Deep sequencing of HPV E6/E7 genes reveals loss of genotypic diversity and gain of clonal dominance in high-grade intraepithelial lesions of the cervix
© The Author(s). 2017
Received: 19 December 2016
Accepted: 7 March 2017
Published: 14 March 2017
Human papillomavirus (HPV) is the carcinogen of almost all invasive cervical cancer and a major cause of oral and other anogenital malignancies. HPV genotyping by dideoxy (Sanger) sequencing is currently the reference method of choice for clinical diagnostics. However, for samples with multiple HPV infections, genotype identification is singular and occasionally imprecise or indeterminable due to overlapping chromatograms. Our aim was to explore and compare HPV metagenomes in abnormal cervical cytology by deep sequencing for correlation with disease states.
Low- and high-grade intraepithelial lesion (LSIL and HSIL) cytology samples were DNA extracted for PCR-amplification of the HPV E6/E7 genes. HPV+ samples were sequenced by dideoxy and deep methods. Deep sequencing revealed ~60% of all samples (n = 72) were multi-HPV infected. Among LSIL samples (n = 43), 27 different genotypes were found. The 3 dominant (most abundant) genotypes were: HPV-39, 11/43 (26%); -16, 9/43 (21%); and -35, 4/43 (9%). Among HSIL (n = 29), 17 HPV genotypes were identified; the 3 dominant genotypes were: HPV-16, 21/29 (72%); -35, 4/29 (14%); and -39, 3/29 (10%). Phylogenetically, type-specific E6/E7 genetic distances correlated with carcinogenic potential. Species diversity analysis between LSIL and HSIL revealed loss of HPV diversity and domination by HPV-16 in HSIL samples.
Deep sequencing resolves HPV genotype composition within multi-infected cervical cytology. Biodiversity analysis reveals loss of diversity and gain of dominance by carcinogenic genotypes in high-grade cytology. Metagenomic profiles may therefore serve as a biomarker of disease severity and a population surveillance tool for emerging genotypes.
KeywordsHuman papillomavirus HPV genotyping High-throughput sequencing Deep sequencing Metagenome Virome LSIL HSIL
The first description of cervical cancer was documented by Hippocrates c. 450 BCE . Its cause remained a mystery for two millennia until 1983 when zur Hausen and colleagues isolated and cloned HPV-16 from cervical carcinoma . HPV is now recognized as the carcinogen of almost all invasive cervical cancer and a major cause of other human malignancies including vulvovaginal, oropharyngeal, penile, and anal cancers [3, 4].
The HPV genome is a ~8,000 base pair (bp), double-stranded, circular DNA. The prototypical genome encodes 6 early genes (E1, E2, E4, E5, E6, and E7) and 2 late genes (L1 and L2) . By convention, HPV classification is based on the L1 gene where a difference of >10% in the viral sequence defines a different genotype . With the advent of sequencing technologies, the list of papillomavirus (PV) genotypes has grown to 333 with 202 types isolated from humans and 131 from animals . The conferment of HPV oncogenic potential is derived from two viral oncoproteins E6 and E7, which inactivate two primary cellular proteins: p53 and RB, respectively [5, 8, 9]. Inhibition followed by degradation of p53 and RB leads to cell-cycle progression, immortalization, and malignant transformation of the HPV-infected cell . The E6 and E7 proteins of carcinogenic HPV possess a structural advantage, namely, conformational plasticity that may be responsible for multi-target binding and transformation in host cells [9, 10]. Hence, sequencing the E6/E7 genes to decode its oncogenic potential through association with known genotypes by genetic proximity may be a preferred diagnostic test.
Sanger sequencing of PCR amplicons is accurate in the detection of single HPV infections and yields easily interpretable data . For samples with multiple HPV infections, genotype identification may be imprecise or indeterminable because of noisy (overlapping signals) chromatograms causing failures in nucleotide alignment . Furthermore, non-dominant genotypes in mixed infections may not be detected by the Sanger method and may be consequently underestimated. Therefore, to properly identify all HPV types in a complex sample, contemporary next-generation sequencing (NGS), also referred to as deep or high-throughput sequencing (HTS), may provide an innovative solution . Although the literature is limited, several NGS platforms have been successful at determining the diversity of HPV genotypes found in human skin, normal cervical cytology, and cytology of an HIV+ woman harboring 16 types [13–16].
In this study, we aimed to determine the HPV genotypes and their proportional composition in single- and multi-infected cervical samples. Specifically, two categories of cytology, i.e. low- and high-grade squamous intraepithelial lesion (LSIL and HSIL) containing HPV DNA were selected for deep (Illumina®) sequencing to explore viral diversity and characterize differences in metagenomes.
