Overexpression of synaptic vesicle protein Rab GTPase 3C promotes vesicular exocytosis and drug resistance in colorectal cancer cells

Citrulline facilitates the glycolysis, proliferation, and metastasis of lung cancer cells by regulating RAB3C

Lung cancer (LC) is one of the major malignant diseases threatening human health. The study aimed to identify the effect of citrulline on the malignant phenotype of LC cells and to further disclose the potential molecular mechanism of citrulline in regulating the development of LC, providing a novel molecular biological basis for the clinical treatment of LC. The effects of citrulline on the viability, proliferation, migration, and invasion of LC cells (A549, H1299) were validated by CCK-8, colony formation, EdU, and transwell assays. The cell glycolysis was assessed via determining the glucose uptake, lactate production, ATP levels, extracellular acidification rate (ECAR), and oxygen consumption rate (OCR). RNA-seq and molecular docking were performed to screen for citrulline-binding target proteins. Western blotting experiments were conducted to examine the expression of related signaling pathway molecules. In addition, the impacts of citrulline on LC growth in vivo were investigated by constructing mouse models. Citrulline augmented the viability of LC cells in a concentration and time-dependent manner. The proliferation, migration, invasion, glycolysis, and EMT processes of LC cells were substantially enhanced after citrulline treatment. Bioinformatics analysis indicated that citrulline could bind to RAB3C protein. Western blotting results indicated that citrulline activated the IL-6/STAT3 pathway by binding to RAB3C. In addition, animal experiments disclosed that citrulline promoted tumor growth in mice. Citrulline accelerated the glycolysis and activated the IL6/STAT3 pathway through the RAB3C protein, consequently facilitating the development of LC.

Rab11 promotes single Mauthner cell axon regeneration in vivo through axon guidance molecule Ntng2b

Effective axon regeneration within the central nervous system (CNS) is pivotal for achieving functional recovery following spinal cord injury (SCI). Numerous extrinsic and intrinsic factors exert influences on the axon regeneration. While prior studies have demonstrated crucial involvement of specific members the Rab protein family in axon regeneration in the peripheral nervous system (PNS), the precise function of Rab11 in CNS axon regeneration in vivo remains elusive. Thus, our study aimed to elucidate the impact of Rab11 on the axon regeneration of Mauthner cells (M-cells) in zebrafish larvae. Our findings demonstrated that overexpression of Rab11bb via single-cell electroporation significantly promoted axon regeneration in individual M-cells. Conversely, knockdown of Rab11bb inhibited the axon regeneration of M-cells. RNA-seq analysis revealed an upregulation of ntng2b following Rab11bb overexpression. As we hypothesized, overexpression of Ntng2b markedly enhanced axon regeneration, while Ntng2b knockdown in the context of Rab11bb pro-regeneration substantially hindered axon regrowth. In conclusion, our study demonstrated that Rab11 promotes axon regeneration of single M-cell in the CNS through the Rab11/axon guidance/Ntng2b pathway.

Membrane Trafficking-Related Genes Predict Tumor Immune Microenvironment and Prognosis in Colorectal Cancer

Colorectal cancer (CRC) is a heterogeneous disease with varying clinical outcomes. The identification of distinct subgroups of CRC patients based on molecular profiling can aid in better understanding the disease and improving patient outcomes. This study aimed to investigate the potential of membrane trafficking-related genes (MTRGs) in sub-grouping colorectal cancer patients based on their overall survival and immune microenvironments. Consensus clustering analysis identified two distinct clusters with different expression profiles of membrane trafficking-related genes. The patients in cluster 1 had a significantly better overall survival than those in cluster 2. Furthermore, the immune microenvironments in the two clusters were also found to be significantly different, with cluster 1 having a higher immune score and more immune cells present. Functional analysis of differentially expressed genes between the two clusters revealed that MTRGs were involved in immune response and metabolic processes, and a risk signature model based on MTRGs was established to predict the prognosis of CRC patients. These findings suggest that MTRGs play a crucial role in the immune microenvironment and overall survival of CRC patients and may provide a potential target for personalized therapy.

Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas

Background: Aldosterone-producing adenomas (APA) are a common cause of primary aldosteronism (PA), a clinical syndrome characterized by hypertension and electrolyte disturbances. If untreated, it may lead to serious cardiovascular complications. Therefore, there is an urgent need for potential biomarkers and targeted drugs for the diagnosis and treatment of aldosteronism. Methods: We downloaded two datasets (GSE156931 and GSE60042) from the GEO database and merged them by de-batch effect, then screened the top50 of differential genes using PPI and enriched them, followed by screening the Aldosterone adenoma-related genes (ARGs) in the top50 using three machine learning algorithms. We performed GSEA analysis on the ARGs separately and constructed artificial neural networks based on the ARGs. Finally, the Enrich platform was utilized to identify drugs with potential therapeutic effects on APA by tARGseting the ARGs. Results: We identified 190 differential genes by differential analysis, and then identified the top50 genes by PPI, and the enrichment analysis showed that they were mainly enriched in amino acid metabolic pathways. Then three machine learning algorithms identified five ARGs, namely, SST, RAB3C, PPY, CYP3A4, CDH10, and the ANN constructed on the basis of these five ARGs had better diagnostic effect on APA, in which the AUC of the training set is 1 and the AUC of the validation set is 0.755. And then the Enrich platform identified drugs tARGseting the ARGs with potential therapeutic effects on APA. Conclusion: We identified five ARGs for APA through bioinformatic analysis and constructed Artificial neural network (ANN) based on them with better diagnostic effects, and identified drugs with potential therapeutic effects on APA by tARGseting these ARGs. Our study provides more options for the diagnosis and treatment of APA.