Jennymchua Week 3 Assignment

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The purpose of this article was to derive a pattern of HIV-1 evolution in seroconverting injection drug users with differing rates of CD4 T-cell decline to provide insights into the type and efficiency of selection forces that influence viral evolution and how the virus adapts to such forces.

Electronic lab notebook

10 biological terms

  • synonymous mutation
    • a nucleotide substitution that does not result in an amino acid substitution in the translation product due to redundancy of the genetic code (Cammack et al., 2008)
  • seroconversion
    • the stage in an immune response when antibodies to the infecting agent are first detected in the bloodstream; typically 4-6 weeks in patients infected with HIV (Martin and Hine, 2008)
  • plasma viral load
    • test that measures the amount (viral load) of genetic material (RNA) in the blood
  • dual infection
    • infection of more than one HIV strain
  • envelope protein
    • any protein (usually a glycoprotein) of the envelope of a virus for the purpose of avoiding the host immune system (Cammack et al., 2008)
  • peripheral blood mononuclear cells (PBMC)
    • the mononuclear cells of the blood (monocytes and lymphocytes) (Lackie, 2010)
  • Tamura-Nei distance measure
    • model that corrects for multiple hits and takes into account the differences in substitution rate between nucleotides and the inequality of nucleotide frequencies (Nei and Kumar, 2000)
  • Sanger chain termination method
    • a general method for deriving the primary sequence of a polypeptide chain based on selective hydrolytic degredation of the chain into msaler peptides (Cammack et al., 2008)
  • Wilcoxon signed-rank test
    • non-parametric test of the null hypothesis that the median is a specified value; based on ranking the observations by their distance above and below the median and comparing the total of the rankings above and below
  • genomic RNA
    • the genetic material of all viruses that do not use DNA as genetic material (King, Mulligan, and Stansfield, 2014)

Article outline

  • What is the importance or significance of this work?
    • HIV is a virus that affects an estimated 1.1 million people in the U.S. alone (, 2016) and unfortunately, there is no cure due to its high variability and adaptability to its host environment. T-cells are essential to an individual's immune system, and HIV attacks them, reducing their count greatly. this study attempts to shed some light on the strains that perhaps are best at mutating so researchers can target them in further treatments.
  • What were the limitations in previous studies that lead them to perform this work?
    • Limitations from previous studies that lead Markham et al. to perform this work include examining and using a small sample size, characterizing HIV-1 genetic evolution using techniques that did not involve direct examination of sequence patterns, or analyzing patients for only a limited amount of time.
  • How did they overcome these limitations?
    • The researchers in this article used a larger sample size of fifteen patients and collected and analyzed data over a span of four years to track the virus's mutations and progression.
  • What is the main result presented in this paper?
    • CD4 T cells and genetic mutations in the HIV virus are inversely related; as more mutations arise, the number of T cells decrease. This paper also articulates how individuals who were rapid or moderate progressors had different patterns of of mutation selection than those who were non-progressors.
  • What were the methods used in this paper?
    • Fifteen participants were selected and categorized into rapid progressors (fewer than 200 CD4 T cells within two years of seroconversion), moderate progressors (between 200-650 CDT 4 T cells during the four-year observation period), and nonprogressors (maintained greater than 650 CD4 T cells throughout the observation period).
    • Nested PCR was used to amplify a 285-bp region of the env gene from the blood cells of each patient.
    • This DNA was then cloned and sequenced using the Sanger chain termination method.
    • Limiting dilution single-round PCR was used to detect input viral DNA copies.
      • Five samples that had PCR product were next subjected to second-round PCR as they only showed one product at the lowest dilution. After this, they still produced more than 125 DNA copies.
    • Reverse transcription-PCR was used to determine plasma viral load.
    • Phylogenetic trees were made using neighbor-joining algorithm and the Tamura-Nei distance measure.
    • A correlation analysis was ran to determine if there was a correlation between genetic diversity and CD4 T cell counts in one year.
    • A Jukes-Cantor correction removed possible bias due to unequal sample sizes at different visits and were then classified as synonymous or nonsynonymous.
    • Substantial variation in two subjects was inspected using reexamination of their histories and phylogenetic tree analyses.
    • Regression lines of divergence/diversity over time and their slopes were combined and the averages for each of the groups according to diminishing CD4 T cell level progression were compared.
  • Briefly state the results shown in each of the figures and tables.
    • Figure 1
      • Shows CD4 T cell trajectory (circles), diversity (diamonds), and divergence (squares) over time since the first visits in each of the fifteen patients. The general pattern for rapid progressors and moderate progressors was that when diversity increases, T cell levels decrease. For non-progressors, T cell trajectory, diversity, and divergence generally followed the same pattern.
    • Table 1
      • Presents baseline data of each of the fifteen participants, who were categorized into their progression status. This information includes the number of observation visits; initial CD4 T cell levels; median intravisit nucleotide differences among clones; the virus copy number; and the annual rate of CD4 T cell decline.
      • The next four columns show the results of the calculations done to determine the slope of change in intravisit nucleotide differences per clone per year and the slope of divergence, as well as the median dS/dN ratios.
      • Rapid progressors showed a pattern of rapid T cell decrease after each visit, as well as a fairly rapid increase in diversity, especially for Patient 1.
      • Diversity and divergence generally showed an increasing trend in all three progression status groups.
      • Viruses from 13 out of the 15 patients were homogenous, while two subjects (9 and 15) had heterogenous viruses, which meant that they were likely dually infected.
    • Figure 2
      • Diversity and divergence slopes trended towards increasing in all three progressor groups.
      • Rapid progressors had the fastest changes for year, followed by moderate then non-progressors for both intravisit nucleotide differences per clone per year and percent nucleotides mutated from baseline consensus sequence per year.
    • Figure 3
      • No predominance of one single strain across a long period of time (pattern of limited progression).
    • Figure 4
      • Re-emphasizes pattern of limited progression in four other patients from original pool. Evolution is shown to not be sustained along a single branch.
  • How do the results of this study compare to the results of previous studies?
    • One previous study by McDonald et al. showed that the intravisit diversity in rapid progressors was less than that in slow progressors, which varies from the results presented in Markham et. al., which state the opposite. This, though, is probably because the patients in the McDonald study weren't followed from the time of seroconversion and had less laboratory visits.
    • Similar results were found in Wolinsky et al., as less viral genetic diversity was present in the most rapidly declining CD4 T cell level counts.
      • However, those individuals in the Wolinsky trial might have been exceptional.
    • The results in this study most similarly align with the results found in Nowak's study in that as genetic diversity and and divergence increased, CD4 T cells decreased.
  • How do the results of this study support published HIV evolution models?
    • The model that Novak presents discusses the relationship that the researchers in the Markham et al. study found were true; as genetic diversity and divergence increased, CD4 T cell levels decreased.
      • This diversity is a result of the host not being able to make antigens against the consantly mutating virus.
  • What are the important implications of this study?
    • The relationship of CD4 T cells and genetic diversity/divergence found in this study, as well as previous studies, can help scientists in the pharmacology field develop drugs that perhaps identify mutations in the cloning process and revert them back to the original sequence. Or, in the instance this is not possible, continue the process of creating protease inhibitors to prevent maturation of the virus so it halts in progression and reproduction.
    • In addition, another area of interest is how to somehow replenish CD4 T cells. I'm not well-versed on palliative care of HIV patients, I do know the importance of T cells and their role in the immune system, so perhaps the authors should focus their research on immune boosting drugs.
  • What future directions should the authors take?
    • Perhaps the authors can look at one specific subgroup of progresors and see if there is any pattern of where the mutations occur or what types of mutations randomly happen and if they have any effects on the body besides the destruction of CD4 T cells.
  • Give a critical evaluation of how well you think the authors supported their conclusions with the data they showed.
    • Luckily for the authors, their conclusions were fully supported by the data they found. Markham et al. used findings from other studies (Nowak et al. and Wolinsky et al.) to build upon their own study, even though nothing novel was discovered. I do think that bringing in the Novak et al. study, along with basing their study off of its model, helped to further strengthen their conclusions.
    • The argument that Markham et al. make is that there exists a relationship between CD4 T cells and genetic diversity in the HIV-1 virus, and their data showed an inverse relationship.


This paper was successful in its goal of finding a relationship between genetic diversity/divergence and CD4 T cells by studying HIV-1 virus mutation progression over a period of time in patients who have different rates of CD4 T cell decline. The relationship observed was inverse; as CD4 T cells decreased, it was also found that there was more genetic diversity/divergence in individuals with rapid and moderate CD4 T cell decline progression than non-progressors. This study has the potential to further research in understanding frequent mutations or why certain virus strains may have the tendency to mutate.


  • I worked with my homework partner Nick Yeo during class to discuss the assignment during the lab period.
  • I used the following online dictionaries to define vocabulary terms: Oxford Reference, Lab Tests Online, The Body Pro, and MegaSoftware.
  • I analyzed a paper by Markham et al. called "Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline."
  • I followed the protocol on the Week 3 wiki.
  • Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

Jennymchua (talk) 21:57, 3 February 2020 (PST)


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