 26 sequences.
    • For subject 10, we chose all distinct clones for visits 4,5 and 6 for a total of 36 sequences.
  • For subjects 6 and 13, we chose the most recent visits that actually had clones sequenced.
    • For subject 6, we chose all distinct clones for visits 5,7, and 9 for a total of 27 sequences.
    • For subject 13, we chose all distinct clones for visits 3,4, and 6 for a total of 24 sequences.
  • Control subject will be subject 5, a moderate progressor, to compare 6 and 7 to because its CD4 decline rate is between that of subjects 6 and 7 and its viral copy number seems most comparable. We chose to use visits 3,4, and 5 for a total of 23 distinct clones.



Other Previously Published Work:

  1. Hill MD and Hernández W. Nucleotide and amino acid mutations in human immunodeficiency virus corresponding to CD4+ decline. Arch Virol. 2006 Jun;151(6):1149-58. DOI:10.1007/s00705-005-0693-8 | PubMed ID:16385396 | HubMed [Paper1]

Question

Reevaluating the standards of categorizing HIV progressors based on CD4 T cell decline rates by examining divergence and diversity in HIV-1 env sequences.

Hypothesis

We predict that subject 7 (moderate progressor), who has a similar CD4 T cell decline rate to subject 10( Rapid progressor), will have similar patterns of diversity and divergence based on sequence analysis. We also predict that subject 6 (Moderate progressor), with a similar decline in CD4 T cell decline rate to Subject 13 (Non progressor), will be similar in the same analyses. Lastly, we predict that Subjects 7 and 6, both Moderate Progressors, will be significantly more divergent from one another. Subject 7 will be less divergent and more closely related to Subject 10. Subject 6 will be less divergent and more closely related to Subject 13. Overall, we predict that the rate of decline of CD4 T cell counts will prove a better method of categorizing subjects into the most accurate HIV progressor status in that divergence and diversity will be most significantly similar in subjects with similar CD4 decline rates. Divergence and diversity will be more significantly different between subjects with more variant CD4 decline rates.

Procedure

1.Upload approximately 30 sequences from each subject from Visit xx. 2.Conduct Clustdist multiple sequence alignment between the following pairs and generate phylogenetic trees:

    • 7:10
    • 6:13
    • 7:6

3.Calculate S, Theta, and the Minimum and Maximum

4.Interpret phylogenetic trees and statistical data


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