Subjects and samples
This study was conducted after approval by the Institutional Review Board of Brooke Army Medical Center (BAMC), Texas. Liquid-based cytology collected for clinical testing at the Department of Pathology was consecutively procured after completion of analysis for cytological diagnosis. Demographic data were abstracted from the electronic health record (AHLTA) of the Department of Defense (DoD) and linked to each specimen. Similarly, histologic data were abstracted for cytohistological correlation. In a previous study, three categories of samples, i.e. negative for intraepithelial lesion or malignancy (NILM), LSIL and HSIL were collected for HPV genotyping and DNA methylation analysis . For this study, we selected only the subset of HPV+ LSIL (n = 55) and HSIL (n = 29) for characterization and comparison of viral diversity because of the high prevalence of HPV positivity.
HPV DNA amplification and detection
HPV DNA dideoxy (Sanger) sequencing and genotyping
PCR products were purified using the GeneRead Size Selection Kit (QIAGEN) and eluted in 100 uL of molecular biology-grade water on the QIAcube. Dideoxy sequencing of the amplicons (~200 ng DNA/sample) was performed using primer GP-E6-3 F at Eurofins Operon (USA). Sequence quality was assessed using Sequence Scanner 2.0 (appliedbiosystems.com) where a “high quality” Trace Score (TS) was defined as ≥20 and a QV20+ value (total number of bases in the sequence with TS ≥20) as ≥100. Quality sequences were entered into BLAST® and queried against HPV sequences in GenBank® (Taxon identifier: 151340) for genotyping as previously described .
HPV DNA sample library preparation and deep sequencing
DNA libraries were prepared from GeneRead-purified PCR products as described above using the Nextera XT kit (Illumina). Briefly, the input DNA was quantitated and analyzed for purity (260/280 nm absorbance ratio ~1.8-2.0) with the Qubit Fluorometer (ThermoFischer). Each DNA sample (1 ng) with a standardized concentration of 0.1-0.2 ng/uL was “tagmented” (fragmented and tagged with sequencing adapters) by the Nextera XT transposome and dual indexed (barcoded) by limited-cycle PCR using the 96-sample Nextera Index Kit. AMPure magnetic beads (Beckman Coulter) were used to purify the DNA libraries and size select (300–500 bp) amplicons in each sample. The DNA libraries were normalized for quantity to ensure equal representation from each sample prior to pooling and sequencing. Paired-end bi-directional sequencing (2 × 300 bp) using the MiSeq Reagent Kit v3 (600-cycle) was performed on the MiSeq (Illumina) for bridge amplification.
Bioinformatics for next-generation genotyping
The MiSeq on-instrument analysis generated a QC report of total reads, total reads passing filter, and % of bases with quality score ≥30 (Q30) meaning an accuracy rate of 99.9% . The de-multiplexed, paired-end sequences were imported into CLC Genomics Workbench 8.0 (QIAGEN) for analysis. The bioinformatics workflow constructed for HPV genotyping consisted of 5 sequential steps: reads import, reads merging, reads QC, de novo assembly with mapping, and BLAST® search (Fig. 1b). A read mapping result following de novo assembly produced contigs and the consensus sequence (Fig. 1c). Only consensus sequences with contigs composed of ≥100 reads from each sample were BLAST® (blastn) searched against the NCBI Viral Genome database. BLAST® hit results (Fig. 1d) were used for HPV genotype assignment . Of note, the minimum coverage and percentage of reads required for accurate HPV genotype identification has not been reported to date. For this study, the minimum coverage (100x) for variant detection was based on the manufacturer’s technical note on coverage requirement for reads mapped to a subset of a genome . The CLC Genomics workflow parameter settings are presented in Additional File 1: Table S1.
