Aids virus mutation rate

Since cell tropism and disease progression are associated [ 65 , 66 ], this might produce a correlation between the HIV-1 mutation rate and disease progression. Computational analysis of consensus V3-loop sequences suggested CCR-5 coreceptor usage in all patients except R5 and, therefore, we found no evidence that systematic differences in cell tropism among patients may drive the observed correlations. In conclusion, we have inferred an extremely high mutation rate in HIV-1 patients, which is mainly caused by A3-driven editing of the viral genome.

We argue that analysis of sequences from plasma grossly underestimates the HIV-1 mutation rate due to the abundance of lethal mutations that are incompatible with the release of viral particles. A3 proteins have also been shown to mutate hepatitis B virus [ 67 ] and nonreverse transcribing DNA viruses such as papillomaviruses [ 68 ] or herpesviruses [ 69 ]. In addition, the double-stranded RNA-specific adenosine deaminase ADAR can edit the genomes of many RNA viruses including measles virus [ 70 ], human parainfluenza virus [ 71 ], respiratory syncytial virus [ 72 ], lymphocytic choriomeningitis virus [ 73 ], and Rift Valley fever virus [ 74 ].

Future work may elucidate whether intracellular sequences also harbor higher-than-average mutation rates in these viruses, similar to A3-edited HIV-1 sequences. As shown for HIV-1, host-mediated hypermutation of viral genomes can be regarded as an antiviral mechanism, but a downside of this process is that it may contribute to viral genetic diversity and pathogenesis. In our study, we have focused only on normal and rapid progressors, but it remains unclear whether similar results will be observed in other disease progression categories such as long-term nonprogressors or elite controllers.

Samples were processed following standard procedures and frozen immediately upon reception. All patients participating in the study gave their informed consent, and protocols were approved by institutional ethics committees. The program was approved by the Institutional Review Boards of the participating hospitals and centers.

Nearly complete HIV genomes were amplified in three overlapping PCR fragments named as regions 1, 2, and 3 using different sets of primer described previously [ 76 ], and which are provided in S1 Table. Positive reactions were picked from the well plates and visually checked in agarose gels for equal concentration. For each patient, fifty clonal PCR products were obtained for each region. To obtain the consensus for each patient, 50, paired reads were subsampled from each library, pooled, and mapped using Bowtie 2 version 2.

Reads mapping to each region were then split using a custom script and the consensus sequence obtained using VICUNA [ 79 ] with default settings. Aligning reads to each half genome region was necessary to properly assemble the 5' and 3' UTR regions. Contigs from both regions were then merged using the contigMerger. V-profiler [ 81 ] was then used to call mutations at each codon position, excluding mutations occurring only at the last 10 bases of reads.

In addition, a consensus sequence for each mix mix consensus was obtained from the nucleotide frequency output file of V-phaser 2 and an overall consensus for each patient derived from these. Positive primary PCRs of fragment 3 from libraries showing at least two stop codons per pool as determined by NGS were reamplified, column-purified, and subjected to Sanger sequencing using the reverse PCR primer.

Chromatograms were analyzed using the Staden package version 2. Sequences were converted to Fasta using Egglib software version 2. These were separated into individual alignments for each patient including the precalculated consensus using a custom script.

For gag , sequences were downloaded from the HIV database that encompassed the entire gene, included at least four sequences per patient, and were derived by single genome amplification, cloning, or limiting dilution. Sequences from individual patients were then separated, and a consensus calculated using Biostrings R package. For each patient in the three datasets, sequences with frameshifts were removed. NSMTs are codons that can mutate to a stop codon via a single nucleotide substitution [ 53 , 54 ].

The presence of these codons was identified in each reference sequence and their abundance calculated by multiplying by the number of sequences for each patient. Mutation rates for RT were calculated similarly but were multiplied by three to account for the fact that only one out of three bases at each NSMT produces an observable stop codon.

These are likely due to selective amplification of nonhypermutated genomes due to primer mismatches and were therefore not included in the analysis. The resulting PCR was directly sequenced to ascertain the presence of a glycine or arginine at amino acid position reference SNP The PCR product was then cloned, and 5 clones per patient were analyzed for the presence of a deletion at amino acid position 15 reference SNP With the exception of R15, all patients were homozygous for a deletion at position 15, indicating an unstable A3H genotype.

R15 was homozygous for the presence of an asparagine at position 15 and hence carried one stable and one unstable allele. Mutation rates inferred from plasma RNA are also provided. For each patient columns , genes, NSMT-containing codons, and the observed number of stop codons are shown.

