Questions and Answers

Scientific Journal Article Questions and Answers:

1. In class, we discussed the reasons that Ne is not equal to N in animal populations.  What were these reasons?  Why do the authors assert that the HIV virion effective population size is not equal to the census population in an individual with a chronic HIV infection?

-                    Four reasons why the Effective Population Size (N_e) is not equal to the actual population size (N) are: 1. Variation in the number of progeny produced by either females, males, or both. 2. Unequal numbers of females and males. N_e is sensitive to the differences in the number of reproductively active females versus males. Overlapping generations. Offspring may mate with parents or relatives. Parents and offspring carry identical alleles at many genes, and the inbreeding effect may occur. 4. Fluctuations in population size. Rate of genetic drift is increased when there is a period of small population size, and N_e is more strongly affected.

-                    The mutation rates and HIV’s ability to evolve directly rely on the population size of the virus. An analysis of plasma viral dynamics suggests that 10¹⁰ virions are produced daily. The amount of T cells with infectious HIV-1 genomes that can replicate is about 10⁷. This implies that the census sizes of the virus and infected cells is 10⁷. For both the env and gag-pol loci, the effective population size has been estimated as 10³. This is because the demography within an individual has a large effect on genetic diversity of the HIV-1 virus. The evolution of the virus within an individual is greater than the virus evolution viewed as a population across individuals. The effective population size in an individual is about 1,000 which is quite small. Genetic drift can have a larger effect on this smaller population, causes evolution to happen more quickly. The authors examined 7 hypotheses about why HIV-1 effective population size is smaller than the actual population size.  

                The census population size of HIV-1 within an individual increases exponentially. Variance in the numbers of viral offspring are greater than the mean numbers of offspring. Viral generations overlap. Genetic variation is reduced due to deleterious mutations. Intermediate-frequency mutants are missing due to positive selection. The population of HIV-1 in an individual experiences negative frequency-dependent selection. Finally, the population of HIV-1 within an infected individual is structured, and a metapopulation exists that experiences migration, extinction, and/or recolonization.

2. What does the env gene code for in HIV?  Would you then expect high levels of mutation rates to be tolerated in env?

-                   The env gene has been linked to many characteristics of the HIV-1 virus that make it such a good virus. It is linked to coreceptor production, as it codes for a precursor to both gp120 and gp41. These two glycoproteins assist the virus with attaching to its host cell. According to Rangel, et al. it plays a major role in the fitness of HIV-1. If env were to be mutated, the virion could lose its ability to produce gp120 and gp41, which means it would no longer be able to attach to and infect the T-cells of its host. High levels of mutation rates would not be tolerated in the env gene in HIV-1, because that gene is crucial to the viral characteristics that HIV-1 possesses, namely the ability to attach to its host cell.

   3.   Given what you know about linkage disequilibrium, why would a high recombination rate indicate 

              loose linkage (page 1173)?

-         Linkage disequilibrium is the population genetic effect of linkage. A population is in disequilibrium when genotypes at two or more loci are associated more frequently than expected from their individual allelic frequencies. One of the causes of linkage disequilibrium is physical linkage. If two genes are located close together on the same chromosome, crossing over between them is rare and breaking them apart is hard resulting in low recombination. When genes are located further apart from each other on the same chromosome, more crossing over and recombination can occur. With increasing distance, the rate of recombination increases. Therefore, a high recombination rate would indicate loose linkage because the genes are still on the same chromosome but are at a large distance apart so a high rate of recombination is possible.

 4.   Which of the five mechanisms of evolution is/are at work in the study populations?  Does this result  

       in high or low metapopulation genetic diversity?

-            The mechanisms of evolution that are present in the study population are genetic drift, gene flow,      mutation, nonrandom mating, and natural selection. Mutation is one of the more prominent forces, giving HIV-1 its powerful ability to mutate and evolve quickly. Genetic drift can occur, and the population is not extremely large so it will have some effect. Selection occurs when the body’s immune system kills the HIV-1 viruses that are not able to evade the body’s natural defenses. Eventually, the HIV viruses that survive will multiply and continue to evade the body’s immune system. Because the HIV viruses exist in multiple populations AKA one metapopulation in one human, gene flow occurs between these populations. When two different strands of HIV infect the same cell, the genome of the progeny viruses created in the cell may contain RNA from these two different strains. In a way this is nonrandom mating, because an HIV strain may be more prone to infect a certain type of cell, and same with a different strain. So they will only be mixing between those two strains, not randomly with the whole population or metapopulation. This study was completed in naturally occurring populations. This results in high metapopulation genetic diversity. 

5.       Briefly explain the Wright-Fisher null model and how it was used in this study.

-                 The Wright-Fisher null model is based off of 7 assumptions similar to Hardy-Weinberg populations, such as random mating and non-overlapping generations, with the important distinction of a finite and constant population size. Each hypothesis in this study was based off of the seven assumptions of this model to explain previous findings of low viral effective population sizes and shortages of intermediate-frequency mutants. The effective population size, the number of individuals in the population that actually contribute genes to the next generation, is the size of an ideal Wright-Fisher population which would experience the same rate of genetic drift as the observed population being studied. This null model is used to compare the size of the viral HIV population as a whole, the infectious population size, and the effective population size to see if variations can be attributed to genetic drift. This article states that the total size (infectious and noninfectious HIV) is at least seven times greater than the effective population (those contributing to the next generation), which is significantly different than this Wright-Fisher null model.

6.      Below you will find a figure from Science entitled “Application and Accuracy of Molecular Phylogenies” (Hillis et al. 1994;  Vol. 264: 671-677).  In the study referenced, the authors considered the allegations of 7 patients (A-G) that they had contracted HIV from their dentist.  Were their allegations correct?  Describe how the authors might have generated this tree.

-                  Based on the study, most of their methods of assessing the phylogenies resulted in extremely high confidence in recovering the dental clade. Despite the methods not taking into account things such as duration of infection, pressure from the host’s immune system, state of the disease or therapy, it appears that their allegations of contracting HIV were correct. The authors likely generated the tree by sampling the HIV sequences from each patient (within 2-3 years before it could significantly mutate and evolve) and compared their similarities in sequence with the dentist’s HIV sequence, as well as HIV sequences from a control population infected with HIV. If patients were significantly closer in sequence to the dentist rather than the control group, they would be paired in the phylogeny directly to the dentist. The further the patient’s sequence was from that of the dentist, the more distant they would be in the molecular phylogeny.