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#CD4 T Cell: CD4 T Cells recognize antigens on the surface of a virus-infected cell and secretes lymphokines that stimulate B Cells and T cells.
#CD4 T Cell: CD4 T Cells recognize antigens on the surface of a virus-infected cell and secretes lymphokines that stimulate B Cells and T cells.


'''Table 1'''
===Outline===
This table separated the 15 individual subjects into progressor categorization based on the lowest level of CD4 Tcell decline attained during the period of observation.  
'''Introduction'''
*HIV-1 adapts to a rapidly changing host environment
This experiment focussed on 15 individuals separated between the CDT4 T Cell decline. These were further separated into
*#Rapid Progressors
*#Moderate Progressors
*#Non-Progressors
*Ideally in a stable environment the "best fit" virus will be predominate.
*Likewise an unstable environment either variable mutants are selected against or the dominant strain is selected.
*This observes the forces against HIV that cause diversity and look at how the virus is adapting.
'''Previous Studies'''
*Previous studies addressed smaller control groups analyzing smaller number of time points per subject.
Limitations also include not addressing sequence patterns.  


'''Methods'''
*15 known injection drug users with varying CD4T Cells were placed into 3 catagories.
*Blood was obtained every six months.
*Amplified 285-bp region of "env" gene from peripheral blood mononuclear cells.
*Phylogenetic trees
*Taxons labels were used to show the time at which the strain was isolated and the replicates used.
*Correlation  Analysis
*Analyzed between genetic diversity and CD4 T Cell count a year as well as mutational Divergence vs. CD4 T cell count.


Patterns of CD4 decline were quite variable among the 15
'''Figures & Table'''
subjects (Fig. 1), with median annual changes in the subjects’ CD4
*Figure 1
T cell number ranging from an increase of 53 cells per year to a
Indicates the diversity, divergence and cell count for the 15 individuals. Presents the divergence of the viral variance from the initial visit. The CD4 T cell count lay across the vertical axis and presented the separation between the progressors. Presented steady progression for the moderate progressors. Presents an inverse relationship.
decrease of 593 cells per year (Table 1). Serum viral load data
*Figure 2
were available for all subjects from one of the first three visits and
Indicates the "mean slope" of diversity and differences of each progressor category and indicates significant differences. Presented mean slope of genetic diversity and presented that between the non progressors and rapid progressors they suggested being on trend. Possibly could have had more of a conclusion to divergence, however, it allows us to critique wether this was enough data to explain the rapid nature of progression using the measure of diversity and divergence.
ranged from 1,702 to 321,443 copies of viral genomic RNAyml
*Figure 3
(Table 1).
Phylogenetic tree of evolution from subject 9. This is the results of taking viral samples presenting everything from the first visit to the last visit. This presented no predominent strain throughout the entire experiment. There is an x axis of time, each sequence that is different is given a branch. Every node of the tree means that those two where more closely related based on how far apart and how different they are from each other. The clones are all very close the main branch. This specific one was classified as moderate.
*Figure 4
Phylogenetic tree of four other randomly selected subjects (5,7,8,14). Presents no single dominant strain, but "randomly" they showed all of the moderates. 


Annual changes in CD4, intravisit nucleotide diversity, and percent nucleotide divergence from the first viruses sequenced after seroconversion
'''Results'''
reflect slopes of regression lines between individual visits. As slopes of CD4 T cell decline were quite variable between visits in the same subject,
*The more genetic diversity of HIV-1 was closer to the rapid decline with CD4 T cell counts.
progressor categorization of subjects was based on the lowest level of CD4 T cell counts attained during the period of observation. Although subject
*Non progressor groups had a low viral load.
7 had a 392yyear CD4 T cell decline, his CD4 T cell level never fell below 200 and therefore he was included in the moderate progressor group.
*Diversity and divergence was negatively correlated with the CD4 T cell count over a year.
His movement to the rapid progressor group would not have altered the statistical support for any of the conclusions reached
*Phylogenetic trees gave no evidence of predominance over a single strain.
*More diversity and divergence in the rapid and non progressors
 
'''Discussion'''
* Increase in diversity and divergence in HIV-1 variants led to CD4 decline
 
* For subjects who contracted AIDS, their diversity and divergence continued to increase
*To control infection, the host cell must control it at an organismal level due to high diversity of virus mutants
* Nonprogressor viral strains showed possible selection against amino acid change, whereas moderate/rapid progressors selected for amino acid change
 
