Impact of Sampling Density on the Extent of HIV Clustering

NovitskyVlad,; Moyo, Sikhulile; LeiQuanhong,; DeGruttolaVictor,; Essex, Max

doi:10.1089/aid.2014.0173

Cited by 61 publications

(74 citation statements)

References 43 publications

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“…A larger set of available HIV-1C sequences (n ϭ 547) included matched viral sequences for amplicon 1 and the V1C5 region of gp120 generated in our previous studies (34,35,80). Clustering patterns were compared for two long loci, amplicon 1 and concatenated amplicon 1 plus V1C5, and for two short regions across the HIV-1 genome, ViroSeq and V1C5.…”

Section: Resultsmentioning

confidence: 99%

“…A combination of conserved (amplicon 1) and variable (amplicon 2) regions could help to deal with different and/or unknown stages of HIV infection in an analyzed set of viral sequences. The choice of a particular bootstrap value and filtering by the threshold of pairwise distances and/or internode certainty (94,95) could depend on the specific scientific question and take into account the specifics of an analyzed set of sequences, including sampling density (35).…”

Section: Discussionmentioning

confidence: 99%

“…The technique of long-range HIV genotyping was applied to specimens collected within three Botswana-Harvard AIDS Institute Partnership (BHP) studies performing viral genotyping: the HIV Prevention Program for Mochudi, Botswana (Mochudi Prevention Project [MPP]; R01 AI083036; principal investigator, M. Essex) (34,35); the GWAS on Determinants of HIV-1 Subtype C Infection study (RC4 AI092715; principal investigator, M. Essex); and the Botswana Combination Prevention Project (BCPP, or Ya Tsie; U01 GH000447; principal investigator, M. Essex) (77). All studies were approved by the Health Research and Development Committee (HRDC) of the Republic of Botswana and the Office of Human Research Administration (OHRA) of the Harvard T.H.…”

Section: Methodsmentioning

confidence: 99%

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Long-Range HIV Genotyping Using Viral RNA and Proviral DNA for Analysis of HIV Drug Resistance and HIV Clustering

et al. 2015

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HIV genotyping is a critical tool for antiviral drug resistance testing that has revolutionized HIV care and advanced HIVrelated research. Routine antiretroviral (ARV) drug resistance testing is useful in choosing an optimal treatment regimen and monitoring its efficiency in clinical practice (1-12). HIV genotyping has been used successfully in research on HIV transmission clusters and HIV transmission dynamics (13-35).Initial broadly used ARV regimens included combinations of nucleoside reverse transcriptase (RT) inhibitors (NRTIs) and nonnucleoside reverse transcriptase inhibitors (NNRTIs). To monitor the emergence of drug resistance mutations associated with NRTIs and NNRTIs, HIV genotyping targeted viral sequences spanning an approximately 1,000-to 1,300-bp region of the HIV-1 genome encoding viral protease and partial RT, using viral RNA as a template for amplification. While the RNA-based approach works well in antiretroviral therapy (ART)-naive individuals, it is less successful if levels of viral replication are low, such as in individuals on ART. The sequence length of traditional RNAbased HIV genotyping for drug resistance is relatively short and does not cover the HIV-1 region encoding viral integrase or the viral envelope, hindering analysis of drug resistance mutations associated with integrase strand transfer inhibitors or entry inhibitors. The global scale up of ARV treatment and successful introduction of integrase strand transfer inhibitors and entry inhibitors into clinical trials and clinical practice necessitate modification of traditional methods of HIV genotyping.Two commercial genotyping assays, ViroSeq HIV-1 from Abbott Molecular and TruGene HIV-1 from Siemens Molecular Diagnostics, have been widely used for analysis of HIV-1-associated drug resistance. Both genotyping kits were extensively tested and validated (36)(37)(38)(39)(40)(41)(42)(43)(44)(45). While the ViroSeq HIV-1 kit is still on the market, Siemens discontinued selling and supporting the TruGene HIV-1 kit in 2014. The ViroSeq HIV-1 kit covers the entire protease-coding region and the RT region encoding the first 320 amino acids. The TruGene HIV-1 sequences span the protease (amino acids 4 to 99)-and RT (amino acids 40 to 240)-coding regions. The CDC supplies WHO-designated and CDC-supported President's Emergency Plan for AIDS Relief (PEPFAR) Genotyping Laboratories with the ATCC HIV-1 Drug Resistance Genotyping kit (46) for drug resistance testing. Many experienced genotyping laboratories have developed their own in-house amplification and sequencing protocols (11,(47)(48)(49)(50)(51)(52)(53)(54)(55)(56), including identification of minor viral variants that are normally missed by commercial genotyping kits (57-61). All of these approaches generally include smaller and more restricted regions for testing HIV-1 drug resistance.Recently, the protocol developed by Gall et al. (62)

