HPV integration generates a cellular super-enhancer which functions as ecDNA to regulate genome-wide transcription.

Human papillomavirus (HPV) integration is a critical step in cervical cancer development; however, the oncogenic mechanism at the genome-wide transcriptional level is still poorly understood. In this study, we employed integrative analysis on multi-omics data of six HPV-positive and three HPV-negative cell lines. Through HPV integration detection, super-enhancer (SE) identification, SE-associated gene expression and extrachromosomal DNA (ecDNA) investigation, we aimed to explore the genome-wide transcriptional influence of HPV integration. We identified seven high-ranking cellular SEs generated by HPV integration in total (the HPV breakpoint-induced cellular SEs, BP-cSEs), leading to intra-chromosomal and inter-chromosomal regulation of chromosomal genes. The pathway analysis revealed that the dysregulated chromosomal genes were correlated to cancer-related pathways. Importantly, we demonstrated that BP-cSEs existed in the HPV-human hybrid ecDNAs, explaining the above transcriptional alterations. Our results suggest that HPV integration generates cellular SEs that function as ecDNA to regulate unconstrained transcription, expanding the tumorigenic mechanism of HPV integration and providing insights for developing new diagnostic and therapeutic strategies.

DeepHPV: a deep learning model to predict human papillomavirus integration sites.

Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integration sites. However, a published bioinformatic tool is not available to date. Thus, we developed an attention-based deep learning model DeepHPV to predict HPV integration sites by learning environment features automatically. In total, 3608 known HPV integration sites were applied to train the model, and 584 reviewed HPV integration sites were used as the testing dataset. DeepHPV showed an area under the receiver-operating characteristic (AUROC) of 0.6336 and an area under the precision recall (AUPR) of 0.5670. Adding RepeatMasker and TCGA Pan Cancer peaks improved the model performance to 0.8464 and 0.8501 in AUROC and 0.7985 and 0.8106 in AUPR, respectively. Next, we tested these trained models on independent database VISDB and found the model adding TCGA Pan Cancer performed better (AUROC: 0.7175, AUPR: 0.6284) than the model adding RepeatMasker peaks (AUROC: 0.6102, AUPR: 0.5577). Moreover, we introduced attention mechanism in DeepHPV and enriched the transcription factor binding sites including BHLHA15, CHR, COUP-TFII, DMRTA2, E2A, HIC1, INR, NPAS, Nr5a2, RARa, SCL, Snail1, Sox10, Sox3, Sox4, Sox6, STAT6, Tbet, Tbx5, TEAD, Tgif2, ZNF189, ZNF416 near attention intensive sites. Together, DeepHPV is a robust and explainable deep learning model, providing new insights into HPV integration preference and mechanism. Availability: DeepHPV is available as an open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepHPV.git, Contact: huzheng1998@163.com, liangjiuxing@m.scnu.edu.cn, lizheyzy@163.com.

DeepEBV: a deep learning model to predict Epstein-Barr virus (EBV) integration sites.

MOTIVATION: Epstein-Barr virus (EBV) is one of the most prevalent DNA oncogenic viruses. The integration of EBV into the host genome has been reported to play an important role in cancer development. The preference of EBV integration showed strong dependence on the local genomic environment, which enables the prediction of EBV integration sites. RESULTS: An attention-based deep learning model, DeepEBV, was developed to predict EBV integration sites by learning local genomic features automatically. First, DeepEBV was trained and tested using the data from the dsVIS database. The results showed that DeepEBV with EBV integration sequences plus Repeat peaks and 2-fold data augmentation performed the best on the training dataset. Furthermore, the performance of the model was validated in an independent dataset. In addition, the motifs of DNA-binding proteins could influence the selection preference of viral insertional mutagenesis. Furthermore, the results showed that DeepEBV can predict EBV integration hotspot genes accurately. In summary, DeepEBV is a robust, accurate and explainable deep learning model, providing novel insights into EBV integration preferences and mechanisms. AVAILABILITYAND IMPLEMENTATION: DeepEBV is available as open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepEBV.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Risk stratification of cervical lesions using capture sequencing and machine learning method based on HPV and human integrated genomic profiles.

