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.

Interactions between host and gut microbiota in gestational diabetes mellitus and their impacts on offspring.

Gestational diabetes mellitus (GDM) is characterized by insulin resistance and low-grade inflammation, and most studies have demonstrated gut dysbiosis in GDM pregnancies. Overall, they were manifested as a reduction in microbiome diversity and richness, depleted short chain fatty acid (SCFA)-producing genera and a dominant of Gram-negative pathogens releasing lipopolysaccharide (LPS). The SCFAs functioned as energy substance or signaling molecules to interact with host locally and beyond the gut. LPS contributed to pathophysiology of diseases through activating Toll-like receptor 4 (TLR4) and involved in inflammatory responses. The gut microbiome dysbiosis was not only closely related with GDM, it was also vital to fetal health through vertical transmission. In this review, we summarized gut microbiota signature in GDM pregnancies of each trimester, and presented a brief introduction of microbiome derived SCFAs. We then discussed mechanisms of microbiome-host interactions in the physiopathology of GDM and associated metabolic disorders. Finally, we compared offspring microbiota composition from GDM with that from normal pregnancies, and described the possible mechanism.

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.

Transcriptome Analysis in Yeast Reveals the Externality of Position Effects.

The activity of a gene newly integrated into a chromosome depends on the genomic context of the integration site. This "position effect" has been widely reported, although the other side of the coin, that is, how integration affects the local chromosomal environment, has remained largely unexplored, as have the mechanism and phenotypic consequences of this "externality" of the position effect. Here, we examined the transcriptome profiles of approximately 250 Saccharomyces cerevisiae strains, each with GFP integrated into a different locus of the wild-type strain. We found that in genomic regions enriched in essential genes, GFP expression tended to be lower, and the genes near the integration site tended to show greater expression reduction. Further joint analysis with public genome-wide histone modification profiles indicated that this effect was associated with H3K4me2. More importantly, we found that changes in the expression of neighboring genes, but not GFP expression, significantly altered the cellular growth rate. As a result, genomic loci that showed high GFP expression immediately after integration were associated with growth disadvantages caused by elevated expression of neighboring genes, ultimately leading to a low total yield of GFP in the long run. Our results were consistent with competition for transcriptional resources among neighboring genes and revealed a previously unappreciated facet of position effects. This study highlights the impact of position effects on the fate of exogenous gene integration and has significant implications for biological engineering and the pathology of viral integration into the host genome.

Long noncoding RNA TFAP2A-AS1 promotes oral squamous cell carcinoma cell growth and movement via competitively binding miR-1297 and regulating TFAP2A expression.

Oral squamous cell carcinoma (OSCC) is an aggressive and common malignancy in the head and neck, characterized by poor prognosis and high incidence. This study aimed to investigate the role of long noncoding RNA TFAP2A-AS1 in OSCC. The competing endogenous RNA network of TFAP2A-AS1 was constructed by bioinformatics analysis. The expressions of miR-1297, TFAP2A-AS1, and TFAP2A were measured by quantitative reverse transcription-polymerase chain reaction. The correlations of TFAP2A-AS1, miR-1297, and TFAP2A with clinicopathological characteristics of OSCC were assessed. RNA immunoprecipitation and dual-luciferase reporter assay were used to identify the target of miR-1297. Cell proliferation was measured by colony formation assay and [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium] assay. Transwell assay and wound healing assay were performed to assess cell movement. TFAP2A-AS1 and TFAP2A were upregulated in OSCC and their expression levels were positively correlated. The levels of TFAP2A-AS1, miR-1297, and TFAP2A were also associated with lymphatic metastasis and the tumor-node-metastasis (TNM) stage of OSCC patients. TFAP2A-AS1 acted as a miR-1297 sponge. OSCC cell growth and movement were inhibited by miR-1297. Changes in the miR-1297 expression abolished the effects of TFAP2A-AS1 on OSCC cells. Additionally, TFAP2A was a target of miR-1297. TFAP2A promoted OSCC cell growth and migration/invasion, indicating that TFAP2A mediated the effects of TFAP2A-AS1 and miR-1297. TFAP2A-AS1 exerts an oncogenic effect in OSCC via the TFAP2A-AS1/miR-1297/TFAP2A axis, which may provide new targets and strategies for OSCC treatments.

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.

