Effect of oligonucleotide primers in determining viral variability within hosts

Quasispecies Analysis of SARS-CoV-2 of 15 Different Lineages during the First Year of the Pandemic Prompts Scratching under the Surface of Consensus Genome Sequences

The tremendous majority of SARS-CoV-2 genomic data so far neglected intra-host genetic diversity. Here, we studied SARS-CoV-2 quasispecies based on data generated by next-generation sequencing (NGS) of complete genomes. SARS-CoV-2 raw NGS data had been generated for nasopharyngeal samples collected between March 2020 and February 2021 by the Illumina technology on a MiSeq instrument, without prior PCR amplification. To analyze viral quasispecies, we designed and implemented an in-house Excel file (“QuasiS”) that can characterize intra-sample nucleotide diversity along the genomes using data of the mapping of NGS reads. We compared intra-sample genetic diversity and global genetic diversity available from Nextstrain. Hierarchical clustering of all samples based on the intra-sample genetic diversity was performed and visualized with the Morpheus web application. NGS mapping data from 110 SARS-CoV-2-positive respiratory samples characterized by a mean depth of 169 NGS reads/nucleotide position and for which consensus genomes that had been obtained were classified into 15 viral lineages were analyzed. Mean intra-sample nucleotide diversity was 0.21 ± 0.65%, and 5357 positions (17.9%) exhibited significant (>4%) diversity, in ≥2 genomes for 1730 (5.8%) of them. ORF10, spike, and N genes had the highest number of positions exhibiting diversity (0.56%, 0.34%, and 0.24%, respectively). Nine hot spots of intra-sample diversity were identified in the SARS-CoV-2 NSP6, NSP12, ORF8, and N genes. Hierarchical clustering delineated a set of six genomes of different lineages characterized by 920 positions exhibiting intra-sample diversity. In addition, 118 nucleotide positions (0.4%) exhibited diversity at both intra- and inter-patient levels. Overall, the present study illustrates that the SARS-CoV-2 consensus genome sequences are only an incomplete and imperfect representation of the entire viral population infecting a patient, and that quasispecies analysis may allow deciphering more accurately the viral evolutionary pathways.

Quantifying Next Generation Sequencing Sample Pre-Processing Bias in HIV-1 Complete Genome Sequencing

Genetic analyses play a central role in infectious disease research. Massively parallelized “mechanical cloning” and sequencing technologies were quickly adopted by HIV researchers in order to broaden the understanding of the clinical importance of minor drug-resistant variants. These efforts have, however, remained largely limited to small genomic regions. The growing need to monitor multiple genome regions for drug resistance testing, as well as the obvious benefit for studying evolutionary and epidemic processes makes complete genome sequencing an important goal in viral research. In addition, a major drawback for NGS applications to RNA viruses is the need for large quantities of input DNA. Here, we use a generic overlapping amplicon-based near full-genome amplification protocol to compare low-input enzymatic fragmentation (Nextera™) with conventional mechanical shearing for Roche 454 sequencing. We find that the fragmentation method has only a modest impact on the characterization of the population composition and that for reliable results, the variation introduced at all steps of the procedure—from nucleic acid extraction to sequencing—should be taken into account, a finding that is also relevant for NGS technologies that are now more commonly used. Furthermore, by applying our protocol to deep sequence a number of pre-therapy plasma and PBMC samples, we illustrate the potential benefits of a near complete genome sequencing approach in routine genotyping.

miR-ID: a novel, circularization-based platform for detection of microRNAs

MicroRNAs (miRNAs) are important regulators of gene expression and have great potential as biomarkers, prognostic indicators, and therapeutic targets. Determining the expression patterns of these molecules is essential for elucidating their biogenesis, regulation, relation to disease, and response to therapy. Although PCR-based assays are commonly used for expression profiling of miRNAs, the small size, sequence heterogeneity, and (in some cases) end modifications of miRNAs constrain the performance of existing PCR methods. Here we introduce miR-ID, a novel method that avoids these constraints while providing superior sensitivity and sequence specificity at a lower cost. It also has the unique ability to differentiate unmodified small RNAs from those carrying 2'-OMe groups at their 3'-ends while detecting both forms. miR-ID is comprised of the following steps: (1) circularization of the miRNA by a ligase; (2) reverse transcription of the circularized miRNA (RTC), producing tandem repeats of a DNA sequence complementary to the miRNA; and (3) qPCR amplification of segments of this multimeric cDNA using 5'-overlapping primers and a nonspecific dye such as SYBR Green. No chemically modified probes (e.g., TaqMan) or primers (e.g., LNA) are required. The circular RNA and multimeric cDNA templates provide unmatched flexibility in the positioning of primers, which may include straddling the boundaries between these repetitive miRNA sequences. miR-ID is based on new findings that are themselves of general interest, including reverse transcription of small RNA circles and the use of 5'-overlapping primers for detection of repetitive sequences by qPCR.

