Correction: Differential roles of coding and non-coding transcripts in obesity: insights from RNA-seq analysis of Macaca fascicularis hepatocytes.
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Published Erratum↗
Biomedical subjects
Pengfei Zhang
Publications and source records attributed to Pengfei Zhang.
Phosphoproteomics identification of ERK-dependent activation of Rps6kb1 in cardiac hypertrophy.
Cardiomyocyte growth is tightly controlled by multiple signaling pathways. Identification of master kinases in this process is essential in exploring potential targets for the treatment of pathological cardiac hypertrophy and heart failure. Here we identified the mTOR-independent activation of ribosomal protein S6 kinase b1 (Rps6kb1) during cardiomyocyte growth. By utilizing phosphoproteomics in primary neonatal rat ventricular myocytes, we revealed Rps6kb1 as one of most activated kinases under growth stimulation. We further demonstrated the role of Rps6kb1 phosphorylation in pathological cardiac hypertrophy and heart failure. We showed that the phosphorylation of multiple sites in Rps6kb1, including T367 in the kinase domain and S418/T421/S424 in the C-terminal domain, is not directly regulated by the activity of mTOR but coupled with the activation of the MEK1/ERK axis. In mice, cardiomyocyte-specific deletion of Rps6kb1 significantly inhibited both constitutively active ERK- and pressure overload-induced cardiac hypertrophy. In contrast, cardiomyocyte-specific overexpression of wild-type Rps6kb1, rather than the phosphorylation-defective mutant, elevated cardiac hypertrophy and augmented pressure overload-induced heart failure. In conclusion, our findings reveal that the MEK/ERK axis primes Rps6kb1 activation through phosphorylation of 2 separate domains of Rps6kb1, which may play an essential role in cardiac hypertrophy and heart failure under hemodynamic stress.
Animals↗
RNA-DNA hybrid binding domain broadens the editing window of base editors.
Adenine base editors (ABEs) and cytosine base editors (CBEs) are prominent tools for precise genome editing but are hindered by limited editing activity at positions proximal to the protospacer adjacent motif (PAM). This study investigates the potential of enhancing base editors editing activity by fusing them with RNA-DNA hybrid binding domains (RHBDs). Specifically, fusing ABE8e with the RHBD of Homo sapiens RNaseH1 (RHBD1) significantly increased A-to-G editing efficiency in the PAM-proximal region (A9-A15) by up to 3.5-fold, while reducing off-target cytosine editing. Additionally, RHBD1 is compatible with ABEmax, BE4max, and dual base editor (eA&C-BEmax), enhancing their editing activity at the PAM-proximal bases. Notably, RHBD1-fused BE4max led to a 3.1-fold improvement in C-to-T editing efficiency at PAM-proximal region (C9-C12). Furthermore, we demonstrated that RHBD1-fused ABE8e could effectively edit disease-related single nucleotide variations (SNVs) in human cells and validated its efficacy in adult mouse liver. These findings highlight the significance of the RHBD in expanding editing window and the applicability of base editors for gene therapy and disease modeling.
Gene Editing↗
Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.
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.
Adenocarcinoma of Lung↗