Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

Motivation: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage. Results: Here, we describe a computational method, PrabHot (Prediction of protein-RNA binding hot spots), that can effectively detect hot spot residues on protein-RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein-RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result. Availability and implementation: The PrabHot webserver is freely available at http://denglab.org/PrabHot/. Contact: leideng@csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

Characteristics of innate, humoral and cellular immunity in children with non-severe SARS-CoV-2 infection.

The symptoms of children infected with SARS-CoV-2 are mainly asymptomatic, mild, moderate, and a few severe cases. To understand the immune response characteristics of children infected with SARS-COV-2 who do not develop severe cases, 82 children infected with the SARS-CoV-2 delta strain were recruited in this study. Our results showed that high levels of IgG, IgM, and neutralization antibodies appeared in children infected with SARS-CoV-2. SARS-CoV-2 induced upregulation of both pro-inflammatory factors including TNF-α and anti-inflammatory factors including IL-4 and IL-13 in the children, even IL-10. The expression of INF-α in infected children also showed a significant increase compared to healthy children. However, IL-6, one of the important inflammatory factors, did not show an increase in infected children. It is worth noting that a large number of chemokines reduced in the SARS-CoV-2-infected children. Subsequently, TCR Repertoire, TCRß bias, and preferential usage were analyzed on data of TCR next-generation sequencing from 8 SARS-CoV-2-infected children and 8 healthy controls. We found a significant decrease in TCR clonal diversity and a significant increase in TCR clonal expansion in SARS-CoV-2-infected children compared to healthy children. The most frequent V and J genes in SARS-CoV-2 children were TRBV28 and TRBJ2-1. The most frequently VßJ gene pairing in SARS-CoV-2 infected children was TRBV20-1-TRBJ2-1. The strong antiviral antibody levels, low expression of key pro-inflammatory factors, significant elevation of anti-inflammatory factors, and downregulation of many chemokines jointly determine that SARS-CoV-2-infected children rarely develop severe cases. Overall, our findings shed a light on the immune response of non-severe children infected with SARS-CoV-2.

TRIM proteins in lung cancer: Mechanisms, biomarkers and therapeutic targets.

The tripartite motif (TRIM) family is defined by the presence of a Really Interesting New Gene (RING) domain, one or two B-box motifs and a coiled-coil region. TRIM proteins play key roles in many biological processes, including innate immunity, tumorigenesis, cell differentiation and ontogenetic development. Alterations in TRIM gene and protein levels frequently emerge in a wide range of tumors and affect tumor progression. As canonical E3 ubiquitin ligases, TRIM proteins participate in ubiquitin-dependent proteolysis of prominent components of the p53, NF-κB and PI3K/AKT signaling pathways. The occurrence of ubiquitylation events induced by TRIM proteins sustains internal balance between tumor suppressive and tumor promoting genes. In this review, we summarized the diverse mechanism of TRIM proteins responsible for the most common malignancy, lung cancer. Furthermore, we also discussed recent progress in both the diagnosis and therapeutics of tumors contributed by TRIM proteins.

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches.

BACKGROUND: Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions. METHODS: In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods. RESULTS: We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions. CONCLUSION: The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Functional characterization of a potent anti-tumor polysaccharide in a mouse model of gastric cancer.

