BIOL368/F14:Nicole Anguiano Week 3

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Annotated Bibliography for HIV Evolution Project

HIV Questions

  1. What about HIV makes it so difficult to treat?
  2. What is the difference between HIV and AIDS? How are they similar, and how are they different?
  3. Do certain things increase the likelihood of HIV developing into AIDS?


  • Using a keyword search answer the following:
    • What original keyword(s) did you use? How many results did you get?
      • In PubMed, I used the keyword search "env and evolution". This returned 2,090 results. Using the same keyword search "env and evolution" in Google Scholar returned 120,000 results. The same keyword search in Web of Science returned 71 results.
    • Which terms in which combinations were most useful to narrow down the search? How many results did you get after narrowing the search?
      • In PubMed, using the adding the terms "and markham rb" to the search for a full search term of "env and evolution and markham rb" proved to be the most effective. It narrowed the number of results from 2,090 to 4. Adding "and markham rb" to Google Scholar narrowed the number of results to 10,000, which was useful but not as effective as I would like. Adding "and hiv and 1998" for a final search of "env and evolution and markham rb and hiv and 1998" narrowed the results to 1,730, indicating that an advanced search would be required for a specific search using Google Scholar. Narrowing the search to "env and evolution and hiv" in Web of Science actually increased the number of results to 566, but removing the "and hiv" adding in "markham rb" as the author narrowed the number of results to 3. From this, it can be concluded that the most effective keyword search is ""env and evolution and markham rb" with markham rb being denoted as the author in Web of Science is the most effective keyword search.
  • Use the advanced search functions for each of these three databases/tools and answer the following:
    • Which advanced search functions were most useful to narrow down the search? How many results did you get?
      • In PubMed, the most useful advanced search functions were "Title/Abstract", "Author" and "Date - Publication". Filling out those fields as "env", "Markham RB", and "1998" respectively returned 3 results. In Google Scholar, the fields "Find Articles with all of the words", "Return articles authored by", and "Return articles dated between" were the most useful. Filling them out with "hiv and env", "rb markham", and "1998" returned 4 results, much more efficient than the previous 1,730 that had been found by the keyword search. The Web of Science advanced search used tags to search. The TS (topic), AU (author) and PY (publication year) tags proved most effective. Performing the search "TS=(hiv AND env) AND AU=(Markham RB) AND PY=1998" returned 1 single result.
  • Perform a prospective search on the Markham et al. (1998) article and answer the following:
    • How many articles does the Markham et al. (1998) article cite?
      • The Markham et al. (1998) article cited 51 articles.
    • How many articles cite the Markham et al. (1998) article?
      • Currently, 70 articles cite the Markham et al. (1998) article.


Primary Research Articles
  1. Archary, D., Gordon, M. L., Green, T. N., Coovadia, H. M., Goulder, P. J., & Ndung'u, T. (2010 Nov 4). HIV-1 subtype C envelope characteristics associated with divergent rates of chronic disease progression. Retrovirology, 7. doi:10.1186/1742-4690-7-92
  2. Neher R. A. & Leitner T. (2010 Jan 29). Recombination Rate and Selection Strength in HIV Intra-patient Evolution. PLoS Comput Biol, 6. doi:10.1371/journal.pcbi.1000660
  3. Bunnik E. M., Euler Z., Welkers M. R., Boeser-Nunnink B. D., Grijsen M. L., Prins J. M., & Schuitemaker H. (2010 Aug 29). Adaptation of HIV-1 envelope gp120 to humoral immunity at a population level. Nature Medicine, 16, 995–997. doi:10.1038/nm.2203
Review Article

Garg H., Mohl J., & Joshi A. (2012 Nov 9). HIV-1 induced bystander apoptosis. Viruses, 4, 3020-3043. doi:10.3390/v4113020

