A short-term assessment of nascent HIV-1 transmission clusters among newly diagnosed individuals using envelope sequence-based phylogenetic analyses

The identification of transmission clusters (TCs) of HIV-1 using phylogenetic analyses can provide insights into viral transmission network and help improve prevention strategies. We compared the use of partial HIV-1 envelope fragment of 1,070 bp with its loop 3 (108 bp) to determine its utility in inferring HIV-1 transmission clustering. Serum samples of recently (n = 106) and chronically (n = 156) HIV-1-infected patients with status confirmed were sequenced. HIV-1 envelope nucleotide-based phylogenetic analyses were used to infer HIV-1 TCs. Those were constructed using ClusterPickerGUI_1.2.3 considering a pairwise genetic distance of £10% threshold. Logistic regression analyses were used to examine the relationship between the demographic factors that were likely associated with HIV-1 clustering. Ninety-eight distinct consensus envelope sequences were subjected to phylogenetic analyses. Using a partial envelope fragment sequence, 42 sequences were grouped into 15 distinct small TCs while the V3 loop reproduces 10 clusters. The agreement between the partial envelope and the V3 loop fragments was significantly moderate with a Cohen’s kappa (j) coefficient of 0.59, p < .00001. The mean age (<38.8 years) and HIV-1 B subtype are two factors identified that were significantly associated with HIV-1 transmission clustering in the cohort, odds ratio (OR) = 0.25, 95% confidence interval (CI, 0.04–0.66), p = .002 and OR: 0.17, 95% CI (0.10–0.61), p = .011, respectively. The present study confirms that a partial fragment of the HIV-1 envelope sequence is a better predictor of transmission clustering. However, the loop 3 segment may be useful in screening purposes and may be more amenable to integration in surveillance programs

HIV-1 envelope sequence-based diversity measures for identifying recent infections

Identifying recent HIV-1 infections is crucial for monitoring HIV-1 incidence and optimizing public health prevention efforts. To identify recent HIV-1 infections, we evaluated and compared the performance of 4 sequence-based diversity measures including percent diversity, percent complexity, Shannon entropy and number of haplotypes targeting 13 genetic segments within the env gene of HIV-1. A total of 597 diagnostic samples obtained in 2013 and 2015 from recently and chronically HIV-1 infected individuals were selected. From the selected samples, 249 (134 from recent versus 115 from chronic infections) env coding regions, including V1-C5 of gp120 and the gp41 ectodomain of HIV-1, were successfully amplified and sequenced by next generation sequencing (NGS) using the Illumina MiSeq platform. The ability of the four sequence-based diversity measures to correctly identify recent HIV infections was evaluated using the frequency distribution curves, median and interquartile range and area under the curve (AUC) of the receiver operating characteristic (ROC). Comparing the median and interquartile range and evaluating the frequency distribution curves associated with the 4 sequence-based diversity measures, we observed that the percent diversity, number of haplotypes and Shannon entropy demonstrated significant potential to discriminate recent from chronic infections (p<0.0001). Using the AUC of ROC analysis, only the Shannon entropy measure within three HIV-1 env segments could accurately identify recent infections at a satisfactory level. The env segments were gp120 C2_1 (AUC = 0.806), gp120 C2_3 (AUC = 0.805) and gp120 V3 (AUC = 0.812). Our results clearly indicate that the Shannon entropy measure represents a useful tool for predicting HIV-1 infection recency

A significant reduction in the frequency of HIV-1 drug resistance in Québec from 2001 to 2011 is associated with a decrease in the monitored viral load.

