Predicting the Onset of AIDS

Slides:



Advertisements
Similar presentations
Changes In Protein Sequences Of the HIV-1 gp120 V3 Region In Non-Progressor Types Nicki S.Harmon Samantha M. Hurndon.
Advertisements

Wisconsin HIV/AIDS Surveillance Annual Review: Slide Set New diagnoses, prevalent cases, and deaths through December 2014 April 2015 P Wisconsin.
Genetic Similarities Between HIV-1 Viruses in the Onset of AIDS Isabel Gonzaga BIOL : Bioinformatics Laboratory Loyola Marymount University October.
Categorical Data. To identify any association between two categorical data. Example: 1,073 subjects of both genders were recruited for a study where the.
Maximum Likelihood. Likelihood The likelihood is the probability of the data given the model.
Assessing cognitive models What is the aim of cognitive modelling? To try and reproduce, using equations or similar, the mechanism that people are using.
Single nucleotide polymorphisms Usman Roshan. SNPs DNA sequence variations that occur when a single nucleotide is altered. Must be present in at least.
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.
ACTG 333 The Antiviral Effect of Switching from Saquinavir to the New Formulation of Saquinavir vs. Switching to Indinavir After >1 year of Saquinavir.
Analysis of HIV Evolution Bobak Seddighzadeh and Kristoffer Chin Department of Biology Loyola Marymount University Bio February 23, 2010.
Wisconsin Department of Health Services HIV/AIDS Surveillance Annual Review New diagnoses, prevalent cases, and deaths through December 31, 2013 April.
Examining Subjects of HIV-1 With Possible Predominant Viral Strains Samantha Hurndon Isaiah Castaneda.
Using Molecular Information to Investigate the Evolutionary Origin of the HIV Virus.
The Human Genome Project & Pedigrees Chapter 11 & 12.
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.
Kow-Tong Chen, M.D., Ph.D., Hsiao-Ling Chang, Ph.D., Chu-Tzu Chen, M.P.H., and Ying-An Chen, M.P.H. Volume 23, Number 3, 2009 AIDS PATIENT CARE and STDs.
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.
Lecture 7: Parametric Models for Covariance Structure (Examples)
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.
DNA Replication. When and why must the DNA molecule be copied? Before cell division the DNA must be copied so that any new cells will have an identical.
Residue Sequence and Structure in the Re evaluation the Categorization of HIV Progression in Subjects Based on CD4 T cell Decline Rates Angela Garibaldi.
© 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
Examining Subjects of HIV-1 With Possible Predominant Viral Strains Samantha Hurndon Isaiah Castaneda.
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.
Examining the Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano BIOL398: Bioinformatics Laboratory.
Examining Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano Bioinformatics Laboratory Loyola.
HIV and STI Department The case for HIV testing A presentation for the clinical team in your practice.
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 Genetic Similarity and Difference of the Three Progressor Groups at the First and Middle Visits Nicole Anguiano BIOL398: Bioinformatics Laboratory.
Date of download: 6/3/2016 From: Report of the NIH Panel To Define Principles of Therapy of HIV Infection* Ann Intern Med. 1998;128(12_Part_2):
Investigations of HIV-1 Env Evolution Evolutionary Bioinformatics Education: A BioQUEST Curriculum Consortium Approach Grand Valley State University August.
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,
Date of download: 7/6/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Effect of Antiretroviral Therapy on Viral Load, CD4.
NEW NATIONAL CURRICULUM ASSESSMENT FRAMEWORK 2016.
Journal Club Presentation BIOL368/F16: Bioinformatics Laboratory
Linear Equations in Two Variables (Day 1) 1.3
Learning objectives Define HIV treatment goals
Supplemental Digital Content 1. Table: Primers
Amino Acid Sequences in V3 Loop Conformation
Pipelines for Computational Analysis (Bioinformatics)
The V3 Region Expresses More Diversity in Amino Acid Sequence in AIDS Diagnosed Patients than in Non-Trending and AIDS Progressing Patients HIV Structure.
Do HIV+ Rapid Progressors Show More Divergence than Non-Progressors?
Amino Acid Sequences in V3 Loop Conformation
Ranking Tumor Phylogeny Trees by Likelihood
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,
Nature of Science Quiz & Answers
Investigations of HIV-1 Env Evolution
Loyola Marymount University
LMU Department of Biology
Understanding Indicator 6: Early Childhood Special Education Settings for Children Ages Birth-Five Hello and welcome to Understanding Indicator 6: Early.
Predicting the Onset of AIDS
Loyola Marymount University
Amino Acid Sequences in V3 Loop Conformation
Aim What happens when a bacteria or virus mutates?
Қош келдіңіздер.
Wednesday, April 10, 2019.
Understanding Indicator 6: Early Childhood Special Education Settings for Children Ages Birth-Five Hello and welcome to Understanding Indicator 6: Early.
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,
Understanding Indicator 6: Early Childhood Special Education Settings for Children Ages Birth-Five Hello and welcome to Understanding Indicator 6: Early.
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
Presentation transcript:

Predicting the Onset of AIDS Robert Arnold, Alex Cardenas, Zeb Russo LMU Biology Department 10/5/2011

Outline What causes a subject to develop AIDS from HIV and what separates AIDS from HIV? Focusing on dS/dN ratio The definition of AIDS, the subjects affected, and their similarities, ALIVE information New hypothesis involving the division of subjects into those with AIDS, trending towards AIDS, and AIDS free trending away Research comparison, proving assumptions incorrect Further comparisons between the subjects with AIDS and those without

dS/dN ratio related to AIDS development determined that low dS/dN ratios, subjects that select either for nonsynonymous mutation or not against it were the subjects to develop AIDS The subjects picked were 4, 9, 11, and 14, all with 0.0 dS/dN ratios along with subject 10 with a 0.2 and subject 1 with a 0.3

AIDS and CD4 counts CDC definition of AIDS is a CD4 count below 200 Once diagnosed, cannot be reversed Makes our first hypothesis irrelevant since all ‘rapid progressors’ drop below 200, AKA all 6 have AIDS Subjects 1, 3, 4, 10, 11, 15

Revised hypothesis separating those with AIDS from others Separated into 3 categories Those with AIDS: 1, 3, 4, 10, 11, 15 Those trending to AIDS: 7, 8, 9, 14 Those free of and trending away from AIDS: 2, 5, 6, 12, 13 New vision; which subjects developed AIDS? Began to focus on ALIVE research to go beyond Markham’s 4 year period

Development of two new questions Since we can tell who has AIDS, we would now like to determine whether there are any similar clones of the env gene across the AIDS subjects Does a median ds/dn ratio below 1.0 or lower determine whether you will get AIDS or not?

Our division of the Patients

Random clonal comparison To determine whether there were any similarities between clones of those who developed AIDS during the study and those at risk, we performed a ClustalW on a random selection of two clones from each subject

2 Clones Rooted Tree

Comparison of dS/dN Subject No. of observations CD4 Median intravisit nucleotide differences among clones Virus copy number (×103) Annual rate of CD4 T cell decline Slope of change in intravisit nucleotide differences per clone per year Slope of divergence (% nucleotides mutated from baseline consensus sequence per year) Median dS/dN AIDS   Subject 4 4 1,028 0.9 6.8 −593 4.64 2.09 Subject 10 5 833 1.71 99.3 −363 3.16 1 0.2 Subject 11 753 2.27 62.2 1.11 0.32 Subject 15 707 15.16 171 −362 −2.94 0.68 0.7 Subject 3 819 1.82 302.5 −294 0.53 0.74 Subject 1 3 464 5.64 307.6 −117 5.1 1.55 0.3 At Risk Subject 7 1,072 317.6 −392 −0.79 1.35 1.3 Subject 8 7 538 1.24 209 −92 1.68 1.16 0.5 Subject 9 8 489 9.49 265 −11 1.58 1.21 Subject 14 9 523 50.9 −51 1.69 0.6 Not at Risk Subject 2 715 1.64 21.6 30 1.32 0.49 1.8 Subject 5 749 2.5 260.6 −41 0.06 1.4 Subject 6 405 2.82 321.4 52 1.92 0.82 0.4 Subject 12 6 772 2.8 44 0.62 0.13 Subject 13 671 0.87 1.7 53 0.28 3.5

Neither Assumption is correct Using the original data from the Bedrock website, we determined who actually developed AIDS over the full study 1, 3, 4, 6, 7, 8, 9, 10, 11, 14, 15 Only 2, 5, 12 and 13 avoided the progression to AIDS over the course of the study

2 Clones Rooted Tree Redux

Comparison of dS/dN Subject No. of observations CD4 Median intravisit nucleotide differences among clones Virus copy number (×103) Annual rate of CD4 T cell decline Slope of change in intravisit nucleotide differences per clone per year Slope of divergence (% nucleotides mutated from baseline consensus sequence per year) Median dS/dN AIDS   Subject 4 4 1,028 0.9 6.8 −593 4.64 2.09 Subject 10 5 833 1.71 99.3 −363 3.16 1 0.2 Subject 11 753 2.27 62.2 1.11 0.32 Subject 15 707 15.16 171 −362 −2.94 0.68 0.7 Subject 3 819 1.82 302.5 −294 0.53 0.74 Subject 1 3 464 5.64 307.6 −117 5.1 1.55 0.3 At Risk Subject 7 1,072 317.6 −392 −0.79 1.35 1.3 Subject 8 7 538 1.24 209 −92 1.68 1.16 0.5 Subject 9 8 489 9.49 265 −11 1.58 1.21 Subject 14 9 523 50.9 −51 1.69 0.6 Not at Risk Subject 2 715 1.64 21.6 30 1.32 0.49 1.8 Subject 5 749 2.5 260.6 −41 0.06 1.4 Subject 6 405 2.82 321.4 52 1.92 0.82 0.4 Subject 12 6 772 2.8 44 0.62 0.13 Subject 13 671 0.87 1.7 53 0.28 3.5