Matthew K. Oki Individual Journal 3

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Contents

Matthew K. Oki I.J. Week 3

Purpose

  • The purpose of this weeks assignment was to explore bioinformatics tools on the site: Biology Workbench
  • Also, we were meant to critically read a scientific paper and learn about the sequencing of HIV.

Methods & Results

  1. Activity 1/Part 2:
    • Look up paper on the NCBI website
    • Click on the "Nucleotide" Tab
      • This takes you to a list of all the HIV sequences found in this study.
    • Select 4-6 sequences and press download as FASTA
    • Each sequence has a list of information such as source organism, accession number, reference author, etc.

Workbench as discussed later

      • The sequence's accession number that I picked was AF016768.
      • The subject of each HIV sequence is noted in the definition section. For example, this subject was subject 1 of visit 1 - clone 9, otherwise written as S1V1-9.
  1. Activity 1/Part 3:
    • Create an account on Biology Workbench
    • Select the "Nucleic Acid" Tab
    • Select Add and press Run
    • Upload a file with the sequencing for 6 HIV sequences from the study above
    • Select checkbox of all sequences
    • Select ClustalW and press Run
    • Finally, select Submit
      • The images below were produced from the previous instructions:

My Trial Tree

Data & Files

Scientific Conclusion

  • Our main finding today was the large amount of sequences for HIV. There are many different sequences of HIV postulated in a relatively short amount of time, showing the quick mutation ability of HIV.
  • Our purpose was fulfilled by both learning about the sequences of HIV and practicing our skills at the bioinformatics tools.

Preparation for Week 4 Journal

Vocabulary

  1. seroconversion
  2. coreceptors
  3. heterogeneous
  4. monophyletic
  5. CD4 T cells
  6. synonymous mutation
  7. non-synonymous mutation
  8. nested primer
  9. chemostat
  10. epitope

Outline

Intro

  1. What is the importance or significance of this work?
    • The importance of this work was to obtain a better general understanding of HIV through its sequence. It also looked at the inverse relationship between CD4 T cells and the increase of HIV sequences.
  2. What were the limitations in previous studies that led them to perform this work?
    • This paper took up a few objections with previous studies.
      • The first of these is the sample size. Previous studies used much too small cohorts to study.
      • Next, some of the previous studies did not use any techniques that examined the sequence patterns directly.
      • Finally, some of the studies that did examine sequences did not take enough samples over a number of days.
  3. How did they overcome these limitations?
    • This study overcame these limitations with one large fix.
      • They increased the sample size of both the people studied and the number of sequences they observed.
  4. What is the main result presented in this paper? (Hint: look at the last sentence of the introduction and restate it in plain English.)
    • “This analysis demonstrates that different patterns of selection are observed between nonprogressor and moderately or rapidly progressing subjects and that, contrary to previous reports, the attainment of higher levels of genetic diversity is most frequently associated with more rapid CD4 T cell decline.” (Markham et al., 1998)
    • In plain English, this means that the sequences for each group (nonprogressor, moderate, and rapid progressor) were found to be different. Also, the higher genetic diversity (more HIV sequences) there was, the lower the CD4 T cell count was.

Methods

  • The Study Population
    • 15 participants from a group of injection drug users
      • Participants had a blood sample drawn every 6 months for the study
    • These 15 people were split into three categories: rapid progressors, moderate progressors, and nonprogressors
      • rapid progressors – those who had a CD4 T cell count of 200 or lower within 2 years of seroconversion
      • moderate progressors – CD4 T cell count of 200-650 during the 4 year observation period
      • nonprogressors – CD4 T cell count about 650 during the observation period
  • Sequencing of HIV-1 env genes
    • A nested PCR was used to amplify the 285-bp region of the env gene
    • The gene was obtained from an infected peripheral blood mononuclear cell
    • These sequences were replicated into pUC19
    • Finally, the Sanger method was used to sequence
  • Plasma Viral Load
    • Determined through reverse-transcription PCR
  • Generation of Phylogenetic Trees
    • Constructed with the MEGA computer package
      • Accounts for bias of base competition and transition/transversion
    • Each taxa was colored by time of observation
  • Correlation Analysis
    • The correlation between diversity and divergence at a given time point
  • Determination of dS/dN ratios
    • The initial consensus sequence was compared to the sequences of each observed strain.
  • Comparison of the Rate of Change of Divergence and Diversity
    • A regression line was fit for divergence and diversity
    • Diversity – the difference between any two clones of one visit
    • Divergence – difference between the original consensus sequence and any clone of any visit
  1. What were the methods used in the study?
    • There were a number of methods used in this study including a couple different PCR methods, creation of phylogenetic trees, general analysis of ratios and figures, and the sequencing of the genes.

