Human papillomavirus

associated alternative RNA

splicing in early carcinogenesis

NST Part II Pathology: Cancer and Genetic Disease

Project Report

Summer 2019

[935 words]

Hira Hasan

University of Cambridge

Girton College

Supervisor: Dr Stephen Smith

Group leader: Professor Nick Coleman

1. Introduction

Cervical cancer is the fourth most common cause of cancer mortality in women

(Olmedo-Nieva, Muñoz-Bello, Contreras-Paredes, & Lizano, 2018). Human papillomavirus

(HPV) is present in more than 99% of cervical cancer samples making it the most frequent viral

cause of cancer (Clifford, Smith, Aguado, & Franceschi, 2003). HPV16 (HR-HPV) accounts

for 55% of the cases of HPV-positive cervical cancers (Graham & Faizo, 2017). Although the

majority of the sexually active population is likely to contract HPV at some point, the most

likely outcome is clearance within 2 years (Stanley, 2012). Furthermore, even when clinical

lesions occur, the majority undergo spontaneous regression (Ho et al., 2011).

HPV oncoproteins E6 and E7 have transforming properties and their pleiotropic activity

is necessary for malignant conversion (Yim & Park, 2005).

Alternative splicing has been implicated in HPV-associated cancer progression due to

expression of splice variants of E6 (termed E6*) whose overexpression leads to tumorigenesis

(Graham & Faizo, 2017). Since over 90% of human mRNAs are alternatively spliced it is

reasonable to suggest that a possible consequence of HPV infection is that host splicing will be

affected and that this may contribute to the carcinogenic property of the virus (Hallegger,

Llorian, & Smith, 2010).

Different E6/E6* patterns have been discovered in premalignant lesions and HPV

related cancers (Olmedo-Nieva et al., 2018). However, the association with cancer

development and the role of the E6* proteins in cancer development remain unclear.

Serine-arginine rich (SR) proteins help define exons in pre-mRNA in order to outline

exon-intron boundaries (Graham & Faizo, 2017). Heterogenous ribonucleoproteins (hnRNPs)

on the other hand, generally block exon-intron definition. Therefore SR proteins and hnRNPs

usually act antagonistically to control splicing (Eperon et al., 2000).

Project Objectives

• To characterise the relationship between HPV oncoprotein isoform levels and splicing

factors

• To suggest mechanisms of viral oncoprotein regulation by analysing splicing factor

binding sites in the genome of HPV using in silico modelling in combination with the

above associations.

2. Materials and Methods

2.1 W12 cell line

W12 is a human keratinocyte cell line obtained from a low-grade cervical lesion.

(Stanley, Browne, Appleby, & Minson, 1989). It represents early events in HPV infection,

which is vital as clinical manifestations of HPV infection in vivo occur decades after initial

infection. The clones have a highly variable oncogene activity and phenotype, making them

ideal to measure the effect of viral oncoproteins on host alternative splicing factors and control

of host gene isoform expression on an otherwise homogenous genetic background. Clones were

selected and used as they represented a range of oncoprotein expression (Smith, Scarpini,

Groves, Odle, & Coleman, 2016).

2.2 cDNA synthesis followed by RT-qPCR

cDNA was synthesised from RNA using QuantiTect Reverse Transcription kit (Qiagen)

following standard protocol. RNA expression from the selected W12 clones was measured by

SYBR Quantitative PCR. Having tested two reference genes (YWHAZ and HMBS) by PCR

and verified their stability of expression in previous work in the W12 cell line (Smith et al.,

2016), YWHAZ alone was used in all experiments for time efficiency. This choice was further

validated with data from The Cancer Genome Atlas.

2.4 Splice Aid 2

Splice Aid 2 (a database of human splicing factors and RNA target motifs) was used to

probe for potential binding sites.

2.5 Statistical Analysis

Pearson correlation coefficients were calculated (Mukaka, 2012), and statistical

significance (P <.05) established with the Pearson product moment test. P-values were

corrected for multiple hypothesis testing using the False Discovery Rate method (Benjamini &

Hochberg, 1995), adjusted to an FDR of 0.05.

3. Results

Figure 1. Heatmap showing the Pearson correlation between various splicing

factors and HPV16 transcripts in the W12 clones. ‘Full E6’ refers to the full

length E6 transcript, while ‘all E6’ refers to all isoforms of E6. The black stars

indicate results that are statistically significant (P value <.05).

Figure 3. Heatmap showing Pearson correlation between splicing factors in

the W12 clones. Black stars indicate statistically significant results (P value <.05).

Figure 2. Heatmap showing correlation between HPV16 transcripts in the

W12 clones. Black stars indicate statistically significant data (P value <.05).

