Genetic diversity of HIV in seminal plasma remains higher than in blood after short-term antiretroviral therapy.

OBJECTIVE: To provide insight on viral kinetics and genetic diversity of HIV in seminal plasma at baseline and 1 month after initiating antiretroviral therapy (ART). PATIENTS AND METHODS: Blood and seminal samples from patients with newly diagnosed HIV were obtained before ART initiation (T0) and 1 month after ART initiation (T1). HIV env genetic diversity was studied using deep sequencing Nextera and V3 chemistry in a MiSeq Illumina platform. The number of viral quasispecies (5% cut-off) and Shannon Index were used to analyse diversity. RESULTS: Forty-seven ART-naive patients were recruited between September 2016 and November 2018. At enrolment, the number of quasispecies in blood (median 4 (IQR 2-5)) was lower than in the seminal compartment (median 6, (IQR 4-8)) (p<0.01); the Shannon Index was also higher (p<0.001) in the seminal compartment than in blood (1.77 vs 0.64). At T1, for the 13 patients with detectable HIV in both blood/seminal plasma, viral diversity remained higher (p=0.139) in seminal plasma (median 2 (IQR 1-4.5)) than in blood (median 1 (IQR 1-1.5)) Integrase inhibitors (INI)-based regimens achieved higher levels of undetectability and led more frequently to lower variability (p<0.001) than protease inhibitors (PI) or non-nucleoside reverse transcriptase inhibitors (NNRTI). CONCLUSION: We provide here further evidence of a larger genetic diversity in seminal plasma, both at diagnosis and short term after ART initiation. Our results strengthen previous findings on HIV diversity in seminal plasma. In addition, INIs decrease variability more rapidly than PI and NNRTI in both blood and seminal plasma.

Infection with multiple hepatitis C virus genotypes detected using commercial tests should be confirmed using next generation sequencing.

Current HCV genotyping methods may have some limitations in detecting mixed infections. We aimed to determine the accuracy of genotyping and the detection of mixed-genotype infections using the Abbott-RealTime HCV Genotype II assay (Abbott-RT-PCR) in comparison with a Roche-Next Generation Sequencing assay (Roche-NGS). Plasma samples collected from 139 HCV-infected patients tested with Abbott-RT-PCR, 114 with single genotype (GT) and 25 with mixed GTs were genotyped using Roche-NGS. Roche-NGS confirmed all single GTs obtained with Abbott-RT-PCR. One case of Abbott GT 4 was found as GT 1a using Roche-NGS. Genotype 5 was confirmed using Roche-NGS in 75% cases (3 out of 4 cases). Twenty-five patients were identified as having mixed HCVinfections using Abbott-RT-PCR. The concordance between Abbott-RT-PCR and Roche-NGS was 76% (19 out of 25 cases). Three mixed-GT infections identified with the Abbott assay (two (1b + 4); one (1a + 3)) were reported as pure 1b using Roche-NGS. Very divergent results were found for the other three samples. When compared to Roche-NGS, Abbott-RT-PCR has performed excellently for the determination of patients infected with single GTs. For patients that are categorized as having a mixed infection using Abbott-RT-PCR, we recommend an NGS assay as a confirmation test.

Usefulness of Integrase resistance testing in proviral HIV-1 DNA in patients with Raltegravir prior failure.

BACKGROUND: In our study, we have hypothesized that proviral DNA may show the history of mutations that emerged at previous failures to a Raltegravir containing regimen, in patients who are currently undetectable and candidates to simplification to a Dolutegravir containing regimen, in order to decide on once a day or twice a day dosing. METHODS: We have performed a pilot, observational, retrospective, non interventional study, including 7 patients infected by HIV-1, all with a history of previous failure to a RAL containing regimen, that were successfully salvaged and had reached viral suppression. A genotypic viral Integrase region study was available for each patient at the moment of RAL failure. After an average (IQR) time of 48 months (29-53) Integrase resistance mutations in proviral DNA were studied. RESULTS: All the patients were infected by HIV-1 B subtypes, with a mean age of 55 (range 43 to 56), originating from Spain, and 4 were women. Median viral load (log) and CD4 count at the moment of the study on proviral DNA was of 1.3 log cp/ml (range 0-1.47) and 765.5 cells/µL (range; 436.75-1023.75). The median time (IQR) between previous failure to RAL and the study on proviral DNA was 48 (29-53) months. At Raltegravir failure, N155H was detected in four patients, and other secondary mutations were detected in five patients (71.4 %). In proviral DNA, N155H was detected by population sequencing in three patients (42.8 %), and UDS demonstrated a 9.77 % relative abundance of N155H in the remaining patient. Sanger sequencing correctly identified all the secondary mutations. CONCLUSION: This is a pilot study that demonstrates the possibility of properly identifying N155H and some secondary mutations 29-53 months after failure.

Using NS5B Sequencing for Hepatitis C Virus Genotyping Reveals Discordances with Commercial Platforms.

