AVM BIOL368 Week 3

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Purpose

  • The purpose of this activity was to take data which has previously been published and researched and learn how to sequence this DNA within the Workbench application and familiarize ourselves with these new applications. This assignment was also used to gain an understanding of the procedures used for uploading data files.

Methods and Results

Activity 1; Part 3:

  1. Visit National Center for Biotechnology Information.
  2. Search and open link to paper, Markham et. al.
  3. Click "nucleotide".
    • Accession number of sequence chosen: AF016768.
    • Subject of study: S1V1-9.
    • This information was found in the Genbank under "Definition".
  4. Under "Nucleotide" open the link "FATSA" to find the sequences.
    • Select 4-6 boxes and save to local hard drive as a single text file.
      • Open the file with a word processor.
        • Confirm that the sequences are in FATSA format.

Activity 1; Part 3:

  1. Log in to Biology Workbench Workbench.
  2. Select "nucleic tools".
    • Select "add new sequences", then select "run".
      • Select "browse" and upload the previously saved file.
        • Scroll to the bottom of the browser and select "save".
          • Select a sequence using "view the sequence" and press "run".
  3. Select the "ClustalW" tool.
    • Within this tool select "help".
  4. Use ClustalW to run a multiple sequence alignment.
Avm.biol358.hiv.sequences.png
    • Look at similarities between the sequences". Unrooted tree shown to right.
      • Create a new session to save the sequences under for further use.
Avm.biol358.hivunrootedtree.activity1section3.png

Data and Files

FASTA sequences

Conclusion

In this activity the process of analyzing nucleic acid data and inputting information into Biology Workbench was done to familiarize ourselves with this information for the project we will be working on in the upcoming weeks. The HIV sequences used were taken from a previous research study which has been cited below. With the use of the application Workbench and the tool ClusterW within it, phylogenetic trees were developed to show the relationships between the sequences mapped. The visualization of an unrooted phylogenetic tree better shows the relationships between each sequence than a rooted tree might convey. On the tree screen shot above, you can tell that the three sequences to the right are more closely related and the two sequences on the left are most likely not related. It will be important to choose meaningful ID's during future use of this application, as it was hard to identify which sequence was which when the accession numbers were used as the ID's on the phylogenetic tree. This activities purpose was to expose us to this software so that we would ultimately be familiarize ourselves with it before we start our own HIV research.

Journal Club

Definitions

  1. Seroconversion: "The stage in an immune response when antibodies to the infecting agent are first detected in the bloodstream. For example, people infected with HIV typically seroconvert about 4–6 weeks following the initial infection, when antibodies against viral proteins are first produced."
  2. Polymorph: "One of several possible crystal forms of an element, compound, or mineral, all of which have the same chemical composition."
  3. Heterogeneity: "Variety or diversity, lack of uniformity; the variety of qualities found in an environment (habitat patches) or a population (genotypic variation). Contrast homogeneity."
  4. Coreceptors: "A receptor molecule on the surface of a cell that enhances the activity of another receptor following binding of its extracellular ligand."
  5. Lymphopenia: "(lymphocytopenia) n. a decrease in the number of lymphocytes in the blood, which may occur in a wide variety of diseases."
  6. Hematopoiesis: "The body system concerned with production, metabolism, circulation, and eventual destruction of circulating blood cells that have reached their natural life expectancy. The main components of the system are the bone marrow, spleen, and lymphatic glands."
  7. Senescent: "A glycosylated polypeptide antigen that appears on the surface of senescent and damaged red cells as result of calpain proteolysis of the cytoplasmic region of band III. Circulating IgG binds to the antigen and triggers the removal of aged erythrocytes from the circulation by Kuppfer cells of the liver."
  8. Cytolysis: " The dissolution or disruption of cells, especially by an external agent."
  9. Viraemia: "The presence of viruses in the blood."
  10. Chemokine: "Any of a class of cytokines with functions that include attracting white blood cells to sites of infection."

Outline

Introduction

  • HIV-1 has high mutation and replication rates
  • Mutations which survived are title "best fit"
    • This would occur in a stable dost environment
  • Unstable host environments result in instability
    • The environment of HIV-1 virus is one of this type
    • instability is due to coreceptors
  • Noticing how diversity affects HIV-1 evolution could provide information on the virus's evolution and how it adapts
  • Previous similar study was at a smaller scale
    • Analysis has been adapted to gain better results for this study

Methods

  • Study is comprised of 15 participants from injection drug users
    • AIDS Linked to Intravenous Experiences study participants
  • Applicants followed in 6-month intervals
    • Blood is drawn to study virology and immunology
  • All participants had different levels of CD4 T-cells
  • Rapid Progressors: <200 CD4 T-cells
  • Moderate Progressors: 200-650 CD4 T-cells
  • Non-progressors: >650 CD4 T-cells
  • HIV-1 genes were sequenced using PCR methods and the Sanger method
  • Plasma viral load's were done using reverse transcription PCR
  • Phylogenetic Trees were created using MEGA computer package, the neighbor-joining algorithm and the Tamura-Nei distance measure
  • Correlation analysis was determined between generic diversity and CD4 T-cell count
  • Each sequence was compared with each observed strain and classified as synonymous or non-synonymous
    • Jukes-Cantor correction
  • Determined if viruses in each subject divided independently or monophyleticly
  • Rate of change in divergence and diversity was obtained

