Courtney L. Merriam Week 3

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Contents

Preparation for Week 4 Journal Club

Vocabulary

  1. Make a list of at least 10 biological terms
    1. Coreceptors- any chemokine receptor that when expressed with a CD4 receptor on a cell’s surface allow for HIV entry into the cell interior. The major coreceptors that do this are CXCR4 and CCR5. 2
    2. PBMC- stands for peripheral blood mononuclear cell. A PBMC is a peripheral blood cell that has a round nucleus. Examples of PBMCs are T cells, B cells, and NK cells (lymphocytes), and simple white blood cells (monocytes). PBMC are distinct from red blood cells (erythrocytes) and platelets, which have no nucleus. 3
    3. Sanger Method- a technique for DNA sequencing developed by Frederick Sanger in 1977 that incorporates chain-elongating inhibitors known as dideoxynucleotides, which help in the identification of individual nucleotides 4
    4. PCR- Stands for polymerase chain reaction. A method used to create thousands of copies of a particular DNA strand from a single initial sequence. 5
    5. Substitution model- a description of the way that a length of nucleotides changes into another 6
    6. Tamura-Nei Distance measure- a special model used to correct genetic data to account for base composition and transition/transversion bias. There are twice as many possibilities for transversions, but transitions occur more often. This method helps account for this disparity. 7
    7. Jukes-Cantor correction- another substitution model for DNA, considered one of the simplest in that it assumes both equal base frequencies and equal mutation rates, leaving only overall substitution rate as the variable. 8
    8. Monophyletic- refers to a group of organisms that have descended from a common ancestor, particularly a group that is distinct from any other. Think of a single branch on a phylogenetic tree. 9
    9. (long term) Nonprogressors- In reference to HIV research, a nonprogressor is an individual who has tested positive for the HIV virus but has been able to maintain a relatively high CD4 number (an excess of 500) without the aid of extraneous antiretroviral therapy over a length of several months or years 10
    10. Seroconversion- seroconversion refers to the length of time between inception of the HIV virus within an individual and when HIV antibodies become prevalent enough to be detected. Often times takes only a few weeks, and may also involve fevers and rashes.1
    11. Nonsynonymous mutations- When messenger RNA is making copies of DNA, either an insertion or a deletion occurs for a nucleotide. This single addition or deletion causes a shift on the entire side of the DNA, which shifts the nucleotide pairs and causes drastic differences in the codons. Obviously has a more substantial impact on the organism than a synonymous mutation. 11

Outline

Main Result

In observed individuals with progressing amounts of the HIV-1 virus, CD4 T cells declined in relation to how many mutations an individual strain of the HIV virus acquired after splitting from a previous strain, as well as the specific kinds of mutations that occurred that enabled increased fitness within the environment. Essentially, increased genetic diversity of the HIV-1 virus is linked with quicker decline of CD4 protein producing T cells.

Introduction

HIV-1 mutate and replicate rapidly, meaning they can adapt quickly to a changing host environment.

  • In an environment that changes very little, the strain of HIV-1 that is most adapted already would begin to dominate over less adapted strains, who wouldn’t be as well represented in the total gene pool
  • In an environment that changes a lot, it is difficult to determine what might happen to the various strains of the virus.
    • Environment around HIV-1 virus disrupted by immune response’s selective forces of a variety of coreceptors (component that when in conjunction with CD4 protein allow HIV-1 virus into cell interior).
    • If the selective force of the coreceptor’s presence respond randomly, the current dominant strain experiences reduction in numbers; minority strains see a less severe reduction in their numbers. Known as frequency dependent selection, as was so far untested until this experiment was conducted.
      • As these minority strains multiply in the absence of the previous majority strain, the diversity of the pool expands
      • This diversity leads to the immune system’s inability to respond to the diversity of remaining threats, as was proposed by Nowak in 1991.