From initial human papillomavirus (HPV) infection and precursor stages, the development of cervical cancer takes decades. High-sensitivity HPV DNA testing is currently recommended as primary screening method for cervical cancer, whereas better triage methodologies are encouraged to provide accurate risk management for HPV-positive women. Given that virus-driven genomic variation accumulates during cervical carcinogenesis, we designed a 39 Mb custom capture panel targeting 17 HPV types and 522 mutant genes related to cervical cancer. Using capture-based next-generation sequencing, HPV integration status, somatic mutation and copy number variation were analyzed on 34 paired samples, including 10 cases of HPV infection (HPV+), 10 cases of cervical intraepithelial neoplasia (CIN) grade and 14 cases of CIN2+ (CIN2: n = 1; CIN2-3: n = 3; CIN3: n = 9; squamous cell carcinoma: n = 1). Finally, the machine learning algorithm (Random Forest) was applied to build the risk stratification model for cervical precursor lesions based on CIN2+ enriched biomarkers. Generally, HPV integration events (11 in HPV+, 25 in CIN1 and 56 in CIN2+), non-synonymous mutations (2 in CIN1, 12 in CIN2+) and copy number variations (19.1 in HPV+, 29.4 in CIN1 and 127 in CIN2+) increased from HPV+ to CIN2+. Interestingly, 'common' deletion of mitochondrial chromosome was significantly observed in CIN2+ (P = 0.009). Together, CIN2+ enriched biomarkers, classified as HPV information, mutation, amplification, deletion and mitochondrial change, successfully predicted CIN2+ with average accuracy probability score of 0.814, and amplification and deletion ranked as the most important features. Our custom capture sequencing combined with machine learning method effectively stratified the risk of cervical lesions and provided valuable integrated triage strategies.

VIS Atlas: A Database of Virus Integration Sites in Human Genome from NGS Data to Explore Integration Patterns.

Integration of oncogenic DNA viruses into the human genome is a key step in most virus-induced carcinogenesis. Here, we constructed a virus integration site (VIS) Atlas database, an extensive collection of integration breakpoints for three most prevalent oncoviruses, human papillomavirus, hepatitis B virus, and Epstein-Barr virus based on the next-generation sequencing (NGS) data, literature, and experimental data. There are 63,179 breakpoints and 47,411 junctional sequences with full annotations deposited in the VIS Atlas database, comprising 47 virus genotypes and 17 disease types. The VIS Atlas database provides (1) a genome browser for NGS breakpoint quality check, visualization of VISs, and the local genomic context; (2) a novel platform to discover integration patterns; and (3) a statistics interface for a comprehensive investigation of genotype-specific integration features. Data collected in the VIS Atlas aid to provide insights into virus pathogenic mechanisms and the development of novel antitumor drugs. The VIS Atlas database is available at https://www.vis-atlas.tech/.

Massively parallel CRISPR off-target detection enables rapid off-target prediction model building.

BACKGROUND: CRISPR (clustered regularly interspaced short palindromic repeats) genome editing holds tremendous potential in clinical translation. However, the off-target effect has always been a major concern. METHODS: Here, we have developed a novel sensitive and specific off-target detection method, AID-seq (adaptor-mediated off-target identification by sequencing), that can comprehensively and faithfully detect the low-frequency off targets generated by different CRISPR nucleases (including Cas9 and Cas12a). FINDINGS: Based on AID-seq, we developed a pooled strategy to simultaneously identify the on/off targets of multiple gRNAs, as well as using mixed human and human papillomavirus (HPV) genomes to screen the most efficient and safe targets from 416 HPV gRNA candidates for antiviral therapy. Moreover, we used the pooled strategy with 2,069 single-guide RNAs (sgRNAs) at a pool size of about 500 to profile the properties of our newly discovered CRISPR, FrCas9. Importantly, we successfully built an off-target detection model using these off-target data via the CRISPR-Net deep learning method (area under the receiver operating characteristic curve [AUROC] = 0.97, area under the precision recall curve [AUPRC] = 0.29). CONCLUSIONS: To our knowledge, AID-seq is the most sensitive and specific in vitro off-target detection method to date. And the pooled AID-seq strategy can be used as a rapid and high-throughput platform to select the best sgRNAs and characterize the properties of new CRISPRs. FUNDING: This work was supported by The National Natural Science Foundation of China (grant nos. 32171465 and 82102392), the General Program of Natural Science Foundation of Guangdong Province of China (grant no. 2021A1515012438), Guangdong Basic and Applied Basic Research Foundation (grant no. 2020A1515110170), and the National Ten Thousand Plan-Young Top Talents of China (grant no. 80000-41180002).

