A novel methodology for large-scale phylogeny partition

Prosperi, Mattia; Ciccozzi, Massimo; Fanti, Iuri; Saladini, Francesco; Pecorari, Monica; Borghi, Vanni; Giambenedetto, Simona Di; Bruzzone, Bianca; Capetti, Amedeo; Vivarelli, Angela; Rusconi, Stefano; Re, Maria Carla; Gismondo, Maria Rita; Sighinolfi, Laura; Gray, Rebecca; Salemi, Marco; Zazzi, Maurizio; Luca, Andrea De

doi:10.1038/ncomms1325

Cited by 120 publications

(128 citation statements)

References 40 publications

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“…A viral lineage (group or subtree) with at least two viral sequences and specified statistical support was considered to be an HIV cluster. Clusters were identified using a depth-first algorithm (87,88), a method for traversing or searching tree or graph data structures starting from the root. This approach eliminated double counting of viral sequences in clusters when the clusters had internal structure with strong support.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

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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“…Specifically, a median pairwise patristic distance cutoff of 0.74 resulted in clusters at this percentage level. The algorithm is essentially as outlined previously (37), except that internal nodes in the tree were not accounted for in our implementation. The algorithm was implemented in R (clustertree.R code for clustering phylogenetic trees, Glenn Lawyer, figshare [2012], http://dx.doi.org/10.6084/m9.figshare.97 225).…”

Section: Methodsmentioning

confidence: 99%

Composite Bacterial Hopanoids and Their Microbial Producers across Oxygen Gradients in the Water Column of the California Current

Kharbush

Ugalde

Hogle

et al. 2013

Appl Environ Microbiol

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bHopanoids are pentacyclic triterpenoid lipids produced by many prokaryotes as cell membrane components. The structural variations of composite hopanoids, or bacteriohopanepolyols (BHPs), produced by various bacterial genera make them potentially useful molecular biomarkers of bacterial communities and metabolic processes in both modern and ancient environments. Building on previous work suggesting that organisms in low-oxygen environments are important contributors to BHP production in the marine water column and that there may be physiological roles for BHPs specific to these environments, this study investigated the relationship between trends in BHP structural diversity and abundance and the genetic diversity of BHP producers for the first time in a low-oxygen environment of the Eastern Tropical North Pacific. Amplification of the hopanoid biosynthesis gene for squalene hopene cyclase (sqhC) indicated far greater genetic diversity than would be predicted by examining BHP structural diversity alone and that greater sqhC genetic diversity exists in the marine environment than is represented by cultured representatives and most marine metagenomes. In addition, the genetic relationships in this data set suggest microaerophilic environments as potential "hot spots" of BHP production. Finally, structural analysis of BHPs showed that an isomer of the commonly observed BHP bacteriohopanetetrol may be linked to a producer that is more abundant in low-oxygen environments. Results of this study increase the known diversity of BHP producers and provide a detailed phylogeny with implications for the role of hopanoids in modern bacteria, as well as the evolutionary history of hopanoid biosynthesis, both of which are important considerations for future interpretations of the marine sedimentary record.

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“…[1][2][3][4][5][6] The identification of transmission clusters can support epidemiologically linked transmission events, 7 identify putative transmission chains, 8 and reveal mixing between key risk groups and geographic subpopulations. 9 Transmission clusters of HIV infections are typically defined using either genetic distances among sequences 4 or genetic distances in addition to branch support values (e.g., bootstrap values).…”

Section: Introductionmentioning

confidence: 99%

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Rose¹,

Lamers²,

Dollar³

et al. 2017

AIDS Research and Human Retroviruses

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We compared the behavior of two approaches (Cluster Picker and HIV-TRACE) at varying genetic distances to identify transmission clusters. We used three HIV gp41 sequence datasets originating from the Rakai Community Cohort Study: (1) next-generation sequence (NGS) data from nine linked couples; (2) NGS data from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n = 1,022) containing 91 epidemiologically linked heterosexual couples. We calculated the optimal genetic distance threshold to separate linked versus unlinked NGS datasets using a receiver operating curve analysis. We evaluated the number, size, and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%-5.3%) on all three datasets. We further tested the effect of using all NGS, versus only a single variant for each patient/time point, for datasets (1) and (2). The optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4%, respectively. HIV-TRACE tended to detect larger and fewer clusters, whereas Cluster Picker detected more clusters containing only two sequences. For NGS datasets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distances, respectively. However, at 5.3% genetic distance, 20% of couples in dataset (3) did not cluster using either program, and for >1/3 of couples cluster assignment were discordant. We suggest caution in choosing thresholds for clustering analyses in a generalized epidemic.

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A novel methodology for large-scale phylogeny partition

Cited by 120 publications

References 40 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

Composite Bacterial Hopanoids and Their Microbial Producers across Oxygen Gradients in the Water Column of the California Current

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

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