Methods

15 people picked from a group of individuals who participated in injection drug use in a study in Maryland

  • The participants were sorted into three groups
    • Rapid Progressors- individuals measured 200 or less CD4 T cells within 2 years of seroconversion
    • Moderate Progressors- individuals having between 200 and 650 CD4 T cells during 4 years of observation
    • Nonprogressors- individuals who kept an excess of 650 CD4 T cells during the observed period
  • A 285 base pair region of the env gene (enables retroviruses to attach to host cells) multiplied using Polymerase chain reaction
    • Continued cycles of PCR were run at varying temperatures for varying times to determine viral multiplication and mutation
    • The viral clones produced by PCR were found to originate from an initial viral genome template
    • Plasma Viral Load determined by reverse transcription PCR
    • Phylogenetic tree of results show viral clones from the different participants diverged independently between one participant and another, subject 1 and 2 were known to be genetically related individuals
      • Each branch was colored differently depending on the time the data was collected
      • Correlation Analysis
      • Two variables analyzed to understand the relation of time point and genetic diversity
        • X0 (divergence of mutation)
        • Y1 (count of CD4 T cells)
  • dS/dN Ratios
    • Differences in strains were classified synonymous or non synonymous
    • Values of dS/dN averaged to account for unequal sample sizes from different sampling visits
  • Examination of Source of Greater Initial Diversity in S9 and S15
    • Significant genetic variation observed in S9 and S15
      • May be infected with two different viruses
      • Found that their respective viruses were monophyletic
  • Comparison of Rate of Change in Divergence and Diversity
    • Each participant received a regression line comparing divergence and diversity over time
    • Slopes of these regression lines were compared to random effects models

Results

  • Variable CD4 T cell decline across all participants
  • Large difference in viral load between moderate/rapid progress ors and nonprogressors
    • little difference between moderate and rapid
  • Median diversity observed as changing from -2.94 to 5.10 nucleotides per clone per year
    • Both diversity and divergence were observed to increase with time
  • Rapid and moderate progress ors seemed to experience roughly the same divergence and diversity rates
    • Nonprogressors had noticeably lower rates
  • Increased diversity/divergence correlate with reduced CD4 T cell counts
  • dS/dN ratio for rapid and moderate progress ors indicate nonsynonymous mutation
    • Nonprogressors seemed to select against nonsynonymous mutations
  • Studies of phylogenetic tree data showed larger presence of a single strain
    • Subjects 10 and 15 were outliers

Discussion

  • Increased genetic diversity and divergence correlated to reduced CD4 T cell count
  • Moderate and rapid progress ors appeared to be unable to select against amino acid changes
    • Nonprogressors were able to
  • McDonald et all study found differing data
    • Similar data- correlation between divergence and drop in CD4 T cell number
    • Differing data- slow progress ors had higher diversity than rapid progress ors
      • May be linked to incomplete data, lack of enough participants
  • Wolinsky et all study found differing data
    • Less diversity was recorded in both rapid and slow progress ors
    • Individuals whose data most substantially supported this may have been exceptions
  • This study supports Nowak Study
    • Host immune response cannot handle the variety of viral clones’ respective mutated resistances to the T cell’s capabilities
    • Increased genetic diversity and divergence of virus leads to decreased CD4 T cell count
    • Decrease in viral genetic diversity once T cells are overwhelmed

Figures and Tables

  • Fig. 1
    • A graphical examination of CD4 T cell trajectory compared with diversity and divergence rates. Rapid progressors had a characteristic drop in CD4 T cells as time passed.
  • Table 1
    • A layout of the statistical data for each individual test subject. At the time the study began, all individuals had an adequate CD4T cell count. Their rate of decline from further observation classified them into rapid, moderate, or nonprogressor.
  • Fig. 2
    • Graph of slope of divergence and slope of diversity in all three progresso categories. Rapid progressors showed significantly higher rates of diversity than moderate and nonprogressors, and divergence that was far higher than nonprogressors but only slightly higher than moderate progressors
  • Fig. 3
    • A phylogenetic tree of subject 9’s HIV-1 mutation progression. The data of the single mutation reflects a denial of the idea that S9 may have been infected with two different viral strains.
  • Fig. 4
    • A phylogenetic tree of 4 random subjects from the initial 15 participants. Data shows that the participants had single mutations in between samplings, and the branching patterns were diverse between subjects.
Figure 2A: "Comparison among different progresso groups of the mean slope per year (􏳢SE) of intravisit viral genetic diversity (A) and the percent of nucleotides that diverged from the original postseroconversion consensus sequence"








Purpose

The purpose of this experiment was to study aspects of sequence evolution by by working with HIV sequence data. Also to learn possible sources of HIV for the 15 subjects and design own research project and become familiar with the ClustalW tool.

