The HIV Clock. Introduction. SimBio Virtual Labs

Transcription

1 SimBio Virtual Labs The HIV Clock Introduction AIDS is a new disease. This pandemic, which has so far killed over 25 million people, was first recognized by medical professionals in The virus responsible for most cases of AIDS is HIV-1 (diagramed at right). Where did this devastating new pathogen originate? HIV-1 belongs to a family of viruses known as SIVs (for simian immunodeficiency viruses) that infect primates. In 1992, reporter Tom Curtis, writing in Rolling Stone, suggested that HIV-1 might be a direct descendent of an SIV that infects African green monkeys or some other African primate. Curtis alleged that this SIV had gotten into humans as a contaminant in oral polio vaccines administered in central Africa during the late 1950s and early 1960s. These polio vaccines were made by growing a weakened strain of polio virus in cultured primate kidney cells. Many of the vaccines were grown in kidney cells taken from African green monkeys. If these kidney cells harbored SIV, then this virus could have been inoculated into patients along with the weakened polio. The evolutionary tree diagram at the right rules out African green monkey SIV as an ancestor of HIV-1. This tree, produced by Beatrice Hahn and colleagues, shows the evolutionary relationships among six samples of HIV-1, three samples of HIV-2, and SIVs from chimps, African green monkeys, and several other primates. If you start at the branch ends or tips and measure the total distance along branches connecting different virus samples, the closer related samples will be connected by shorter distances, and more distantly related ones by larger distances. The 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 1

2 HIV-1 samples (at the top of the diagram) are much more closely related to the chimp SIVs than to the viruses from African greens or any other monkey. Indeed, the HIV-1 samples all arise within the chimp SIV branch of the tree. This shows that HIV-1 in humans originated in chimps. How did humans contract a chimpanzee virus? Reporter Edward Hooper has argued, in his 1999 book The River and subsequent writings, that Curtis was on the right track. Hooper believes that some of the early polio vaccines distributed in central Africa were grown in chimpanzee kidney cells, that some of these cells were infected with SIV, and that SIV virions rode the vaccines into human bodies, where they became the founding strains of HIV-1. This lab will explore one line of evidence biologist Bette Korber and colleagues used to test Hooper s hypothesis. Korber used a molecular clock to date the last common ancestor of the most prevalent group of HIV-1 strains. According to the simplest version of Hooper s hypothesis, this common ancestor should have lived in the late 1950s. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 2

3 Exercise 1: A Model of Molecular Evolution by Genetic Drift The first step toward developing the tools we need to estimate a date for the origin of HIV is to recognize that a viral infection is a population of individual virus particles (virions). The infection may be initiated by one or a few particles that invade the person s body. Soon the invaders begin to reproduce, establishing a large population. When mutations occur during viral reproduction, the population becomes genetically variable. The population of virions can then evolve. In thinking about how a population of virions might evolve, we will imagine that it does so in the absence of natural selection. [This no-selection assumption is unlikely to be entirely true, but it will hold for stretches of viral genome in which the variation does not affect function.] To see how populations evolve in the absence of selection, we will examine change over time in the composition of a simple model population. [ 1 ] Launch SIMBIO VIRTUAL LABS. Select HIV from the EVOBEAKER LABS options. [ 2 ] You will see a large box labeled HIV Patient. This box contains a population of virions living inside a patient. To view information about an individual particle, or virion, click on the SELECT tool (the arrow button). Then double-click (or control-click) on one of the virions. [ 3 ] A new window will appear giving you access to a 100-nucleotide-long piece of the virion s genome. You can scroll back and forth to move from one end of the sequence to the other. After examining the sequence, close the window by clicking the CLOSE button. [ 4 ] Look again at your population of virions, and notice that they are all the same color. In this model, this indicates that they are all genetically identical to each other. They are identical because they are all recent descendants of the virion that initiated your patient s infection. [ 5 ] Notice too that all but one of the virions in your population is small. The creators of EVOBEAKER have taken some artistic license and used size to distinguish virions that are under development versus virions that are complete and thus ready to infect new cells and reproduce. The small virions are immature; the large one is mature. [ 6 ] To see the EVOBEAKER model of viral evolution in action, click the GO button to start the model running. As the simulation runs, the first thing you will see is the immature viruses becoming mature. Once all the virions are mature you will see them begin to reproduce. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 3