Baseline prediction of combination therapy outcome in hepatitis C virus 1b infected patients by discriminant analysis using viral and host factors

Background

Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy −especially among genotype 1 infected patients−, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment failure, as well as to assess whether therapy outcome could be predicted at baseline.

Methodology

Forty-three HCV subtype 1b (HCV-1b) chronically infected patients treated with pegylated-interferon alpha plus ribavirin were retrospectively studied (21 responders and 22 non-responders). Host (gender, age, weight, transaminase levels, fibrosis stage, and source of infection) and viral-related factors (viral load, and genetic variability in the E1–E2 and Core regions) were assessed. Logistic regression and discriminant analyses were used to develop predictive models. A “leave-one-out” cross-validation method was used to assess the reliability of the discriminant models.

Principal Findings

Lower alanine transaminase levels (ALT, p = 0.009), a higher number of quasispecies variants in the E1–E2 region (number of haplotypes, nHap_E1–E2) (p = 0.003), and the absence of both amino acid arginine at position 70 and leucine at position 91 in the Core region (p = 0.039) were significantly associated with treatment failure. Therapy outcome was most accurately predicted by discriminant analysis (90.5% sensitivity and 95.5% specificity, 85.7% sensitivity and 81.8% specificity after cross-validation); the most significant variables included in the predictive model were the Core amino acid pattern, the nHap_E1–E2, and gamma-glutamyl transferase and ALT levels.

Conclusions and Significance

Discriminant analysis has been shown as a useful tool to predict treatment outcome using baseline HCV genetic variability and host characteristics. The discriminant models obtained in this study led to accurate predictions in our population of Spanish HCV-1b treatment naïve patients.

Evidence of recombination in intrapatient populations of hepatitis C virus

Hepatitis C virus (HCV) is a major cause of liver disease worldwide and a potential cause of substantial morbidity and mortality in the future. HCV is characterized by a high level of genetic heterogeneity. Although homologous recombination has been demonstrated in many members of the family Flaviviridae, to which HCV belongs, there are only a few studies reporting recombination on natural populations of HCV, suggesting that these events are rare in vivo. Furthermore, these few studies have focused on recombination between different HCV genotypes/subtypes but there are no reports on the extent of intra-genotype or intra-subtype recombination between viral strains infecting the same patient. Given the important implications of recombination for RNA virus evolution, our aim in this study has been to assess the existence and eventually the frequency of intragenic recombination on HCV. For this, we retrospectively have analyzed two regions of the HCV genome (NS5A and E1-E2) in samples from two different groups: (i) patients infected only with HCV (either treated with interferon plus ribavirin or treatment naïve), and (ii) HCV-HIV co-infected patients (with and without treatment against HIV). The complete data set comprised 17712 sequences from 136 serum samples derived from 111 patients. Recombination analyses were performed using 6 different methods implemented in the program RDP3. Recombination events were considered when detected by at least 3 of the 6 methods used and were identified in 10.7% of the amplified samples, distributed throughout all the groups described and the two genomic regions studied. The resulting recombination events were further verified by detailed phylogenetic analyses. The complete experimental procedure was applied to an artificial mixture of relatively closely viral populations and the ensuing analyses failed to reveal artifactual recombination. From these results we conclude that recombination should be considered as a potentially relevant mechanism generating genetic variation in HCV and with important implications for the treatment of this infection.

