Unravelling the potential of circular RNAs as novel biomarkers of radiation exposure and -sensitivity and their functional characterization in the radiation response

Inalegwu Auchi

Promoter

de vos winnok, (UA), winnok.devos@uantwerpen.be

SCK•CEN Mentor

Quintens Roel
roel.quintens@sckcen.be
+32 14 33 28 37

SCK•CEN Co-mentor

Monsieurs Pieter
pieter.monsieurs@sckcen.be
+32 14 33 21 08

Expert group

Radiobiology

PhD started

2017-10-01

Short project description

Biomarkers of radiation exposure are useful both to indicate a recent exposure, such as in the case of a nuclear accident and for assessing exposures after a long time, for instance to improve dose reconstruction in retrospective epidemiological studies. Nowadays, with growing threats of nuclear terrorism, as acknowledged during the Nuclear Security Summit in Washington last April, the identification of efficient biomarkers for radiation exposure for the purpose of a fast initial screening (triage) of exposed individuals in need of extra medical attention is becoming increasingly important. Current golden standard methods for biological dosimetry such as cytogenetics assays fall short in several aspects related to emergency situations, in that their analysis is very laborious, time-consuming and expensive and therefore not amenable for fast screening of large cohorts. The last decade, gene expression signatures have emerged as potential interesting biomarkers that could be useful for the abovementioned purposes1–6. We have recently taken this research a step further with the identification of exon expression signatures as robust radiation biomarkers7. We could furthermore show that these exon signatures are more sensitive than gene signatures, and therefore more suitable in the case of low-dose exposures.

Beside biomarkers for exposure, also biomarkers for radiation sensitivity are important for a number of reasons. Current radiotherapy treatment plans are adjusted to the most sensitive individuals in such a way that only about 5% of the patients will develop severe side effects on the healthy tissue. On the other hand, also cancer cells can display differences in radiation sensitivity. In both cases, this may lead to treatments being suboptimal. Thus, identifying biomarkers of sensitivity can play a major role in maximizing tumor control, while minimizing radiation toxicity8.

Circular RNAs (circRNAs) are a recently described class of non-coding RNA molecules that are generated by the canonical splicing machinery in a process called "back-splicing", resulting in a covalently closed circular RNA molecule, which lacks a poly-A tail9,10. CircRNAs are widely expressed, and their expression profiles can vary according to the cell/tissue-type and developmental timing11–16. Many circRNAs are conserved between worms, flies, and mammals13,16–18. One of the main disadvantages of classical mRNA biomarkers is their inherent instability. This is especially problematic for radiation exposure biomarkers for retrospective dosimetry, in which the biomarker should reflect a past event, rather than a physiological state. Due to their covalently closed circular structure, circRNAs are resistant to exonuclease degradation, and therefore remarkably stable17. This, together with observations that circRNAs are highly abundant in blood cells19 and furthermore enriched in exosomes from human serum20 makes them a species of RNA molecules with a very high potential as biomarkers in general, and radiation biomarkers in particular. Hence, in this PhD project, we will identify circRNA biomarkers for radiation exposure and radiosensitivity and furthermore characterize the functions of some of the most promising ones.

References

1.  Boldt, S., et al. Int. J. Radiat. Biol. 88, 267–276 (2012).

2.  Dressman, H. K. et al. PLoS Med. 4, e106 (2007).

3.  Knops, K., et al. Radiat. Res. 178, 304–312 (2012).

4.  Meadows, S. K. et al. PloS One 3, e1912 (2008).

5.  Paul, S. & Amundson, S. A. Int. J. Radiat. Oncol. Biol. Phys. 71, 1236–1244 (2008).

6.  Paul, S. & Amundson, S. A. Int. J. Radiat. Biol. 87, 791–801 (2011).

7.  Macaeva, E. et al. Sci. Rep. 6, 19251 (2016).

8.  Ow, T. J., et al. Arch. Pathol. Lab. Med. 139, 1379–1388 (2015).

9.  Ashwal-Fluss, R. et al. Mol. Cell 56, 55–66 (2014).

10.         Starke, S. et al. Cell Rep. 10, 103–111 (2015).

11.         Dang, Y. et al. Genome Biol. 17, 130 (2016).

12.         Fan, X. et al. Genome Biol. 16, 148 (2015).

13.         Rybak-Wolf, A. et al. Mol. Cell 58, 870–885 (2015).

14.         Salzman, J., et al. PLoS Genet. 9, e1003777 (2013).

15.         Salzman, J., et alPloS One 7, e30733 (2012).

16.         Westholm, J. O. et al. Cell Rep. 9, 1966–1980 (2014).

17.         Jeck, W. R. et al. RNA N. Y. N 19, 141–157 (2013).

18.         Wang, P. L. et al. PloS One 9, e90859 (2014).

19.         Memczak, S., et al. PloS One 10, e0141214 (2015).

20.         Li, Y. et al. Cell Res. 25, 981–984 (2015).

Objective

Objectives

The following objectives will be addressed:

  1. Identification of circRNA biomarkers of acute/chronic exposure from blood and exosomes in mice and humans

  2. Identification of circRNA biomarkers for radiosensitivity in (cancer) cells with different radiosensitivity, or (single) cells screened for radiation damage

  3. Functional characterization of the most interesting biomarkers

 

Methodology

The first two aims will mainly consist of expression profiling (RNA-seq, microarray and/or qRT-PCR) on human and mouse blood and exosome samples at different time points after acute and chronic radiation exposure (objective 1), in (cancer) cell lines with different radiation sensitivity, or cells screened for radiation damage using high-content fluorescence microscopy (objective 2). State-of-the-art software tools21 will be applied to identify differentially expressed circRNAs, while supervised classification methods will be used to evaluate their potential to predict exposure dose or radiation sensitivity status. Furthermore, to differentiate between pre-existing and nascent circRNAs, we will perform metabolic labeling experiments using 4sU.

Functional characterization of novel biomarkers (objective 3) will be performed by knockout, silencing, or overexpression strategies in relevant cell types using different methodologies where suitable (CRISPR/Cas9, lentiviral vectors, LNA GapmeRs). We will evaluate their potential role in the cellular radiation response (DNA damage repair, cell cycle arrest, apoptosis) as well as in general mechanisms relevant for normal and cancer cells (proliferation, epithelial-to-mesenchymal transition (EMT), migration). Methods used will involve gamma-H2AX staining (DNA damage)22,23, flow cytometry (cell cycle analysis), caspase-3 staining (apoptosis), clonal survival assays, staining for epithelial (E-cadherin) and mesenchymal (N-cadherin, Vimentin, Nestin) markers (EMT), migration assays. Subcellular expression of circRNAs at the single molecule level will be evaluated using RNA fluorescence in situ hybridization.

References

21.         Szabo, L. & Salzman, J. Nat. Rev. Genet. 17, 679–692 (2016).

22.         Dieriks, B., et al. Mutat. Res. 715, 19–24 (2011).

23.         Dieriks, B., et al. Mutat. Res. 687, 40–48 (2010).