4 Reproduction is modeled as follows. Each virion has an equal chance of being replicated. A mature virion is chosen at random and copied to generate the first offspring. Then another one is chosen at random and copied, generating the second offspring. If you watch closely, you will see offspring blip into existence and float away from their parents. The model repeats this process until there are 50 offspring. By chance, certain virions may be copied more than once. Other adults may never be copied. Once there are 50 offspring, all the adults die. The offspring mature and the process is repeated. You can keep track of how much time has passed by watching the modeled time counter at the bottom of your screen. A generation takes about 150 time-steps. In this model, the virions have no enemies, and experience no competition. They are born, have a chance to reproduce, then die. At first glance, you might expect that this virion population will not evolve. There is little or no variation, and no selection. Variation and selection are necessary ingredients for adaptive evolution. However, the model does incorporate mutation. As you ve seen, each virion has a genome, represented by a piece of RNA 100 nucleotides long. Each time an adult produces an offspring, its genome is copied. But the copying is not perfect; occasionally an A is substituted for a U, or a U for a G, and so on. These mistakes, or mutations, add genetic variation to the population. When a mutation occurs, the virion containing the new nucleotide sequence is given a new color. Watch closely as the simulation runs. Most offspring are identical to their parent, but occasionally you will see new mutants appear among the offspring in the population. Although no genotypes are at a selective advantage over any others, non-adaptive evolution can occur due to random chance. Some genotypes may reproduce more often than others and increase in frequency while others may reproduce less often and become rare, or disappear altogether. If the composition of the virion population changes over time, it does so because mutation creates new variants. Most of the new variants disappear, but occasionally virions of a particular nucleotide sequence will have a run of good luck and become more abundant. [ 7 ] Click on the RESET button to reset the model. Start the model running again by clicking on the GO button. Watch the simulation run for at least 1,000 time steps. [ 7.1 ] What happened to most of the new variants that appeared did they become common, remain rare, or vanish altogether? Why? 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 4

5 [ 7.2 ] Did any variants look as though they might become common? What would have to happen for one of them to take over the population? [NOTE: Remember that there is no selection in this model.] [ 7.3 ] Was your population more variable at the end of 1,000 time steps than it was at the beginning? If it was, why? This mechanism of evolution at work in our model is known as genetic drift. To understand genetic drift in more detail, we need to gather data covering longer time spans. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 5

6 Exercise 2: Evolution by Genetic Drift Over Long Time Spans [ 1 ] In the EXPERIMENT menu, select SEQUENCE DIFFERENCES. [ 2 ] In the new window that appears, click the GO button to run the simulation. You will notice that things happen faster in this window than they did in the window for the first experiment. Instead of letting you watch individual virions as they are produced, grow up, reproduce, and die, the display in this new experiment skips from each generation s adult population to the next generation s adult population. The underlying model, however, is exactly the same as in Exercise 1. [ 3 ] Notice, also, that the time counter in the control panel at the bottom keeps track of generations, not time steps. With the simulation moving more quickly, we can track the population over much longer spans of time. Watch the simulation run for a while. [ 3.1 ] What happens to the colors represented in the population over time? Why does this happen? [ 4 ] We need a precise way to track how mutations accumulate in our model population over time. Click the STOP button. [ 5 ] Make sure the SELECT tool (the arrow button) is activated. Pick a virion and use your mouse to drag the particle into the Sequence Comparator box on the upper right. Because the virion you picked is the first one placed into the Sequence Comparator, the Sequence Comparator stores it as the reference sequence. The virion s data, including its icon color, the generation in which it was collected, and its RNA sequence should appear above the box you dragged it into. [ 6 ] Pick a virion with a different color and drag it into the gray box in the Sequence Comparator. Its data will appear as the first line inside the box itself. The third column in the Sequence Comparator is labeled Diffs. It reports the number of nucleotide positions at which the reference sequence and the current sequence differ. [ 6.1 ] How many differences are there between the reference sequence and the one you just selected? [ 7 ] Use the horizontal scroll bar under the Sequence Comparator to move back and forth along the RNA sequences until you find a nucleotide position at which your new sequence differs from the reference sequence; the nucleotide at this position in the new sequence will be highlighted. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 6