Preparation for Week 4 Journal Club


  1. Seroconversion - "The change of a serologic test from negative to positive, indicating the development of antibodies in response to infection or immunisation" (Biology Online)
  2. Nonprogressor - "An HIV-infected individual who remains symptom-free over the long term and does not progress to develop AIDS" (Merrian-Webster)
  3. Recombinant - "A cell or an individual with a new combination of genes not found together in either parent, usually applied to linked genes." (Biology Online)
  4. Coreceptors - "A cell surface receptor, which, when bound to its respective ligand, modulates antigen receptor binding or affects cellular activation after antigen-receptor interactions." (MediLexicon)
  5. Chemostat - "A device for keeping a bacterial population growing at a reduced rate over an indefinite period of time" (Novick, A. and Szilard, L., Science Magazine)
  6. Hypervariable (region) - "The regions of the immunoglobulin molecule that contain most of the residues involved in the antibody binding Site." (Biology Online)
  7. Epidemiological - "Relating to or involving epidemiology. " (Biology Online) Epidemiology - "The study of the distribution and determinants of health-related states and events in populations and the control of health problems, the study of epidemic disease." (Biology Online)
  8. Heterogeneity - "The condition or state of being different in kind or nature." (Biology Online)
  9. Primer - "Short pre-existing polynucleotide chain towhich new deoxyribonucleotides can be added by dNA polymerase." (Biology Online)
  10. Epitope - "That part of an antigenic molecule to which the t-cell receptor responds, a site on a large molecule against which an antibody will be produced and to which it will bind." (Biology Online)


  • Due to HIV's rapidly mutating nature, it is easily capable of adapting to changes in its host environment.
    • Stable hosts will have a relatively similar spread of HIV strains.
    • Unstable hosts, usually caused by "differential display of co-receptors," will show a variety of different HIV strains due to the selection of different strains in the unstable environment.
      • Examining this diversity can give a window into the selection pressures that cause HIV to evolve.
  • Previous studies either did not directly examine sequence patterns or were performed over a very short period of time with a very small number of time points.
    • These limitations resulted in the performing of this study, as potentially new insights could be gained by remedying these limitations.
  • This study used 15 HIV infected patients for up to four years.
    • It determined that there is a difference in selection pressures between those who have an active, progressing infection (unstable hosts) and those who had an extremely mild infection (stable hosts), and found that those who had the greatest diversity in HIV strains had much higher levels of CD4 T cell decline.
  • The 15 participants were IV drug users that had blood drawn and tested every 6 months. The participants varied from those who had rapid progression to those who were nonprogressors.
    • Nonprogressors had CD4 T cell levels above 650 throughout the study, moderate progressors had CD4 T cells levels that fell to 200-650 throughout the study, and rapid progressors had fewer than 200 CD4 T cell levels by the end of the study.
  • The env gene was sequenced using nested PCR from peripheral blood mononuclear cells (PBMC).
    • The primers used were BamHI and/or EcoRI.
    • The PCR was run for 35 cycles. The resulting DNA clones were converted into pUC19 and sequenced by the Sanger chain termination method.
  • Reverse transcription–PCR was used to find the viral load.
  • Phylogenetic trees were created using MEGA program using the neighbor-joining algorithm combined with the Tamura-Nei distance measure.
  • Taxa were labeled with the time of isolation and the number of identical strains, with colors being used to indicate what time point is being used.
    • 76 time points were taken from the 15 patients.
  • The relationship between genetic diversity and random mutation was determined using the CD4 T cell count.
    • With X0 indicating genetic diversity/random mutation and Y0 being the CD4 T cell count at the specific time point of that X0, and Y1 indicating the CD4 T cell count the next year, Y0 was placed in a category based on CD4 T cell count and compared with Y1, which was organized the same way. The relationship between them and the corresponding X's indicate the relationship between genetic diversity and random mutation.
  • Figure 1 showed the CD4 T call number, diversity, and divergence. Rapid progressors had largely variable diversity and divergence, and a rapid decline in CD4 T cell count.. Moderate progressors displayed an average amount of diversity and divergence, with a slow decline in CD4 T cell count. Nonprogressors had relatively stable diversity and divergence, with little to no drop in CD4 T cell count, or occasionally even an increase in CD4 T cell count.
  • Table 1 shows the nucleotide diversity, CD4 T cell count change, and nucleotide divergence after the first initial testing in the nonprogressors, moderate progressors, and rapid progressors. Rapid progressors have the highest level of CD4 T cell decline, but the slopes of divergence and nucleotide diversity were relatively spread out amongst all the groups.
  • Figure 2 compares the slope of the diversity and divergence of the various levels of progressor groups. The nonprogressors had the lowest slope for both, the moderate progressors had the middle slope, and the rapid progressors had the largest slope and standard deviation.
  • Each consensus sequence was compared to the other strains, and the differences between them were considered either synonymous or nonsynonymous.
    • The resulting values after performing the Jukes–Cantor correction, dS and dN, were averaged to remove bias due to variable sample sizes on each time point.
  • Two of the subjects had very high genetic variation initially, so they were tested to see whether they were infected with two separate HIV strains.
    • Phylogenetic trees revealed that this was not the case.
  • Patterns of CD4 T cell decline varied widely, with the worst of the rapid progressors having a loss of up to 593 cells per year to the nonprogressors having a gain of 53 cells a year.
  • The nonprogressor group had a lower viral load, but the differences in viral load were not significant between moderate and rapid progressors.
  • The clones of the envgene were compared at each time point using genetic diversity and divergence.
    • Genetic diversity varied between -2.94 to 5.10 nucleotides per clone per year, and divergence ranged from 0.13% to 2.09% of the nucleotides per clone per year.
  • The viruses from 13 of the 15 subjects were homogenous, and the remaining 2 were heterogeneous.
    • The reasoning behind the heterogeneous nature of the viruses of the remaining two patients is still unknown, but infection with two separate strains at once, development of recombinants, and mistimed seroconversion were ruled out.
  • Diversity and divergence uniformly increased over time among all three groups, though the increase was larger in the moderate progressors, and larger still in the rapid progressors.
    • The differences in increase in both diversity and divergence were significant between nonprogressors and progressors, as well as between moderate progressors and nonprogressors.
    • The differences in diversity and divergence between moderate progressors and rapid progressors was not significant.
  • Diversity and divergence both had a significant negative correlation with the levels of CD4 T cells.
    • The greater the diversity and divergence, the greater the CD4 T cell decline.
  • The ratio between the dS and dN of nonprogressors was 1.6, indicating that nonsynonymous mutations were selected against, suggesting that the viruses containing the changes in env protein structure had no selective advantage.
  • The difference between the dS/dN ratio was significantly different among the three groups.
    • dS was not significantly different between each group, making dN the deciding factor. Nonprogressors had a much larger dS than dN, whereas the moderate and rapid progressors did not, showing a difference between the progressor groups and nonprogressors.
  • 10 of the 15 subjects had no one viral strain gain dominance throughout the visits.
  • Figure 3 showed the phylogenetic tree of subject 9 and displayed one mutation that branched into several strains.
    • Figure 4 showed the phylogenetic trees of subjects 5, 7, 8, and 14. Mutations occur in every subject, with no one subject displaying a single branch that gains dominance.
  • Higher levels of diversity and divergence were directly related to higher levels of CD4 T cell decline.
  • Nonsynonymous mutation rates were significantly higher in progressors than nonprogressors, but synonymous mutation rates were similar amongst all groups.
    • Nonprogressors selected against changes in the env gene sequence, while progressors selected for changes in the env gene sequence.
  • The findings went counter to two previous studies.
    • McDonald et al compared env gene variation among progressors and nonprogressors at two time points over 10 years.
      • Divergence was higher among rapid progressors, which agrees with Markham's study.
      • The diversity, however, was lower in the rapid progressors than in the slow progressors, with conclusions varying among the different sections of the env gene that are focused on.
      • This difference was likely due to the long time between time points and because it did not begin at the time of seroconversion.
    • Wolinsky et al studied six adults using similar methods to Markham's study.
      • The study found that two of the rapidly progressing adults had lower diversity.
      • One of the subjects of Markham's study also had lower diversity than the nonprogressors, but had a higher viral load.
      • It is possible that the two subjects from Wolinsky's study and the one subject from Markham's study may represent individuals who never developed a resistance to any of the viral strains, allowing for a single strain to become dominant.
  • The correlation between CD4 T cell decline and increased diversity and divergence were upheld by Nowack.
    • He hypothesized that the viruses developed epitopes that couldn't be contained by the host, causing the immune system to fail.
    • Increased level of diversity was common amongst those subjects that's infection developed into AIDS, and as the AIDS infection progressed, levels of diversity and divergence continued to increase.
  • The results of the study corresponded to the results of Sala et al, which showed that there was an independent evolution of the virus throughout the body.
  • The increase in genetic diversity may be due to the immune system focusing only on the most abundant strain of the virus, allowing other strains to diversify.
    • Nonprogressors may have a more comprehensive immune response that focuses on many strains instead of just one.