===Table 1===
*This table separated the 15 individual subjects into progressor categorization based on the lowest level of CD4 Tcell decline attained during the period of observation.
*Patterns of CD4 decline were quite variable among the 15 subjects, with median annual changes in the subjects’ CD4 T cell number ranging from an increase of 53 cells per year to a decrease of 593 cells per year (Table 1). Serum viral load data were available for all subjects from one of the first three visits and ranged from 1,702 to 321,443 copies of viral genomic RNAyml
*Annual changes in CD4, intravisit nucleotide diversity, and percent nucleotide divergence from the first viruses sequenced after seroconversion reflect slopes of regression lines between individual visits. As slopes of CD4 T cell decline were quite variable between visits in the same subject, progressor categorization of subjects was based on the lowest level of CD4 T cell counts attained during the period of observation.
*Although subject 7 had a 392yyear CD4 T cell decline, his CD4 T cell level never fell below 200 and therefore he was included in the moderate progressor group. His movement to the rapid progressor group would not have altered the statistical support for any of the conclusions reached
 
{{Kevin A Paiz-Ramirez}}


===Journal Assignments===
{| style="width: 50em"
| [[KP Ramirez Week 2]]
| [[KP Ramirez Week 6]]
| [[KP Ramirez Week 10]]
| [[KP Ramirez Week 14]]
|-
| [[KP Ramirez Week 3]]
| [[KP Ramirez Week 7]]
| [[KP Ramirez Week 11]]
| [[KP Ramirez Week 15]]
|-
| [[KP Ramirez Week 4]]
| [[KP Ramirez Week 8]]
| [[KP Ramirez Week 12]]
| [[KP Ramirez Week 16]]
|-
| [[KP Ramirez Week 5]]
| [[KP Ramirez Week 9]]
| [[KP Ramirez Week 13]]
|}


back to [[User:Kevin A Paiz-Ramirez|KP Ramirez]]
back to [[User:Kevin A Paiz-Ramirez|KP Ramirez]]

Latest revision as of 11:00, 9 February 2010

What do i know about the HIV virus?

In Class Activity

  • I know that HIV is a precursor to AIDS which is an immune disorder. It's transferred through blood and seminal fluids and considered a global pandemic. Aside from this very basic information there isn't much more that I know directly about the virus. I am unsure if there are variations in strains of HIV, as well as the difference between those who have been living with the virus for many years, versus those who only lasted a brief period of time.

Questions

  • Has there been any major developments in finding a cure for HIV?
  • How do some individuals living with HIV delay the onset of AIDS?
  • How many strains of HIV are there?


Assignment

I worked through the Bioinformatics for dummies book and immediately noticed several differences. The book itself begins with a brief introduction to the PubMED database including how to begin a query. The screen shots featured in the book were out of date, this is common as one aspect of Bioinformatics is the constantly evolving interface of databases. Secondly I ran through the sample queries it asks in the book, namely the search of the name Abergel and dUTPase together. In the book it cites that this will provide only one result which would in turn present the full abstract right away, however, since the publication there now yields two results.

Searches

I began searching PubMed and used the keywords Human Immunodeficiency Virus Type 1, after looking through the results I decided on the following paper after reading through the abstract[1]. It's important to note that the full text for the following article was not available from PubMed directly, instead I was able to access the full text through Google Scholar in the following link.

[2]

Comparisons

I felt that there were many ways to approach my comparisons between the databases. The first, as cited earlier was that PubMed did not have the full text available for the article I chose, however, this was available through Google Scholar and Web of Knowledge. Google Scholar was extremely easy to use as the article was found through the key words Human Immunodeficiency Virus Type 1 and additionally could be accessed by typing the name of the entire article. Web of Knowledge had a different query, extremely similar to literature databases, where you can type in Author searchs, Key words, and other through many search bars. While this gives the ability to be more comprehensive, it is not as user friendly as the queries from PubMed or Google Scholar. In order to best compare my results I used the same key word search (Human Immunodeficiency Virus Type 1)for Google Scholar and the ISI Web of ScienceThe following were the top ten results for each.

PubMed

  • PubMed felt like the most comprehensive and authoritative of the databases, however, I still found some difficulty such as missing full text.
  1. Mining protein dynamics from Sets of crystal structures using 'Consensus Structures'
  2. The anti-viral protein of trichosanthin penetrates into human immunodeficiency virus type 1
  3. Retroviral intasome assembly and inhibition of DNA strand transfer
  4. Gender differences in drug toxicity
  5. Anti-HIV-1 activity of low molecular weight sulfated chitooligosaccharides
  6. A novel, rapid method to detect infectious HIV-1 from plasma from persons infected with HIV-1
  7. InChI-based optimal descriptors: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors by correlation balance
  8. Role of etravirine in combination antiretroviral therapy
  9. Etravirine in first-line therapy
  10. Chemical characteristics, mechanism of action and antiviral activity of etravirine

