Kristoffer Chin: Week 3

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Contents

In-Class Activity

  • Pre-research HIV knowledge
    • HIV is a well known viral disease that can be spread through blood to blood contact or sexual intercourse. In modern society, it is usually transmitted through unsafe sex, blood transfusions, and the sharing of needles when using drugs. HIV is a a retrovirus which slowly weakens the immune system of the person infected which eventually leads to AIDS. HIV is a disease that was first found in the population of monkeys which was then introduced in humans. The first case of this happened in a region of Asia where cultures ate the brains of monkeys as a form of delicacy. I am not exactly sure how monkeys transmitted the virus to others, but eating the brains of monkeys introduced it to the human population. It as then slowly transmitted to humans through the form of sex without condoms. There are currently many efforts in trying to find a cure for the disease, but there are medicines available in order to lessen the damage of the virus allowing a longer lifespan to those that are infected.
    • My knowledge of HIV is very small at the moment nd thee are a few things I would like to know more about it:
  1. What is the average lifespan of those that are infected with HIV?
  2. Even though there is no cure at the moment, has there been anyone that has been cured of the disease?
  3. Are there any other type of retroviruses that have been found to create a disease for humans that is not HIV?
  4. How are HIV infected individuals affected through medicine?
  5. What kind of lifestyle changes must an HIV infected person must be applied?
  • Bioinformatics for dummies
    • Using PubMed to search for articles is quite easy according to the book.

Access the webpage: http://www.ncbi.nlm.nih.gov/pubmed/ The instructions gives an example of searching for dUTPase which leads to a result screen

PubMed Search result

Next shows an easier way to view the abstracts of the all the search results allowing comparison making it faster than clicking on every single article

PubMed Search result with abstract overview

Although this is an easy way to search, it is not the most specific because there are plenty of articles on dUTPase with many authors. To lessen the search and make it more specific, adding the last name of the author along with the enzyme allows the search or specific articles. This helps lower the hit count

PubMed search with author AND topic

And if luck is at your side, the result can lead to a full text of the article and not only the abstract allowing a better research on the topic

PubMed Full text article
  • Articles regarding HIV
    • The following article explains that the HIV medication HAART can cause complications with those taking it. The complications dealing with lipid synthesis which alters the metabolism of the person
  1. Richmond SR, Carper MJ, Lei X, Zhang S, Yarasheski KE, and Ramanadham S. . pmid:20117238. PubMed HubMed [Paper1]
  2. Van Vaerenbergh K. . pmid:11813503. PubMed HubMed [Paper2]
  3. Aiuti F and Mezzaroma I. . pmid:16848276. PubMed HubMed [Paper3]
All Medline abstracts: PubMed HubMed
  • The use of google scholar was much different that PubMed. Google scholar felt a little too broad when trying to find review articles on the given journal. It provided a huge list that made it hard to narrow down because their advance search was not as specific. The other problem is that I had to click on each link in order to find more information on the article rather than reading the summary on each link like in PubMed. I believe it is better to use PubMed before google scholar to find any journal articles.
  • Using ISI Web of knowledge was probably just as useful, if not better than PubMed. ISI Web of knowledge search engine was very specific in finding the journals and specifically searched for academic journals dealing with science. The advance search also gave many options from year of publication, author, to topic. It was easier to find a review article with this search engine rather than Google scholar.

Journal Club Week 4-Prep

Terms Definitions

  1. CD4 T-Cells - A T-cell lymphocyte, a type of white blood cell for the immune system, with CD4 receptors that recognizes antigens of viral infected cells. http://www.biology-online.org/dictionary/CD4_T_cell
  2. env - Retroviral gene that encodres for viral envelope of glycoproteins http://www.biology-online.org/dictionary/Env
  3. nonsynonymous mutation - also known as missense mutation. A point mutation in amino acid that causes and overall change in the protein that is being translated http://www.biology-online.org/dictionary/Missense_mutation
  4. Nested PCR - A type of polymerase chain reaction that uses two sets of primer sequentially, outter then inner. http://www.mblab.gla.ac.uk/~julian/Dict.html
  5. Sanger chain termination method - Termination of an in-vitro DNA synthesis with modified nucleotide substrates. The termination is sequence specific http://www.biology-online.org/biology-forum/about10178.html?p=77258&hilit=Termination+sequence#p77258&sid=718e34104e7115e33d220dca27395765
  6. BamHI - A restriction enzyme that cuts at the GGATCC nucleotide sequence and is used for cloning http://www.mblab.gla.ac.uk/~julian/Dict.html
  7. EcoRI - A restriction enzyme that makes staggered cuts on dsDNA by cutting between G and A sequences http://www.biology-online.org/dictionary/Ecori_restriction_enzyme
  8. Seroconversion - Change from negative to positive in a serologic test. Shows an indication of production of antibodies in response to an infection http://www.biology-online.org/dictionary/Seroconversion
  9. Reverse Transcription PCR - a type of PCR in which an ssDNA is used to form a dsDNA through the help of enzymes. http://www.biology-online.org/dictionary/Reverse_transcription
  10. Peripheral Blood Mononuclear Cell - A mix of monocyte and lymphocytes. Blood leucotyes without granulocytes. http://www.mblab.gla.ac.uk/~julian/Dict.html

Outline

  1. Abstract
    1. The main purpose is to find a pattern of evolution in HIV-1
      1. Patterns were to be observe in those that have HIV and have different rates of declining CD4 T-cells from the virus
      2. More diversity found in those that had their HIV turn into AIDS, progressors, than those whose HIV, but not progressing into AIDS, nonprogressors
    2. Two types of mutations found
      1. Nonsynonymous mostly found in progressors
      2. Synonymous mostly found in nonprogressors
    3. Evolution that did occur, was not dominant in expression than any other variance
    4. Lower CD4 T-cells means more mutations
    5. Results can help with understanding how HIV virus adapts to environment
  2. Introduction
    1. HIV is dependable on the host and has no control to its environment
      1. To adapt to the different environments that is constantly changed by the host, it must mutate
      2. Mutation is possible through HIVs properties, high mutation and replication
      3. Properties of HIV have been found in different viruses as well
    2. Studying the HIVs abaility to mutate and adapt to the environment can help understand what type of forces that influence viruses to evolve.
    3. It also helps to understand how viruses adapt in general
    4. Previous studies had small number of subjects and no detailed analysis on the sequences
    5. 15 subjects chosen with different rates of CD4 T-cell declines to find a pattern between those that are progressors and nonprogressors
    6. Hypothesis: There will be more genetic diversity in those infected that have a faster rate of loss in CD4 T-cell
  3. Methods
    1. Population
      1. 15 subjects, 6 month interval
      2. Rapid progressors = <200 CD4 T-Cells in 2 years
      3. Moderate progressors = 200-650 CD4 T-Cells in 4 years
      4. Nonprogressors = >650 throughout all years
    2. Sequencing genes
      1. Nested PCR technique used
        1. primers used were BAMHI or ECORI
        2. sequences clones in pUC19
        3. sequenced with Sanger chain termination method
      2. Genes taken from peripheral blood mononuclear cells (PBMC)
        1. PBMC used because it was found that when recent infection occurs, the DNA of these are a close match to the RNA of the virus
    3. Plasma viral load found with reverse transcription-PCR
    4. Phylogenetic trees
      1. MEGA computer package was used to create the trees
        1. uses an alogrithim and Tamura-Nei distance measure
    5. Correlation analysis
      1. Relate the number of CD4 T-cell count, rate of decline, to the amount of diversity or mutation found in each subject
    6. Determination of ds/dN ratio (synonymous/nonsynonymous)
      1. Used Jokes-Cantor correction to find ratio
      2. The ratio given was the median because distribution was highly variable
    7. Examine Diversity in subjects 9 and 15
      1. They showed high siversity in first visit
      2. Trying to figure out the reason for this diversity, possible multiple viral infection
    8. Rate of diversity
      1. Use of regression line
  4. Results
    1. env region analyzed due to its tolerance to mutation
    2. Figure 1
      1. A graph showing the amount of CD4 –T cell counts, diversity, and divergence from the subjects through time since seroconversion and cell count
        1. The two main counts used to distinguish progressors were 200 and 650.
        2. Diversity is classified as a difference between clones in the nucleotides
        3. Divergence classified as changes in nucleotide in the clone that can be found related to the original sequence.
      2. Overall graph showed skewed results which is why the median is represented instead of average
    3. Table 1
      1. Table shows data on all subjects through the count and rate of CD4 T- cell decline, ratio of ds/dN, slopr of diversity and divergence
      2. Overall table shows that there was a significance in the increase of diversity in rapid progressor and moderate progressor than nonprogressors
        1. A trend in increase diversity and divergence was found in rapid progressors, but not significant enough in the difference with moderate progressors
      3. Negative correlation found in diversity and divergence with the decline in cell count, but not significant enough
      4. No significance found in ds/dN ratio but there was a trend of choosing against dN
    4. Figure 2
      1. Graph shows an increase in diversity and divergence from nonprogressors to rapid progressors
        1. error bars for rapid progressors is high compared to the others
    5. Figure 3
      1. Shows a phylogenetic tree of subject 9
        1. diversity was found in this subject, but there was no mutation that dominated compared to the rest
    6. Figure 4
      1. Shows the phylogenetic tree commonly found in the subjects, shows no real dominate mutation
      2. Tree shows that the mutations were limited and did not go further
  5. Discussion
    1. Increase in diversity and divergence found would the decrease of CD4 T cells.
    2. Synonymous substitution was found to be almost the same with all three groups of subjects, but nonsynonymous was found in higher amounts in progressors
    3. Conflicting results with McDonals
      1. Gene variation was found to be almost the same in progressors
      2. Agreed that diversity was dependent only on env region, method used was probably what made the difference
        1. Less time periods used
    4. Similar results with Wolinsky and Nowak
      1. More genetic diversity with slow declining CD4 T cell
      2. When with full blown AIDS, the genetic diversity and divergence continues to increase
    5. Although HIV was extracted from PBMC, that is not necessarily the best site of HIV
      1. Optimal site can be found anywhere to allow viral enhancement
    6. Replication and diversity used in order to recognize the correct immune response in the hose


