A novel prognostic factor TIPE2 inhibits cell proliferation and promotes apoptosis in pancreatic ductal adenocarcinoma (PDAC)

Genome-wide analysis of RNA-binding proteins co-expression with alternative splicing events in mitral valve prolapse

Objectives

We investigated the role and molecular mechanisms of RNA-binding proteins (RBPs) and their regulated alternative splicing events (RASEs) in the pathogenesis of mitral valve prolapse (MVP).

Methods

For RNA extraction, we obtained peripheral blood mononuclear cells (PBMCs) from five patients with MVP, with or without chordae tendineae rupture, and five healthy individuals. High-throughput sequencing was used for RNA sequencing (RNA-seq). Differentially expressed genes (DEGs) analysis, alternative splicing (AS) analysis, functional enrichment analysis, co-expression of RBPs, and alternative splicing events (ASEs) analysis were conducted.

Results

The MVP patients exhibited 306 up-regulated genes and 198 down-regulated genes. All down- and up-regulated genes were enriched in both Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, MVP was closely associated with the top 10 enriched terms and pathways. In MVP patients, 2,288 RASEs were found to be significantly different, and four suitable RASEs (CARD11 A3ss, RBM5 ES, NCF1 A5SS, and DAXX A3ss) were tested. We identified 13 RNA-binding proteins (RBPs) from the DEGs and screened out four RBPs (ZFP36, HSPA1A, TRIM21, and P2RX7). We selected four RASEs based on the co-expression analyses of RBPs and RASEs, including exon skipping (ES) of DEDD2, alternative 3' splice site (A3SS) of ETV6, mutually exclusive 3'UTRs (3pMXE) of TNFAIP8L2, and A3SS of HLA-B. Furthermore, the selected four RBPs and four RASEs were validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and showed high consistency with RNA sequencing (RNA-seq).

Conclusion

Dysregulated RBPs and their associated RASEs may play regulatory roles in MVP development and may therefore be used as therapeutic targets in the future.

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development.

Tumor Necrosis Factor-α-Induced Protein 8-Like 2 Ameliorates Cardiac Hypertrophy by Targeting TLR4 in Macrophages

Background

Tumor necrosis factor-α-induced protein 8-like 2 (TIPE2), a novel immunoregulatory protein, has been reported to regulate inflammation and apoptosis. The role of TIPE2 in cardiovascular disease, especially cardiac hypertrophy, has not been elucidated. Thus, the aim of the present study was to explore the role of TIPE2 in cardiac hypertrophy.

Methods

Mice were subjected to aortic banding (AB) to induce an adverse hypertrophic model. To overexpress TIPE2, mice were injected with a lentiviral vector expressing TIPE2. Echocardiographic and hemodynamic analyses were used to evaluate cardiac function. Neonatal rat cardiomyocytes (NRCMs) and mouse peritoneal macrophages (MPMs) were isolated and stimulated with angiotensin II. NRCMs and MPM were also cocultured and stimulated with angiotensin II. Cells were transfected with Lenti-TIPE2 to overexpress TIPE2.

Results

TIPE2 expression levels were downregulated in hypertrophic mouse hearts and in macrophages in heart tissue. TIPE2 overexpression attenuated pressure overload-induced cardiac hypertrophy, fibrosis, and cardiac dysfunction. Moreover, we found that TIPE2 overexpression in neonatal cardiomyocytes did not relieve the angiotensin II-induced hypertrophic response in vitro. Furthermore, TIPE2 overexpression downregulated TLR4 and NF-κB signaling in macrophages but not in cardiomyocytes, which led to diminished inflammation in macrophages and consequently reduced the activation of hypertrophic Akt signaling in cardiomyocytes. TLR4 inhibition by TAK-242 did not enhance the antihypertrophic effect of TIPE2 overexpression.

Conclusions

The present study indicated that TIPE2 represses macrophage activation by targeting TLR4, subsequently inhibiting cardiac hypertrophy.