Long non-coding RNA TFAP2A-AS1 plays an important role in oral squamous cell carcinoma: research includes bioinformatics analysis and experiments.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is the most common neck and head malignancies, and the prognosis is not good. Studies shown that the long non-coding RNA (lncRNA) TFAP2A-AS1 is involved in the progression of multiple cancers. However, the role of lncRNA TFAP2A-AS1 in OSCC remains unclear. We aimed to explore the functions and expression in OSCC. METHODS: The lncRNA profiles for OSCC patients were acquired from the TCGA. Based on these data, the data mining of TFAP2A-AS1 in patients with OSCC were performed. The functions of TFAP2A-AS1 were determined by bioinformatics analysis. The expression and roles in cell growth were tested by RT-qPCR and MTS assay. Cell invasion and migration were tested by wound healing and transwell assays. RESULTS: The consequences displayed that TFAP2A-AS1 was upregulated in the TCGA datasets. The expression of TFAP2A-AS1 was higher in OSCC samples. Bioinformatics analysis shown that TFAP2A-AS1 might be associated with the P53 signaling pathway. Cell culture experiments indicated that deficiency of TFAP2A-AS1 inhibited cell growth, invasion, and migration, and overexpression of it could opposite results in SCC-25 cells. CONCLUSION: The results suggested that TFAP2A-AS1 was overexpressed in OSCC cells, which could facilitate OSCC cell proliferation, migration, and invasion.

IncRNA ZNF667-AS1 Suppresses Epithelial Mesenchymal Transformation by Targeting TGF-ß1 in Oral Squamous Cell Carcinoma.

BACKGROUND: IncRNAs perform complex functions and play an essential role in all stages of tumor progression. However, there are few studies that discuss the function of lncRNA ZNF667-AS1in oral squamous cell carcinoma (OSCC). This study aimed at analyzing the expression and biological behavior of lncRNA ZNF667-AS1 in OSCC. METHODS: IncRNA ZNF667-AS1 expression level in OSCC tissues and cell lines was explored by real-time PCR. The role of lncRNA ZNF667-AS1 on prognosis was elucidated. Cell proliferation assay, plate colony formation assay, wound-healing assay, and transwell migration assay were used to detect cell proliferation ability, cell clone formation ability, migration ability, and invasion ability, respectively. The effect of lncRNA ZNF667-AS1 on epithelial mesenchymal transformation (EMT) process was evaluated by western blot and real-time PCR. RESULTS: The expression levels of lncRNA ZNF667-AS1 were decreased in malignant tumor tissues. The OSCC patients with high expression of lncRNA ZNF667-AS1 had a longer survival time. IncRNA ZNF667-AS1 inhibited cell proliferation, cell clone formation ability, invasion and migration. Furthermore, lncRNA ZNF667-AS1 could inhibit the EMT process by suppressing transforming growth factor-ß-1 (TGF-ß1) expression, and TGF-ß1 treatment could partially restore the inhibitory effect. CONCLUSIONS: IncRNA ZNF667-AS1 may act as an antioncogene inhibiting the ability of proliferation, cell clone formation, invasion and migration, and suppress the process of EMT by targeting TGF-ß1. IncRNA ZNF667-AS1 could be a potential therapeutic target and a new predictive biological marker of OSCC.

MicroRNA-222-3p participates in the development of oral squamous cell carcinoma by targeting CDKN1B.

OBJECTIVE: The aim of this study was to explore the potential role and regulatory mechanism of microRNA (miR)-222-3p in oral squamous cell carcinoma (OSCC). METHODS: The expression level and prognostic significance of miR-222-3p was detected in OSCC tissues. CCK-8, transwell, and flow cytometry assays were used to explore the effect of miR-222-3p on cell proliferation, migration, invasion, and apoptosis. The influence of miR-222-3p on cyclin-dependent kinase inhibitor 1B (CDKN1B) expression was evaluated by luciferase assays, real-time polymerase chain reaction, and Western blot. RESULTS: We found that miR-222-3p was overexpressed in OSCC tissues, comparing with normal tissues. Kaplan-Meier curves showed that OSCC patients with high expression of miR-222-3p (P = .003) showed worse overall survival than those patients with low expression of miR-222-3p. Multivariate analysis showed that miR-222-3p (P = .037) expression was an independent prognostic factor of OSCC patients. miR-222-3p promoted cell proliferation, migration and invasion and induced the apoptosis of SCC-15 and Tca-83 cells. Furthermore, luciferase reporter assays indicated that CDKN1B is targeted by miR-222-3p in OSCC cells. Overexpression of CDKN1B inhibited OSCC cell proliferation, migration, and invasion and promoted cell apoptosis rate. CONCLUSIONS: miR-222-3p affects OSCC cell proliferation, migration, invasion, and apoptosis through targeting CDKN1B, and may be a potential prognostic biomarker for OSCC patients.

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.

A two-CpG-based prognostic signature for oral squamous cell carcinoma overall survival.