FrCas9 is a CRISPR/Cas9 system with high editing efficiency and fidelity.

Genome editing technologies hold tremendous potential in biomedical research and drug development. Therefore, it is imperative to discover gene editing tools with superior cutting efficiency, good fidelity, and fewer genomic restrictions. Here, we report a CRISPR/Cas9 from Faecalibaculum rodentium, which is characterized by a simple PAM (5'-NNTA-3') and a guide RNA length of 21-22 bp. We find that FrCas9 could achieve comparable efficiency and specificity to SpCas9. Interestingly, the PAM of FrCas9 presents a palindromic sequence, which greatly expands its targeting scope. Due to the PAM sequence, FrCas9 possesses double editing-windows for base editor and could directly target the TATA-box in eukaryotic promoters for TATA-box related diseases. Together, our results broaden the understanding of CRISPR/Cas-mediated genome engineering and establish FrCas9 as a safe and efficient platform for wide applications in research, biotechnology and therapeutics.

Gene knock-out chain reaction enables high disruption efficiency of HPV18 E6/E7 genes in cervical cancer cells.

A genome editing tool targeting the high-risk human papillomavirus (HPV) oncogene is a promising therapeutic strategy to treat HPV-related cervical cancer. To improve gene knockout efficiency, we developed a gene knockout chain reaction (GKCR) method for continually generating mutagenic disruptions and used this method to disrupt the HPV18 E6 and E7 genes. We verified that the GKCR Cas9/guide RNA (gRNA) cassettes could integrated into the targeted loci via homology-independent targeted insertion (HITI). The qPCR results revealed that the GKCR method enabled a relatively higher Cas9/gRNA cassette insertion rate than a control method (the common CRISPR-Cas9 strategy). Tracking of Indels by DEcomposition (TIDE) assay results showed that the GKCR method produced a significantly higher percentage of insertions or deletions (indels) in the HPV18 E6 and E7 genes. Furthermore, by targeting the HPV18 E6/E7 oncogenes, we found that the GKCR method significantly upregulated the P53/RB proteins and inhibited the proliferation and motility of HeLa cells. The GKCR method significantly improved the gene knockout efficiency of the HPV18 E6/E7 oncogenes, which might provide new insights into treatment of HPV infection and related cervical cancer.

Non-homologous dsODN increases the mutagenic effects of CRISPR-Cas9 to disrupt oncogene E7 in HPV positive cells.