We aimed to evaluate the correct assignment of HCV genotypes by three commercial methods-Trugene HCV genotyping kit (Siemens), VERSANT HCV Genotype 2.0 assay (Siemens), and Real-Time HCV genotype II (Abbott)-compared to NS5B sequencing. We studied 327 clinical samples that carried representative HCV genotypes of the most frequent geno/subtypes in Spain. After commercial genotyping, the sequencing of a 367 bp fragment in the NS5B gene was used to assign genotypes. Major discrepancies were defined, e.g. differences in the assigned genotype by one of the three methods and NS5B sequencing, including misclassification of subtypes 1a and 1b. Minor discrepancies were considered when differences at subtype levels, other than 1a and 1b, were observed. The overall discordance with the reference method was 34% for Trugene and 15% for VERSANT HCV2.0. The Abbott assay correctly identified all 1a and 1b subtypes, but did not subtype all the 2, 3, 4 and 5 (34%) genotypes. Major discordances were found in 16% of cases for Trugene HCV, and the majority were 1b- to 1a-related discordances; major discordances were found for VERSANT HCV 2.0 in 6% of cases, which were all but one 1b to 1a cases. These results indicated that the Trugene assay especially, and to a lesser extent, Versant HCV 2.0, can fail to differentiate HCV subtypes 1a and 1b, and lead to critical errors in clinical practice for correctly using directly acting antiviral agents.

Pyrosequencing analysis reveals changes in intestinal microbiota of healthy adults who received a daily dose of immunomodulatory probiotic strains.

The colon microbiota plays a crucial role in human gastrointestinal health. Current attempts to manipulate the colon microbiota composition are aimed at finding remedies for various diseases. We have recently described the immunomodulatory effects of three probiotic strains (Lactobacillus rhamnosus CNCM I-4036, Lactobacillus paracasei CNCM I-4034, and Bifidobacterium breve CNCM I-4035). The goal of the present study was to analyze the compositions of the fecal microbiota of healthy adults who received one of these strains using high-throughput 16S ribosomal RNA gene sequencing. Bacteroides was the most abundant genus in the groups that received L. rhamnosus CNCM I-4036 or L. paracasei CNCM I-4034. The Shannon indices were significantly increased in these two groups. Our results also revealed a significant increase in the Lactobacillus genus after the intervention with L. rhamnosus CNCM I-4036. The initially different colon microbiota became homogeneous in the subjects who received L. rhamnosus CNCM I-4036. While some orders that were initially present disappeared after the administration of L. rhamnosus CNCM I-4036, other orders, such as Sphingobacteriales, Nitrospirales, Desulfobacterales, Thiotrichales, and Synergistetes, were detected after the intervention. In summary, our results show that the intake of these three bacterial strains induced changes in the colon microbiota.

Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients.

INTRODUCTION: Use of deep sequencing is becoming a critical tool in clinical virology, with an important impact in the HIV field for routine diagnostic purposes. Here, we present the comparison of deep and Sanger sequencing in newly diagnosed HIV patients, and the use of DeepChek v1.3 & VisibleChek for their interpretation and integration with virological and clinical data. PATIENTS AND METHODS: Plasma samples from 88 newly diagnosed HIV-1-infected patients were included in the study. Median age (IQR) was 37 (27-47), median CD4 count (IQR) was 387 (220-554), and 85% were males. Median Viral Load (Log, IQR) was 5.03 (4.51-5.53). Deep sequencing was obtained using a GS-Junior (Roche). Sequences were preprocessed with the 454 AVA software; aligned reads were uploaded into the DeepChek v1.3 system (ABL SA). Sanger sequences (Trugene), were uploaded in parallel. Stanford algorithm (version 7.0) resistance interpretation to first line drugs and all the mutations (score≥5) were analyzed. For deep sequencing, 1%, 5% and 10% thresholds were chosen for resistance interpretation. RESULTS: Using VisibleChek for analysis, we were able to describe the detection of any mutation using Sanger in 37/88 patients, with a total number of 50 Stanford ≥5 mutations, K103N and E138A being the most prevalent (n=4). Using UDS-1%, we found 72/88 patients with at least one mutation (total of 206 Stanford ≥5 mutations). Using Sanger data, 9/88 patients (10.22%) showed any resistance to NNRTIs, while none showed resistance to NRTIs or PIs. Using UDS-10% increased resistance to NRTIs [3/88 (3.40%)], to NNRTIs 12/88 (13.63%), and to a lesser extent to PIs [1/88 (1.13%)]. Using UDS-5% increased resistance to NRTIs [4/88 (4.54%)] and to NNRTIs [12/88 (13.63%)], but not to PIs. Using UDS-1% increased resistance to all classes: NRTIs [14/88 (15.90%)], NNRTIs [26/88 (30.68%)], and PIs [6/88 (6.81]. CONCLUSIONS: DeepChek and VisibleChek allow for an easy, reliable and rapid analysis of UDS data from HIV-1. Compared to Sanger data, UDS detected a higher number of resistance mutations. UDS with a 5 &10% threshold resulted in an increase in the number of patients with any degree of resistance mainly to NRTI, NNRTIs. Going down as low as 1% increased resistance to all classes. A correct definition of clinically relevant thresholds for the interpretation of minor variant detection for different classes of ARVs is needed.