Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Apr 1;12(4):853.
doi: 10.3390/cancers12040853.

The Core-Clock Gene NR1D1 Impacts Cell Motility In Vitro and Invasiveness in A Zebrafish Xenograft Colon Cancer Model

Affiliations

The Core-Clock Gene NR1D1 Impacts Cell Motility In Vitro and Invasiveness in A Zebrafish Xenograft Colon Cancer Model

Alireza Basti et al. Cancers (Basel). .

Abstract

Malfunctions of circadian clock trigger abnormal cellular processes and influence tumorigenesis. Using an in vitro and in vivo xenograft model, we show that circadian clock disruption via the downregulation of the core-clock genes BMAL1, PER2, and NR1D1 impacts the circadian phenotype of MYC, WEE1, and TP53, and affects proliferation, apoptosis, and cell migration. In particular, both our in vitro and in vivo results suggest an impairment of cell motility and a reduction in micrometastasis formation upon knockdown of NR1D1, accompanied by altered expression levels of SNAI1 and CD44. Interestingly we show that differential proliferation and reduced tumour growth in vivo may be due to the additional influence of the host-clock and/or to the 3D tumour architecture. Our results raise new questions concerning host-tumour interaction and show that core-clock genes are involved in key cancer properties, including the regulation of cell migration and invasion by NR1D1 in zebrafish xenografts.