7 [ 7.1 ] What is the nucleotide at this position in the reference sequence? What is it in the new sequence? [ 8 ] Now that you know how to use the Sequence Comparator, click the Comparator s CLEAR button. Then click Yes in the dialog box that appears. [ 9 ] Click the RESET button and then pick a virion at random from your population. A good way to do this is to pick the virion that happened to land closest to the upper right corner. Drag this virion into the Sequence Comparator to store it as a reference. [ 10 ] You are going to run the simulation for another 1,000 generations, periodically stopping the simulation to drag a virion into the Sequence Comparator. [ 10.1 ] Use the axes below to show how you think the number of differences between these new sequences and the reference sequence will change over time. Explain your reasoning in the space below the graph. [ 11 ] Click the GO button to run the simulation, let it run for about 100 generations, then click the STOP button. Randomly pick a virion and drag it into the Sequence Comparator. [ 12 ] Repeat the above steps until you have accumulated at least 10 additional samples collected over a span of at least 1,000 generations since you stored the reference sequence. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 7

8 [ 13 ] Once you have accumulated at least 10 additional samples, click the PLOT button underneath the Sequence Comparator. This will plot the data you have accumulated in the graph on your screen. [ 13.1 ] What is the pattern in the graph? [ 13.2 ] Compare the graph on your screen to the graph you drew on the previous page. Describe their similarities and differences. Was your prediction generally correct? [ 14 ] Click the FIT LINE button to the right of the scatterplot on your screen. This will use a standard statistical method to draw the best fit line through the data points on your graph. At the top of the graph, just under the label Differences vs. Generation you should see some numbers. The only one that concerns us here is the second one, labeled s. This is the slope of the best fit line. Recall that the slope of a line is the rise over the run; steeper lines have higher slopes. In our graph, the slope gives the number of sequence differences that have accumulated in the population per generation. [ 14.1 ] Record the slope of your best fit line here: [ 15 ] It would be a good idea at this point to save a copy of your graph. In order to copy your graph, mouse over the center of your graph and right-click (Windows) or Control-Click (OSX). Choose Copy View to Clipboard. Then open a document in a word processing program where you can paste the picture and use the PASTE command to place the screen shot in the document. Label the image Molecular evolution without selection, Trial 1. Your instructor may want you to print and turn in this and other screen shots. Before proceeding, switch back to the SELECT tool. [ 16 ] Click on the RESET button to obtain a new population. [ 17 ] Pick a virion at random and drag it into the Sequence Comparator for storage as a reference sequence. [ 18 ] Click the GO button to run the simulation, let it go for about 100 generations, then click the STOP button. Pick a virion at random and drag it into the Sequence Comparator. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 8

9 [ 19 ] Repeat until you have accumulated at least 10 additional samples collected over a span of at least 1,000 generations since you stored the reference sequence. [ 20 ] Once you have accumulated at least 10 additional samples, click the PLOT button. Compare your new graph to the one you saved for Trial 1. Think about what happened in your two experiments. [ 20.1 ] What was similar between them? What was different? What generalizations can you make about how populations evolve by genetic drift? [ 21 ] Click the FIT LINE button. [ 21.1 ] What is the slope of your best-fit line for Trial 2? [ 22 ] Mouse over the center of your graph and right-click (Windows) or Control-Click (OSX), choose Copy View to Clipboard, then paste the resulting screen shot into your text document. Label the image Molecular evolution without selection, Trial 2. [ 22.1 ] If someone gave you frozen samples of virions taken from your patient at different times, could you make an educated guess as to how far apart in time the samples were collected? Explain. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 9

10 [ 23 ] The following graphs plot data from real viruses. The one on the left, based on research by Raj Shankarappa and colleagues, tracks molecular evolution in a population of HIV virions inside an individual patient. The one on the right, by Walter Fitch and colleagues, tracks molecular evolution in the global flu virus population. Compare these graphs to your own. [ 23.1 ] Why might an evolutionary biologist think of these graphs as molecular clocks? Until now, our model population has experienced mutation and genetic drift, but not natural selection. Next, we will explore what will happen if we add selection to the model. [ 24 ] Imagine that new mutations were deleterious. That is, imagine that a virion carrying a mutation in its genome is less likely to reproduce than a virion with no mutations. [ 24.1 ] Would this change in our model affect the rate at which new mutations accumulate in the population? Explain. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 10