Google Scholar

  • Google Scholar was the most user friendly of the databases, I really appreciated that the results highlighted the queries.
  1. Viral dynamics in human immunodeficiency virus type 1 infection
  2. … with the initial control of viremia in primary human immunodeficiency virus type 1 …
  3. Reduction of maternal-infant transmission of human immunodeficiency virus type 1 …
  4. … and serologic markers in infection with human immunodeficiency virus type 1
  5. Viral load and heterosexual transmission of human immunodeficiency virus type 1
  6. … levels of viremia in patients with primary human immunodeficiency virus type 1 …
  7. … associated with control of viremia in primary human immunodeficiency virus type 1 …
  8. Quantitation of human immunodeficiency virus type 1 in the blood of infected …
  9. Reduction of maternal-infant transmission of human immunodeficiency virus type 1 …
  10. … characterization of long-term survivors of human immunodeficiency virus type 1 …

Web of Science

  • Web of science was effective, however, of all three I found it the least user friendly.
  1. The vpx protein of HIV-2
  2. Autoreactivity in an HIV-1 broadly reactive neutralizing antibody variable region heavy chain induces immunologic tolerance
  3. Low prevalence of varicella zoster virus and herpes simplex virus type 2 in saliva from human immunodeficiency virus-infected persons in the era of highly active antiretroviral therapy
  4. Broadly Neutralizing Monoclonal Antibodies 2F5 and 4E10 Directed against the Human Immunodeficiency Virus Type 1 gp41 Membrane-Proximal External Region Protect against Mucosal Challenge by Simian-Human Immunodeficiency Virus SHIVBa-L
  5. Evidence for an Activation Domain at the Amino Terminus of Simian Immunodeficiency Virus Vpx
  6. Tiered Categorization of a Diverse Panel of HIV-1 Env Pseudoviruses for Assessment of Neutralizing Antibodies
  7. Protease Cleavage Sites in HIV-1 gp120 Recognized by Antigen Processing Enzymes Are Conserved and Located at Receptor Binding Sites
  8. Breadth of Human Immunodeficiency Virus-Specific Neutralizing Activity in Sera: Clustering Analysis and Association with Clinical Variables
  9. Application of the Dipeptidyl Peptidase IV (DPPIV/CD26) Based Prodrug Approach to Different Amine-Containing Drugs
  10. HIV-1 Protease Inhibitors with a Transition-State Mimic Comprising a Tertiary Alcohol: Improved Antiviral Activity in Cells
  • The Markaim article was cited 52 times according to Web of Science prospective search, the following are the top 5 articles that included the citation.
  1. A comparative study of HIV-1 clade C env evolution in a Zambian infant with an infected rhesus macaque during disease progression
  2. Multiple-infection and recombination in HIV-1 within a longitudinal cohort of women
  3. HIV-1 evolution in gag and env is highly correlated but exhibits different relationships with viral load and the immune response
  4. Relationship of Injection Drug Use, Antiretroviral Therapy Resistance, and Genetic Diversity in the HIV-1 pol Gene
  5. Dynamic Correlation between Intrahost HIV-1 Quasispecies Evolution and Disease Progression

Article Assignment

Top 10 Definitions

  1. Seroconversion: The development of detectable specific antibodies to microorganisms in the blood serum as a result of infection or immunization. Serology (the testing for antibodies) is used to determine antibody positivity.
  2. Phyletic : Assess how to denote the evolution of sequential changes in a line of descent, during which one species is altered.
  3. Epitopes: An epitope, also known as antigenic determinant, is the part of a macromolecule that is recognized by the immune system, specifically by antibodies, B cells, or T cells.
  4. Phylogenetic: The study of evolutionary relatedness among various groups of organisms, which is discovered through molecular sequencing data and morphological data matrices.
  5. Serological: The scientific study of blood serum. In practice, the term usually refers to the diagnostic identification of antibodies in the serum.
  6. Hypervariable Region: The region that pertains to immunoglobulin molecules that contain most of the residues involved in the antibody binding sites.
  7. Monophyletic: a monophyletic group is a taxon (group of organisms) which forms a clade, meaning that it consists of an ancestor and all its descendants.
  8. Epidemiology: is the study of factors affecting the health and illness of populations, and serves as the foundation and logic of interventions made in the interest of public health and preventive medicine.
  9. Virology: The study of viruses and virus-like agents: their structure, classification and evolution, their ways to infect and exploit cells for virus reproduction, the diseases they cause, the techniques to isolate and culture them, and their use in research and therapy.
  10. CD4 T Cell: CD4 T Cells recognize antigens on the surface of a virus-infected cell and secretes lymphokines that stimulate B Cells and T cells.