Results

  • CD4 patterns were not consistent among participants
    • Median CD4 T-cell ranged from an increase of 53 cells per year to another participants CD4 T-cell level decreasing 593 cells per year
  • Sequence analysis concentrated on viral env region (V3)
    • Region is a site fro host-virus interaction
    • Can tolerate mutations well
  • Viruses from 13/15 participants were homogeneous
    • Suggests possible infection at two sites
    • Further study denied this
  • Diversity and divergence increased in all progressors
  • Patients who showed a decrease in CD4 T-cells also had an increase in genetic diversity and divergence
  • Phylogenetic trees showed that a dominant strain was not present

Discussion

  • Association between low CD4 T-cell levels and high divergence and genetic diversity.
  • Inconsistant findings for "most fit" viral strain proliferation and progress ors
    • Non synonymous substitution rates were higher in progressors
  • Conflicting findings with McDonald's research
    • Diversity differences
  • Conflicting findings with Wolinsky
    • Less viral genetic diversity
  • Consistent with Nowak's model
  • To beat an infection, the host must overcome it at the organism level

Questions

  • What is the importance or significance of this work?
    • This work was done to determine if the diversity of mutations present in HIV-1 had any direct correlation to the decrease in CD 4 T-cells. The study specifically looked at genetic divergence and diversity of these mutations which were found to make the HIV-1 virus progress more quickly. The examination of the diversity which occurs during HIV-1 evolution could then be used to determine the types of selection influencing the virus and how it adapts.
  • What were the limitations in previous studies that led them to perform this work?
    • In the previous study there were limitations with the number of subjects, the use of incorrect techniques for examination of these subjects sequence patterns and too little amount of time points from these subjects data.
  • How did they overcome these limitations?
    • To overcome these limitations they used a larger number of subjects (15 participants), visited them in 6-month intervals to have more time points and used different techniques to better collect sequence information.
  • What is the main result presented in this paper? (Hint: look at the last sentence of the introduction and restate it in plain English.)
    • The main result found in this paper was that the more genetic diversity present in the viral population, the quicker the CD 4 T-cells decline.
  • What were the methods used in the study?
    • The methods used in this study included PCR, generation of phylogenetic trees with the use of the MEGA computer package, Tamura-Nei distance measure and neighbor-joining algorithm. It also used correlation analysis between genetic diversity and mutational divergence. Lastly, it used the Sanger chain termination method as the sequencing method.
  • Briefly state the result shown in each of the figures and tables.
    • Fig. 1: This figure displays each subjects CD 4 T-cell trajectory, diversity and divergence over time. It shows that Subject 1 had a late difference in diversity over 3.57 years, putting it off scale.
    • Table 1: This gives numerical data for the seroconverters, it summarizes that subject 7 was categorized as a moderate progresso group rather than a rapid progressor.
    • Fig 2: This figure shows that diversity and divergence both increased over time for non-progressors, moderate progress ors and rapid progressors. The rapid progress ors had a significantly higher rate of divergence than the other groups.
    • Fig 3: Subject 9's phylogenetic tree suggests a single mutation between S9V2-1 and S9V2-2.
    • Fig 4: This phylogenetic tree shows that evolution is interrupted on the branches. For example, Subjects 5 and 7 had interruptions of evolution between visits 4 and 5 and subject 8 between visits 6 and 7 and subject 14 between visits 5 and 6. This tree also displayed a single mutation in the horizontal distance at the bottom of the tree.
  • How do the results of this study compare to the results of previous studies (See Discussion).
    • The studies came to different conclusion on diversity which they credit to the fact that the other study did not begin collecting data at the point of seroconversion. The previous study also had significantly fewer time points.
  • How do the results of this study support published HIV evolution models?
    • The finding that a decline in CD4 T-cells is associated with an increase in diversity and divergence supports Nowak's model.
  • What are the limitations in this study? (your critical evaluation of the study).
    • The only thing I think was a limitation was the number of subjects studied. I know they increased their number compared to the previous study, but 15 still seems like a relatively small number to be able to have any significant findings.
  • What future work do you suggest?
    • The only thing I can recommend is to continue the research with more subjects and see how much their findings will change.

Acknowledgments

  • Worked in collaboration with Anu
  • Template followed from week 3 assignment BIOL368/F16
  • Help from our professor, Dr. Dahlquist in class.
  • Note: While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

Avery Vernon-Moore 19:24, 13 September 2016 (EDT)

References

  1. Protocol followed using: 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.
  2. National Center for Biotechnology Information
  3. Biology Workbench
  4. 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
  5. Vlahov, D., Anthony, J.C., Munoz, A., Margolick, J., Nelson, K.E., Celentano, D.D., Solomon, L., Polk, B.F. (1991). The ALIVE study, a longitudinal study of HIV-1 infection in intravenous drug users: description of methods and characteristics of participants. NIDA Res Monogr 109, 75-100.

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