11 [ 25 ] Imagine that new mutations were beneficial. That is, imagine that a virion carrying a mutation in its genome is more likely to reproduce than a virion with no mutations. [ 25.1 ] Would this change in our model affect the rate at which new mutations accumulate in the population? Explain. NEW MUTATIONS ARE... FITNESS CHANGE TRIAL 1 TRIAL 2...Deleterious 5%...Neutral 0...Beneficial + 5% [ 25.2 ] Record the slopes of the best fit lines that you recorded in Questions 14.1 and 21.1 into the table above under Trial 1 and Trial 2 in the second row down (= Neutral). [ 26 ] There is a popup menu in the EVOBEAKER window under the population of virions in the HIV patient; it is labeled FITNESS CHANGE FOR MUTANTS. Make new mutations deleterious by selecting 5% from the popup menu. [ 27 ] RESET your simulation and drag a random virion into the Sequence Comparator to serve as the reference sequence. [ 28 ] Run the simulation for 100 generations, then STOP it and drag a random virion into the Sequence Comparator. [ 29 ] Repeat this process until you have accumulated at least 10 additional samples collected over a span of at least 1,000 generations since you stored the reference sequence. [ 30 ] Once you have accumulated at least 10 additional samples, click the PLOT button, then click the FIT LINE button. [ 30.1 ] Record the slope in the table above, placing it into the grid under Deleterious, Trial 1. [ 30.2 ] Repeat the steps and record the resulting slope in the table above under Deleterious, Trial 2. [ 31 ] Make new mutations beneficial by selecting +5% from the FITNESS CHANGE FOR MUTANTS popup menu. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 11

12 [ 32 ] Run two more trials of this experiment, being sure to RESET before each run. [ 32.1 ] Record the resulting slopes in the table on the previous page. [ 32.2 ] Based on the data in your table, how does natural selection affect the rate of molecular evolution in our virus population? [ 32.3 ] Look back at the predictions you recorded. Were they correct? Why or why not? 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 12

13 Exercise 3: Diverging Lineages In Experiment 2 we saw how a population of virions evolves by mutation and genetic drift to become ever more genetically distinct from its ancestral state. In this exercise we will look at how two populations that share a common ancestor evolve relative to each other. [ 1 ] In the EXPERIMENT menu, select DIVERGING LINEAGES. [ 2 ] The window that appears is similar to the one you worked with in Exercise 2, except that now you have two patients on the left side of the window instead of just one. At present, only the top patient is infected with HIV. [ 3 ] Infect HIV Patient 2 by dragging a virion from Patient 1 into Patient 2. [ 4 ] RUN the simulation for about 50 generations, then click the STOP button to stop it. [ 5 ] Drag a random virion from Patient 1 into the Sequence Comparator to serve as a reference. Then drag a random virion from Patient 2 into the Comparator. Click the PLOT button to enter a data point representing the difference in the scatterplot. [ 5.1 ] How many sequence differences are there between the virion from Patient 1 and the virion from Patient 2? [ 5.2 ] How do you expect the number of sequence differences between virions from the two populations to change over time? Explain your reasoning. [ 6 ] Clear the Sequence Comparator by clicking the CLEAR button, then hitting Yes in the dialog box that appears. Then run the simulation for another 50 generations. Drag a random virion from each population into the comparator, then click PLOT to add the data point to your graph. [ 7 ] Repeat until you have accumulated and plotted at least 10 additional comparisons over a span of at least 500 generations. Then click the FIT LINE button. [ 7.1 ] What is the pattern in your graph? What is the slope of the fitted line? What does this number mean? [ 7.2 ] Compare your graph to your expectation in Question 5.1. Was your prediction correct? If not, why? 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 13

14 [ 8 ] The graphs at right, by Thomas Leitner and Jan Albert, shows the accumulating genetic divergence between pairs of HIV viruses taken from different patients as a function of how long it had been since the viral populations in each pair of patients had shared a common ancestor. The top graph shows sequence divergence within a gene called V3. The bottom graph shows sequence divergence within a gene called p17. [ 8.1 ] How do these graphs compare to the one from your experiment? [ 8.2 ] Why might the V3 genes in different HIV populations evolve away from each other more quickly than the p17 genes? [ 8.3 ] Imagine you are a doctor with two patients, each infected with HIV. Do you think you could use a molecular clock to estimate how long it had been since the viral populations in your patients had shared a common ancestor? Explain how. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 14