Outline

Introduction

  • HIV-1 adapts to a rapidly changing host environment

This experiment focussed on 15 individuals separated between the CDT4 T Cell decline. These were further separated into

    1. Rapid Progressors
    2. Moderate Progressors
    3. Non-Progressors
  • Ideally in a stable environment the "best fit" virus will be predominate.
  • Likewise an unstable environment either variable mutants are selected against or the dominant strain is selected.
  • This observes the forces against HIV that cause diversity and look at how the virus is adapting.

Previous Studies

  • Previous studies addressed smaller control groups analyzing smaller number of time points per subject.

Limitations also include not addressing sequence patterns.

Methods

  • 15 known injection drug users with varying CD4T Cells were placed into 3 catagories.
  • Blood was obtained every six months.
  • Amplified 285-bp region of "env" gene from peripheral blood mononuclear cells.
  • Phylogenetic trees
  • Taxons labels were used to show the time at which the strain was isolated and the replicates used.
  • Correlation Analysis
  • Analyzed between genetic diversity and CD4 T Cell count a year as well as mutational Divergence vs. CD4 T cell count.

Figures & Table

  • Figure 1

Indicates the diversity, divergence and cell count for the 15 individuals. Presents the divergence of the viral variance from the initial visit. The CD4 T cell count lay across the vertical axis and presented the separation between the progressors. Presented steady progression for the moderate progressors. Presents an inverse relationship.

  • Figure 2

Indicates the "mean slope" of diversity and differences of each progressor category and indicates significant differences. Presented mean slope of genetic diversity and presented that between the non progressors and rapid progressors they suggested being on trend. Possibly could have had more of a conclusion to divergence, however, it allows us to critique wether this was enough data to explain the rapid nature of progression using the measure of diversity and divergence.

  • Figure 3

Phylogenetic tree of evolution from subject 9. This is the results of taking viral samples presenting everything from the first visit to the last visit. This presented no predominent strain throughout the entire experiment. There is an x axis of time, each sequence that is different is given a branch. Every node of the tree means that those two where more closely related based on how far apart and how different they are from each other. The clones are all very close the main branch. This specific one was classified as moderate.

  • Figure 4

Phylogenetic tree of four other randomly selected subjects (5,7,8,14). Presents no single dominant strain, but "randomly" they showed all of the moderates.

Results

  • The more genetic diversity of HIV-1 was closer to the rapid decline with CD4 T cell counts.
  • Non progressor groups had a low viral load.
  • Diversity and divergence was negatively correlated with the CD4 T cell count over a year.
  • Phylogenetic trees gave no evidence of predominance over a single strain.
  • More diversity and divergence in the rapid and non progressors

Discussion

  • Increase in diversity and divergence in HIV-1 variants led to CD4 decline
  • For subjects who contracted AIDS, their diversity and divergence continued to increase
  • To control infection, the host cell must control it at an organismal level due to high diversity of virus mutants
  • Nonprogressor viral strains showed possible selection against amino acid change, whereas moderate/rapid progressors selected for amino acid change

Table 1

  • This table separated the 15 individual subjects into progressor categorization based on the lowest level of CD4 Tcell decline attained during the period of observation.
  • Patterns of CD4 decline were quite variable among the 15 subjects, with median annual changes in the subjects’ CD4 T cell number ranging from an increase of 53 cells per year to a decrease of 593 cells per year (Table 1). Serum viral load data were available for all subjects from one of the first three visits and ranged from 1,702 to 321,443 copies of viral genomic RNAyml
  • Annual changes in CD4, intravisit nucleotide diversity, and percent nucleotide divergence from the first viruses sequenced after seroconversion reflect slopes of regression lines between individual visits. As slopes of CD4 T cell decline were quite variable between visits in the same subject, progressor categorization of subjects was based on the lowest level of CD4 T cell counts attained during the period of observation.
  • Although subject 7 had a 392yyear CD4 T cell decline, his CD4 T cell level never fell below 200 and therefore he was included in the moderate progressor group. His movement to the rapid progressor group would not have altered the statistical support for any of the conclusions reached

Journal Assignments

KP Ramirez Week 2 KP Ramirez Week 6 KP Ramirez Week OFF
KP Ramirez Week 3 KP Ramirez Week 7 KP Ramirez Week 11
KP Ramirez Week 4 KP Ramirez Week 8 KP Ramirez Week 12
KP Ramirez Week 5 KP Ramirez Week 9 KP Ramirez Week 13

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  3. Week 4
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  5. Week 6
  6. Week 7
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  10. Week 11
  11. Week 12
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