Delineation of the HPV11E6 and HPV18E6 Pathways in Initiating Cellular Transformation

Mwapagha, Lamech M.; Tiffin, Nicki; Parker, M. Iqbal

doi:10.3389/fonc.2017.00258

Cited by 9 publications

(9 citation statements)

References 67 publications

(56 reference statements)

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“…It is thought that loss of epithelial integrity may allow HPV particles to enter and infect the basal cells [ 60 ]. Subsequently, HPV may use its oncoproteins (specifically E6 and E7) to trigger oncogenic cellular transformation through deregulation of cellular signalling pathways and deregulation of tumour suppressor genes [ 61 ]. In the present study, functional cell motility pathway was enriched in women without HR-HPV infection.…”

Section: Discussionmentioning

confidence: 99%

Predictive functional analysis reveals inferred features unique to cervicovaginal microbiota of African women with bacterial vaginosis and high-risk human papillomavirus infection

et al. 2021

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Mounting evidence suggests that Lactobacillus species may not necessarily be the sine qua non of healthy cervicovaginal microbiota (CVM), especially among reproductive-age African women. A majority of African women have high-diversity non-Lactobacillus-dominated CVM whose bacterial functions remain poorly characterized. Functional profiling of the CVM is vital for investigating human host-microbiota interactions in health and disease. Here, we investigated the functional potential of L. iners-dominated and high-diversity non-Lactobacillus-dominated CVM of 75 African women with and without bacterial vaginosis (BV) and high-risk human papillomavirus (HR-HPV) infection. Functional contents were predicted using PICRUSt. Microbial taxonomic diversity, BV, and HR-HPV infection statuses were correlated with the inferred functional composition of the CVM. Differentially abundant inferred functional categories were identified using linear discriminant analysis (LDA) effect size (LEfSe) (p-value <0.05 and logarithmic LDA score >2.0). Of the 75 women, 56 (74.7%), 35 (46.7%), and 29 (38.7%) had high-diversity non-Lactobacillus-dominated CVM, BV, and HR-HPV infection, respectively. Alpha diversity of the inferred functional contents (as measured by Shannon diversity index) was significantly higher in women with high-diversity non-Lactobacillus-dominated CVM and BV than their respective counterparts (H statistic ≥11.5, q-value <0.001). Ordination of the predicted functional metagenome content (using Bray-Curtis distances) showed that the samples segregated according to the extent of microbial taxonomic diversity and BV (pseudo-F statistic ≥19.6, q-value = 0.001) but not HR-HPV status (pseudo-F statistic = 1.7, q-value = 0.159). LEfSe analysis of the inferred functional categories revealed that transport systems (including ABC transporters) and transcription factors were enriched in high-diversity CVM. Interestingly, transcription factors and sporulation functional categories were uniquely associated with high-diversity CVM, BV, and HR-HPV infection. Our predictive functional analysis reveals features unique to high-diversity CVM, BV and HR-HPV infections. Such features may represent important biomarkers of BV and HR-HPV infection. Our findings require proof-of-concept functional studies to examine the relevance of these potential biomarkers in women’s reproductive health and disease.

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Section: Discussionmentioning

confidence: 99%

Predictive functional analysis reveals inferred features unique to cervicovaginal microbiota of African women with bacterial vaginosis and high-risk human papillomavirus infection

et al. 2021

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“…Transcription factor binding sites, protein modifications, alterations of the DNA structure as well as methylation patterns can now be analysed and measured at the genome level [ 91 , 92 , 93 , 94 , 95 , 96 ]. Several studies including our own have identified deletions, insertions, copy number variations, splicing variants and translocations associated with certain cancers, and in so doing identified new drug targets [ 97 , 98 , 99 , 100 , 101 , 102 ]. The development of novel and unrivalled technologies, allowing genome-wide analysis, has enabled the unbiased discovery of drug targets.…”

Section: Natural Products Drug Discovery Research and Development mentioning

confidence: 99%

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

Thomford

Senthebane

Rowe

et al. 2018

IJMS

925

556

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The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of “active compound” has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of ‘organ-on chip’ and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

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“…In principle, there are different ways to investigate the role of HPV oncogenes in host cell transformation. The most common approaches were either to express or to knock down E6 and/or E7 to study the changes in host transcriptome profiling (19–23). In addition, available cell lines or clinical samples with different progression states were used to examine the role of either distinct HPV oncogenes or the whole viral genome in an episomal or integrated state (22, 24, 25).…”

Section: Introductionmentioning

confidence: 99%

Combined Transcriptome and Proteome Analysis of Immortalized Human Keratinocytes Expressing Human Papillomavirus 16 (HPV16) Oncogenes Reveals Novel Key Factors and Networks in HPV-Induced Carcinogenesis

Yang

Klimentová

Göckel-Krzikalla

et al. 2019

mSphere

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Although the role of high-risk human papillomaviruses (hrHPVs) as etiological agents in cancer development has been intensively studied during the last decades, there is still the necessity of understanding the impact of the HPVE6andE7oncogenes on host cells, ultimately leading to malignant transformation. Here, we used newly established immortalized human keratinocytes with a well-defined HPV16E6E7expression cassette to get a more complete and less biased overview of global changes induced by HPV16 by employing transcriptome sequencing (RNA-Seq) and stable isotope labeling by amino acids in cell culture (SILAC). This is the first study combining transcriptome and proteome data to characterize the impact of HPV oncogenes in human keratinocytes in comparison with their virus-negative counterparts. To enhance the informative value and accuracy of the RNA-Seq data, four different bioinformatic workflows were used. We identified potential novel upstream regulators (e.g., CNOT7, SPDEF, MITF, and PAX5) controlling distinct clusters of genes within the HPV-host cell network as well as distinct factors (e.g., CPPED1, LCP1, and TAGLN) with essential functions in cancer. Validated results in this study were compared to data sets from The Cancer Genome Atlas (TCGA), demonstrating that several identified factors were also differentially expressed in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and HPV-positive head and neck squamous cell carcinomas (HNSCs). This highly integrative approach allows the identification of novel HPV-induced cellular changes that are also reflected in cancer patients, providing a promising omics data set for future studies in both basic and translational research.IMPORTANCEHuman papillomavirus (HPV)-associated cancers still remain a big health problem, especially in developing countries, despite the availability of prophylactic vaccines. Although HPV oncogenes have been intensively investigated for decades, a study applying recent advances in RNA-Seq and quantitative proteomic approaches to a precancerous model system with well-defined HPV oncogene expression alongside HPV-negative parental cells has been missing until now. Here, combined omics analyses reveal global changes caused by the viral oncogenes in a less biased way and allow the identification of novel factors and key cellular networks potentially promoting malignant transformation. In addition, this system also provides a basis for mechanistic research on novel key factors regulated by HPV oncogenes, especially those that are confirmedin vivoin cervical cancer as well as in head and neck cancer patient samples from TCGA data sets.

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Delineation of the HPV11E6 and HPV18E6 Pathways in Initiating Cellular Transformation

Cited by 9 publications

References 67 publications

Predictive functional analysis reveals inferred features unique to cervicovaginal microbiota of African women with bacterial vaginosis and high-risk human papillomavirus infection

Predictive functional analysis reveals inferred features unique to cervicovaginal microbiota of African women with bacterial vaginosis and high-risk human papillomavirus infection

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

Combined Transcriptome and Proteome Analysis of Immortalized Human Keratinocytes Expressing Human Papillomavirus 16 (HPV16) Oncogenes Reveals Novel Key Factors and Networks in HPV-Induced Carcinogenesis

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