Genetic variability of hepatitis C virus before and after combined therapy of interferon plus ribavirin

We present an analysis of the selective forces acting on two hepatitis C virus genome regions previously postulated to be involved in the viral response to combined antiviral therapy. One includes the three hypervariable regions in the envelope E2 glycoprotein, and the other encompasses the PKR binding domain and the V3 domain in the NS5A region. We used a cohort of 22 non-responder patients to combined therapy (interferon alpha-2a plus ribavirin) for which samples were obtained before initiation of therapy and after 6 or/and 12 months of treatment. A range of 25-100 clones per patient, genome region and time sample were sequenced. These were used to detect general patterns of adaptation, to identify particular adaptation mechanisms and to analyze the patterns of evolutionary change in both genome regions. These analyses failed to detect a common adaptive mechanism for the lack of response to antiviral treatment in these patients. On the contrary, a wide range of situations were observed, from patients showing no positively selected sites to others with many, and with completely different topologies in the reconstructed phylogenetic trees. Altogether, these results suggest that viral strategies to evade selection pressure from the immune system and antiviral therapies do not result from a single mechanism and they are likely based on a range of different alternatives, in which several different changes, or their combination, along the HCV genome confer viruses the ability to overcome strong selective pressures.

Genetic variability in hepatitis C virus and its role in antiviral treatment response

Hepatitis C virus (HCV) is a major health problem worldwide, infecting an estimated 170 million people. The high genetic variability of HCV contributes to the chronicity of hepatitis C. Here, we report results from a large-scale sequence analysis of 67 patients infected with HCV genotype 1, 23 with subtype 1a and 44 with subtype 1b. Two regions of the HCV genome were analysed in samples prior to combined therapy with alpha interferon plus ribavirin, one compressing the hypervariable regions (HVR1, HVR2 and HVR3) of the E2 glycoprotein and another one including the interferon-sensitive determining region (ISDR) and the V3 domain of the NS5A protein. Genetic diversity measures showed a clear tendency to higher genetic variability levels in nonresponder patients to antiviral treatment than in responder patients, although highly disperse values were present within each response group for both subtypes. A more detailed analysis of amino acid composition revealed the presence of several subtype-specific variants in a few positions, but no discriminating positions between responder and nonresponder patients were detected. Our results also revealed that most amino acid positions were highly conserved, especially for subtype 1a. We conclude that the outcome of the antiviral treatment might depend not only on the nature of one or a few independent positions, but more likely on the combination of several positions along the HCV genome. Moreover, the own host's ability to generate an appropriate systemic response, in combination with the action of antivirals, is also likely to be essential for treatment outcome.

Contribution of insertions and deletions to the variability of hepatitis C virus populations

Little is known about the potential effects of insertions and deletions (indels) on the evolutionary dynamics of hepatitis C virus (HCV). In fact, the consequences of indels on antiviral treatment response are a field of investigation completely unexplored. Here, an extensive sequencing project was undertaken by cloning and sequencing serum samples from 25 patients infected with HCV subtype 1a and 48 patients with subtype 1b. For 23 patients, samples obtained after treatment with alpha interferon plus ribavirin were also available. Two genome fragments containing the hypervariable regions in the envelope 2 glycoprotein and the PKR-BD domain in NS5A were sequenced, yielding almost 16 000 sequences. Our results show that insertions are quite rare, but they are often present in biologically relevant domains of the HCV genome. Moreover, their frequency distributions between different time samples reflect the quasispecies dynamics of HCV populations. Deletions seem to be subject to negative selection.

Epidemic dynamics of two coexisting hepatitis C virus subtypes

Hepatitis C virus (HCV) infection affects about 3% of the human population. Phylogenetic analyses have grouped its variants into six major genotypes, which have a star-like distribution and several minor subtypes. The most abundant genotype in Europe is the so-called genotype 1, with two prevalent subtypes, 1a and 1b. In order to explain the higher prevalence of subtype 1b over 1a, a large-scale sequence analysis (100 virus clones) has been carried out over 25 patients of both subtypes in two regions of the HCV genome: one comprising hypervariable region 1 and another including the interferon sensitivity-determining region. Neither polymorphism analysis nor molecular variance analysis (attending to intra- and intersubtype differences, age, sex and previous history of antiviral treatment) was able to show any particular difference between subtypes that might account for their different prevalence. Only the demographic history of the populations carrying both subtypes and analysis of molecular variance (AMOVA) for risk practice suggested that the route of transmission may be the most important factor to explain the observed difference.