AIMS: Natural polysaccharides are emerging as a new class of immunomodulatory agents due to their potent immunostimulatory effects and suitable biocompatibility. The aim of this study was to identify potent and selective anticancer activity of a bioactive polysaccharide. MATERIALS AND METHODS: In vitro, viability assay was performed to screen 16 of bioactive polysaccharides in a panel of normal and cancer cell lines. Foci formation, soft agar, BrdU incorporation, cell cycle analyses, and ß-galactosidase staining were performed to validate the screening results. In vivo, both murine gastric cancer syngeneic and a human gastric tumor xenografts models were applied. Tumor histology, immunohistochemical staining, cytokine array and flow cytometry analyses were assayed. KEY FINDINGS: BSP (bamboo shaving polysaccharides) was identified as the most selective polysaccharide for inhibiting the growth of six gastric cancer cell lines while having no toxic effect on normal gastric mucosal cells. Similarly, BSP had more potent killing effect on a subset of human stomach cancer cells than liver or lung cancer cells. In vivo, BSP significantly inhibited tumor growth and prolonged the survival of mice bearing a gastric tumor; these effects are mediated by tumor cell apoptosis and remodeling of the tumor microenvironment by boosting both immune cell subpopulations and cytokine production in murine gastric cancer syngeneic model. A significant decrease of F4/80-positive tumor-associated macrophages was also observed. SIGNIFICANCE: The findings of this study suggest that the potent and selective anti-tumor activity of bioactive polysaccharides such as BSP warrants clinical testing for the treatment of gastric cancer.

SDADB: a functional annotation database of protein structural domains.

Annotating functional terms with individual domains is essential for understanding the functions of full-length proteins. We describe SDADB, a functional annotation database for structural domains. SDADB provides associations between gene ontology (GO) terms and SCOP domains calculated with an integrated framework. GO annotations are assigned probabilities of being correct, which are estimated with a Bayesian network by taking advantage of structural neighborhood mappings, SCOP-InterPro domain mapping information, position-specific scoring matrices (PSSMs) and sequence homolog features, with the most substantial contribution coming from high-coverage structure-based domain-protein mappings. The domain-protein mappings are computed using large-scale structure alignment. SDADB contains ontological terms with probabilistic scores for more than 214 000 distinct SCOP domains. It also provides additional features include 3D structure alignment visualization, GO hierarchical tree view, search, browse and download options.Database URL: http://sda.denglab.org.

Probing the functions of long non-coding RNAs by exploiting the topology of global association and interaction network.

Long non-coding RNAs (lncRNAs) are involved in many biological processes, such as immune response, development, differentiation and gene imprinting and are associated with diseases and cancers. But the functions of the vast majority of lncRNAs are still unknown. Predicting the biological functions of lncRNAs is one of the key challenges in the post-genomic era. In our work, We first build a global network including a lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network according to the expressions and interactions, then extract the topological feature vectors of the global network. Using these features, we present an SVM-based machine learning approach, PLNRGO, to annotate human lncRNAs. In PLNRGO, we construct a training data set according to the proteins with GO annotations and train a binary classifier for each GO term. We assess the performance of PLNRGO on our manually annotated lncRNA benchmark and a protein-coding gene benchmark with known functional annotations. As a result, the performance of our method is significantly better than that of other state-of-the-art methods in terms of maximum F-measure and coverage.

Phosphorylation of glutaminase by PKCε is essential for its enzymatic activity and critically contributes to tumorigenesis.

Glutamine metabolism plays an important role in cancer development and progression. Glutaminase C (GAC), the first enzyme in glutaminolysis, has emerged as an important target for cancer therapy and many studies have focused on the mechanism of enhanced GAC expression in cancer cells. However, little is known about the post-translational modification of GAC. Here, we report that phosphorylation is a crucial post-translational modification of GAC, which is responsible for the higher glutaminase activity in lung tumor tissues and cancer cells. We identify the key Ser314 phosphorylation site on GAC that is regulated by the NF-κB-PKCε axis. Blocking Ser314 phosphorylation by the S314A mutation in lung cancer cells inhibits the glutaminase activity, triggers genetic reprogramming, and alleviates tumor malignancy. Furthermore, we find that a high level of GAC phosphorylation correlates with poor survival rate of lung cancer patients. These findings highlight a previously unappreciated mechanism for activation of GAC by phosphorylation and demonstrate that targeting glutaminase activity can inhibit oncogenic transformation.

COMMD9 promotes TFDP1/E2F1 transcriptional activity via interaction with TFDP1 in non-small cell lung cancer.

