Examining the Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano BIOL398: Bioinformatics Laboratory.

Slides:



Advertisements
Similar presentations
Chapter 2 The Process of Experimentation
Advertisements

Changes In Protein Sequences Of the HIV-1 gp120 V3 Region In Non-Progressor Types Nicki S.Harmon Samantha M. Hurndon.
Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring"
Principal Component Analysis (PCA) for Clustering Gene Expression Data K. Y. Yeung and W. L. Ruzzo.
Genetic Similarities Between HIV-1 Viruses in the Onset of AIDS Isabel Gonzaga BIOL : Bioinformatics Laboratory Loyola Marymount University October.
Personal perspective: an HIV-positive family
Molecular Clock I. Evolutionary rate Xuhua Xia
Protein-DNA interactions: amino acid conservation and the effects of mutations on binding specificity Nicholas M. Luscombe and Janet M. Thornton JMB (2002)
Evaluating Hypotheses
Materials and Methods Abstract Conclusions Introduction 1. Korber B, et al. Br Med Bull 2001; 58: Rambaut A, et al. Nat. Rev. Genet. 2004; 5:
An Introduction to the HIV Problem Space Oakwood University: Faculty Quantitative Institute Aug. 10–12, 2009.
Examination of Amino Acid Differences as a Means of Determining Functional Changes in HIV-1 Protein Sequences Chris Rhodes and Isaiah Castaneda Loyola.
Principal Component Analysis (PCA) for Clustering Gene Expression Data K. Y. Yeung and W. L. Ruzzo.
Analysis of HIV Evolution Bobak Seddighzadeh and Kristoffer Chin Department of Biology Loyola Marymount University Bio February 23, 2010.
AIDS supplement. History of HIV Originated in Africa in the late 1950’s Originally found in nonhuman primates and may have mutated First documented in.
Introduction to Basic Science Emily L. Lowe, Ph.D. Microbiology, Immunology and Molecular Genetics UCLA.
Examining Subjects of HIV-1 With Possible Predominant Viral Strains Samantha Hurndon Isaiah Castaneda.
Unconserved Amino Acid Sequences in V3 Domain of gp120 Show No Significant Correlation to Altered Folding and Function Bobak Seddighzadeh Alex George.
Using Molecular Information to Investigate the Evolutionary Origin of the HIV Virus.
STEPHANIE HINTZEN BIOL 471 SIV and HIV: Differences in Diversity and Divergence.
Re evaluating the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi & Ryan Willhite Loyola Marymount University.
A Mutational Investigation of an HIV Patient’s GP120 Glycoprotein and it’s Implications on CD4 Binding Salita Kaistha Usrinus College, Collegeville PA.
Predicting the Onset of AIDS Robert Arnold, Alex Cardenas, Zeb Russo LMU Biology Department 10/5/2011.
Supporting Scientific Collaboration Online SCOPE Workshop at San Diego Supercomputer Center March 19-22, 2008.
Patterns of selection for or against amino acid change among different CD4 T-cell count progressor groups Michael Pina, Salomon Garcia Journal Club Presentation.
Diversity and Divergence in HIV-1 Viral Variants between patients with high CD4+ T Cell Variability and Patients with Rapid CD4+ T Cell Decline Kevin Paiz-Ramirez.
Mutations on the V3 loop of gp120 may predict progression to AIDS Derese Getnet, Dr. Rebecca Roberts, Structural Biology, Ursinus College, Collegeville.
Amino Acid Sequences in V3 Loop Conformation Alex Cardenas, Bobby Arnold and Zeb Russo Loyola Marymount University Department of Biology BIO /02/11.
Predicting the Onset of AIDS Robert Arnold, Alex Cardenas, Zeb Russo LMU Biology Department 10/5/2011.
Residue Sequence and Structure in the Re evaluation the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi.
Examining Subjects of HIV-1 With Possible Predominant Viral Strains Samantha Hurndon Isaiah Castaneda.
Research on killer HIV antibodies provides promising new ideas for vaccine design New discoveries about the immune defenses of rare HIV patients who produce.
Analyzing Differences in Protein Sequences Between Subjects with Varying T Cell Counts J’aime Moehlman Amanda Wavrin Loyola Marymount University March.
Changes In Protein Sequences Of the HIV-1 gp120 V3 Region In Non-Progressor Types Nicki S.Harmon Samantha M. Hurndon LMU Department of Biology BIOL 368.
Gene Frequency vs. Natural Selection Team Married 2 The Game.
Examining Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano Bioinformatics Laboratory Loyola.
The HIV virus. Objectives At the end of this session the participants will be able to: 1. Understand basic HIV structure 2. Describe the significance.
Residue Sequence and Structure in the Re evaluation the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi.
Student Project Showcase – April 2008 Classroom Pearls for Life Sciences and Mathematics Students J. de Varona, E. Demirci, S. Koksal S2 Figure 1: Photo.
CATEGORY: VACCINES & THERAPEUTICS HIV-1 Vaccines Shokouh Makvandi-Nejad, University of Oxford, UK HIV-1 Vaccines © The copyright for this work resides.
Evolution and transmission in HIV Steve Paterson Review; Rambaut 2004 Nature Reviews Genetics 5: ‘The causes and consequences of HIV evolution’
Examining Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano BIOL398: Bioinformatics Laboratory.
Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A, Templeton A,
Abstract Our research mainly applies Maximum Likelihood Method (MLE), Dynamic Programming, and Neighbor Joining Method in an attempt of shortening the.
Multiple Sequence Alignment Dr. Urmila Kulkarni-Kale Bioinformatics Centre University of Pune
Journal Club Presentation BIOL368/F16: Bioinformatics Laboratory
Colin Wikholm William Fuchs
Amino Acid Sequences in V3 Loop Conformation
The V3 Region Expresses More Diversity in Amino Acid Sequence in AIDS Diagnosed Patients than in Non-Trending and AIDS Progressing Patients HIV Structure.
Multiple Sequence Alignment
Do HIV+ Rapid Progressors Show More Divergence than Non-Progressors?
Amino Acid Sequences in V3 Loop Conformation
Global network analysis of drug tolerance, mode of action and virulence in methicillin-resistant S.aureus Overton et al., 2011 I.M. Overton, S. Graham,
Predicting the Onset of AIDS
Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A, Templeton A,
The Evolution of Populations
Structure of V3-containing HIV-1 gp120 core
Loyola Marymount University
LMU Department of Biology
Predicting the Onset of AIDS
Loyola Marymount University
Loyola Marymount University
Amino Acid Sequences in V3 Loop Conformation
Aim What happens when a bacteria or virus mutates?
Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A, Templeton A,
Predicting the Onset of AIDS
Residue Sequence and Structure in the Re evaluation the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi.
Chloe Jones, Isabel Gonzaga, and Nicole Anguiano
Loyola Marymount University
Presentation transcript:

Examining the Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano BIOL398: Bioinformatics Laboratory Loyola Marymount University 10/01/14

Talk Outline Background information on HIV and how it led to the development of the question and hypothesis. Examination of each progressor group with itself at the first visit and middle (~2 year) visit. Examination of the comparison between progressor groups at the first visit and the middle (~2 year) visit. An analysis of the results and the conclusions that can be drawn from them.

Talk Outline Background information on HIV and how it led to the development of the question and hypothesis. Examination of each progressor group with itself at the first visit and middle (~2 year) visit. Examination of the comparison between progressor groups at the first visit and the middle (~2 year) visit. An analysis of the results and the conclusions that can be drawn from them.

What is HIV? HIV is a retrovirus spread through sexual contact or intravenous drug use. It infects the immune system, targeting T cells that express the surface protein CD4. Its high mutation and replication rate allow it to quickly adapt to the host immune system. The env protein is a particularly active area for mutations. An HIV infected person whose CD4 T cell count falls below 200 is diagnosed with AIDS.

Describing the Three Progressor Groups In Markham et al’s paper, those infected with HIV were placed into three broad categories: Rapid progressors Moderate progressors Nonprogressors There seemed to be no initial indication of whether one person would end up in one progressor group or another.

Motivation Behind the Question and Hypothesis Why study progressor groups? Figuring out what makes one person more likely to be relatively unaffected by the infection (a nonprogressor) could be the breakthrough needed to finding a functional cure for all patients. Secondly, knowing what makes one person more predisposed to AIDS than another could lead to more effective treatments at an individual level. Markham et all did not compare across groups to see what was similar or different between them.

Statement of the Question and Hypothesis The question: Are clones from subjects in the same group the most similar, or are they as dissimilar as clones from other groups? Does the amount of variation and similarity, if any, change from the time of the first visit to a visit after about 2 years of infection with the virus? The hypothesis: The clones will be most similar to clones within its own group, and the groups will differentiate at a rate dependent on their group’s progressor level.

