Increased expression of lnc-FANCI-2, a newly discovered long noncoding RNA, is associated with cervical lesion progression from cervical intraepithelial neoplasia stage 1 (CIN1, low grade), CIN2–3 (high grade), to cervical cancer. Viral oncoprotein E7 of high-risk human papillomaviruses (HR-HPVs) and host transcription factor YY1 are two major factors promoting lnc-FANCI-2 expression. Using CRISPR-Cas9 technology, we knocked out the expression of lnc-FANCI-2 in the HPV16-positive cervical cancer cell line, CaSki cells. The selected knockout (KO) single-cell clones displayed altered cell morphology and proliferation with changes of cellular soluble receptors, but normal HPV16 E6 and E7 expression. Relative to the parental cells, lnc-FANCI-2 KO cells exhibited significantly increased RAS signaling and epithelial-mesenchymal transition, but decreased response to IFN signaling, along with increased p-Akt and p-Erk1/2 (two RAS signaling effectors), IGFBP3, MCAM, VIM, and CCND2 (cyclin D2) and decreased expression of RAC3. lnc-FANCI-2 in CaSki interacts with cellular proteins H13, HNRH1, K1H1, MAP4K4, and RNPS1. MAP4K4 knockdown led to enhance the expression of p-Erk1/2 and p-Akt. High lnc-FANCI-2 and low MCAM levels in cervical cancer tissues were found to be associated with patients’ survival. A key function of lnc-FANCI-2 intrinsically regulates RAS signaling to impact cervical lesion progression and cervical cancer prognosis.
ABSTRACT Restricting the localization of the evolutionarily conserved centromeric histone H3 variant CENP-A to centromeres prevents chromosomal instability (CIN). The mislocalization of CENP-A to non-centromeric regions contributes to CIN in yeasts, flies and human cells. Even though overexpression and mislocalization of CENP-A have been reported in cancers, the mechanisms responsible for its mislocalization remain poorly understood. Here, we used an imaging-based high-throughput RNAi screen to identify factors that prevent mislocalization of overexpressed YFP-tagged CENP-A (YFP–CENP-A) in HeLa cells. Among the top five candidates in the screen – the depletion of which showed increased nuclear YFP–CENP-A fluorescence – were the histone chaperones CHAF1B (or p60) and CHAF1A (or p150). Follow-up validation and characterization experiments showed that CHAF1B-depleted cells exhibited CENP-A mislocalization, CIN phenotypes and increased enrichment of CENP-A in chromatin fractions. The depletion of DAXX, a histone H3.3 chaperone, suppressed CENP-A mislocalization and CIN in CHAF1B-depleted cells. We propose that in CHAF1B-depleted cells, DAXX promotes mislocalization of the overexpressed CENP-A to non-centromeric regions, resulting in CIN. In summary, we identified regulators of CENP-A localization and defined a role for CHAF1B in preventing DAXX-dependent CENP-A mislocalization and CIN.
In March 2022, a decision of the Romanian Constitutional Court was published in the Official Gazette of Romania, Part I, which brings to the attention of practitioners, but also of the general public, the issue of the removal from office of one of the Presidents of the two Chambers of Parliament. Such a removal is of particular interest in parliamentary life and beyond, and it has also been addressed in the case-law of the Constitutional Court since 2005. The present paper aims mainly to present the novelties of this decision, and, in the alternative, to show that only the Constitutional Court can draw the limits between which such a removal from office can take place, as it is the guarantor of the supremacy of the Constitution, a value provided by the Fundamental Law.
Motivated by risk assessment of coastal flooding, we consider time-consuming simulators with a spatial output. The aim is to perform sensitivity analysis (SA), quantifying the influence of input parameters on the output. There are three main issues. First, due to computational time, standard SA techniques cannot be directly applied on the simulator. Second, the output is infinite dimensional, or at least high dimensional if the output is discretized. Third, the spatial output is non-stationary and exhibits strong local variations. We show that all these issues can be addressed all together by using functional PCA (FPCA). We first specify a functional basis, such as wavelets or B-splines, designed to handle local variations. Secondly, we select the most influential basis terms, either with an energy criterion after basis orthonormalization, or directly on the original basis with a penalized regression approach. Then FPCA further reduces dimension by doing PCA on the most influential basis coefficients, with an ad-hoc metric. Finally, fast-to-evaluate metamodels are built on the few selected principal components. They provide a proxy on which SA can be done. As a by-product, we obtain analytical formulas for variance-based sensitivity indices, generalizing known formula assuming orthonormality of basis functions.