COMMD protein family is an evolutionarily conserved gene family implicated in a number of critical processes including inflammation, copper homeostasis, sodium balance, endosomal sorting and cancer. In an effort to profile the expression pattern of COMMD family in several non-small cell lung cancer (NSCLC) cell lines, we found that compared with that in human bronchial epithelial (HBE) cells, the mRNA expression levels of five COMMD genes including COMMD3, COMMD4, COMMD5, COMMD6 and COMMD8 were significantly down-regulated, whereas COMMD9 was up-regulated in NSCLC cell lines. Here we reported that the expression of COMMD9 protein was significantly increased in various NSCLC cell lines and tissue samples. SiRNA-induced knocking down of COMMD9 inhibited proliferation and migration, arrested cell cycle at G1/S transition and induced autophagy in NSCLC cells. Mechanistically, COMMD9 interacted with the TFDP1 through COMM domain, and DNA-binding domain of TFDP1 was required for this interaction. Moreover, decreased expression COMMD9 attenuated TFDP1/E2F1 activation accompanied with enhanced p53 signaling pathway. Taken together, these findings demonstrate that COMMD9 participates in TFDP1/E2F1 activation and plays a critical role in non-small cell lung cancer.

Dihydromyricetin ameliorates oleic acid-induced lipid accumulation in L02 and HepG2 cells by inhibiting lipogenesis and oxidative stress.

AIMS: Dihydromyricetin (DMY), a flavonoid component isolated from Ampelopsis grossedentata, was recently reported to ameliorate nonalcoholic fatty liver disease (NAFLD) in patients. However, the underlying mechanisms of this action remain unknown. Here, we evaluate the effect of DMY on an in vitro model of NAFLD and investigate the signal transduction pathways underlying DMY treatment. MAIN METHODS: Oleic acid (OA) induced hepatic steatosis was established in L02 and HepG2 cells as in vitro model of NAFLD. Cell apoptosis, lipid accumulation and oxide stress were evaluated by flow cytometry, oil red O staining, and cellular biochemical assays, respectively. Signaling pathways involved in lipid metabolism including PPARγ, AMPK, and AKT were investigated by Western blot and RT-qPCR. KEY FINDINGS: DMY protected cells against apoptosis and lipid accumulation induced by oleic acid. DMY decreased the levels of cellular triglycerides (TG), cholesterol (TC) and malondialdehyde (MDA), while at the same time increasing the level of superoxide dismutase (SOD). DMY suppressed the expression of PPARγ and the phosphorylation of AKT, and promoted the phosphorylation of AMPK. SIGNIFICANCE: Our study suggests that DMY ameliorates OA-induced hepatic steatosis by inhibiting cell apoptosis, lipid accumulation and oxide stress. Furthermore, the effect of DMY is likely associated with its role in the regulating of PPARγ, AMPK and AKT signaling pathways.

Inhibition of mitochondrial glutaminase activity reverses acquired erlotinib resistance in non-small cell lung cancer.

The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) erlotinib has been approved based on the clinical benefit in non-small cell lung cancer (NSCLC) patients over the past decade. Unfortunately, cancer cells become resistant to this agent via various mechanisms, and this limits the improvement in patient outcomes. Thus, it is urgent to develop novel agents to overcome erlotinib resistance. Here, we propose a novel strategy to overcome acquired erlotinib resistance in NSCLC by inhibiting glutaminase activity. Compound 968, an inhibitor of the glutaminase C (GAC), when combined with erlotinib potently inhibited the cell proliferation of erlotinib-resistant NSCLC cells HCC827ER and NCI-H1975. The combination of compound 968 and erlotinib not only decreased GAC and EGFR protein expression but also inhibited GAC activity in HCC827ER cells. The growth of erlotinib-resistant cells was glutamine-dependent as proved by GAC gene knocked down and rescue experiment. More importantly, compound 968 combined with erlotinib down-regulated the glutamine and glycolysis metabolism in erlotinib-resistant cells. Taken together, our study provides a valuable approach to overcome acquired erlotinib resistance by blocking glutamine metabolism and suggests that combination of EGFR-TKI and GAC inhibitor maybe a potential treatment strategy for acquired erlotinib-resistant NSCLC.

