Carolyne week 3

Purpose

The purpose of this activity is to understand how Markham et. al. studied differences in HIV-1 viral evolution in altered in drug-using patients experiencing different rates of decline in their CD4+ T Cell levels.

Background Terms

1. Frequency-dependent selection: A type of natural selection where the frequency of the genotype in within the population influences the fitness of that genotype (Colman, 2015).
2. Viral load: The number of viral particles in blood plasma, often used as a sign of disease progression in HIV ("Viral load," n.d.).
3. Synonymous Mutation: A point mutation that results in changes an amino acid codon into another codon that still codes for the same amino acid, so the amino acid sequence of the protein is unchanged (King et. al., 2014).
4. Nonsynonyous Mutation: A missense mutation (King et. al., 2014).
5. Synonymous Mutation: a point mutation, usually in the third position of a nucleotide triplet, in which a nucleotide-pair substitution results in changing a codon for an amino acid into a different codon for the same amino acid (King et. al., 2014).
6. Seroconversion: The stage in an immune response when antibodies to the infecting agent are first detected in the bloodstream (Martin and Hine, 2014).
7. Epitope: the antigentic determinant on an antigen to which the paratope on the antibody binds (King et. al., 2014).
8. Taxon: Any named taxonomic group of any rank in the hierarchical classification of organisms (Martin and Hine, 2014).
9. CD4 T cell: A CD marker that occurs on T-helper cells and is involved in MHC class II restricted interactions (Cammack et. al. 2008).
10. Peripheral Blood Mononuclear Cells: A mixture of monocytes and lymphocytes; blood leucocytes from which granulocytes have been separated and removed (Lackie 2007.)

Markham et. al. Outline

1. Introduction
   1. HIV-1 has a high mutation rate that allows the virus to survive and adapt to changes in its host
   2. If the environment of the host is unstable, then it could affect the evolution of HIV-1 through selection
   3. It is important to determine how the immune system may exert selection pressure on HIV-1
      1. Understanding type and efficiency of the immune system's selection forces may explain how HIV-1 evolves and adapts to the selection forces. This could also explain how it survives and preserves its genetic diversity in the host, making it difficult to eradicate.
   4. Several earlier studies looking at HIV-1 evolution have limitations
      1. Very small sample size of infected people
      2. Not looking at sequences to describe how HIV-1 is undergoing genetic evolution
      3. Only looking at a small number of time points for each subject
   5. This paper looks at 15 individuals. They start from seroconversion and frequently collect data over a long period of time for each subject.
   6. Main finding of the paper: Nonprogressing HIV-1 infections show selection patterns that are different from moderately and rapidly progressing HIV-1 infections. In addition, this paper shows that rapid declines in CD4 T cell counts are related to higher levels of viral genetic diversity.
2. Methods
   1. Subjects
      1. This study had a sample size of 15 subjects that were all participants in a program aimed at those infected or at risk of infection with HIV-1 after using drugs. They had frequent visits for periods of up to four years
         1. Overcomes previous studies limited by small sample sizes or lack of time points for each subject
   2. Sequencing of env Genes
      1. Researchers sequenced the viral DNA through Nested PCR, which allowed them to add cut sites for EcoR1 and BamH1 in the env primers. Amplified DNA cloned into pUC19 vector, then sequenced by Sanger sequencing to confirm the sequence. RT-PCR was used to confirm the viral load.
         1. Overcomes limitations in previous studies that did not directly analyze the DNA sequence to describe HIV-1 genetic evolution
      2. PCR Settings: For rounds 1 and 2 of PCR, for each sample...
         1. Start by 2 min @ 95°C
         2. For 35 cycles: denature 30s @ 94°C, anneal 30s @ 60°C, elongate 45s @ 72°C
         3. End by holding for 10 min @ 72°C, then store at 4°C
   3. Viral Phylogenetics
      1. Used Mega computer package, Tamura-Nei model, and neighbor-joining algorithm to create phylogenetic trees of each subject's HIV virus with minimal biases. Taxon labels included subject, visit when the strain was isolated, and the number of replicates sampled.
   4. Statistics and Mathematics
      1. They defined the following variables for the correlation analysis:
         1. X0=divergence or diversity
         2. Y0=CD4 count at the time when X0 was determined
         3. Y1=CD4 count one year later
      2. They compared 76 time points for 15 individuals and divided the Y0 counts into 4 categories so they could determine how X0 related to Y1 in individuals with similar Y0 counts
      3. ds/dN determined by categorizing differences between strains as nonsynonymous or synonymous and correcting for bias
      4. ds/dN results were averaged over all observed strains at a visit
   5. Initial Diversity In Subjects 9 and 15
      1. Since subjects 9 and 15 had much greater levels of diversity compared to other subjects at the beginning of the study, they wanted to determine if the subjects had been infected by different viruses
      2. They found that viruses from subjects 9 and 15 were monophyletic. They did this by constructing phylogenetic trees comparing clones from 9 and 15 with clones from other subjects
      3. Excluding 15 doesn't change the ds/dN analysis despite 15 having a diversity value of 11.4 6-months after seroconversion.
   6. Divergence vs. Diversity
      1. To compare the rate of change of diversity and divergence, they plotted divergence/diversity over time and fit a regression line to the data
      2. Used random effects models to compare the average rate of decline in CD4 T cell count for each group
3. Results
   1. Among the subjects, there was a wide variation in the average annual change in CD4 T cell number
   2. At early time points, nonprogressors had significantly lower viral loads compared to either progressor group (rapid and moderate)
   3. In all three groups, diversity and divergence increased over time. When comparing the groups: increase in nonprogressor < increase in moderate progressor < increase in rapid progressor
      1. Rate of increase between moderate and rapid progressors wasn't significantly different
   4. Greater amounts of diversity or divergence observed in subjects meant that they were likely to have a greater decline in CD4 T cells over the next year
   5. HIV evolution in the nonprogressor group did not show the selective advantage for nonsynonymous changes, while rapid and moderate progressors seem to show a selective advantage for nonsynonymous changes
   6. For 10 subjects, phylogenetic trees showed that there was no single strain that was predominant over an extended period of time. This suggests that host factors can select against clones that predominate at a specific visit, but can't fight against all of the viruses present
      1. Strains that were seen at later patient visits tended to be closely related to strains seen in earlier patient visits
4. Discussion
   1. Increased genetic diversity and divergence in the HIV-1 strains present correlated with a greater decrease in CD4 T cells
   2. Results imply selection against changes in amino acids in the nonprogressing strains and selection for these changes or against a lack of changes in the progressing strains
   3. The idea that HIV progression is caused by a fit viral strain rapidly proliferating is not supported by the results of the study.
      1. But based on the Wolinsky et. al. study, this model could explain rapid decline observed patients couldn't develop an immune response
   4. Markham et. al. results support Nowak's model, which proposes the idea that CD4 T cell decline was related to increased genetic diversity in the virus
      1. However, patients that had increasing viral genetic diversity throughout HIV and AIDS development don't support Nowak's hypothesis that increasing diversity leads to the development of critical viral epitopes. Instead, it seems to support the idea that frequency-dependent selection leads to progression
   5. Another model by Sala et. al. proposed the idea that viral variants of HIV in one body part evolve independently from viral variants in other parts of the body
      1. Observations seem to be consistent with this model
      2. Since immune responses do not seem to be able to clear HIV-1, the model supports the idea that this may be because the immune response doesn't target all viral variants in the body
      3. HIV in nonprogressors may not replicate or mutate as much to evade the immune system

