KP Ramirez Week 6
Question: Are their differences in HIV-1 diversity or divergence between participants with high CD4 T cell variability within the study (between visits) as compared to participants with linear ‘progression’ (defined as CD4 T cell counts which fall rapidly, or linearly, over time (slope ~ -1)?
Prediction: We predict that participants with high variability in T cell count between visits will show a lower HIV-1 diversity and divergence than participants with linear progression. This is predicted under two assumptions
- (1) High diversity and divergence of HIV-1 variants indicates a more rapidly progressing virus (and thus a steadily falling CD4 T cell count)
- (2) high variability in T-Cell count will indicate a participant’s immune system was able to manage this virus better than a participant with a steadily falling CD4 counts. If we do see high diversity in participants with high variability in T cell count between visits, we predict that these will be predominantly synonymous mutations as opposed to non-synonymous mutations (which we would expect to see with linear progressors).
- Linear Progressors: (slope: -1) Subject: 4, 10
- High Variability between visits: Subject 12, 8
- (Low Variability between visits: 5
- Error fetching PMID 12803997:
Between subjects, subject ten is missing sequence data for visit three.
I began by inputting all of the data into the biology workbench. This included downloading the sequence data for all the visits of the subjects chose. Ran subject 4 S=59 Number of clones total 47
began working on subject 12, noted 5 visits total S=36 Number of clones 29
Began working on subject 8 noted 5 visits total S= 36 Number of clones 29
Began working on subject 5 noted 5 visits total S=54 Number of clones 29
Began working on subject 6 noted 5 visits total S=50 Number of clones 29
Following this step i complied the data into aligned sequences and preformed the clustdist tool to format the min and max differences. This involved individually assessing each matrix in excel to properly compute the min and max.
- Subject:4 Min:0.007 Max:0.074 Theta:12.86
- Subject:10 Min:0.004 Max:0.032 Theta:14.61
- Subject:12 Min:0.007 Max:0.056 Theta:9.17
- Subject:8 Min:0.004 Max:0.025 Theta:9.17
- Subject:5 Min:0.007 Max:0.056 Theta:13.75
- Subject:6 Min:0.007 Max:0.049 Theta:12.73
dS/dN was compiled by Janelle as well as θ value
- There are three stages to human immunodeficiency virus. The first stage termed seroconversion lasts for 7-8 weeks and is characterized by high viral titers within the body, the initiation of a host immun reaction to the virus, and conversation from testing negative to testing positive for antibodies to HIV. The second stage, termed latency can last for a variable period of years and is characterized by relatively consant viral titers within the body and by decrease in host CD4 Tcell counts in most individuals. The third stage is termed AIDS and the final collapse of the immune system, high HIV viral titers and a series of opportunistic infections due to the host's serverely suppressed immune system.
- This study focuses on the glycoprotein coat that affects HIV1 interaction with the hosts immune system and the target of host cell type preference. The 3rd variable domain on gp120 termed the V3 loop contains a single sight to which antibodies respond.
- In light of many important functions coded for by the V3 loop of the envelope gp120 gene there is evidence for selection in this gene region although the type and pattern of inferred selection has varied.
- Given the diversity of selective forces that can potentially operate on this gene this heterogeneity among studies is not surprising because many studies involved only a small number of infected subjects, the subjects chosen did not reflect the range of disease progressions observed in the overall HIV positive population and/or a limited number of time points were analyzed for each subject (Markham). 15 subjects followed from seroconversion at ~6 month intervals.
- Markham used analyses of synonymous and nonsynonymous differences and rates of divergence to reveal selective heterogeneity as a function of disease progression heterogeneity as a function of disease progression category.
- The purpose of this article of this article is to examine selection in the same 15 subjects as in the markham study but to investige a broader range of potential selective contexts. The contexts that we examine include overall disease progession categories; the CD4 and CD8 T-cell counts observed at each visit; evolution within NSI, SI and transitional viral forms; and interactions among these factors.
- Similar to markham (injection drug users)
6 month interval visits, blood drawn all acquired HIV through drugs except 2 sexually involved. early samples taken from 1989 last in 1993 Rapid progressors were defined as having attained a level of <200 CD4TCell within 2 years of seroconversion Moderate progressors had CD4T-cell counts levels decline to 200-650 during a period of observation and nonprogressors maintained CD4Tcell levels decline to 200-650 during the period of observation and non progressors maintained counts at >650 throughout the observation Subject seven was chosen because he was in the middle.
- The CD4 TCell brackets were consistent with Markham The CD8TCell brackets were (1) >1050 cells/μl (2) between 650 and 1050 cells and (3) <650 cells/ These catagories were chosen because they result in roughly equal numbers of observed mutations in all three catagories.
- Analysis of figures ...
Solid if within a visit, dashed if its at the tip intravisit , dotted if its between visit, the numbers placed ontop of it are the nucleotide positions that change. so between like 37 to 42 it shows the position that the nucleotides that changed. The bolded it if it was nonsynonymous. its not bolded if its synonymous of silent mutation.
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