C1QBP regulates apoptosis of renal cell carcinoma via modulating xanthine dehydrogenase (XDH) mediated ROS generation.

Background: Complement component 1 Q subcomponent binding protein (C1QBP) plays a vital role in the progression and metabolism of cancer. Studies have shown that xanthine dehydrogenase (XDH)-derived reactive oxygen species (ROS) accelerates tumor growth, and also induces mutations or produces cytotoxic effects concurrently. However, the role of C1QBP in metabolism, oxidative stress, and apoptosis of renal cell carcinoma (RCC) cells have not yet been explored. Methods: Metabolomics assay was applied to investigate the role of C1QBP in RCC metabolism. C1QBP knockdown and overexpression cells were established via lentiviral infection and subjected to apoptosis and ROS assay in vitro. RNA stability assay was applied to characterize the mechanism of C1QBP regulating XDH transcription. In vivo, orthotopic tumor xenografts assay was performed to investigate the role of C1QBP in RCC progression. Results: Metabolomics investigation revealed that C1QBP dramatically diminished the hypoxanthine content in RCC cells. C1QBP promoted the mRNA and protein expression of hypoxanthine catabolic enzyme XDH. Meanwhile, C1QBP may affect XDH transcription by regulating the mRNA level of XDH transcriptional stimulators IL-6, TNF-α, and IFN-γ. Moreover, the expression of C1QBP and XDH was lower in RCC tumors compared with the tumor-associated normal tissues, and their down-regulation was associated with higher Fuhrman grade. C1QBP significantly increased ROS level, apoptosis, and the expression of apoptotic proteins such as cleaved caspase-3 and bax/bcl2 via regulating XDH. Conclusion: C1QBP promotes the catabolism of hypoxanthine and elevates the apoptosis of RCC cells by modulating XDH-mediated ROS generation.

PlantAPAdb: A Comprehensive Database for Alternative Polyadenylation Sites in Plants.

Alternative cleavage and polyadenylation (APA) is increasingly recognized as an important regulatory mechanism in eukaryotic gene expression and is dynamically modulated in a developmental, tissue-specific, or environmentally responsive manner. Given the functional importance of APA and the rapid accumulation of APA sites in plants, a comprehensive and easily accessible APA site database is necessary for improved understanding of APA-mediated gene expression regulation. We present a database called PlantAPAdb that catalogs the most comprehensive APA site data derived from sequences from diverse 3' sequencing protocols and biological samples in plants. Currently, PlantAPAdb contains APA sites in six species, Oryza sativa (japonica and indica), Arabidopsis (Arabidopsis thaliana), Medicago truncatula, Trifolium pratense, Phyllostachys edulis, and Chlamydomonas reinhardtii APA sites in PlantAPAdb are available for bulk download and can be queried in a Google-like manner. PlantAPAdb provides rich information of the whole-genome APA sites, including genomic locations, heterogeneous cleavage sites, expression levels, and sample information. It also provides comprehensive poly(A) signals for APA sites in different genomic regions according to distinct profiles of cis-elements in plants. In addition, PlantAPAdb contains events of 3' untranslated region shortening/lengthening resulting from APA, which helps to understand the mechanisms underlying systematic changes in 3' untranslated region lengths. Additional information about conservation of APA sites in plants is also available, providing insights into the evolutionary polyadenylation configuration across species. As a user-friendly database, PlantAPAdb is a large and extendable resource for elucidating APA mechanisms, APA conservation, and gene expression regulation.

scLINE: A multi-network integration framework based on network embedding for representation of single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is fast becoming a powerful technology that revolutionizes biomedical studies related to development, immunology and cancer by providing genome-scale transcriptional profiles at unprecedented throughput and resolution. However, due to the low capture rate and frequent drop-out events in the sequencing process, scRNA-seq data suffer from extremely high sparsity and variability, challenging the data analysis. Here we proposed a novel method called scLINE for learning low dimensional representations of scRNA-seq data. scLINE is based on the network embedding model that jointly considers multiple gene-gene interaction networks, facilitating the incorporation of prior biological knowledge for signal extraction. We comprehensively evaluated scLINE on eight single-cell datasets. Results show that scLINE achieved comparable or higher performance than competing methods, including PCA, t-SNE and Isomap, in terms of internal validation metrics and clustering accuracy. The low dimensional representations learned by scLINE are effective for downstream single-cell analysis, such as visualization, clustering and cell typing. We have implemented scLINE as an easy-to-use R package, which can be incorporated in other existing scRNA-seq analysis pipelines or tools for data preprocessing.

scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.

BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data. RESULTS: We presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools. CONCLUSIONS: scNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization.

p32 regulates glycometabolism and TCA cycle to inhibit ccRCC progression via copper-induced DLAT lipoylation oligomerization.

A key player in mitochondrial respiration, p32, often referred to as C1QBP, is mostly found in the mitochondrial matrix. Previously, we showed that p32 interacts with DLAT in the mitochondria. Here, we found that p32 expression was reduced in ccRCC and suppressed progression and metastasis in ccRCC animal models. We observed that increasing p32 expression led to an increase in oxidative phosphorylation by interacting with DLAT, thus, regulating the activation of the pyruvate dehydrogenase complex (PDHc). Mechanistically, reduced p32 expression, in concert with DLAT, suppresses PDHc activity and the TCA cycle. Furthermore, our research discovered that p32 has a direct binding affinity for copper, facilitating the copper-induced oligomerization of lipo-DLAT specifically in ccRCC cells. This finding reveals an innovative function of the p32/DLAT/copper complex in regulating glycometabolism and the TCA cycle in ccRCC. Importantly, our research provides important new understandings of the underlying molecular processes causing the abnormal mitochondrial metabolism linked to this cancer.

Rho GTPase-activating protein 4 is upregulated in Kidney Renal Clear Cell Carcinoma and associated with poor prognosis and immune infiltration.

BACKGROUND: Kidney Renal Clear Cell Carcinoma (KIRC) is a malignant tumor that seriously threatens human health. Rho GTPase-activating protein 4 (ARHGAP4) plays an important role in the occurrence and development of tumors. OBJECTIVE: The purpose of this study was to explore the role of ARHGAP4 in the progression of KIRC and its diagnostic and prognostic value. METHODS: Multiple analytical methods and in vitro cell assays were used to explore the expression of ARHGAP4 and its value in the progression, diagnosis and prognosis of KIRC. The biological function of ARHGAP4 was studied by GO analysis and KEGG pathway analysis, and then the relationship between ARHGAP4 and immune infiltration was analyzed. RESULTS: The expression of ARHGAP4 was significantly up-regulated in KIRC. We found that the high expression of ARHGAP4 was related to the progression of KIRC and suggested a poor prognosis. Compared with normal tissues, ARHGAP4 had a better diagnostic value in KIRC. The biological function of ARHGAP4 was related to immunity, and its expression was also closely related to tumor immune infiltration and immune checkpoints. CONCLUSIONS: Our study demonstrated that ARHGAP4 may be a biomarker, which is related to the progression, diagnosis and prognosis of KIRC. Its biological functions are related to tumor immune infiltration.