Predicting splicing factor binding sites in HPV genome

Figure 4. Schematic illustrating potential splicing factor binding sites. Red

stars indicate the nucleotide position of the splice donor site or the splice acceptor

site for the E6* isoforms. Grey boxes show the recognised sequence of mRNA

while the coloured ovals illustrate the various SFs hypothesised to bind here.

4. Discussion

I investigated SFs reported in the literature to be associated with HPV gene splicing

and/or carcinogenesis. This represents the most comprehensive analysis to date across a broad

selection of SFs and HPV transcripts in a large set of W12 clones. I was able to confirm and

characterise the relationship between SRSF1, SRSF9, HNRPA1, HNRPA2B1 and HNRNPD

with oncoprotein levels in the W12 model. SRSF2, SRSF3, SRSF10 and KHDRBS1 on the

other hand, were not confirmed via my methodology. E6*III and E6*IV did not appear to be

regulated in the same manner as the other transcripts suggesting a distinct mechanism of

regulation in this subset of W12 clones. I identified networks of SFs that are associated and

may be acting in concert with one another. Lastly, I provided evidence for ten binding sites that

warrant further investigation. These include binding sites for; SRSF9 at the splice donor site,

E6*I, E6*II and E6*X branchpoints, HNRPA1 at the splice donor site, E6*II and E6*X

branchpoints, SRSF1 and SRSF2 at E6*I branchpoint and HNRPA2B1 at the E6*X

branchpoint.

These results lay the ground work for investigation into host protein isoform switching

that may be a key way in which the virus promotes carcinogenesis.

5. Appendix

Figure 5. Poster presentation of my research project presented at the BAD

99th annual conference in Liverpool 2nd – 4th July 2019

Figure 6. A 360o tour of the Coleman-Murray Lab in the Department of

Pathology, Cambridge, where my project was carried out.

6. References

Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A

Practical and Powerful Approach to Multiple Testing. Journal of the Royal

Statistical Society. Series B (Methodological), 57(1), 289–300.

Clifford, G. M., Smith, J. S., Aguado, T., & Franceschi, S. (2003). Comparison

of HPV type distribution in high-grade cervical lesions and cervical cancer:

a meta-analysis. British Journal of Cancer, 89(1), 101–105.

Eperon, I. C., Makarova, O. V, Mayeda, A., Munroe, S. H., Cáceres, J. F.,

Hayward, D. G., & Krainer, A. R. (2000). Selection of alternative 5’ splice

sites: role of U1 snRNP and models for the antagonistic effects of SF2/ASF

and hnRNP A1. Molecular and Cellular Biology, 20(22), 8303–8318.

Graham, S. V., & Faizo, A. A. A. (2017). Control of human papillomavirus

gene expression by alternative splicing. Virus Research, 231, 83–95.

Hallegger, M., Llorian, M., & Smith, C. W. J. (2010). Alternative splicing:

global insights. FEBS Journal, 277(4), 856–866.

Ho, G. Y. F., Einstein, M. H., Romney, S. L., Kadish, A. S., Abadi, M.,

Mikhail, M., … Albert Einstein Cervix Dysplasia Clinical Consortium.

(2011). Risk Factors for Persistent Cervical Intraepithelial Neoplasia

Grades 1 and 2. Journal of Lower Genital Tract Disease, 15(4), 268–275.

Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of

correlation coefficient in medical research. Malawi Medical Journal : The

Journal of Medical Association of Malawi, 24(3), 69–71.

Olmedo-Nieva, L., Muñoz-Bello, J., Contreras-Paredes, A., & Lizano, M.

(2018). The Role of E6 Spliced Isoforms (E6*) in Human Papillomavirus-

Induced Carcinogenesis. Viruses, 10(1), 45.

Smith, S. P., Scarpini, C. G., Groves, I. J., Odle, R. I., & Coleman, N. (2016).

Identification of host transcriptional networks showing concentration-

dependent regulation by HPV16 E6 and E7 proteins in basal cervical

squamous epithelial cells. Scientific Reports, 6(1), 29832.

Stanley, M. A. (2012). Epithelial Cell Responses to Infection with Human

Papillomavirus. Clinical Microbiology Reviews, 25(2), 215–222.

Stanley, M. A., Browne, H. M., Appleby, M., & Minson, A. C. (1989).

Properties of a non-tumorigenic human cervical keratinocyte cell line.

International Journal of Cancer, 43(4), 672–676.

Yim, E.-K., & Park, J.-S. (2005). The role of HPV E6 and E7 oncoproteins in

HPV-associated cervical carcinogenesis. Cancer Research and Treatment :

Official Journal of Korean Cancer Association, 37(6), 319–324.