BACKGROUND: HIV drug resistance represents a major threat for effective treatment. We assessed the trends in the frequency of drug resistance mutations and the monitored viral load (VL) in treatment-naïve (TN) and treatment-experienced (TE) individuals infected with HIV-1 in Québec, Canada, between 2001 and 2011. METHODS AND FINDINGS: Resistance data were obtained from 4,105 and 5,086 genotypic tests performed on TN and TE patients, respectively. Concomitantly, 274,161 VL tests were carried out in the Province. Changes over time in drug resistance frequency and in different categories of VL were assessed using univariate logistic regression. Multiple logistic regression was used to evaluate associations between the rates of certain mutations and antiretroviral prescriptions. From 2001 to 2011, the proportion of undetectable VL test results continually increased, from 42.1% to 75.9%, while a significant decrease in the frequency of resistance mutations associated with protease inhibitors [PI (from 54% to 16%)], nucleoside [NRTI (from 78% to 37%) and non-nucleoside reverse transcriptase inhibitors [NNRTI (from 44% to 31%)] was observed in TE patients. In TN individuals, the overall frequency of transmitted drug resistance was 13.1%. A multiple logistic regression analysis indicated that the introduction of co-formulated emtricitabine/tenofovir or emtricitabine/tenofovir/efavirenz was positively associated with the decrease of the frequency of the M184I/V mutations observed overtime (p = 0.0004). CONCLUSIONS: We observed a significant decrease in the frequency of drug resistance mutations in TE patients, concomitant with a decrease in the proportion of patients with detectable viremia. These findings may be related to both the increased potencies and adherence to therapy associated with newer antiretroviral regimens. Nevertheless, our data demonstrate that broad use of antiretrovirals does not increase the level of circulating drug resistant variants

Frequency polygons (ggplot2) of Shannon entropy index of <i>env</i> sequences of recent HIV-1 infected individuals compare to chronically infected ones by <i>env</i> segments.

<p>The Y axis represents the density of observations (frequency) and the X axis the Shannon entropy index distribution as sequence-based diversity measure. The blue color represents plot and distribution for recent HIV-1 infected population and the red color plot and distribution for chronic infected ones.</p

ROC curves comparing the predictive performance of different combinations of sequence-based diversity measures of HIV-1 gp120 conserved subdomains to identify HIV-1 infection recency.

<p>Five combinations of sequence-based diversity measures were analyzed. Shannon entropy + percent diversity + percent complexity: P1; percent diversity+ number of haplotypes+ percent complexity: P2; number of haplotypes+ percent complexity: P3; Shannon entropy+ percent complexity: P4 and percent diversity+ percent complexity: P5. Seven HIV-1 <i>env</i> segments were considered: gp120-C2_1; gp120-C2_2; gp120-C2_3; gp120-C3_1; gp120-C3_2; gp120-C4 and gp120-C5. ROC = receiver operating characteristics; AUC = area under the curve. AUC values between 0.8 and 1 were considered performance measures.</p

ROC curves comparing the predictive performance of different combinations of sequence-based diversity measures of HIV gp120-C2-1, gp120-C2_3 and gp120-V3 segments for identifying HIV-1 subtype B infection recency.

<p>Five combinations of sequence-based diversity measures were analyzed: P1, percent complexity; P2, percent diversity; P3, number of haplotypes; P4, Shannon entropy; P5, Shannon entropy+ percent diversity and P6, Number of haplotypes+ percent diversity. Three HIV-1 env segments were considered: gp120-C2_1, gp120- C2_3 and gp120-V3 -. ROC = receiver operating characteristics; AUC = area under the curve. AUC values between 0.8 and 1 were considered performance measures.</p

Schematic figure showing all <i>env</i> segments used for diversity estimates.

<p>Segments length corresponds to that of strain HXB2 of HIV-1 nucleotides positions. Segments used are denoted by asterisks. Env domain abbreviations: SP, signal peptide; C1–C5, conserved domains 1 to 5; V1–V5, variable domains 1 to 5; FP, fusion peptide; HR1, heptad repeat 1 (NHR); DL, disulfide loop; HR2, heptad repeat 2 (CHR); MPER, membrane proximal ectodomain region; TM, transmembrane domain; CD, cytoplasmic domain. Image were friendly adapted from Michael Caffrey[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189999#pone.0189999.ref061" target="_blank">61</a>]; Trends in Microbiology, Volume 19, Issue 4, Pages 191–197 (April 2011) 10.1016/j.tim.2011.02.001.</p