Oral squamous cell carcinoma (OSCC) represents one of the most common head and neck cancer that with dire prognosis due partly to the lack of reliable prognostic biomarker. Here, we aimed to develop a CpG site-based prognostic signature through which we could accurately predict overall survival (OS) of patients with OSCC. We obtained OSCC-related DNA methylation and gene expression data sets from the public accessible Gene Expression Omnibus. Correlations between methylation level of CpG sites and OS of patients with OSCC were assessed by univariate Cox regression analysis followed by robust likelihood-based survival analysis on those CpG sites with permutation P < 0.05 for further screening the optimal CpG sites for OSCC OS prediction based on the risk score formula that composed of the methylation level of optimal CpG sites weighted by their regression coefficients. Besides, differential expression genes (DEGs) and differential methylation genes (DMGs) in OSCC samples compared with normal samples were obtained and shared genes were considered as vital genes in OSCC tumorgenesis and progression. As a result, two CpG sites including cg17892178 and cg17378966 that located in NID2 and IDO1, respectively, were identified as the optimal prognostic signatures for OSCC OS. In addition, 12 overlapping genes between DEGs and DMGs that closely associated with inflammation or blood and tissue development-related biological processes were obtained. In conclusions, this study should provide valuable signatures for OSCC diagnosis and treatment.

ECT2 promotes the occurrence and is a prognostic biomarker of head and neck squamous cell carcinoma.

Background: Epithelial Cell Transforming Sequence 2 (ECT2) has been implicated in various tumorigenic processes, including proliferation, migration, and invasion. However, its specific role in head and neck squamous cell carcinoma (HNSCC) remains unclear. Methods: This study integrates transcriptomic and single-cell RNA sequencing (scRNA-seq) data to explore the potential role of ECT2 in HNSCC. Differential expression analysis, cell-based assays (including CCK-8 for proliferation, transwell for migration, invasion assays, and flow cytometry for apoptosis and cell cycle analysis), and enrichment analysis were employed to investigate ECT2 expression levels and its regulatory effects on cellular phenotypes. Additionally, Mendelian randomization analysis was utilized to identify genes causally related to HNSCC using publicly available Genome-Wide Association Study (GWAS) data. Results: ECT2 is highly expressed in HNSCC samples and its downregulation inhibits proliferation, migration, invasion, induces apoptosis, and affects the cell cycle transition in HSC-3 cells. Furthermore, differential analysis revealed significant differences in the immune microenvironment and drug sensitivity between high and low ECT2 expression groups. The pathways enriched in different groups include CCR and its related chemokines, as well as HLA in antigen presentation and immune response. There are also significant differences in the sensitivity to drugs such as bortezomib and dasatinib between the two groups. Prognostic models constructed from prognosis-related genes showed significant differences in prognosis between high and low-risk groups. Integration of scRNA-seq data identified Monocyte clusters as high-scoring cell clusters based on genes interacting with ECT2.Mendelian randomization analysis identified three genes (LGALS2, SLC11A1, and TKT) causally related to HNSCC within this cell cluster. Conclusion: The findings suggest that ECT2 overexpression is associated with the survival rate of HNSCC, indicating its potential as a prognostic biomarker for this malignancy.

Demethylase FTO-mediated m6A modification of SIK1 modulates placental cytotrophoblast syncytialization in type 2 diabetes mellitus.

Type 2 diabetes mellitus (T2DM) represents a common complication during pregnancy that affects fetoplacental development. We demonstrated the existence of impaired trophoblast syncytialization under hyperglycemic conditions. However, the exact mechanism remains unknown. RNA N6-methyladenosine (m6A) is an emerging regulatory mechanism of mRNA and participates in various biological processes. We described the global m6A modification pattern in T2DM placenta by the combined analysis of methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and RNA sequencing (RNA-Seq). Both the m6A modification and expression of SIK1, which is critical for syncytialization, were significantly decreased in trophoblast exposed to hyperglycemic conditions. In addition, the m6A demethylase fat mass and obesity-associated protein (FTO) affects the expression and mRNA stability of SIK1 by binding to its 3'-untranslated region (UTR) m6A site. This work reveals that the FTO-m6A-SIK1 axis plays critical roles in regulating syncytialization in the placenta.

The role and mechanism of circ-BNC2 on the malignant progression of oral squamous cell carcinoma.

