Advertisement

Molecular Biology

, Volume 52, Issue 3, pp 393–397 | Cite as

Mutation Frequencies in HIV-1 Genome in Regions Containing Efficient RNAi Targets As Calculated from Ultra-Deep Sequencing Data

  • O. V. Kretova
  • M. A. Gorbacheva
  • D. M. Fedoseeva
  • Y. V. Kravatsky
  • V. R. Chechetkin
  • N. A. Tchurikov
Genomics. Trascriptomics

Abstract

HIV-1 is one of the most variable viruses. The development of gene therapy technology using RNAi for AIDS/HIV-1 treatment is a potential alternative for traditional anti-retroviral therapy. Anti-HIV-1 siRNA should aim to exploit the most conserved viral targets. Using the deep sequencing of potential RNAi targets in 100-nt HIV-1 genome fragments from the clinical HIV-1 subtype A isolates in Russia, we found that the frequencies of all possible transversions and transitions in certain RNAi targets are 3–38 times lower than in adjacent sequences. Therefore, these targets are conserved. We propose the development of these RNAi targets for AIDS/HIV-1 treatment. Deep sequencing also enables the detection of the characteristic mutational bias of RT during the replication of viral RNA.

Keywords

gene therapy HIV-1 RNAi mutations RT transversions transitions gene therapy deep sequencing 

Abbreviations

HIV-1

human immune deficiency virus type 1

A1–A6

100-bp HIV-1 genome fragments with effective RNAi targets

p17

matrix protein

RT

reverse transcriptase

int

integrase

vpu

enhancer protein of viral particle release

p120

protein that facilitates HIV entry into a host cell

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bobbin M.L, Burnett J.C., Rossi J.J. 2015. RNA interference approaches for treatment of HIV-1 infection. Genome Med. 7, 50.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Kretova O.V., Fedoseeva D.M., Gorbacheva M.A., et al. 2017. Ultra-deep sequencing data of HIV-1-infected patients from Russia reveals six highly conserved targets of RNAi that are also present in many HIV-1 strains worldwide. Mol. Ther. Nucl. Acids. 8, 330–344.CrossRefGoogle Scholar
  3. 3.
    Kretova O.V., Chechetkin V.R., Fedoseeva D.M., et al. 2016. Analysis of variability in HIV-1 subtype A strains in Russia suggests a combination of deep sequencing and multi-target RNA interference for silencing of the virus. AIDS Res. Hum. Retroviruses. 33, 194–201.CrossRefPubMedGoogle Scholar
  4. 4.
    Tchurikov N.A., Fedoseeva D.M., Gashnikova N.M., et al. 2016. Conserved sequences in the current strains of HIV-1 subtype A in Russia are effectively targeted by artificial RNAi in vitro. Gene. 583, 78–83.CrossRefPubMedGoogle Scholar
  5. 5.
    Kravatsky Y.V., Chechetkin V.R., Fedoseeva D.M., Gorbacheva M.A., Kretova O.V., Tchurikov N.A. 2016. Mutation frequencies in HIV-1 subtype A genomes in the regions containing the efficient RNAi targets. Mol. Biol. (Moscow). 50, 417–421. doi 10.7868/S0026898416020117CrossRefGoogle Scholar
  6. 6.
    Andrews S. 2010. FastQC: A quality control tool for high throughput sequence data. http://www.bioinformatics. babraham.ac.uk/projects/fastqc.Google Scholar
  7. 7.
    Martin M. 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12.CrossRefGoogle Scholar
  8. 8.
    Katoh K., Standley D. 2013. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Li H., Handsaker B., Wysoker A., et al. 2009. 1000 Genome Project Data Processing Subgroup. The sequence alignment/map (SAM) format and SAMtools. Bioinformatics. 25, 2078–2079.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kent W.J., Zweig A.S., Barber G., Hinrichs A.S., Karolchik D. 2010. BigWig and BigBed: Enabling browsing of large distributed data sets. Bioinformatics. 26, 2204–2207.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Kravatsky Y.V., Chechetkin V.R., Fedoseeva D.M., et al. 2017. A bioinformatic pipeline for monitoring of the mutational stability of viral drug targets with deepsequencing technology. Viruses. 9, pii: E357. doi 10.3390/v9120357Google Scholar
  12. 12.
    Okonechnikov K., Golosova O., Fursov M., et al. 2012. Unipro UGENE: A unified bioinformatics toolkit. Bioinformatics. 28, 1166–1167.CrossRefPubMedGoogle Scholar
  13. 13.
    Johnson N.L., Leone F.C. 1977. Statistics and Experimental Design in Engineering and the Physical Sciences. New York: Wiley.Google Scholar
  14. 14.
    Pathak V.K., Temin H.M. 1990. Broad spectrum of in vivo forward mutations, hypermutations, and mutational hotspots in a retroviral shuttle vector after a single replication cycle: Substitutions, frameshifts, and hypermutations. Proc. Natl. Acad. Sci. U. S. A. 87, 6019–6023.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Kim T., Mudry R.A., Jr., Rexrode C.A., Pathak V.K. 1996. Retroviral mutation rates and A-to-G hypermutations during different stages of retroviral replication. J. Virol. 70, 7594–7602.PubMedPubMedCentralGoogle Scholar
  16. 16.
    Rezende L. F., Prasad V.R. 2004. Nucleoside-analog resistance mutations in HIV-1 reverse transcriptase and their influence on polymerase fidelity and viral mutation rates. Int. J. Biochem. Cell Biol. 36, 1716–1734.CrossRefPubMedGoogle Scholar
  17. 17.
    Abram M.E., Ferris A.L., Shao W., et al. 2010. Nature, position, and frequency of mutations made in a single cycle of HIV-1 replication. J. Virol. 84, 9864–9878.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Cuevas J.M., Geller R., Garijo R., et al. 2015. Extremely high mutation rate of HIV-1 in vivo. PLoS Biol. 13, e1002251.CrossRefGoogle Scholar
  19. 19.
    Garforth S.J., Lwatula C., Prasad V.R. 2014. The Lysine 65 residue in HIV-1 reverse transcriptase function and in nucleoside analog drug resistance. Viruses. 6, 4080–4094.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  • O. V. Kretova
    • 1
  • M. A. Gorbacheva
    • 1
  • D. M. Fedoseeva
    • 1
  • Y. V. Kravatsky
    • 1
  • V. R. Chechetkin
    • 1
  • N. A. Tchurikov
    • 1
  1. 1.Engelhardt Institute of Molecular BiologyRussian Academy of SciencesMoscowRussia

Personalised recommendations