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. 2016 Dec 13;11(12):e0166902.
doi: 10.1371/journal.pone.0166902. eCollection 2016.

An Efficient Microarray-Based Genotyping Platform for the Identification of Drug-Resistance Mutations in Majority and Minority Subpopulations of HIV-1 Quasispecies

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Free PMC article

An Efficient Microarray-Based Genotyping Platform for the Identification of Drug-Resistance Mutations in Majority and Minority Subpopulations of HIV-1 Quasispecies

Verónica Martín et al. PLoS One. .
Free PMC article

Abstract

The response of human immunodeficiency virus type 1 (HIV-1) quasispecies to antiretroviral therapy is influenced by the ensemble of mutants that composes the evolving population. Low-abundance subpopulations within HIV-1 quasispecies may determine the viral response to the administered drug combinations. However, routine sequencing assays available to clinical laboratories do not recognize HIV-1 minority variants representing less than 25% of the population. Although several alternative and more sensitive genotyping techniques have been developed, including next-generation sequencing (NGS) methods, they are usually very time consuming, expensive and require highly trained personnel, thus becoming unrealistic approaches in daily clinical practice. Here we describe the development and testing of a HIV-1 genotyping DNA microarray that detects and quantifies, in majority and minority viral subpopulations, relevant mutations and amino acid insertions in 42 codons of the pol gene associated with drug- and multidrug-resistance to protease (PR) and reverse transcriptase (RT) inhibitors. A customized bioinformatics protocol has been implemented to analyze the microarray hybridization data by including a new normalization procedure and a stepwise filtering algorithm, which resulted in the highly accurate (96.33%) detection of positive/negative signals. This microarray has been tested with 57 subtype B HIV-1 clinical samples extracted from multi-treated patients, showing an overall identification of 95.53% and 89.24% of the queried PR and RT codons, respectively, and enough sensitivity to detect minority subpopulations representing as low as 5-10% of the total quasispecies. The developed genotyping platform represents an efficient diagnostic and prognostic tool useful to personalize antiviral treatments in clinical practice.

Conflict of interest statement

Competing Interests: We have the following interests: This study was supported by Biotherapix, S.L.U. Patricia Garrido, María Pernas and José Luis Torán were employed by Biotherapix, S.L.U. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Figures

Fig 1
Fig 1. Examples of the density of normalized hybridization signals from the training set and their corresponding distribution functions for positive and negative data.
A) General reference curves; B) Probe-specific curves for probe Y188a, showing no overlap between positive and negative distribution functions. C) Probe-specifc curves for probe M230-3, with high overlap between positive and negative distribution functions (probe discarded during the quality control, see text). The probe-specific curves for the 124 probes that passed the quality control are shown in Figure G in S1 File. Color code: Red, density of normalized negative hybridization signals; Blue, fit of the negative data to a log-normal distribution; Black, density of normalized positive hybridization signals; Green, fit of the positive data to a normal distribution.
Fig 2
Fig 2. Detection sensitivity, estimated based on binary mixtures of pure samples.
A) Theoretical hybridization tables of the pure samples used in the mixtures (1.95c9/2.94c64 and pWT/pINS) presenting 7 discriminating probes. Color code: Red, expected positive hybridization; White, expected negative hybridization. B) Rate at which each sample in the mixture produces a positive hybridization with each probe identified in panel A. Bar shows the transition from white (positive signal not detected) to red (positive signal detected in all the hybridization experiments).
Fig 3
Fig 3. Classification accuracy of the hybridized clinical samples that contained minority subpopulations within their mutant spectra.
Columns: probes included in the microarray corresponding to the PR (A) and RT (B) regions that passed the stepwise filtering protocol. Rows: hybridized samples. Color code: Dark green, correctly classified signal (TP or TN); Dark red, FN signal; Red, FP signal; Black, UD signal; Light green, no data (i.e. individual spots discarded during the quality control or by probe absence in some versions of the microarray).
Fig 4
Fig 4. Detection of minority variants in clinical samples by the genotyping microarray.
Columns: probes included in the microarray belonging to the PR (A) and RT (B) regions that passed the stepwise filtering protocol. Color code: Green, hybridization signal produced at the corresponding probe when the complementary target is present within a given rate (shown on the right side of each panel) in the quasispecies; White, lack of detection of an expected signal; Black, no data available (the sequence complementary to the queried codon is not present in any of the clinical samples at the corresponding percentage range).

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Grant support

This work was supported by the Spanish Ministerio de Ciencia e Innovación (MICINN grant BIO2010-20696 to CB), Ministerio de Economía y Competitividad (MINECO grants BIO2013-47228-R to CB and SAF2014-52400-R to ED), Comunidad Autónoma de Madrid (PLATESA, grant S2013/ABI-2906 to ED), Biotherapix, S.L.U. and FEDER funds from the European Union. Work at CIBERehd is funded by the Instituto de Salud Carlos III (ISCIII). MICINN provided funding for VM under grants RyC2010-06516 and AGL2015-64290-R. CP is supported by Miguel Servet program of the ISCIII (CP14/00121), co-financed by the European Regional Development Fund (ERDF). Funding for open access charge: Spanish National Research Council (CSIC). The funders provided support in the form of salaries and research materials for authors [MICINN and MINECO for VM, CP, MF-A, HGDS, VP, MM, DA, ED and CB; ISCIII for CP, JG-P and JA; Biotherapix, S.L.U. for PG, MP and JLT], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.
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