Species diversity and phylogenetic analysis
The term species in biodiversity analysis refers to a sampling unit under study. Herein, the sample unit is the HPV genotype rather than species. HPV community is defined as the assemblage of different genotypes found in each sample by deep sequencing and grouped as LSIL or HSIL for comparative analysis. Count-based genotype diversity and dominance within a HPV community were quantified by the Shannon-Wiener Index (SWI) and Berger-Parker Index (BPI), respectively and compared using Solow’s randomization test . Composition-based genotype dissimilarity between communities (β-diversity) was calculated using the Analysis of Similarity (ANOSIM) method based on percentage differences between samples [23, 24]. ANOSIM is a non-parametric statistical test for significant differences in species composition (%) between two or more groups/sites of sampling units. The ANOSIM statistic R compares the mean ranks of species similarities between and within groups . Principal component analysis (PCA) was used to determine the most influential variables (HPV types) in the LSIL or HSIL group. PCA was performed on the covariance matrix of natural log-transformed abundance data [ln(x + 1)] of HPV-types within each sample [24, 25]. Log transformation was applied to reduce the influence of highly abundant genotypes (skewed data). Biodiversity analyses were performed using Species Diversity and Richness 4.0 and Community Analysis Package 5.0. [22, 24]
The evolutionary relationship of HPV E6/E7 from deep sequencing of LSIL and HSIL samples was inferred using the Neighbor-Joining method . The evolutionary distances were computed using the Maximum Composite Likelihood method . Codon positions included were 1st + 2nd + 3rd + Noncoding. Positions containing gaps or missing data were eliminated. Bootstrap analysis using 1,000 replicates was performed to evaluate the reliability of the inferred trees . The bootstrap value attached to each node is the confidence (%) in the subtree rooted at the node. Evolutionary analyses were conducted in MEGA6 .
The classification of HPV carcinogenic potential was based on the World Health Organization (WHO) International Agency for Research on Cancer (IARC) Working Group Reports [8, 30]. Specifically, HPV types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68 were deemed carcinogenic; HPV types 26, 30, 34, 53, 66, 67, 69, 70, 73, 82, 85, and 97 were possibly carcinogenic; HPV types 6, 11 were not classifiable; and all others were probably not carcinogenic. The not classifiable agents are generally considered not carcinogenic based on limited epidemiological and experimental data. The rationale for categorizing HPV 6 as not classifiable instead of probably not carcinogenic was the low (0.45% [95% CI: 0.35-0.56]) but not zero incidence found in invasive cervical cancers worldwide . It is postulated that HPV-6 and other low-risk genotypes may rarely cause cancer due to unusual “virus-host circumstances” . Therefore, the not classifiable/probably not carcinogenic genotypes are generally considered non-carcinogenic and thus grouped together for this study. The probably not carcinogenic group include HPV species alpha-1, -2, -3, -4, -8, -10 (other than HPV 6, 11), -13, -14/15 [8, 30].
Data were summarized using means (95% CI), medians (IQR), and proportions. Normality of data distribution was determined by the skewness and kurtosis test. For nonparametric data, the median test (Fisher’s exact, 2-tailed) or chi-squared test were used for hypothesis testing as appropriate. Agreement between 2 dichotomous categorical variables was calculated using simple agreement (%) and kappa coefficient. Kappa coefficients are categorized by the following nomenclature: poor (κ < 0.00); slight (0.00 ≤ κ ≤ 0.20); fair (0.21 ≤ κ ≤ 0.40); moderate (0.41 ≤ κ ≤ 0.60); substantial (0.61 ≤ κ ≤ 0.80); and almost perfect (κ > 0.80) . A p-value <0.05 was considered statistically significant. Statistical analyses were performed using STATA/IC 13.0 (StataCorp, Texas).
Mixed HPV infections are frequent and unresolved by dideoxy (Sanger) sequencing
Cytohistological correlation and HPV E6/E7 deep sequencing results
Histology (biopsy or excision)a
Documented, n (%)
Not documented, n (%)
CIN 0, n (%)
CIN I, n (%)
CIN II/III, n (%)
Expected Agreement, %
Deep Sequencing Reads for LSIL/HSIL
25th %tile, n
75th %tile, n
HPV Genotype Concordance Analysis
Deep vs. Sanger sequencinge
Expected Agreement, %
Deep sequencing of HPV E6/E7 loci reveals loss of HPV diversity and gain of clonal dominance in HSIL
The deep sequencing read statistics of all samples are summarized in Table 1. The total number of passed-filtered reads was 21 million, which is consistent with the maximum number of reads (25 million) for the MiSeq Reagent Kit used. The median number of merged reads for 72 samples derived from E6/E7 loci amplification was 242,665 (IQR, 185,144-324,210), and the proportion of mapped to merged reads was 79% (192,236/242,665). The number of mapped reads per sample was sufficient to discover up to 8 genotypes. The HPV genotypes based on BLAST® are listed in Additional File 3: Table S2.
Validation of our next-generation genotyping results showed high concordance between the deep and dideoxy sequencing methods. Comparing the dominant HPV genotypes derived from the two methods, the inter-assay agreement was highly concordant for LSIL (κ = 0.91, p <0.001) and HSIL (κ = 0.85, p <0.001) (Table 1). This finding indicates that dideoxy sequencing may be used to determine the dominant genotype within mixed infections.
