Isai Lopez Individual Journal 3

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The purpose of this assignment is to access the online databases of HIV sequences and familiarize ourselves with the program and layout

Methods and Results

  1. Type in the following website into your browser
  2. Find the paper given in the instruction packet. To do this, type in the full text of the name of the article in quotation marks under the search box on the website and find the article under the "PubMed" section under the "Literature" header.
  3. At the main page for the Markham article, select the "nucleotide" tab under the "related information" section along the right side of the page.
  4. Select any of the entries for HIV sequences by ticking the box next to the name. Do this for 4-6 entries
    • The accession number of the sequence I chose was AF089136.1
    • The sequence came from subject 3 in the study. The sequence of letters and numbers after the "isolate" tells you information about who the HIV was collected from including subject number, the visit number, and the clone number of the HIV sequence
  5. Scroll to the bottom of the page and download the FASTA format of these sequences by clicking on the "send to" button and select FASTA as the sequence type.
  6. Now follow the link to the Biology Workbench
  7. Scroll to the bottom and select the "nucleic tools" button
  8. Select the "add new sequence" button and upload your saved FASTA sequences file
  9. Save the data to import it into your Workbench session
  10. Select any of the sequences you've imported and then select the view command
  11. Select all the boxes for your sequences and then select the "ClustalW" command. This will give you a comparison of genomic sequences between the strains you've selected including similarity, using both a score and an unrooted tree model




The purpose of the assignment was to become familiar with the types of information databases that exist and how to access nucleotide data of sequenced genomes, in this case for HIV strains. This data can be compared using programs readily available online.

Data and Files


Biological Terms

  1. Frequency-dependent selection: A term used to describe a genotype who's frequency depends on the frequency of the genotype in a population. (
  2. Seroconvergence: The amount of time following initial infection after which antibodies for a specific pathogen (in this case the HIV virus) become noticeable. (
  3. Nested PCR: A modification to the traditional PCR practice which uses that replaces one or both of the primers used in PCR to amplify sections of the DNA more accurately when performing PCR (
  4. Jukes-Cantor correction: A method for accounting for time in calculating the evolutionary distance between two homologous pieces of DNA (
  5. Rapid Progessor: Term given to the approximately 1% HIV patients who's condition turns into AIDS merely 2-3 years following initial infection for yet unknown reasons. (
  6. Synonymous mutation: A change in an amino acid coding sequence that doesn't change the resulting amino acid. (
  7. PBMC: Peripheral Blood Mononuclear cell. Term used to describe any blood cell with a round nucleus. These cells include macrophages, monocytes, and lymphocytes and play an important role in the immune response. (
  8. Epitope: The component of an antigen that reacts with and binds to a receptor and initiates the immune response (
  9. Escape variant: A variant of a strain of nucleic acid that allows the organism to escape detection by the body's immune response. (
  10. Env region: The region of the HIV genome that codes for specific membrane proteins in the membrane of the HIV virus (



  • HIV’s ability to quickly mutate and adapt to a changing environment is one of its greatest threats as a pathogen.
  • The method by which HIV is able to “outsmart” host defenses is not well understood, however several mechanisms have been proposed by which the body targets only the most frequently appearing strain of the virus, allowing the smaller untargeted strains to continue adapting, eventually overcoming the body’s ability to target and kill the strain.
  • Studying changes in virus sequence over time can give information as to how HIV is able to adapt and how efficiency changes with the dynamic environment of the human immune system.
  • The importance of the work presented in this paper is difficult to overstate, as it gives the reader, and therefore the greater scientific community, an insight into the speed and diversity of mutation in the genome of HIV. Furthermore, what the research tells us is that the virus’ capability to diverge further from the original strain directly correlates to its ability to cause a drop in CD4 T cells in the individual. The quicker a strain of HIV is able to mutate and adapt to a host environment, the quicker the disease progresses.
  • This specific experiment was done to address a couple shortcomings that the team felt previous research had failed to address. Specifically, the number of individuals in previous work with HIV strains was too low to produce results that are widely acceptable as a general rule. Also, research on the evolution of strains of HIV-1 weren’t done by methods that analyzed the genomic sequence of the virus. Lastly, the paper expresses that subjects in previous studies were examined too infrequently to produce a coherent timeline of the evolution of HIV-1 in their body.
  • To overcome these shortcomings in previous studies, firstly, the number of subjects was addressed by choosing a sample size of 15 individuals, with different rates of progression of the virus. Secondly, in addressing the issue of lack of sequencing, the team used a technique known as nested PCR to amplify specific sequences of HIV genome to better examine differences in RNA. Lastly, to address the problematic infrequency in sequence analysis, the subject in this study were studied regularly (every six months) over a period of 4 years.
  • The main result presented in the paper is that greater declines in CD4 T cells are correlated with a greater and further degree of mutation from the original genome that allows the HIV virus to adapt more efficiently to the immune system of the infected host.