Methods Results

Part II: GeneBank

  • Look at the GeneBank records once you reach the nucleic acid data associated with the Markham et al. paper choose one of the GeneBank records and view both the full record and the FASTA formatted sequence.
  1. What was the accusation number of the sequence you chose
    • AF016768
  2. What subject of the study was that HIV sequence from? Which section of the record contains information about who the HIV was collected from?
    • Subject 1; Clone 9 from USA

Part III: Introduction to the Biology Workbench

  • Log into the Biology Workbench Biology Workbench
  • Set up account link
  • Download several (4 to 6) sequences in FASTA format by selecting several at the same time in the summary view so they are saved into a single text file.
  • Scroll down to nucleic sequence data link
  • Select add new sequence and press the Run Button
  • Enter Sequence in new page
  • Choose The Browse button to select the file saved earlier from NCBI. (name) use Upload button to open it and read the data into this page
  • Once the labels and sequences appear in the data files, choose Save to import that data into your Biology Workbench session
  • Select the sequence and use the command view the sequence to confirm that sequence was successful.
  • Find the ClustalW tool and select all of your sequences and run a multiple sequence alignment using ClustalW.
  • Submit and look over the unrooted tree diagram (shown in Data and Files section)

Data and Files

Unrooted Tree Diagram

Image:Courtney L. Merriam Week 3 Sequence.fast.txt


Conclusion

The essential purpose of this exercise was to acquaint students with the software and tools that we will be using for future exercises in this class. In this activity we analyzed nucleic acids and entered the data into Biology Workbench, in essence practicing for future activities. We developed phylogenetic trees to obtain a visual representation of the nucleic acid data we retrieved in order to analyze it more simply. The unrooted tree diagram in the Data and Files section highlights the similarity between three nucleic acid sequences that are tethered closely on the right side of the diagram, and the relative differences of the two sequences on the left. The length of the branch before the sequence itself connotes the degree of difference between itself and the other four genetic sequences. The experience we gained during this exercise will help us as we move forward in future research.

Acknowledgments

I collaborated with Mia Huddleston in class on this assignment. While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

References

  1. Biology Online (2005) Biology Online Dictionary. Retrieved from http://www.biology-online.org/dictionary/Seroconversion on September 14, 2016.
  2. Biology Online (2005) Biology Online Dictionary. Retrieved from http://www.biology-online.org/dictionary/Cd2 on September 14, 2016.
  3. Biology Online (2006) Biology Online Dictionary. Retrieved from http://www.biology-online.org/dictionary/peripheral_blood_cell on September 14, 2016.
  4. DNA Learning Center (2009) Sanger method of DNA sequencing, 3D animation with narration. Retrieved from https://www.dnalc.org/view/15479-Sanger-method-of-DNA-sequencing-3D-animation-with-narration.html on September 14, 2016.
  5. Biology Online (2005) Biology Online Dictionary. Retrieved from http://www.biology-online.org/dictionary/pcr on September 14, 2016.
  6. Biology Online. (2005) Biology Online Dictionary. Retrieved from http://www.biology-online.org/dictionary/Base-pair_substitution on September 14, 2016.
  7. MEGA Software (2009) Tamura-Nei distance. Retrieved from http://www.biology-online.org/dictionary/pcr on September 14, 2016.
  8. MEGA Software (2011) Jukes-Cantor distance. Retrieved from http://www.biology-online.org/dictionary/pcr on September 14, 2016.
  9. Biology Online (2014) Biology Online Dictionary. Retrieved from http://www.biology-online.org/dictionary/Monophyletic on September 14, 2016.
  10. Indian Journal of Medical Research (2013) Long term non-progresso HIV infection. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3818590/on September 14, 2016.
  11. Scientist Solutions (2005) What are synonymous and non synonymous substitutions. Retrieved from http://www.scientistsolutions.com/forum/bioinformatics-sequence-analysis/what-are-synonymous-and-non-synonymous-substitutionson September 14, 2016.
  12. CLUSTAL W: Julie D. Thompson, Desmond G. Higgins and Toby J. Gibson, modified; any errors are due to the modifications. PHYLIP: Felsenstein, J. 1993. PHYLIP (Phylogeny Inference Package) version 3.5c. Distributed by the author. Department of Genetics, University of Washington, Seattle.
  13. Donovan S and Weisstein AE (2003) Exploring HIV Evolution: An Opportunity for Research. In Jungck JR, Fass MR, and Stanley ED, eds. Microbes Count! West Chester, Pennsylvania: Keystone Digital Press.

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Courtney L. Merriam

Clas Page: Bioinformatics Laboratory

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