BACKGROUND: Circular RNAs (circRNAs) play a key part in the progression of oral squamous cell carcinoma (OSCC). However, the role of circ-BNC2 (circRNA ID hsa_circ_0086414) in OSCC progression is still unclear. METHODS: Plasmid transfection was used to induce overexpression of circ-BNC2. RNA expression of circ-BNC2, microRNA-142-3p (miR-142-3p) and GNAS complex locus (GNAS) was detected by quantitative real-time polymerase chain reaction. Protein expression was assessed by western blot assay or immunohistochemistry assay. Cell proliferation was investigated by 3-(4,5-dimethylthazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, colony formation assay and flow cytometry analysis. Cell migratory and invasive abilities and cell apoptosis were assessed by transwell assay and flow cytometry analysis, respectively. Oxidative stress was evaluated by superoxide dismutase activity detection assay, lipid peroxidation malondialdehyde assay and cellular reactive oxygen species assay. The binding relationship between miR-142-3p and circ-BNC2 or GNAS was proved by dual-luciferase reporter assay and RNA immunoprecipitation assay. The impacts of circ-BNC2 overexpression on tumor growth in vivo were unveiled by a xenograft mouse model assay. RESULTS: Circ-BNC2 expression was downregulated in OSCC tissues and cells when compared with adjacent healthy tissues and normal human oral keratinocytes. Circ-BNC2 overexpression repressed the proliferation, migration and invasion of OSCC cells but induced cell apoptosis and oxidative stress. Additionally, circ-BNC2 overexpression inhibited tumor growth in vivo. Furthermore, circ-BNC2 bound to miR-142-3p, and miR-142-3p targeted GNAS. MiR-142-3p mimic attenuated circ-BNC2 overexpression-mediated effects on the proliferation, migration, invasion, apoptosis and oxidative stress of OSCC cells. The regulation of miR-142-3p in OSCC cell tumor properties involved GNAS. Further, circ-BNC2 introduction promoted GNAS expression by inhibiting miR-142-3p. CONCLUSION: Circ-BNC2 suppressed OSCC malignant progression by upregulating GNAS expression in a miR-142-3p-dependent manner, which suggested that circ-BNC2 might be a novel target for OSCC therapy.

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/.

Construction of the miRNA-mRNA regulatory network and analysis of hub genes in oral squamous cell carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) severely affects the quality of life and the 5-year survival rate is low. Exploring the potential miRNA-mRNA regulatory network and analyzing hub genes and clinical data can provide a theoretical basis for further elucidating the pathogenesis of OSCC. METHODS: The miRNA expression datasets of GSE113956 and GSE124566 and mRNA expression datasets of GSE31056, GSE37991 and GSE13601 were obtained from the Gene Expression Omnibus databases. The differentially expressed miRNAs (DEMs) and mRNAs (DEGs) were screened using GEO2R. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID database. The PPI network was established through STRING database and the hub genes were preliminarily screened out by Cytoscape software. After identifying the hub genes in the TCGA database, we predicted the potential DEM transcription factors, constructed a miRNA-mRNA regulatory network, and analyzed the relationship between the hub genes and clinical data. RESULTS: A total of 28 DEMs and 764 DEGs were screened out, which were composed of 285 up-regulated genes and 479 down-regulated genes. Enrichment analysis showed that up-regulation of DEGs were mainly enriched in extracellular matrix organization and cancer-related pathway, while down-regulation of DEGs were mainly enriched in muscular system process and adrenaline signal transduction. After preliminary screening by PPI network and identification in TCGA, the up-regulated FN1, COL1A1, COL1A2, AURKA, CCNB1, CCNA2, SPP1, CDC6, and down-regulated ACTN2, TTN, IGF1, CAV3, MYL2, DMD, LDB3, CSRP3, ACTA1, PPARG were identified as hub genes. The miRNA-mRNA regulation network showed that hsa-miR-513b was the DEM with the most regulation, and COL1A1 was the DEG with the most regulation. In addition, CDC6, AURKA, CCNB1 and CCNA2 were related to overall survival and tumor differentiation. CONCLUSIONS: The regulatory relationship of hsa-miR-513b/ CDC6, CCNB1, CCNA2 and the regulatory relationship of hsa-miR-342-5p /AURKA were not only verified in the miRNA-mRNA regulatory network but also related to overall survival and tumor differentiation. These results indicated that they participated in the cellular regulatory process, and provided a molecular mechanism model for the study of pathogenesis.

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.

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.

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.

DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites.

BACKGROUND: The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation. RESULTS: An attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By learning local genomic features automatically, DeepHBV was trained and tested using HBV integration site data from the dsVIS database. Initially, DeepHBV showed an AUROC of 0.6363 and an AUPR of 0.5471 for the dataset. The integration of genomic features of repeat peaks and TCGA Pan-Cancer peaks significantly improved model performance, with AUROCs of 0.8378 and 0.9430 and AUPRs of 0.7535 and 0.9310, respectively. The transcription factor binding sites (TFBS) were significantly enriched near the genomic positions that were considered. The binding sites of the AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra, and Foxo3 were highlighted by DeepHBV in both the dsVIS and VISDB datasets, revealing a novel integration preference for HBV. CONCLUSIONS: DeepHBV is a useful tool for predicting HBV integration sites, revealing novel insights into HBV integration-related carcinogenesis.