Genome editing tools targeting high-risk human papillomavirus (HPV) oncogene could be a promising therapeutic strategy for the treatment of HPV-related cervical cancer. We aimed to improve the editing efficiency and detect off-target effects concurrently for the clinical translation strategy by using CRISPR-Cas9 system co-transfected with 34nt non-homologous double-stranded oligodeoxynucleotide (dsODN). We firstly tested this strategy on targeting the Green Fluorescent Protein (GFP) gene, of which the expression is easily observed. Our results showed that the GFP+ cells were significantly decreased when using GFP-sgRNAs with dsODN, compared to using GFP-sgRNAs without donors. By PCR and Sanger sequencing, we verified the dsODN integration into the break sites of the GFP gene. And by amplicon sequencing, we observed that the indels% of the targeted site on the GFP gene was increased by using GFP-sgRNAs with dsODN. Next, we went on to target the HPV18 E7 oncogene by using single E7-sgRNA and multiplexed E7-sgRNAs respectively. Whenever using single sgRNA or multiplexed sgRNAs, the mRNA expression of HPV18 E7 oncogene was significantly decreased when adding E7-sgRNAs with dsODN, compared to E7-sgRNAs without donor. And the indels% of the targeted sites on the HPV18 E7 gene was markedly increased by adding dsODN with E7-sgRNAs. Finally, we performed GUIDE-Seq to verify that the integrated dsODN could serve as the marker to detect off-target effects in using single or multiplexed two sgRNAs. And we detected fewer on-target reads and off-target sites in multiplexes compared to the single sgRNAs when targeting the GFP and the HPV18 E7 genes. Together, CRISPR-Cas9 system co-transfected with 34nt dsODN concurrently improved the editing efficiency and monitored off-target effects, which might provide new insights in the treatment of HPV infections and related cervical cancer.

The comparison of ZFNs, TALENs, and SpCas9 by GUIDE-seq in HPV-targeted gene therapy.

Zinc-finger nucleases (ZFNs), transcription activator-like endonucleases (TALENs), and CRISPR-associated Cas9 endonucleases are three major generations of genome editing tools. However, no parallel comparison about the efficiencies and off-target activity of the three nucleases has been reported, which is critical for the final clinical decision. We for the first time developed the genome-wide unbiased identification of double-stranded breaks enabled by sequencing (GUIDE-seq) method in ZFNs and TALENs with novel bioinformatics algorithms to evaluate the off-targets. By targeting human papillomavirus 16 (HPV16), we compared the performance of ZFNs, TALENs, and SpCas9 in vivo. Our data showed that ZFNs with similar targets could generate distinct massive off-targets (287-1,856), and the specificity could be reversely correlated with the counts of middle "G" in zinc finger proteins (ZFPs). We also compared the TALENs with different N-terminal domains (wild-type [WT]/αN/ßN) and G recognition modules (NN/NH) and found the design (αN or NN) to improve the efficiency of TALEN inevitably increased off-targets. Finally, our results showed that SpCas9 was more efficient and specific than ZFNs and TALENs. Specifically, SpCas9 had fewer off-target counts in URR (SpCas9, n = 0; TALEN, n = 1; ZFN, n = 287), E6 (SpCas9, n = 0; TALEN, n = 7), and E7 (SpCas9, n = 4; TALEN, n = 36). Taken together, we suggest that for HPV gene therapies, SpCas9 is a more efficient and safer genome editing tool. Our off-target data could be used to improve the design of ZFNs and TALENs, and the universal in vivo off-target detection pipeline for three generations of artificial nucleases provided useful tools for genome engineering-based gene therapy.

Hyperbranched poly(ß-amino ester) based polyplex nanopaticles for delivery of CRISPR/Cas9 system and treatment of HPV infection associated cervical cancer.

Persistent high-risk HPV infection is the main factor for cervical cancer. HPV E7 oncogene plays an important role in HPV carcinogenesis. Down-regulation of E7 oncogene expression could induce growth inhibition in HPV-positive cells and thus treats HPV related cervical cancer. Here we developed a non-virus gene vector based on poly(amide-amine)-poly(ß-amino ester) hyperbranched copolymer (hPPC) for the delivery of CRISPR/Cas9 system to specifically cleave HPV E7 oncogene in HPV-positive cervical cancer cells. The diameter of polyplex nanoparticles (NPs) formed by hPPCs/linear poly(ß-amino ester) (PBAE) and plasmids were approximately 300 nm. These hPPCs/PBAE-green fluorescence protein plasmids polyplex NPs showed high transfection efficiency and low toxicity in cells and mouse organs. By cleaving HPV16 E7 oncogene, reducing the expression of HPV16 E7 protein and increasing intracellular retinoblastoma 1 (RB1) amount, hPPCs/PBAE-CRISPR/Cas9 therapeutic plasmids polyplex NPs, especially highly branched hPPC1-plasmids polyplex NPs, exhibited strong growth inhibition of cervical cancer cells in vitro and xenograft tumors in nude mice. Together, the hPPCs/PBAE polyplex NPs to deliver HPV16 E7 targeted CRISPR/Cas9 system in this study could potentially be applied to treat HPV-related cervical cancer.