Keywords: apoptosis; circadian clock; colon cancer; micrometastasis; proliferation; zebrafish xenograft.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
BMAL1 promoter activity shows different oscillation patterns in HCT116 knockdown cell lines. (A) Bioluminescence readouts for the promoter activity of BMAL1 over the course of 120 h in HCT116 control and knockdown (shBMAL1, shPER2 and shNR1D1) cell lines. Periods were calculated with ChronoStar software (TControl = 24.9 ± 0.2 h, TshBMAL1 = ND, TshPER2 = 24.7 ± 0.2 h, TshNR1D1 = 23.6 ± 0.1 h, n = 3, mean ± SEM). (B) Period, phase and amplitude analysis of circadian bioluminescence data of HCT116 knockdown cells over the course of 120 h using Chronostar. (C) Gene expression analysis of core-clock genes PER2, CRY1, NR1D1, CLOCK, and BMAL1 in HCT116 control and knockdown cell lines at 24h after synchronisation. ND, not defined, ns or no asterisk p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001; two-tailed unpaired t-test.
Figure 2
Figure 2
Core-clock gene knockdown affects cell proliferation in HCT116 cells. (A) Proliferation analyses of HCT116 cell lines after shRNA) knock-down of core-clock genes (BMAL1, PER2 and NR1D1) over 5 days (n > 8, mean ± SEM, p < 0.001 for shBMAL1, shPER2 and shNR1D1 comparing AUC to control, two-tailed unpaired t-test). (B) Cell cycle phase distribution of KD cell lines compared to control (n = 3, mean ± SEM, no asterisk p > 0.05, * p < 0.05, *** p < 0.001; two-tailed unpaired t-test). (C) 30-hour time-course gene expression analysis for MYC, WEE1 and TP53 in different HCT116 KD cells (n = 3, mean ± SEM, a cosine curve was fitted to all data sets and displayed as a full line for p < 0.05, the data points were connected with closed lines if p > 0.05). (D) The average expression level for MYC, WEE1 and TP53 in each KD cell line compared to the control (n = 3, mean ± SEM, no asterisk p > 0.05, * p < 0.05, ** p < 0.01, *** p < 0.001; two-tailed unpaired t-test). (E) Apoptosis analysis of HCT116 cell lines after shRNA KD of BMAL1, PER2 and NR1D1 (n  > 8, mean  ±  SEM, p < 0.05 for shBMAL1 and shNR1D1, two-way ANOVA and corrected for multiple testing using Benjamini and Yekutieli method. Measurements obtained by counting caspase3/7 green objects per mm2 every 2 h in the course of 6 days using the IncuCyte S3 device. (F) Representation of the apoptosis assay using the IncuCyte S3 software over the course of 6 days. Green dots represent apoptotic cells expressing caspase3/7. Scale bar: 500 µm.
Figure 3
Figure 3
Core-clock gene knockdown affects cell migration in HCT116 cells. (A) Migration properties of control and shRNA KD HCT116 cell lines (shBMAL1, shPER2 and shNR1D1). Measurements were obtained using a scratch wound assay (IncuCyte). Quantification was performed by measuring the relative wound density over the course of 6 days (n  > 5, mean  ±  SEM, p < 0.05 for shBMAL1, shPER2 and shNR1D1 compared to control, two-way ANOVA and corrected for multiple testing using Benjamini and Yekutieli method). (B) Representation of the scratch wound assay using the IncuCyte S3 software over the course of 6 days. Blue mask indicates the initial scratch wound area. Gold mask indicates wound border area (cell-free area). Scale bar: 1 mm.
Figure 4
Figure 4
(AD)—Representative confocal images of zebrafish larvae xenografts. Human CRC HCT116 control and KD cells (shPER2, shBMAL1 and shNR1D1) were labelled with DiI dye (red) and injected into the PVS of 2dpf Tg(Fli:eGFP) zebrafish (n = 12). At 4dpi mitotic figures (E), % of apoptosis (F—Activated caspase 3), tumour size (G—number of tumour cells) and metastatic potential (HJ) were quantified. Metastatic potential was quantified by injecting cells into the Perivitelline Space (PVS) only, with no cells in circulation (I) and cells injected directly into circulation (J). All images show xenografts anterior to the left, posterior to the right, dorsal up and ventral down. Each dot represents one xenograft. Results are from one single independent experiment. Statistical analysis was performed using Mann–Whitney test (ns or no asterisk p > 0.05, * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001). Error bars: mean ± SEM. Scale bars: 50µm. (K) Overview of the time-course for the in vivo study. HCT116 cells were injected into zebrafish larvae at 48h post-fertilisation (48 hpf). After 3 days, xenograft larvae (XG) were collected every 3h for the course of 24h during dark/dark conditions. Non-injected zebrafish larvae were used as controls (NI), n = 45 larvae/time-point. Time-course mRNA expression levels compared to the mean for: (L) zebrafish per2 gene in non-injected zebrafish larvae (black line and dots) and zebrafish xenografts injected with HCT116 cells (red line and dots), (M) human PER2 gene in zebrafish xenografts injected with HCT116 cells (red line and dots) and in human in HCT116 WT cells (purple line and dots). The average hPER2 expression between XG and HCT116 WT cells is depicted as a bar plot. Cosinor fitting curves were applied for the determination of oscillation parameters (Table S5, Table S7 for list of primers for RT-qPCR, no asterisk p > 0.05). (NO) Shown are the in silico expression profiles for (N) PER2 (O) MYC and WEE1 in Ctrl (T = 23.7 h, black) and Ctrlfast conditions (T = 13.8 h, blue). (P) HCT116 spheroid growth rates over time upon core-clock gene knockdown (shBMAL1, shPER2 and shNR1D1) compared to the control. n = 5, mean  ±  SEM, p < 0.05 for shPER2 compared to control using two-way ANOVA and corrected for multiple testing using Benjamini and Yekutieli method.

References

    1. Hanahan D., Weinberg R.A. Hallmarks of cancer: The next generation. Cell. 2011;144:646–674. doi: 10.1016/j.cell.2011.02.013. - DOI - PubMed
    1. Cederroth C.R., Albrecht U., Bass J., Brown S.A., Dyhrfjeld-Johnsen J., Gachon F., Green C.B., Hastings M.H., Helfrich-Forster C., Hogenesch J.B., et al. Medicine in the Fourth Dimension. Cell Metab. 2019;30:238–250. doi: 10.1016/j.cmet.2019.06.019. - DOI - PMC - PubMed
    1. Lowrey P.L., Takahashi J.S. Genetics of circadian rhythms in Mammalian model organisms. Adv. Genet. 2011;74:175–230. doi: 10.1016/B978-0-12-387690-4.00006-4. - DOI - PMC - PubMed
    1. Albrecht U. Timing to perfection: The biology of central and peripheral circadian clocks. Neuron. 2012;74:246–260. doi: 10.1016/j.neuron.2012.04.006. - DOI - PubMed
    1. Bass J. Circadian topology of metabolism. Nature. 2012;491:348–356. doi: 10.1038/nature11704. - DOI - PubMed

LinkOut - more resources