TRIM59 Promotes the Proliferation and Migration of Non-Small Cell Lung Cancer Cells by Upregulating Cell Cycle Related Proteins.

TRIM protein family is an evolutionarily conserved gene family implicated in a number of critical processes including inflammation, immunity, antiviral and cancer. In an effort to profile the expression patterns of TRIM superfamily in several non-small cell lung cancer (NSCLC) cell lines, we found that the expression of 10 TRIM genes including TRIM3, TRIM7, TRIM14, TRIM16, TRIM21, TRIM22, TRIM29, TRIM59, TRIM66 and TRIM70 was significantly upregulated in NSCLC cell lines compared with the normal human bronchial epithelial (HBE) cell line, whereas the expression of 7 other TRIM genes including TRIM4, TRIM9, TRIM36, TRIM46, TRIM54, TRIM67 and TRIM76 was significantly down-regulated in NSCLC cell lines compared with that in HBE cells. As TRIM59 has been reported to act as a proto-oncogene that affects both Ras and RB signal pathways in prostate cancer models, we here focused on the role of TRIM59 in the regulation of NSCLC cell proliferation and migration. We reported that TRIM59 protein was significantly increased in various NSCLC cell lines. SiRNA-induced knocking down of TRIM59 significantly inhibited the proliferation and migration of NSCLC cell lines by arresting cell cycle in G2 phase. Moreover, TRIM59 knocking down affected the expression of a number of cell cycle proteins including CDC25C and CDK1. Finally, we knocked down TRIM59 and found that p53 protein expression levels did not upregulate, so we proposed that TRIM59 may promote NSCLC cell growth through other pathways but not the p53 signaling pathway.

Cdc42 induces EGF receptor protein accumulation and promotes EGF receptor nuclear transport and cellular transformation.

Cdc42 is a Ras-related small GTP-binding protein. A previous study has shown that Cdc42 binding to the γ subunit of the coatomer protein complex (γCOP) is essential for Cdc42-regulated cellular transformation, but the molecular mechanism involved is not well understood. Here, we demonstrate that constitutively-active Cdc42 binding to γCOP induced the accumulation of epithelial growth factor receptor (EGFR) in the cells, sustained EGF-stimulated extracellular signal-regulated kinase (ERK), JUN amino-terminal kinase (JNK) and phosphoinositide 3-kinase (PI3K) signaling and promoted cell division. Moreover, constitutive Cdc42 activity facilitated the nuclear translocation of EGFR, and this indicates a novel mechanism through which Cdc42 might promote cellular transformation.

How does cancer cell metabolism affect tumor migration and invasion?

Cancer metastasis is the major cause of cancer-associated death. Accordingly, identification of the regulatory mechanisms that control whether or not tumor cells become "directed walkers" is a crucial issue of cancer research. The deregulation of cell migration during cancer progression determines the capacity of tumor cells to escape from the primary tumors and invade adjacent tissues to finally form metastases. The ability to switch from a predominantly oxidative metabolism to glycolysis and the production of lactate even when oxygen is plentiful is a key characteristic of cancer cells. This metabolic switch, known as the Warburg effect, was first described in 1920s, and affected not only tumor cell growth but also tumor cell migration. In this review, we will focus on the recent studies on how cancer cell metabolism affects tumor cell migration and invasion. Understanding the new aspects on molecular mechanisms and signaling pathways controlling tumor cell migration is critical for development of therapeutic strategies for cancer patients.

Evolutionary method for finding communities in bipartite networks.

An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks. Here, we show that the finding of communities in such networks can be unified in a general framework-detection of community structure in bipartite networks. Moreover, we propose an evolutionary method for efficiently identifying communities in bipartite networks. To this end, we show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization. To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection. The high efficiency of the MAGA is based on the following three improvements we make. First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate. This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them. Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase. Third, we present a modified mutation rule which by incorporating related operations can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process. Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks.