Clinical significance of serum ADP-ribosylation and NAD glycohydrolase activity in patients with colorectal cancer

PLK1/NF-κB feedforward circuit antagonizes the mono-ADP-ribosyltransferase activity of PARP10 and facilitates HCC progression

Dysregulation of PARP10 has been implicated in various tumor types and plays a vital role in delaying hepatocellular carcinoma (HCC) progression. However, the mechanisms controlling the expression and activity of PARP10 in HCC remain mostly unknown. The crosstalk between PLK1, PARP10, and NF-κB pathway in HCC was determined by performing different in vitro and in vivo assays, including mass spectrometry, kinase, MARylation, chromatin immunoprecipitation, and luciferase reporter measurements. Functional examination was performed by using small chemical drug, cell culture, and mice HCC models. Correlation between PLK1, NF-κB, and PARP10 expression was determined by analyzing clinical samples of HCC patients with using immunohistochemistry. PLK1, an important regulator for cell mitosis, directly interacts with and phosphorylates PARP10 at T601. PARP10 phosphorylation at T601 significantly decreases its binding to NEMO and disrupts its inhibition to NEMO ubiquitination, thereby enhancing the transcription activity of NF-κB toward multiple target genes and promoting HCC development. In turn, NF-κB transcriptionally inhibits the PARP10 promoter activity and leads to its downregulation in HCC. Interestingly, PLK1 is mono-ADP-ribosylated by PARP10 and the MARylation of PLK1 significantly inhibits its kinase activity and oncogenic function in HCC. Clinically, the expression levels of PLK1 and phosphor-p65 show an inverse correlation with PARP10 expression in human HCC tissues. These findings are the first to uncover a PLK1/PARP10/NF-κB signaling circuit that underlies tumorigenesis and validate PLK1 inhibitors, alone or with NF-κB antagonists, as potential effective therapeutics for PARP10-expressing HCC.

Epigenetics in acute kidney injury

PURPOSE OF REVIEW

Recent advances in epigenetics indicate the involvement of several epigenetic modifications in the pathogenesis of acute kidney injury (AKI). The purpose of this review is to summarize our understanding of recent advances in the epigenetic regulation of AKI and provide mechanistic insight into the role of acetylation, methylation, and microRNA expression in the pathological processes of AKI.

RECENT FINDINGS

Enhancement of protein acetylation by pharmacological inhibition of histone deacetylases leads to more severe tubular injury and impairment of renal structural and functional recovery. The changes in promoter DNA methylation occur in the kidney with ischemia/reperfusion. microRNA expression is associated with regulation of both renal injury and regeneration after AKI.

SUMMARY

Recent studies on epigenetic regulation indicate that acetylation, methylation, and microRNA expression are critically implicated in the pathogenesis of AKI. Strategies targeting epigenetic processes may hold a therapeutic potential for patients with AKI.

ADPRtool: A novel predicting model for identification of ASP-ADP-Ribosylation sites of human proteins

Post-translational modifications (PTMs) occur in the vast majority of proteins, and they are essential for many protein functions. Computational prediction of the residue location of PTMs enhances the functional characterization of proteins. ADP-Ribosylation is an important type of PTM, because it is implicated in apoptosis, DNA repair, regulation of cell proliferation, and protein synthesis. However, mass spectrometric approaches have difficulties in identifying a vast number of protein ADP-Ribosylation sites. Therefore, a computational method for predicting ADP-Ribosylation sites of human proteins seems useful and necessary. Four types of sequence features and an incremental feature selection technique are utilized to predict protein ADP-Ribosylation sites. The final feature set for ADPR prediction modeling is optimized, based on a minimum redundancy maximum relevance criterion, so as to make more accurate predictions on aspartic acid ADPR modified residues. Our prediction model, ADPRtool, is capable to predict Asp-ADP-Ribosylation sites with a total accuracy of 85.45%, which is as good as most computational PTM site predictors. By using a sequence-based computational method, a new ADP-Ribosylation site prediction model - ADPRtool, is developed, and it has shown great accuracies with total accuracy, Matthew's correlation coefficient and area under receiver operating characteristic curve.