Figure Explanations

  • Figure 1
    • Figure 1 shows the CD4 T cell level (solid line with circles), diversity (dashed line with diamonds), and divergence (dashed line with squares) since the first time point for each subject.
      • The x axis shows the number of years. The left vertical axis shows the CD4 T cell count, with the solid lines showing the levels levels used to distinguish between progressor groups (above 650 for nonprogressors, between 650 and 200 for moderate progressors, and under 200 for rapid progressors). The right vertical axis displays the diversity and divergence values.
      • Rapid progressors have higher levels of diversity and divergence overall (with the exception of subjects 3 and 10), with much lower CD4 T cell counts as time passes.
      • Moderate progressors show an average increase in diversity and divergence, but also show a steady CD4 T cell decline.
      • The three nonprogressors were not consistent in their diversity and divergence. Subject 2 had increased values of diversity and divergence, subject 12 showed consistent levels of diversity and divergence, and subject 13 showed decreased levels of diversity and divergence. They overall had relatively consistent (and in some cases increasing) levels of CD4 T cells.
      • Diversity was determined using the average number of nucleotides that differed between any two env gene clones from a visit.
      • Divergence was determined by the mean percentage of nucleotides per clone at any visit that were different from the consensus env gene taken at the first visit.
  • Table 1
    • Column One: No. of Observations
      • This is the number of time points taken for each subject.
    • Column Two: CD4
      • This is the annual change in CD4 T cell count level, found by adding together the total differences per year.
    • Column Three: Median intravisit nucleotide differences among clones
      • This column shows the median level of diversity among the clones. This was found by taking the highest and lowest diversity values and finding their difference.
      • Diversity varies among the rapid progressors, moderate progressors, and nonprogressors.
    • Column Four:Virus copy number (x103)
      • The virus copy number is the viral load for each subject, in thousands (62.2 = 62,200).
      • The viral load varies widely among the progressor groups, with some nonprogressors having higher viral loads than progressors.
      • The viral load was determined by reverse transcription PCR.
    • Column Five: Annual Rate of CD4 T Cell Decline
      • The annual rate of CD4 T cell level decline gives the average decline of the CD4 T cell levels between visits.
      • This was found by comparing the CD4 T cell levels between visits and taking the difference between them.
      • The final level of CD4 T cells determined a person's progression rate, with less than 200 being rapid progression, 200-650 being moderate progression, and over 650 being nonprogressing.
      • The nonprogressors consistently have an increase in CD4 T cell levels. Moderate progressors range from increase to decrease, with Subject 7 showing an incredibly high level of decrease in comparison to the rest of the moderate progressors (-392). Rapid progressors have the highest rates of decline.


Nicole Anguiano
BIOL 368, Fall 2014

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