Evolutionary relationship of HPV E6/E7 sequences correlate with carcinogenic potential
Currently, published data on deep sequencing of HPV in abnormal cytology are limited and varied. Previous studies have analyzed target (non-generalizable) populations, used different NGS platforms and assays, and reported findings that may not be translatable between platforms [13–16, 41]. However, we did find several notable similarities. A metagenomics study of healthy persons from the NIH Human Microbiome Project revealed a high prevalence of HPV in the vagina (41.5%) with 43 types; high abundance of HPV-34, 53, 45, and 52; and a high rate (~50%) of mixed infections . Fonseca et al. investigated the prevalence of HPV in isolated, indigenous Amazonian women of northern Brazil . Among the 607 cytology samples, 3.3% were abnormal, which included 7 cases of LSIL, 2 cases of HSIL, and 1 case of carcinoma. The overall HPV prevalence was 39.7% with 60 different genotypes and a high rate (45%) of multiple infections. Only a limited number of HPVs were found in HSIL and/or carcinoma (HPV-16 and 31) and ASC-US/LSIL (HPV-16, 18, and 31). Finally, Meiring and colleagues used deep sequencing to identify 16 HPV types in a South African HIV+ woman and demonstrated that prevalent HPV types in HIV+ women are undetectable by commercial tests that complicate surveillance measures . Taken together, these data demonstrate that NGS reveals a diverse and prevalent existence of mixed HPV infections in healthy females. With progression of cytopathology, diversity diminishes to a few virulent types, namely HPV-16 and other α-7, -9 species as observed in our LSIL/HSIL samples and Sjoeborg’s study using linear array genotyping . Finally, for immunocompromised hosts, the HPV virome may be more diverse and inclusive of less-virulent genotypes.
In regards to age, our subjects with LSIL were younger than those with HSIL. This finding is consistent with the natural history of HPV infection with a characteristic peak age of < 25 years for HPV infection/LSIL and 25–35 years for HSIL . Ironically, we found no difference in the median age of subjects between those who had and who did not have carcinogenic HPV genotypes by NGS. A large population-based study conducted in New Mexico showed similar age distributions as our sample for both carcinogenic and low-risk HPV groups with the peak age range being 21–24 years followed by a rapid decline . In fact, over 35% of women <21 years of age and 32% between the ages of 21–24 years tested positive for any carcinogenic HPV on cytology . Collectively, the disparate age distributions support the notion that infection with a carcinogenic HPV occurs predominantly in adolescence/early adulthood; whereas, disease, i.e. HSIL develops in later adulthood.
The strength of this investigation lies in the clinical specimens studied and sequencing method used. First, we intentionally focused on precancerous cervical lesions, i.e. LSIL and HSIL because they have been understudied to date by deep sequencing. The remarkable genotypic diversity and coinfection rates found in this study have direct implications for vaccine development and epitope selection, as well as post-immunization surveillance for efficacy and emerging replacement-types . The recently FDA-approved 9-Valent HPV vaccine (9vHPV) containing HPV 6, 11, 16, 18, 31, 33, 45, 52, and 58 virus-like particles (VLPs) is a significant improvement over the quadrivalent vaccine. However, our study only found 7/27 and 4/17 genotypes (~25%) in LSIL and HSIL, respectively, covered by the 9vHPV epitopes. More importantly, HPV-35 and -39 found highly prevalent in LSIL and HSIL are not covered. Another additive concern about vaccine ineffectiveness is the low vaccine uptake and adherence rates in the U.S. [46, 47]. Consequently, active, population-based surveillance is necessary to detect potential redistribution of carcinogenic HPVs as a result of under-achieved herd immunity [45, 48–50]. Second, we used the most accurate NGS platform commercially available . The least cumbersome DNA library preparation was chosen to streamline the laboratory workflow. The bioinformatics workflow created for HPV genotyping with automatable steps systematized the computational analysis. The simple methods developed and tested in this study may be adopted for studying HPV viromes at all susceptible anatomical sites where surveillance is essential . Conversely, the high concordance rate found between deep and Sanger sequencing suggests that Sanger remains a reliable method of detecting the dominant genotype in single and mixed infections if decipherable by BLAST®. In resource limited settings, Sanger sequencing will remain salient. Although PCR/Sanger sequencing is still the current reference standard for HPV clinical diagnostics, next-generation genotyping may soon gain acceptance in the clinical realm as a new reference method according to the 2015 FDA Draft Guidance on HPV in vitro diagnostic devices . Before adoption, however, we opine that further investigation is required to determine the threshold for variant detection (minimum coverage) and variant filtering (percentage of reads in a mixed population) for accurate next-generation HPV genotyping. Two independent studies using admixtures of Hepatitis C Virus (HCV) and influenza A plasmids suggest that for accurate detection using Illumina’s sequencing technology, the limit of variant detection and filtering should be set ~100x and 0.5%, respectively [52, 53].