An effective vaginal gel to deliver CRISPR/Cas9 system encapsulated in poly (ß-amino ester) nanoparticles for vaginal gene therapy.

BACKGROUND: Gene therapy has held promises for treating specific genetic diseases. However, the key to clinical application depends on effective gene delivery. METHODS: Using a large animal model, we developed two pharmaceutical formulations for gene delivery in the pigs' vagina, which were made up of poly (ß-amino ester) (PBAE)-plasmid polyplex nanoparticles (NPs) based two gel materials, modified montmorillonite (mMMT) and hectorite (HTT). FINDINGS: By conducting flow cytometry of the cervical cells, we found that PBAE-GFP-NPs-mMMT gel was more efficient than PBAE-GFP-NPs-HTT gel in delivering exogenous DNA intravaginally. Next, we designed specific CRISPR/SpCas9 sgRNAs targeting porcine endogenous retroviruses (PERVs) and evaluated the genome editing efficacy in vivo. We discovered that PERV copy number in vaginal epithelium could be significantly reduced by the local delivery of the PBAE-SpCas9/sgRNA NPs-mMMT gel. Comparable genome editing results were also obtained by high-fidelity version of SpCas9, SpCas9-HF1 and eSpCas9, in the mMMT gel. Further, we confirmed that the expression of topically delivered SpCas9 was limited to the vagina/cervix and did not diffuse to nearby organs, which was relatively safe with low toxicity. INTERPRETATION: Our data suggested that the PBAE-NPs mMMT vaginal gel is an effective preparation for local gene therapy, yielding insights into novel therapeutic approaches to sexually transmitted disease in the genital tract. FUNDING: This work was supported by the National Science and Technology Major Project of the Ministry of science and technology of China (No. 2018ZX10301402); the National Natural Science Foundation of China (81761148025, 81871473 and 81402158); Guangzhou Science and Technology Programme (No. 201704020093); National Ten Thousand Plan-Young Top Talents of China, Fundamental Research Funds for the Central Universities (17ykzd15 and 19ykyjs07); Three Big Constructions-Supercomputing Application Cultivation Projects sponsored by National Supercomputer Center In Guangzhou; the National Research FFoundation (NRF) South Africa under BRICS Multilateral Joint Call for Proposals; grant 17-54-80078 from the Russian Foundation for Basic Research.

In vitro and in vivo growth inhibition of human cervical cancer cells via human papillomavirus E6/E7 mRNAs' cleavage by CRISPR/Cas13a system.

Sustained infection of high-risk human papillomavirus (HR-HPVs), especially HPV16 and HPV18, is a major cause of cervical cancer. E6 and E7 oncoproteins, encoded by the HPV genome, are critical for transformation and maintenance of malignant phenotypes of cervical cancer. Here, we used an emerging programmable clustered regularly interspaced short palindromic repeat (CRISPR)/Cas13a system to cleave HPV 16/18 E6/E7 messenger RNAs (mRNAs). The results showed that customized CRISPR/Cas13a system effectively and specifically knocked down HPV 16/18 E6/E7 mRNAs, inducing growth inhibition and apoptosis in HPV16-positive SiHa and HPV18-positive HeLa Cell lines, but not in HPV-negative C33A cell line. Simultaneously, we detected downregulation of E6/E7 oncoproteins and upregulation of tumor suppressor P53 and RB proteins. In addition, we used subcutaneous xenograft tumor growth assays to find that the weight and volume of tumors in the SiHa-16E6CR1 group knocked down by the CRISPR/Cas13a system were significantly lower than those in the SiHa-VECTOR group lacking crRNA. Our study demonstrated that targeting HPV E6/E7 mRNAs by the CRISPR/Cas13a system may be a candidate therapeutic strategy for HPV-related cervical cancer.