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Re evaluating the Categorization
of HIV Progression in Subjects
Based on CD4 T cell Decline
Rates
Angela Garibaldi & Ryan Willhite
Loyola Marymount University
BIOL 398-01/S10
March 2, 2010
Outline
 Review of the Markham method of labeling compared
with CD4 T cell decline rate categorization of
progressors.
 Selection Process
 Prediction
 Statistical Approach
 Results
 Discussion/ Comparison to More Recent Studies
 References
Categorizing Progressors by CD4 T
cell Count
 Patterns of HIV-1 evolution in individuals with
differing rates if CD4 T cell decline
 Rapid Progressors
 Fewer than 200 CD4 T cells, within 2 years of
seroconversion
 Moderate Progressors
 CD4 T cell levels 200-650 during 4 year period
 Non-progressors
 CD4 T cell levels above 650
Selecting Subjects to Analyze
Selecting Subject Clones
 Selected the most recent visits that had sequenced
clones. (Many had 0 clones for last 3+ visits)
 Utilized only “Distinct Sequences”
What we predict…
 Subj. 6 (Moderate Test) and 13 (Non-Progressor)
will be less divergent and have less diversity than
when 6 is compared to another Moderate (5,7)
 Subj. 7 (Moderate Test) and 10 (RapidProgressor) will be less divergent and have less
diversity than when 7 is compared to another
Moderate (5,6)
 Subj. 6 and 7 will be more divergent and have
higher diversity in comparison to values
generated in the above.
Statistical Approach
 Utilized BedRock
 Conduct Clustdist multiple sequence
alignment for comparison and frequency
values used to :
 Calculate




''S''
''Theta” to measure Divergence
''Minimum'' and ''Maximum”
S/Number of clones to interpret Diversity
Results
Subject
6 vs 13
7 vs 10
6 vs 7
6 vs 5
7 vs 5
10 vs 5
13 vs 5
Number of
Clones
53
62
53
49
48
59
43
S
Theta
93
96
93
90
83
103
79
20.48
20.43
20.48
20.18
18.69
22.15
18.22
Min
difference
38
27
26
23
34
30
34
Max
difference
50
42
41
42
49
48
46
Range
12
15
15
19
15
12
12
Divergence
 Min. and Max. values show that 6 and 10 are most
divergent
 Considers Frequencies
Minimum and Maximum Divergence
100
90
80
70
60
50
40
30
20
10
0
6vs13
6vs5
6vs7
7vs10
7vs5
10vs5
13vs5
Divergence using Theta Values
Divergence Based on Theta Values
25
22.15
20.48
20.18
20.48
20.43
18.69
20
18.22
15
10
5
0
6vs13
6vs5
6vs7
7vs10
7vs5
10vs5
13vs5
Diversity shows a clearer picture
 Diversity similarities between (6,5) & (13,5)
Diversity Ratios (S/Clones)
1.9
1.85
1.8
1.75
1.7
1.65
1.6
1.55
1.5
1.45
1.4
6vs13
6vs5
6vs7
7vs10
7vs5
10vs5
13vs5
Revisiting the Results
 Divergence does not prove to be an accurate
method of categorizing
 Theta did not deliver insight
 Diversity levels are similar in certain
categories
Implications of using CD4 Tcell Decline
Rate to Categorize
 This method is
 Better than Markham’s method of categorization
 Especially in categorizing moderates from rapids
 Not as successful
 without a larger sample size
 Not much success in comparing all
 In the future
 Find a way to calculate the significance
 A larger sample size
 Use a program that would allow a comparison with higher number
of clones
 Few clones available from subjects may complicate the reliability.
 Focus on most recent visits and acquire clones for these visits
More Recent Study
 Nucleotide and amino acid mutations in human
immunodeficiency virus corresponding to
CD4+ decline
M. D. Hill and W. Hern´andez
Ponce School of Medicine, Ponce, Puerto Rico

Published online January 3, 2006 _c Springer-Verlag 2006
Comparing our findings to more
recent studies
 Change in diversity of nucleotide sequences
among HIV forms within individuals as their
CD4+ counts progressed
 There is a trend for the average distance to
increase with dropping CD4+ values
 Among all progressors, 94.1% of subjects
demonstrated increased diversity
 The rapid progressors had a statistically significant
higher loop charge
 Four of the rapid progressors had T-tropism
How Does this Compare?…
 Found that progression is easier to evaluate
than non-progression in terms of diversity
 The moderate and rapid progressor were
most divergent
 Therefore there is an accumulation of
differences over a period of time
 Perhaps there needs to be further
investigation in:
 RNA and DNA sequences
 A closer look at regions described in paper such
as loop charge
References
 Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A,
Templeton A, Margolick J, Vlahov D, Quinn T, Farzadegan H, and Yu
XF. Patterns of HIV-1 evolution in individuals with differing rates of
CD4 T cell decline. Proc Natl Acad Sci U S A 1998 Oct 13; 95(21)
12568-73. pmid:9770526.
 Hill MD and Hern�ndez 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
pmid:16385396. PubMed HubMed PubGet [Paper1]
 HIV project handout for statistical analysis info