scholarly journals PHD finger protein 5A promoted lung adenocarcinoma progression via alternative splicing

2019 ◽  
Vol 8 (5) ◽  
pp. 2429-2441 ◽  
Author(s):  
Shuangshuang Mao ◽  
Yuan Li ◽  
Zhiliang Lu ◽  
Yun Che ◽  
Jianbing Huang ◽  
...  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shuang Qu ◽  
Zichen Jiao ◽  
Geng Lu ◽  
Bing Yao ◽  
Ting Wang ◽  
...  

Abstract Background Although using a blockade of programmed death-ligand 1 (PD-L1) to enhance T cell immune responses shows great promise in tumor immunotherapy, the immune-checkpoint inhibition strategy is limited for patients with solid tumors. The mechanism and efficacy of such immune-checkpoint inhibition strategies in solid tumors remains unclear. Results Employing qRT-PCR, Sanger sequencing, and RNA BaseScope analysis, we show that human lung adenocarcinoma (LUAD) all produce a long non-coding RNA isoform of PD-L1 (PD-L1-lnc) by alternative splicing, regardless if the tumor is positive or negative for the protein PD-L1. Similar to PD-L1 mRNA, PD-L1-lnc in various lung adenocarcinoma cells is significantly upregulated by IFNγ. Both in vitro and in vivo studies demonstrate that PD-L1-lnc increases proliferation and invasion but decreases apoptosis of lung adenocarcinoma cells. Mechanistically, PD-L1-lnc promotes lung adenocarcinoma progression through directly binding to c-Myc and enhancing c-Myc transcriptional activity. Conclusions In summary, the PD-L1 gene can generate a long non-coding RNA through alternative splicing to promote lung adenocarcinoma progression by enhancing c-Myc activity. Our results argue in favor of investigating PD-L1-lnc depletion in combination with PD-L1 blockade in lung cancer therapy.


2018 ◽  
Vol 46 (13) ◽  
pp. 6608-6626 ◽  
Author(s):  
Ruiqiong Liu ◽  
Jie Gao ◽  
Yang Yang ◽  
Rongfang Qiu ◽  
Yu Zheng ◽  
...  
Keyword(s):  

2019 ◽  
Vol 47 (22) ◽  
pp. 11574-11588 ◽  
Author(s):  
Wieteke Anna Maria Hoeijmakers ◽  
Jun Miao ◽  
Sabine Schmidt ◽  
Christa Geeke Toenhake ◽  
Sony Shrestha ◽  
...  

Abstract Epigenetic regulatory mechanisms are central to the development and survival of all eukaryotic organisms. These mechanisms critically depend on the marking of chromatin domains with distinctive histone tail modifications (PTMs) and their recognition by effector protein complexes. Here we used quantitative proteomic approaches to unveil interactions between PTMs and associated reader protein complexes of Plasmodium falciparum, a unicellular parasite causing malaria. Histone peptide pull-downs with the most prominent and/or parasite-specific PTMs revealed the binding preference for 14 putative and novel reader proteins. Amongst others, they highlighted the acetylation-level-dependent recruitment of the BDP1/BDP2 complex and identified an PhD-finger protein (PHD 1, PF3D7_1008100) that could mediate a cross-talk between H3K4me2/3 and H3K9ac marks. Tagging and interaction proteomics of 12 identified proteins unveiled the composition of 5 major epigenetic complexes, including the elusive TBP-associated-factor complex as well as two distinct GCN5/ADA2 complexes. Furthermore, it has highlighted a remarkable degree of interaction between these five (sub)complexes. Collectively, this study provides an extensive inventory of PTM-reader interactions and composition of epigenetic complexes. It will not only fuel further explorations of gene regulation amongst ancient eukaryotes, but also provides a stepping stone for exploration of PTM-reader interactions for antimalarial drug development.


2006 ◽  
Vol 26 (22) ◽  
pp. 8623-8638 ◽  
Author(s):  
Smitha P. Sripathy ◽  
Jessica Stevens ◽  
David C. Schultz

ABSTRACT KAP1/TIF1β is proposed to be a universal corepressor protein for the KRAB zinc finger protein (KRAB-zfp) superfamily of transcriptional repressors. To characterize the role of KAP1 and KAP1-interacting proteins in transcriptional repression, we investigated the regulation of stably integrated reporter transgenes by hormone-responsive KRAB and KAP1 repressor proteins. Here, we demonstrate that depletion of endogenous KAP1 levels by small interfering RNA (siRNA) significantly inhibited KRAB-mediated transcriptional repression of a chromatin template. Similarly, reduction in cellular levels of HP1α/β/γ and SETDB1 by siRNA attenuated KRAB-KAP1 repression. We also found that direct tethering of KAP1 to DNA was sufficient to repress transcription of an integrated transgene. This activity is absolutely dependent upon the interaction of KAP1 with HP1 and on an intact PHD finger and bromodomain of KAP1, suggesting that these domains function cooperatively in transcriptional corepression. The achievement of the repressed state by wild-type KAP1 involves decreased recruitment of RNA polymerase II, reduced levels of histone H3 K9 acetylation and H3K4 methylation, an increase in histone occupancy, enrichment of trimethyl histone H3K9, H3K36, and histone H4K20, and HP1 deposition at proximal regulatory sequences of the transgene. A KAP1 protein containing a mutation of the HP1 binding domain failed to induce any change in the histone modifications associated with DNA sequences of the transgene, implying that HP1-directed nuclear compartmentalization is required for transcriptional repression by the KRAB/KAP1 repression complex. The combination of these data suggests that KAP1 functions to coordinate activities that dynamically regulate changes in histone modifications and deposition of HP1 to establish a de novo microenvironment of heterochromatin, which is required for repression of gene transcription by KRAB-zfps.


2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qidong Cai ◽  
Boxue He ◽  
Pengfei Zhang ◽  
Zhenyu Zhao ◽  
Xiong Peng ◽  
...  

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.


2020 ◽  
Vol 27 (12) ◽  
pp. 3321-3336
Author(s):  
Hyunji Lee ◽  
Youngeun Hong ◽  
Gyeyeong Kong ◽  
Dong Hoon Lee ◽  
Minhee Kim ◽  
...  

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