15 Exercise 4: Estimating the Age of Common Ancestors In the next exercise, EvoBeaker will prepare a puzzle for you. You will run the simulation for 500 generations, but you won t be able to see what s happening. Behind the scenes, the model will start out with a single infected patient. At some point during the first 500 generations, a second patient will be infected with a virion from the first patient. After 500 generations, the viral populations inside your patients will become visible. Your job will be to use a molecular clock to estimate, within plus or minus ~100 generations, when Patient 2 acquired HIV from Patient 1. [ 1 ] In the EXPERIMENT menu, select COMMON ANCESTORS. [ 2 ] Click the GO button and then be patient with your patients as the model runs. Soon after 500 generations (as soon as the viral populations become visible), STOP the model. [ 3 ] Drag a random virion from each patient into the Sequence Comparator. [ 3.1 ] How different are the RNA sequences from the two virions? Does this difference, by itself, give you a clue as to how long ago Patient 2 became infected? Explain. [ 4 ] Click the PLOT button to add the first data point to the graph, then CLEAR the Sequence Comparator. [ 5 ] RUN the simulation for another 50 generations, then STOP it. Drag a random virion from each patient into the Sequence Comparator. Click the PLOT button, then clear the Sequence Comparator. [ 6 ] Repeat until you have accumulated at least 10 comparisons spread over 500 generations. [ 7 ] Now click the FIT LINE button and look at your best fit line. It shows how rapidly genetic differences have been accumulating between your two patients while you have been watching. You can also follow the line back to get an idea of what was happening before you started watching. It is dangerous to use a best fit line to extrapolate beyond the range of the data, but in this case the best fit line is all we have. Look under the graph titled Differences vs. Generation to find both the slope of this line (which you used in earlier exercises), and the y-intercept, which is labeled y0. [ 7.1 ] Write the equation of your best fit line here (use the standard form, y = mx + b). 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 15

16 [ 8 ] Keep in mind that y (the dependent variable) is the number of sequence differences, and x (the independent variable) is the generation number. We want to use the previous equation to estimate the time (i.e., the generation) Patient 2 acquired HIV from Patient 1. When would that have been? It would have been the point in time when the genetic difference between viral populations in your two patients was zero. To make your estimate, substitute y = 0 in your best fit line equation and solve for x. [ 8.1 ] What is your estimate of the date of Patient 2 s infection? (Show your work.) [ 9 ] In the real world, we would not be able to verify such an estimate by checking it against The Truth. EvoBeaker, however, offers some advantages over the real world. One of them is that EvoBeaker can record The Truth and reveal it when asked. Click on the Infection Time checkbox located above the Sequence Comparator. [ 9.1 ] How accurate was your estimate of the date Patient 2 got HIV? [ 9.2 ] Can you think of any assumptions of your estimation technique that might explain inaccuracies? Explain. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 16

17 Exercise 5: Estimating the Date of the First HIV Infection You are now ready to try a method similar to that used by Bette Korber to estimate how long ago HIV-1 made its jump from chimpanzees into humans. Again, you will need to run the simulation for a number of generations to generate your puzzle. This time, the simulation begins with a chimpanzee infected with SIV. At some point, a virion jumps from the chimp to one of the humans, where it becomes the first HIV-1. At various times after that, the HIV infection jumps from human to human. Eventually, all the humans are infected. This time, the viral populations will become visible at generation 800. [ 1 ] In the EXPERIMENT menu in the control panel, select HIV FIRST INFECTION. [ 2 ] Click the GO button to start the simulation running...then be patient... Very soon after 800 generations (when the viral populations have become visible), STOP the simulation. [ 3 ] Your job now is to estimate, with reasonable accuracy, when the virus first jumped from chimp to human. Start by dragging a random virion from the chimp to the Sequence Comparator, where it will serve as a reference sequence. Then, drag a random virion from each of the seven human patients into the Comparator. [ 3.1 ] How different is a typical human viral sequence versus the chimp sequence? [ 4 ] Click the PLOT button to add your data points to the graph. Then CLEAR the Sequence Comparator. [ 5 ] RUN the simulation for another 50 generations, then STOP it. Sample another reference sequence from the chimp and compare it to a viral sequence from each of the seven humans. Click the PLOT button to add your data to the graph and CLEAR the Sequence Comparator. [ 6 ] Repeat until you think you have enough data to make an estimate. [ 7 ] Click the FIT LINE button and use the best fit line to extrapolate back to a time when a typical chimp virion and a typical human virion would have been identical. [ 7.1 ] Estimate the date of the first HIV infection and record it here: 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 17