Results

  • There was a varying amount of CD4 decline in each subject
  • The diversity and divergence both increased in all three progressor categories over time
  • The rapid progressors had a higher rate of diversity and divergence than the nonprogressing group
  • The rapid and moderate progressors did not have significantly different rate of divergence
  • The moderate progressors did have a significantly higher rate of divergence than nonprogressors
  • An increased diversity and divergence leads to a decrease in CD4 T cells
  1. Briefly state the result shown in each of the figures and tables.
    • Figure 1: Provides a side-by-side differentiation between diversity and divergence. The first subject was an outlier because of its skewed data points compared to all the other subjects.
    • Table 1: This gives an overview of all of the data pulled from the sequences. Important notes on the graph include the CD4 T cell count/decline rate which was used in the categorization of each progessor group.
    • Figure 2: Diversity is the difference between the sequences of clones in a single visit, while divergence is the difference between the sequence found in the first visit and any clone of any visit. Rapid progressors had the greatest upwards trend of diversity and divergence.
    • Figure 3: Subject #9 has a single mutation between S9V2-1 and S9V2-2 shown by the horizontal distance between the two.
    • Figure 4: Four more examples of the varying mutations of the sequences. Since none of the trees kept one single branch, it showed mutations throughout the visits.

Discussion

  • Ultimately, an increase in genetic diversity and divergence is directly correlated with a decrease in CD4 T cells.
  • The nonprogressors showed a possible immunity to amino acid change
    • This is significant because the body’s immune system can now actively try to fight HIV without its immense amount of mutations
  • There were two similar studies with conflicting data:
    • McDonald et al. found slow progressors had higher diversity than the rapid progressor
    • Wolinsky et al. found less diversity in rapid progressors and slow progress ors
  • This study supports a published HIV evolution model
    • Nowak modeled the same negative correlation between genetic diversity and CD4 T cell count
  1. How do the results of this study compare to the results of previous studies?
    • There were two conflicting studies.
    • McDonald et al. found slow progressors had higher diversity than the rapid progressor
    • Wolinsky et al. found less diversity in rapid progressors and slow progressors
  2. How do the results of this study support published HIV evolution models?
    • Nowak's model puts forth that the increased diversity will lead to an immune response failure (decrease in CD4 T cells).
  3. What are the limitations in this study?
    • The limitations of this study still include the sample size.
      • I still don't believe that 15 total subjects is enough to draw conclusions from, especially when those 15 subjects are split into 3 distinct groups.
  4. What future work do you suggest?
    • It would be interesting to look at subjects who got the disease from different ways. These subjects were all infected or at-risk injection drug users. Would the results be any different if we used a subject pool made of non-drug users.

Acknowledgements

  • I would like to thank my partner, Colin Wikholm, for assistance on this project.
  • I would also like to thank Kam D. Dahlquist, Ph.D. for providing the instructions and information for this assignment both in class and on this document: BIOL368/F16:Week 3.
  • Even though I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
  • Matthew K. Oki 01:58, 20 September 2016 (EDT):

References

  1. Biology Workbench
  2. BIOL368/F16:Week 3
  3. Markham, R.B., Wang, W.C., Weisstein, A.E., Wang, Z., Munoz, A., Templeton, A., Margolick, J., Vlahov, D., Quinn, T., Farzadegan, H., & Yu, X.F. (1998). Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline. Proc Natl Acad Sci U S A. 95, 12568-12573. doi: 10.1073/pnas.95.21.12568
  4. Exploring HIV Evolution

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