BIOL398-01: Bioinformatics Lab

  • Lab Journal
Kristoffer Chin: Week 2 Kristoffer Chin: Week 6 Kristoffer Chin: Week 11
Kristoffer Chin: Week 3 Kristoffer Chin: Week 7 Kristoffer Chin: Week 12
Kristoffer Chin: Week 4 Kristoffer Chin: Week 8 Kristoffer Chin: Week 13
Kristoffer Chin: Week 5 Kristoffer Chin: Week 9 Kristoffer Chin: Week 14


  • Shared Journal
BIOL398-01/S10:Class Journal Week 2 BIOL398-01/S10:Class Journal Week 6 BIOL398-01/S10:Class Journal Week 11
BIOL398-01/S10:Class Journal Week 3 BIOL398-01/S10:Class Journal Week 7 BIOL398-01/S10:Class Journal Week 12
BIOL398-01/S10:Class Journal Week 4 BIOL398-01/S10:Class Journal Week 8 BIOL398-01/S10:Class Journal Week 13
BIOL398-01/S10:Class Journal Week 5 BIOL398-01/S10:Class Journal Week 9 BIOL398-01/S10:Class Journal Week 14


  • Assignments
BIOL398-01/S10:Week 2 BIOL398-01/S10:Week 6 BIOL398-01/S10:Week 11
BIOL398-01/S10:Week 3 BIOL398-01/S10:Week 7 BIOL398-01/S10:Week 12
BIOL398-01/S10:Week 4 BIOL398-01/S10:Week 8 BIOL398-01/S10:Week 13
BIOL398-01/S10:Week 5 BIOL398-01/S10:Week 9 BIOL398-01/S10:Week 14

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