18 [ 7.2 ] Click on the INFECTION TIMES checkbox located above the Sequence Comparator. Find the date of the first human infection, and record it below. How does it compare to your estimate? [ 7.3 ] As mentioned earlier, we cannot expect too much precision from an extrapolation beyond the data. Was your estimate at least accurate enough to say with some confidence that the first human infection happened early, late, or somewhere in the middle of the 800-generation time span? Explain. [ 7.4 ] How could you make your estimate more accurate? 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 18

19 Was HIV-1 Carried From Chimps Into Humans by a Polio Vaccine? Recall from the introduction that Edward Hooper claimed that HIV-1 first gained entry into human bodies as a stow-away in oral polio vaccines tested in Africa in the late 1950s. Bette Korber and colleagues tested this hypothesis by using a molecular clock to estimate the date of the last common ancestor of all HIV-1 strains. Korber lacked one crucial asset you possessed in Exercise 5. She didn t know which chimpanzee SIV population served as the immediate source of the virus that became the first HIV-1. To get around this problem, she used a clever trick. Using genetic sequences from 159 HIV samples from all over the world, Korber reconstructed an evolutionary tree for the human viruses. Using her tree, Korber was able to infer the likely sequence of the last common ancestor. Once she had an estimated sequence for the last common ancestor, Korber could calculate the genetic divergence between the common ancestor and the 159 known sequences. She then plotted these values against the year in which each of the 159 sequences was collected. A copy of this plot appears at left. It is like the plots you prepared in Exercise 2. Each sequence is represented by a letter. The letters correspond to the main branches on the HIV evolutionary tree. Korber then calculated the best fit line through the data. Finally, Korber extrapolated back to the point at which a typical HIV-1 sequence would have been identical to the sequence of the last common ancestor, as shown at right. The dashed box contains the data on the 159 sequences. The heavy black line is the best fit line. Because she was extrapolating well beyond her data, Korber allowed for a wide margin of error, indicated by the gray area. Korber s estimate is far from perfect. But as Andrew Rambaut and colleagues have pointed out, in spite of its flaws, it is the best estimate we have so far. Reassuringly, Korber s best fit line, which was based only on samples collected in the 1980s and 1990s, passes close to the data point for the oldest known HIV-1 sample, which was found in blood taken from a patient in , SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 19

20 [ 1 ] Examine Korber s graph on the previous page. [ 1.1 ] Are the data and her estimate compatible with the hypothesis that HIV-1 jumped from chimps to humans in the late 1950s? If not, can you think of an alternative explanation for the relationship between chimpanzee SIV and human HIV-1? Explain. 2011, SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 20

21 References Curtis, T The origin of AIDS. Rolling Stone (March 19): 54-59, 61, 106, 108. Fitch, W. M., J. M. Leiter, et al Positive Darwinian evolution in human influenza A viruses. Proceedings of the National Academy of Sciences, USA 88: Hahn, B. H., G. M. Shaw et al AIDS as a Zoonosis: Scientific and Public Health Implications. Science 287: Hooper, E The river: A journey to the source of HIV and AIDS. London: Allen Lane. Korber, B, M. Muldoon, et al Timing the ancestor of the HIV-1 pandemic strains. Science 288: Leitner, T., and J. Albert The molecular clock of HIV-1 unveiled through analysis of a known transmission history. Proceedings of the National Academy of Sciences, USA 96: Moore, J The puzzling origin of AIDS. American Scientist 92: Rambaut, A., D. Posada, et al The causes and consequences of HIV evolution. Nature Reviews Genetics 5: Shankarappa, R., J.B. Margolick, et al Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. Journal of Virology 73: , SimBiotic Software for Teaching and Research, Inc. All Rights Reserved. 21