Scientific Conclusion

Among the patients in the study, increasing rates of CD4+ T cell decline were correlated with increasing genetic diversity in HIV-1 strains in the patient. If the virus was not progressing, data suggests there could be selection against nonsynonymous mutations, which could help the virus to evade the immune system. If the virus was progressing, whether moderately or rapidly, data supports that there is selection for the nonsynonymous mutations. Moreover, the subjects didn't show evidence that a single strain was more predominant for a long period of time, suggesting that while the immune system is effective against predominant strains, it can't fight all of the strains present in the individual.

Acknolwedgements

I obtained definitions from the following references: A Dictionary of Biology (6th ed.), Oxford Dictionary of Biochemistry and Molecular Biology (2 ed.), A Dictionary of Genetics (8 ed.), The Dictionary of Cell and Molecular Biology, and biology-online.org dictionary. I worked with Drew and Nathan in class to prepare to present Figure 1. The content for this page was driven by the content featured in the Week 3 assignment page. Except for what is noted above, this individual journal entry was completed by me and not copied from another source. Carolyne (talk) 23:48, 5 February 2020 (PST)

References

1. Markham, R. B., Wang, W. C., Weisstein, A. E., Wang, Z., Munoz, A., Templeton, A., ... & Yu, X. F. (1998). Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline. Proceedings of the National Academy of Sciences, 95(21), 12568-12573. doi: 10.1073/pnas.95.21.12568
2. (2006). CD4. In Cammack, R., Atwood, T., Campbell, P., Parish, H., Smith, A., Vella, F., & Stirling, J. (Eds.), Oxford Dictionary of Biochemistry and Molecular Biology. : Oxford University Press. Retrieved 5 Feb. 2020, from https://electra.lmu.edu:5305/view/10.1093/acref/9780198529170.001.0001/acref-9780198529170-e-3064.
3. King, R., Mulligan, P., & Stansfield, W. (2014). synonymous mutation. In A Dictionary of Genetics. : Oxford University Press. Retrieved 5 Feb. 2020, from https://electra.lmu.edu:5305/view/10.1093/acref/9780199766444.001.0001/acref-9780199766444-e-6654.
4. King, R., Mulligan, P., & Stansfield, W. (2013). nonsynonymous mutation. In A Dictionary of Genetics. : Oxford University Press. Retrieved 5 Feb. 2020, from https://electra.lmu.edu:5305/view/10.1093/acref/9780199766444.001.0001/acref-9780199766444-e-4575.
5. (2014). Viral load. Retrieved from https://www.biology-online.org/dictionary/Viral_load
6. Martin, E., & Hine, R. (2008). taxon. In A Dictionary of Biology. : Oxford University Press. Retrieved 5 Feb. 2020, from https://electra.lmu.edu:5305/view/10.1093/acref/9780199204625.001.0001/acref-9780199204625-e-4366
7. Martin, E., & Hine, R. (2008). seroconversion. In A Dictionary of Biology. : Oxford University Press. Retrieved 6 Feb. 2020, from https://electra.lmu.edu:5305/view/10.1093/acref/9780199204625.001.0001/acref-9780199204625-e-6465.

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