Cross-sectional studies, similarly to other observational studies are susceptible to errors due to chance and bias. We acknowledge that the current study has limitations. First, the potential for selection bias must be considered. Our clinical samples were derived from a South Texas military population represented largely by Active Duty Members and dependents of the U.S. Army and Air Force. The demographics, social, and sexual behavior of our population may not be representative of other segments of the U.S. population. In general, HPV prevalence should be interpreted in the context of ethnogeography. Second, atypical squamous and glandular cells of undetermined significance (ASC-US and AGUS) cytological categories were not studied. The overall frequency of HPV+/ASC-US (1.1%) and HPV+/AGUS (0.05%) is low among screening cytology samples; however, the 5-year risk of histologic HSIL and cancer are significant, i.e. 18 and 45%, respectively . Hence, to fill this knowledge gap, we plan to investigate HPV metagenomes of uncommon cytological categories to further our understanding of viral ecology, and we plan to establish predictive models for cytological outcomes based on metagenomics profiles. Third, we did not study LSIL/HSIL samples with negative E6/E7 amplification results. PCR non-detection (false-negative results) may be attributed to several variables, e.g. insufficient DNA template quantity or quality and primer-target mismatch [11, 55]. To explore and compare the HPV metagenomes in E6/E7 amplicon-positive and -negative cytology, multiply-primed rolling-circle amplification followed by deep sequencing may offer a solution, in particular, to partially deleted or poorly E6/E7-primed HPV genomes .
Deep sequencing has provided a powerful lens through which to peer into viral communities and gain an understanding of a dynamic microcosm imperceptible with conventional methods. The HPV diversity and community characteristics found in LSIL and HSIL have provided vital information relevant to cervical carcinogenesis, biomarker discovery, vaccinology, and surveillance strategies. With revolutionary advances in sequencing and computational technologies, we are now able to decipher and interpret the cryptic codes of an ancient virus in a manner reminiscent of the Shakespearean metaphor, “In nature’s infinite book of secrecy, a little I can read” .
Atypical glandular cells of undetermined significance
Analysis of similarity
Atypical squamous cells of undetermined significance
Basic local alignment search tool
Cervical intraepithelial neoplasia
High-grade squamous intraepithelial lesion
International Agency for Research on Cancer
Low-grade squamous intraepithelial lesion
Negative for intraepithelial lesion or malignancy
Principal component analysis
The authors would like to thank the staff at the Greehey Children's Cancer Research Institute and Bioanalytics and Single-Cell Core of the University of Texas Health Science Center at San Antonio (UTHSCSA) for their invaluable service in supporting the next-generation sequencing experiments; and the staff at the Cytopathology Laboratory of Brooke Army Medical Center for their invaluable service for collecting the clinical samples.
This paper has undergone PAO review at Brooke Army Medical Center and was cleared for publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or reflecting the views of the U.S. Department of the Army, U.S. Department of Defense, or the U.S. government.
Laboratory materials for this work were supported in part by the Dept. of Clinical Investigation Intramural Funding Program at Brooke Army Medical Center, Fort Sam Houston, Texas.
Availability of data and materials
Primary data will not be shared due to provisional patent application filing.
JSG, YW, and THH conceived and designed the study. JSG, YW, ZL, GMP and LP participated in the acquisition of data. JSG, YW, GMP, and THH analyzed and interpreted the data. JSG, YW, GMP, and THH wrote the manuscript. All authors read and approved the final manuscript.
The U. S. Army Medical Research and Material Command has filed a provisional patent application on the invention described herein. The inventor is J. Shen-Gunther. No potential conflicts of interest were disclosed by the other authors.
Consent for publication
Ethics approval and consent to participate
Ethical approval was obtained from the institutional review board of Brooke Army Medical Center (reference number C.2013.022d), Fort Sam Houston, Texas. Waiver of informed consent was granted based on the minimal level of risk to conduct the study and satisfaction of the criteria set forth in U.S. Code of Federal Regulations Title 45, Part 46 46.116(d) on the use of human subjects data and/or specimens collected as part of routine clinical care. The study was conducted in accordance with the ethical standard laid down in the 1964 Declaration of Helsinki and its later amendments.
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