PLK1 regulates CtIP and DNA2 interplay in long-range DNA end resection

Polo-Like Kinase 1 and DNA Damage Response

Polo-like kinase 1 (Plk1), an evolutionarily conserved serine/threonine protein kinase, is a key regulator involved in the mitotic process of the cell cycle. Mounting evidence suggests that Plk1 is also involved in a variety of nonmitotic events, including the DNA damage response, DNA replication, cytokinesis, embryonic development, apoptosis, and immune regulation. The DNA damage response (DDR) includes activation of the DNA checkpoint, DNA damage recovery, DNA repair, and apoptosis. Plk1 is not only an important target of the G2/M DNA damage checkpoint but also negatively regulates the G2/M checkpoint commander Ataxia telangiectasia-mutated (ATM), promotes G2/M phase checkpoint recovery, and regulates homologous recombination repair by interacting with Rad51 and BRCA1, the key factors of homologous recombination repair. This article briefly reviews the function of Plk1 in response to DNA damage.

DNA binding and bridging by human CtIP in the healthy and diseased states

The human DNA repair factor CtIP helps to initiate the resection of double-stranded DNA breaks for repair by homologous recombination, in part through its ability to bind and bridge DNA molecules. However, CtIP is a natively disordered protein that bears no apparent similarity to other DNA-binding proteins and so the structural basis for these activities remains unclear. In this work, we have used bulk DNA binding, single molecule tracking, and DNA bridging assays to study wild-type and variant CtIP proteins to better define the DNA binding domains and the effects of mutations associated with inherited human disease. Our work identifies a monomeric DNA-binding domain in the C-terminal region of CtIP. CtIP binds non-specifically to DNA and can diffuse over thousands of nucleotides. CtIP-mediated bridging of distant DNA segments is observed in single-molecule magnetic tweezers experiments. However, we show that binding alone is insufficient for DNA bridging, which also requires tetramerization via the N-terminal domain. Variant CtIP proteins associated with Seckel and Jawad syndromes display impaired DNA binding and bridging activities. The significance of these findings in the context of facilitating DNA break repair is discussed.

Telocinobufagin, a PLK1 suppressor that inhibits tumor growth and metastasis by modulating CDC25c and CTCF in HNSCC cells

BACKGROUND

The high metastasis and mortality rates of head and neck squamous cell carcinoma (HNSCC) urgently require new treatment targets and drugs. A steroidal component of ChanSu, telocinobufagin (TBG), was verified to have anti-cancer effects in various tumors, but its activity and mechanism in anti-HNSCC were still unknown.

PURPOSE

This study tried to demonstrate the anti-tumor effect of TBG on HNSCC and verify its potential mechanism.

METHODS

The effect of TBG on cell proliferation and metastasis were performed and the TBG changed genes were detected by RNA-seq analysis in HNSCC cells. The GSEA and PPI analysis were used to identify the pathways targeted for TBG-regulated genes. Meanwhile, the mechanism of TBG on anti-proliferative and anti-metastasis were investigated in vitro and in vivo.

RESULTS

The in vitro and in vivo experiments confirmed that TBG has favorable anti-tumor effects by induced G2/M phase arrest and suppressed metastasis in HNSCC cells. Further RNA-seq analysis demonstrated the genes regulated by TBG were enriched at the G2/M checkpoint and PLK1 signaling pathway. Then, the bioinformatic analysis of clinical data found that high expressed PLK1 were closely associated with poor overall survival in HNSCC patients. Furthermore, PLK1 directly and indirectly modulated G2/M phase and metastasis (by regulated CTCF) in HNSCC cells, simultaneously. TBG significantly inhibited the protein levels of PLK1 in both phosphorylated and non-phosphorylated forms and then, in one way, inactivated PLK1 failed to activate G2/M phase-related proteins (including CDK1, CDC25c, and cyclin B1). In another way, be inhibited PLK1 unable promote the nuclear translocation of CTCF and thus suppressed HNSC cell metastasis. In contrast, the anti-proliferative and anti-metastasis effects of TBG on HNSCC cell were vanished when cells high-expressed PLK1.

CONCLUSION

The present study verified that PLK1 mediated TBG induced anti-tumor effect by modulated G2/M phase and metastasis in HNSCC cells.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.