  • 15 subject were chosen from a pool of HIV infected people and studied at 6 month intervals following initial detection of the virus.
  • Three groups were chosen that represented rates of decline of the CD4 T cells.
  • A technique known as nested PCR was used to amplify a specific sequence of a certain gene (env) from blood cells.
  • Reverse transcription-PCR was used to find the plasma viral load.
  • Phylogenetic trees were built with the MEGA computer program
  • Differences in taxa correspond to different colors on the phylogenetic tree
  • The correlation between genetic diversity and the amount of CD4 cells was calculated.
  • A ratio between the number of synonymous and non-synonymous mutations in each strain was calculated, using the Jukes-Cantor correction.
  • A regression line was built comparing the divergence and diversity of the HIV strain over the period of the study for each subject, then each line was compared.

Results/ Figures

  • Fig 1: The graph shows the general trends for CD4 T cell counts, as well as diversity and divergence for each of the 15 subject of the study. The sharpest drop in CD4 cells can be seen in the rapid progressors, as is the increase in diversity and divergence from the consensus sequence.
  • Table 1: This table shows a lot of information, but the general takeaways are that median dS/dN ratios are closest to 0 for subjects that are rapid progressors, and tend to increase with the decrease in progression of the virus. Changes in diversity and divergence are also noted here, and the general trend is that the change over time is positively correlated with the speed of viral progression across most of the subjects
  • Fig. 2: For part A of this figure, change in diversity over a year is measured. The lowest change in diversity is seen in the non-progressors, while the highest is seen in the rapid progressors. Part B shows a similar trend, where the % of nucleotides that mutate/ year from the original seroconversion virus increases with speed of progression of the virus.
  • Fig. 3: The phylogenetic tree here is only built for subject 9 and shows the divergence of the HIV strain in his/ her body. The tree shows that some strains of the virus tend to change little from the initial strain, while others change, then return to something more similar than the first strain. This pattern is observed in most of the subjects for this experiment.
  • Fig. 4: Phylogenetic trees were constructed for 4 more subjects in the study and show a similar trend. Evolution over time does not produce a strain that directly follows a single branch, but is often interrupted as it returns to a genome that more closely resembles an earlier strain.


  • Interestingly, an earlier study by McDonald et al. showed that viral diversity in intervisit studies was actually observed as lower for the rapid progressor group that for the non progressor, although, like this study, a higher degree of genetic divergence could be seen in the rapid progressor strains compared to non-progressor. Another study by Wolinsky et al. found a lower degree of genetic diversity in some of the subjects with quickly progressing HIV compared to non-progressors.
  • A study done by Nowak seems to have produced a model that parallels the findings of this study, linking increased diversity and divergence with a more rapid decline in CD4.
  • This study is limited in size, and although they claimed that a shortcoming of previous studies was cohort size, I think an even larger sample size would improve the study. Further, the study is only taking subjects infected by HIV via intravenous injection, limiting their pool of subjects.
  • Further work could include studying other sequences of the viral genome, as opposed to just the env gene. Also, it would be interesting to see if there is anyway to “predict” the general direction of mutation of the HIV strain, to see if there is a way to intercept and engineer antibodies to destroy the virus.


  • I received help form my homework partner Zachary T Goldstein for the completion of this journal. We met once outside of class to talk about certain questions and expectations as well as reviewing the figure for presentation
  • Dr. Dahlquist helped in class with answering questions as to the formatting of my answers
  • While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source
  • Isai Lopez 02:10, 20 September 2016 (EDT):


  • In class homework instructions found here Week 3
  • Markham paper: Markham, R. B., Wang, W.-C., Weisstein, A. E., Wang, Z., Munoz, A., Templeton, A., … Yu, X.-F. (1998). Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline. Proceedings of the National Academy of Sciences of the United States of America, 95(21), 12568–12573.

Markham Paper on HIV-1