Talk Outline Background information on HIV and how it led to the development of the question and hypothesis Examination of each progressor group with itself at the first visit and middle (~2 year) visit Examination of the comparison between progressor groups at the first visit and the middle (~2 year) visit An analysis of the results and the conclusions that can be drawn from them

How the Subjects, Visits, and Clones were Chosen Subjects were chosen due to progressor group (3 per group) and closeness of a visit to the 2 year mark. The first and middle visits were chosen. Clones were selected at random.

Comparing Each Progressor Group with Itself ClustalW and ClustalDist were run on each group, first on the first visit and then on the middle (~2 year) visit. Sequence analysis were used to find the S value on individual groups. Trees were examined for similarity. The clustal distance matrix was used to find the minimum and maximum differences.

Quantifying the Differences within Progressor Groups First Visit Middle Visit The minimum and maximum values in the clustal distance matrix were multiplied by 291 in the rapid progressors, by 288 in the moderate progressors, and by 285 in the nonprogressors to obtain the minimum and maximum difference. The nonprogressors had a greater number of differences on average than the moderate progressors on both the first visit and the 2 year visit. The rapid progressors were consistently highest.

Talk Outline Background information on HIV and how it led to the development of the question and hypothesis Examination of each progressor group with itself at the first visit and middle (~2 year) visit Examination of the comparison between progressor groups at the first visit and the middle (~2 year) visit An analysis of the results and the conclusions that can be drawn from them

Comparing Between Progressor Groups ClustalW and ClustalDist were run on a pairwise comparison between groups. The clustal distance matrix was used to calculate the minimum and maximum differences. The prediction was that it would be higher than in the individual groups on both maximum and minimum differences.

The Case of Subjects 14 and 15 Subject 15 (Rapid progressor) was consistently more similar to subject 14 than any other rapid progressor.

Quantifying the Differences within Progressor Groups First Visit Middle Visit Both the rapid and nonprogressor groups, and rapid and moderate progressor groups were multiplied by 291 to obtain the min/max difference. The moderate and nonprogressor group was multiplied by 288. The minimum difference when comparing the rapid progressors to any other group was significantly lower than just within the progressor group. The minimum difference was consistently higher when comparing the moderate progressors with any other group.

Talk Outline Background information on HIV and how it led to the development of the question and hypothesis Examination of each progressor group with itself at the first visit and middle (~2 year) visit Examination of the comparison between progressor groups at the first visit and the middle (~2 year) visit An analysis of the results and the conclusions that can be drawn from them

Comparing all the Data Reveals the Similarity and Difference Between Groups Each group was relatively similar between each visit, and the comparisons remained relatively consistent.

How the Data Reflects the Hypothesis and Question The question: Are clones from subjects in the same group the most similar, or are they as dissimilar as clones from other groups? Rapid progressors were always more similar to other groups than their own group. Each group was more different from the other groups than its own. Does the amount of variation and similarity, if any, change from the time of the first visit to a visit after about 2 years of infection with the virus? The amount of similarity and difference between and within each group tends to remain about the same. The hypothesis: The clones will be most similar to clones within its own group, and the groups will differentiate at a rate dependent on their group’s progressor level. In the case of the moderate and nonprogressors, the clones were more similar to their own group, but otherwise the hypothesis was disproven.

Did outside research yield any new insight? Generally, no. Most research focuses specifically on the evolution of the env gene, not on the various progressor groups. Research on the progressor groups tends to only compare within groups, not across them. Markham, et al: Diversity and divergence increase in rapid and moderate progressors. Yet from the data calculated here, it seems that they tend to diversify and diverge in ways that prevented them from being too different or similar to each other as the years passed.

What further study could be done? From here, the conducting of a larger study to obtain more subjects of different groups and comparing them would be the most effective way to prove or disprove the hypothesis. Obtaining reliable data with such a small sample size is very difficult. Repeating this same experiment with more clones from each subject would be ideal. Comparing all clones from all subjects would be difficult due to the difference between number of rapid/moderate progressors and nonprogressors.

Talk Summary Comparing between progressor groups could provide insight into the treatment of HIV. The moderate and nonprogressors were similar to themselves, but the rapid progressors had a higher minimum and maximum difference than the other two. With the exception of the rapid progressors, each group was more similar with its own clones than with the clones of another group, and the groups slightly increased in similarity with each other between visits. Further study would be needed to see if there is a trend in similarity or difference between progressor groups.

References 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, doi: /pnas Exploring HIV Evolution Handout The Bedrock HIV Problem Space (

Acknowledgments Dr. Kam Dahlquist Markham, R. B.