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Long-Range HIV Genotyping Using Viral RNA and Proviral DNA for Analysis of HIV Drug Resistance and HIV Clustering

et al. 2015

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“…It is possible that patterns of HIV clustering might differ between modes of viral transmission and HIV-1 subtypes associated with particular modes of transmission. As we demonstrated recently, 73 sampling density is another critical factor affecting the extent of HIV clustering.…”

Section: Discussionmentioning

confidence: 64%

Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering

Novitsky

Moyo

Lei

et al. 2015

AIDS Research and Human Retroviruses

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To improve the methodology of HIV cluster analysis, we addressed how analysis of HIV clustering is associated with parameters that can affect the outcome of viral clustering. The extent of HIV clustering and tree certainty was compared between 401 HIV-1C near full-length genome sequences and subgenomic regions retrieved from the LANL HIV Database. Sliding window analysis was based on 99 windows of 1,000 bp and 45 windows of 2,000 bp. Potential associations between the extent of HIV clustering and sequence length and the number of variable and informative sites were evaluated. The near full-length genome HIV sequences showed the highest extent of HIV clustering and the highest tree certainty. At the bootstrap threshold of 0.80 in maximum likelihood (ML) analysis, 58.9% of near full-length HIV-1C sequences but only 15.5% of partial pol sequences (ViroSeq) were found in clusters. Among HIV-1 structural genes, pol showed the highest extent of clustering (38.9% at a bootstrap threshold of 0.80), although it was significantly lower than in the near full-length genome sequences. The extent of HIV clustering was significantly higher for sliding windows of 2,000 bp than 1,000 bp. We found a strong association between the sequence length and proportion of HIV sequences in clusters, and a moderate association between the number of variable and informative sites and the proportion of HIV sequences in clusters. In HIV cluster analysis, the extent of detectable HIV clustering is directly associated with the length of viral sequences used, as well as the number of variable and informative sites. Near full-length genome sequences could provide the most informative HIV cluster analysis. Selected subgenomic regions with a high extent of HIV clustering and high tree certainty could also be considered as a second choice.

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“…This kind of growth curve has three qualitative phases: exponential growth, linear growth, and a slow final phase when the susceptible population is almost depleted. The waiting times until the next transmission, which determine the coalescence times in the tree, are MSM/USA (Little et al 2014) MSM/Taiwan (Kao et al 2011) MSM/Beijing (Wang et al 2015) HET/Uganda (Grabowski et al 2014) HET/Malawi (McCormack et al 2002) HET/Botswana (Novitsky et al 2013(Novitsky et al & 2014 IDU/Romania (Niculescu et al 2015) IDU/Estonia (Zetterberg et al 2004) dependent on the growth phase of the epidemic. Therefore, we hypothesize that it is the growth phase at the time of sampling which most affects tree shape, rather than the specific values of I or N ( Supplementary Fig.…”

Section: Analysis Of Ba Model With Synthetic Datamentioning

confidence: 99%

Reconstructing contact network parameters from viral phylogenies

McCloskey

Liang

Poon

2016

Preprint

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Models of the spread of disease in a population often make the simplifying assumption that the population is homogeneously mixed, or is divided into homogeneously mixed compartments. However, human populations have complex structures formed by social contacts, which can have a significant influence on the rate of epidemic spread. Contact network models capture this structure by explicitly representing each contact which could possibly lead to a transmission. We developed a method based on approximate Bayesian computation (ABC), a likelihood-free inference strategy, for estimating structural parameters of the contact network underlying an observed viral phylogeny. The method combines adaptive sequential Monte Carlo for ABC, Gillespie simulation for propagating epidemics though networks, and a kernel-based tree similarity score. We used the method to fit the Barab asi-Albert network model to simulated transmission trees, and also applied it to viral phylogenies estimated from ten published HIV sequence datasets. This model incorporates a feature called preferential attachment (PA), whereby individuals with more existing contacts accumulate new contacts at a higher rate. On simulated data, we found that the strength of PA and the number of infected nodes in the network can often be accurately estimated. On the other hand, the mean degree of the network, as well as the total number of nodes, was not estimable with ABC. We observed sub-linear PA power in all datasets, as well as higher PA power in networks of injection drug users. These results underscore the importance of considering contact structures when performing phylodynamic inference. Our method offers the potential to quantitatively investigate the contact network structure underlying viral epidemics.

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Impact of Sampling Density on the Extent of HIV Clustering

Cited by 61 publications

References 43 publications

Long-Range HIV Genotyping Using Viral RNA and Proviral DNA for Analysis of HIV Drug Resistance and HIV Clustering

Long-Range HIV Genotyping Using Viral RNA and Proviral DNA for Analysis of HIV Drug Resistance and HIV Clustering

Importance of Viral Sequence Length and Number of Variable and Informative Sites in Analysis of HIV Clustering

Reconstructing contact network parameters from viral phylogenies

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