Identification of BAG3 target proteins in anaplastic thyroid cancer cells by proteomic analysis

Long non-coding RNA CYTOR modulates cancer progression through miR-136-5p/MAT2B axis in renal cell carcinoma

BACKGROUND

To explore the role of long noncoding RNAs (lncRNAs) cytoskeleton regulator RNA (CYTOR) in renal cell carcinoma (RCC).

METHODS

The levels of CYTOR in RCC tissues and cell lines were detected by RT-qPCR. 786-O and Caki-1 cells were transfected with CYTOR-shRNA or pcDNA-CYTOR respectively, or co-transfected with CYTOR-shRNA and miR-136-5p inhibitor, or co-transfected with miR-136-5p mimic and pcDNA-MAT2B. MTT assay, Transwell assay and flow cytometry were used to evaluate cell proliferation, invasion and apoptosis. The relationship between lncRNA CYTOR and miRNA-136-5p was detected by dual luciferase reporter gene and RNA pull down assays, and the targeted relationship between miRNA-136-5p and MAT2B was verified by dual luciferase reporter gene assay. The interaction between MAT2B and BAG3 protein was verified by co-IP experiment. The role of lncRNA CYTOR in vivo was also examined.

RESULTS

LncRNA CYTOR was up-regulated in RCC tissues and cell lines, and miR-136-5p was down-regulated in renal carcinoma cell lines and tissues. Downregulation of CYTOR inhibited cell proliferation and invasion and promoted apoptosis. miR-136-5p was sponged by lncRNA CYTOR, which negatively regulated the development of RCC. MAT2B was a target gene of miR-136-5p. MAT2B protein interacted directly with BAG3 protein to affect the proliferation, invasion and apoptosis of RCC cells. In vivo experiments showed that the expression level of miR-136-5p was increased, and MAT2B expression was decreased after CYTOR knockdown, thereby inhibiting the development of RCC.

CONCLUSIONS

LncRNA CYTOR promoted the progression of RCC by targeting miR-136-5p to regulate the target gene MAT2B, which interacted with BAG3 protein.

SpBAG3 assisted WSSV infection in mud crab (Scylla paramamosain) by inhibiting apoptosis

The function of B-cell lymphoma-2 (Bcl-2) family proteins can be divided into two categories: anti-apoptotic and pro-apoptotic. As an anti-apoptotic protein, Bcl2-associated athanogene 3 (BAG3) plays a key role in regulating apoptosis, development, cell movement, and autophagy, and mediating the adaptability of cells to stimulation. However, SpBAG3 has not been reported in mud crab (Scylla paramamosain), and the regulatory effect of SpBAG3 on apoptosis in mud crab and its function in antiviral immunity is still unknown. In this study, SpBAG3 was found, and characterized, which encoded a total of 175 amino acid (molecular mass 19.3 kDa), including a specific conserved domain of the BAG family. SpBAG3 was significantly down-regulated at 0-48 h post-infection with WSSV in vivo. The antiviral effect of SpBAG3 was investigated using RNA interference. The results indicated that SpBAG3 might be involved in assisting the replication of WSSV in the host. SpBAG3 could change the mitochondrial membrane potential (△ψm), and affect cell apoptosis through mitochondrial apoptotic pathways. Therefore, the results of this study suggested that SpBAG3 could assist WSSV infection by inhibiting the apoptosis of the hemocytes in mud crab.

Ensemble disease gene prediction by clinical sample-based networks

BACKGROUND

Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks, are widely used to predict disease genes by analyzing the relationships between known disease genes and other genes in the networks. However, existing methods commonly use a universal static PPI network, which ignore the fact that PPIs are dynamic, and PPIs in various patients should also be different.

RESULTS

To address these issues, we develop an ensemble algorithm to predict disease genes from clinical sample-based networks (EdgCSN). The algorithm first constructs single sample-based networks for each case sample of the disease under study. Then, these single sample-based networks are merged to several fused networks based on the clustering results of the samples. After that, logistic models are trained with centrality features extracted from the fused networks, and an ensemble strategy is used to predict the finial probability of each gene being disease-associated. EdgCSN is evaluated on breast cancer (BC), thyroid cancer (TC) and Alzheimer's disease (AD) and obtains AUC values of 0.970, 0.971 and 0.966, respectively, which are much better than the competing algorithms. Subsequent de novo validations also demonstrate the ability of EdgCSN in predicting new disease genes.

CONCLUSIONS

In this study, we propose EdgCSN, which is an ensemble learning algorithm for predicting disease genes with models trained by centrality features extracted from clinical sample-based networks. Results of the leave-one-out cross validation show that our EdgCSN performs much better than the competing algorithms in predicting BC-associated, TC-associated and AD-associated genes. de novo validations also show that EdgCSN is valuable for identifying new disease genes.

New insights into intranuclear inclusions in thyroid carcinoma: Association with autophagy and with BRAFV600E mutation

Background

Intranuclear inclusions (NI) in normal and neoplastic tissues have been known for years, representing one of the diagnostic criteria for papillary thyroid carcinoma (PTC). BRAF activation is involved among others in autophagy. NI in hepatocellular carcinoma contain autophagy-associated proteins. Our aim was to clarify if NI in thyroid carcinoma (TC) have a biological function.

Methods

NI in 107 paraffin-embedded specimens of TC including all major subtypes were analyzed. We considered an inclusion as positive if it was delimited by a lamin AC (nuclear membrane marker) stained intact membrane and completely closed. Transmission electron microscopy (TEM), immunohistochemistry (IHC), immunofluorescence (IF) and 3D reconstruction were performed to investigate content and shape of NI; BRAF^V600E mutation was analyzed by next generation sequencing.

Results

In 29% of the TCs at least one lamin AC positive intranuclear inclusion was detected; most frequently (76%) in PTCs. TEM analyses revealed degenerated organelles and heterolysosomes within such NI; 3D reconstruction of IF stained nuclei confirmed complete closure by the nuclear membrane without any contact to the cytoplasm. NI were positively stained for the autophagy-associated proteins LC3B, ubiquitin, cathepsin D, p62/sequestosome1 and cathepsin B in 14–29% of the cases. Double-IF revealed co-localization of LC3B & ubiquitin, p62 & ubiquitin and LC3B & p62 in the same NI. BRAF^V600E mutation, exclusively detected in PTCs, was significantly associated with the number of NI/PTC (p = 0.042) and with immunoreactivity for autophagy-associated proteins in the NI (p≤0.035). BRAF-IHC revealed that some of these BRAF-positive thyrocytes contained mutant BRAF in their NI co-localized with autophagy-associated proteins.

Conclusions

NI are completely delimited by nuclear membrane in TC. The presence of autophagy-associated proteins within the NI together with degenerated organelles and lysosomal proteases suggests their involvement in autophagy and proteolysis. Whether and how BRAF^V600E protein is degraded in NI needs further investigation.