Personal perspective: an HIV-positive family

Introduction This module uses an inquiry-based approach using video, mathematical modeling, world mapping, and bioinformatics to follow the progression and understanding of HIV-AIDS. The problem is designed for undergraduate students in the life sciences and mathematics. Using global health cases, visualization data, and mathematical models, students working collaboratively will share what they know about HIV-AIDS.

A mathematical model will enable them to compute the intensity of the HIV-AIDS epidemic, calculate the half-life of disease progression, and measure the effects of interventions. Based on Markham data (as referenced by Sam Donovan), students will be able to design questions to correlate, classify, and compare CD4 counts and various HIV clones as well as build phylogenetic trees. Introduction (cont.)

What we need to know? Potential students questions Effectiveness Resistance Demographics Difference between HIV/AIDS What kind of virus is HIV? Education Transmission Vaccines Prevention What is HIV? Treatment Cost

HIV through maps ucatlas.ucsc.edu

HIV infected people versus income around the world

This map summarizes the global distribution of HIV-1 strains in

Deaths due to HIV/AIDS in

Deaths by STDs Excluding HIV a Syphilis, Map 375, (87% of deaths). b Chlamydia, Map 376, (5% of deaths). c Gonorrhoea, Map 377, (1% of deaths). d Other STDs, no map, (7% of deaths).

Module Activities

Sequence Data Set Protein Nucleic Acids Markham Sequences 1998

Data Analysis AlignmentsStructures TreesDistance matrices Sequences Mathematical Modeling

Sequence analysis Sequence Biology Workbench PDB Consurf Swiss Viewer

General Information on HIV Biology

HIV Infection and AIDS An often asymptomatic infection caused by the Human Immunodeficiency Virus (HIV). AIDS is the advanced state of the disease characterized by a series of opportunistic infections occurring due to an immunosupressed state.

AIDS Definition (CDC) HIV+ and a CD4 count of 200/mm 3 or less* At least one out of 26 defined opportunistic infections such as: – Pneumocystic carinii pneumonia (PCP) – Kaposi’s Sarcoma – Systemic Candidiasis * For people 13 year of age or older

HIV Transmission Sexual transmission – Through the mucosal membranes: vagina, vulva, urethra, rectum and mouth. Blood or blood products – Diminished risk by blood transfusions. Syringes – Intravenous drug users. Vertical transmission from mother to child – Depends on the delivery method.

HIV is not transmitted through… casual contact shearing silverware, towels or clothing. saliva sweat tears swiming pools telephones toilets door knobs insect bites

T-helper Lymphocytes (CD4 cells) are essential for the proper function of the immune system helping in antibody production against infectious agents. The HIV virus targets CD4 lymphocytes destroying them as the newly made viruses exit the cell. As the amount of virus (viral load) increases in a subject, the number of viable CD4 cells (CD4 count) decreases. The HIV virus infects and destroys CD4 cells

HIV infection depends on the recognition of surface molecules on the CD4 cell. The first step in HIV infection is the binding of the CD4 molecule in the lymphocyte surface with the viral receptor protein GP120. The HIV-CD4 complex then binds the CCR5 molecule that serves as a co-receptor for HIV infection. The viral GP120 binds to the co- receptor through a specific protein domain known as the V3 loop. Differences in the viral DNA gene sequence encoding the V3 loop may affect viral recognition of the target cell. Differences in the viral DNA sequence accounts for different strains of the HIV virus. An infected individual often has several viral strains.

Resources Gap minder – hans-rosling-hiv-facts/ hans-rosling-hiv-facts/ Information related to HIV statistics around the world World mapper – NCBI –

Resources Clustal W shape/motif/ ConSurf CD4/gp120 3D-model prediction Work Bench NCBI BLAST Mathematica

Resources Markham RB, Wang WC, Weisstein AE, Wang Z, Munoz A, Templeton A, Margolick J, Vlahov D, Quinn T, Farzadegan H, Yu X-F Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline. Proc. Natl. Acad. Sci. USA 95: The original paper presenting and analyzing the data on which this problem space is based. Burks J, Ward L, Hota S, Gunasekaran G. Mathematical Modeling of HIV/AIDS Epidemic; In Press for publication in NCUR Proceedings AVERT: AVERTing HIV and AIDS: Kuby Immunology, 6th Ed W.H. Freeman and Co. National Institute of Allergy and Infectious Diseases: