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=====Introduction=====
=====Introduction=====
 
*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.


=====Methods=====
=====Methods=====
*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 <i>env</i> gene was sequenced using nested PCR from peripheral blood mononuclear cells (PBMC).
**The primers used were <i>Bam</i>HI and/or <i>Eco</i>RI.
**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 X<sub>0</sub> indicating genetic diversity/random mutation and Y<sub>0</sub> being the CD4 T cell count at the specific time point of that X<sub>0</sub>, and Y<sub>1</sub> indicating the CD4 T cell count the next year, Y<sub>0</sub> was placed in a category based on CD4 T cell count and compared with Y<sub>1</sub>, 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.
*Figure 3 showed the phylogenetic tree of subject 9 and displayed one mutation that branched into several strains.




Revision as of 23:57, 16 September 2014

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?

Questions

  • 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.

Bibliography

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

Terms

  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)

Outline

Introduction
  • 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.
Methods
  • 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.
  • Figure 3 showed the phylogenetic tree of subject 9 and displayed one mutation that branched into several strains.


Results
Discussion

Figure Explanations

Links

Nicole Anguiano
BIOL 368, Fall 2014

Assignment Links
Individual Journals
Class Journals
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