Each library containing at least two stops was analyzed by sequencing the nef gene of each of the five clones i. The patient, library, and number of stop codons found in each clone are shown.

The observed number of clones with zero stops is compared with the number expected under a Poisson model, and a p -value is provided for each library. The per-year CD4 count decay rate was obtained by linear regression dashed lines.

The numerical values shown in this Figure are provided in the S2 Data file. The set-point viral load was obtained by averaging the log load values obtained at least one year postinfection dashed lines. Numerical values can be obtained from the S3 Data file. Each column corresponds to a sequencing library showing at least two total nef stop-codons from the indicated patient. Each stacked bar shows the number of stop codons found in each clone of that library clones with no stops are not represented.

Asterisks indicate libraries in which mutations were significantly clustered in a subset of clones. Numerical values are provided in the S5 Data file. The mutation rate within a sliding window of 50 codons skylines and the average for each gene dashed lines are shown for rapid red and normal blue progressors. We thank Dr. We want to particularly acknowledge the patients in this study for their participation and the HIV BioBank integrated in the Spanish AIDS Research Network and collaborating centers for the generous gifts of clinical samples used in this work.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Center for Biotechnology Information , U. PLoS Biol. Published online Sep Sarah L. Rowland-Jones, Academic Editor. Author information Article notes Copyright and License information Disclaimer. Received Apr 3; Accepted Aug This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

This article has been cited by other articles in PMC. S2 Data: Detailed clinical data. The infection time, viral load, and CD4 count are provided for each patient sample.

S3 Data: List and location of stop codons in each patient. S4 Data: List of stop codons found in each sequencing library.

S5 Data: Stop codons in individual nef sequences. S1 Fig: CD4 counts of the 11 patients included in this study. S2 Fig: Viral load determinations for the 11 patients included in this study. S4 Fig: Distribution of the number of stop codons per library in nef. Abstract Rates of spontaneous mutation critically determine the genetic diversity and evolution of RNA viruses. Author Summary The high levels of genetic diversity of the HIV-1 virus grant it the ability to escape the immune system, to rapidly evolve drug resistance, and to circumvent vaccination strategies.

Introduction RNA viruses exist as extremely diverse populations, with every possible spontaneous mutation along the genome appearing within each patient every day [ 1 ]. Results Inference of the HIV-1 Mutation Rate In Vivo To infer the HIV-1 mutation rate from patient-derived sequences, we used the lethal mutation method [ 27 ], which builds on the principle that the frequency of lethal mutations in a population equals their rate of production, as these cannot be inherited.

Table 1 Patient clinical data and mutation rate summary. Open in a separate window. Fig 1. Fig 2. Distribution of mutation rates across HIV-1 genes. Fig 3. Variation in mutation rate among HIV-1 sequences. Fig 4. Association between HIV-1 mutation rate and disease progression markers.

Discussion The rate of spontaneous mutation is a major determinant of viral diversity and evolution, plays a role in the success of vaccination strategies [ 43 , 44 ], determines the likelihood that live attenuated vaccines revert to virulence [ 45 ], and influences the risk of disease emergence at the epidemiological level [ 46 , 47 ].

Fig 5. Comparison of viral mutation rates and effect of A3-mediated editing. Sanger Sequencing of nef Clones Positive primary PCRs of fragment 3 from libraries showing at least two stop codons per pool as determined by NGS were reamplified, column-purified, and subjected to Sanger sequencing using the reverse PCR primer.

Supporting Information S1 Data Average mutation rates and clinical information for each patient. XLSX Click here for additional data file. S2 Data Detailed clinical data. S3 Data List and location of stop codons in each patient. S4 Data List of stop codons found in each sequencing library. S5 Data Stop codons in individual nef sequences. S1 Fig CD4 counts of the 11 patients included in this study. TIF Click here for additional data file.

S2 Fig Viral load determinations for the 11 patients included in this study. S4 Fig Distribution of the number of stop codons per library in nef.

Acknowledgments We thank Dr. References 1. Perelson AS. Modelling viral and immune system dynamics. Nat Rev Immunol. Virulence and pathogenesis of HIV-1 infection: an evolutionary perspective. The origin of genetic diversity in HIV Mutation rate assays for HIV-1 have been reported; however, none measure directly the forward mutation rate during replication of the virus in cell culture while still retaining the ability to propagate and further study mutant proviruses.

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