Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks

Background Drug combination, offering an insight into the increased therapeutic efficacy and reduced toxicity, plays an essential role in the therapy of many complex diseases. Although significant efforts have been devoted to the identification of drugs, the identification of drug combination is still a challenge. The current algorithms assume that the independence of feature selection and drug prediction procedures, which may result in an undesirable performance. Results To address this issue, we develop a novel Semi-supervised Heterogeneous Network Embedding algorithm (called SeHNE) to predict the combination patterns of drugs by exploiting the graph embedding. Specifically, the ATC similarity of drugs, drug–target, and protein–protein interaction networks are integrated to construct the heterogeneous networks. Then, SeHNE jointly learns drug features by exploiting the topological structure of heterogeneous networks and predicting drug combination. One distinct advantage of SeHNE is that features of drugs are extracted under the guidance of classification, which improves the quality of features, thereby enhancing the performance of prediction of drugs. Experimental results demonstrate that the proposed algorithm is more accurate than state-of-the-art methods on various data, implying that the joint learning is promising for the identification of drug combination. Conclusions The proposed model and algorithm provide an effective strategy for the prediction of combinatorial patterns of drugs, implying that the graph-based drug prediction is promising for the discovery of drugs.

Transcriptome Profiling Based on Larvae at Different Time Points After Hatching Provides a Core Set of Gene Resource for Understanding the Metabolic Mechanisms of the Brood-Care Behavior in Octopus ocellatus

The metabolic processes of organisms are very complex. Each process is crucial and affects the growth, development, and reproduction of organisms. Metabolism-related mechanisms in Octopus ocellatus behaviors have not been widely studied. Brood-care is a common behavior in most organisms, which can improve the survival rate and constitution of larvae. Octopus ocellatus carried out this behavior, but it was rarely noticed by researchers before. In our study, 3,486 differentially expressed genes (DEGs) were identified based on transcriptome analysis of O. ocellatus. We identify metabolism-related DEGs using GO and KEGG enrichment analyses. Then, we construct protein–protein interaction networks to search the functional relationships between metabolism-related DEGs. Finally, we identified 10 hub genes related to multiple gene functions or involved in multiple signal pathways and verified them using quantitative real-time polymerase chain reaction (qRT-PCR). Protein–protein interaction networks were first used to study the effects of brood-care behavior on metabolism in the process of growing of O. ocellatus larvae, and the results provide us valuable genetic resources for understanding the metabolic processes of invertebrate larvae. The data lay a foundation for further study the brood-care behavior and metabolic mechanisms of invertebrates.

Guanxin V Acts as an Antioxidant in Ventricular Remodeling

Background: Our previous studies have shown that Guanxin V (GXV) is safe and effective in the treatment of ventricular remodeling (VR), but its mechanism related to oxidative stress has not been studied deeply.Methods: We applied integrating virtual screening and network pharmacology strategy to obtain the GXV-, VR-, and oxidative stress-related targets at first, and then highlighted the shared targets. We built the networks and conducted enrichment analysis. Finally, the main results were validated by molecular docking and solid experiments.Results: We obtained 251, 11,425, and 9,727 GXV-, VR-, and oxidative stress-related targets, respectively. GXV-component-target-VR and protein–protein interaction networks showed the potential mechanism of GXV in the treatment of VR. The following enrichment analysis results gathered many biological processes and “two GXV pathways” of oxidative stress-related to VR. All our main results were validated by molecular docking and solid experiments.Conclusion: GXV could be prescribed for VR through the mechanism, including complex interactions between related components and targets, as predicted by virtual screening and network pharmacology and validated by molecular docking and solid experiments. Our study promotes the explanation of the biological mechanism of GXV for VR.

Evaluation of expression changes, proteins interaction network, and microRNAs targeting catalase and superoxide dismutase genes under cold stress in rapeseed (Brassica napus L.)

Rapeseed is the third-largest source of plant oil and one of the essential oil plants worldwide. Cold stress is one of the critical factors that affect plant yield. Therefore, improving cold stress tolerance is necessary for yield increase. The present study investigated BnCAT1 and BnCSD1 genes’ expression behavior in a tolerant and sensitive cultivar under cold stress (4 °C). Besides, protein-protein interaction networks of CATs and CSDs enzymes, and their association with other antioxidant enzymes were analyzed. Moreover, the microRNAs targeting BnCAT1 and BnCSD1 genes were predicted. This study indicated many direct and indirect interactions and the association between the components of the plant antioxidant system. However, not only did the CATs and CSDs enzymes have a relationship with each other, but they also interacted directly with ascorbate peroxidase and glutathione reductase enzymes. Also, 23 and 35 effective microRNAs were predicted for BnCAT1 and BnCSD1 genes, respectively. The gene expression results indicated an elevated expression of BnCAT1 and BnCSD1 in both tolerant and sensitive cultivars. However, this increase was more noticeable in the tolerant cultivar. Thus, the BnCSD1 gene had the highest expression in the early hour of cold stress, especially in the 12th h, and the BnCAT1 gene showed the highest expression in the 48th h. This result may indicate a functional relationship between these enzymes.

Hyperuricemia induces lipid disturbances by upregulating the CXCL-13 pathway

The molecular mechanism underlying hyperuricemia-induced lipid metabolism disorders is not clear. The purpose of the current study was to investigate the mechanism of lipid disturbances in a hyperuricemia mice model. RNAseq showed that differentially expressed genes (DEGs) in the fatty acid synthesis signaling pathway were mainly enriched, and CXCL-13 was significantly enriched in protein-protein interaction networks. Western blotting, Q-PCR, and immunofluorescence results further showed that hyperuricemia upregulated CXCL-13 and disturbed lipid metabolism in vivo and in vitro. Furthermore, CXCL-13 alone also promoted the accumulation of lipid droplets and upregulated the expression of FAS and SREBP1, blocking AMPK signaling and activating the PKC and P38 signaling pathways. Silencing CXCL-13 reversed uric-acid-induced lipid droplet accumulation, which further downregulated FAS and SREBP1 expression, inhibited the p38 and PKC signaling, and activated AMPK signaling. In conclusion, hyperuricemia induces lipid metabolism disorders via the CXCL-13 pathway, making CXCL-13 a key regulatory factor linking hyperuricemia and lipid metabolism disorders. These results may provide novel insights for the treatment of hyperuricemia.

Endocytic proteins with prion-like domains form viscoelastic condensates that enable membrane remodeling

Membrane invagination and vesicle formation are key steps in endocytosis and cellular trafficking. Here, we show that endocytic coat proteins with prion-like domains (PLDs) form hemispherical puncta in the budding yeast, Saccharomyces cerevisiae. These puncta have the hallmarks of biomolecular condensates and organize proteins at the membrane for actin-dependent endocytosis. They also enable membrane remodeling to drive actin-independent endocytosis. The puncta, which we refer to as endocytic condensates, form and dissolve reversibly in response to changes in temperature and solution conditions. We find that endocytic condensates are organized around dynamic protein–protein interaction networks, which involve interactions among PLDs with high glutamine contents. The endocytic coat protein Sla1 is at the hub of the protein–protein interaction network. Using active rheology, we inferred the material properties of endocytic condensates. These experiments show that endocytic condensates are akin to viscoelastic materials. We use these characterizations to estimate the interfacial tension between endocytic condensates and their surroundings. We then adapt the physics of contact mechanics, specifically modifications of Hertz theory, to develop a quantitative framework for describing how interfacial tensions among condensates, the membrane, and the cytosol can deform the plasma membrane to enable actin-independent endocytosis.

A Molecular View of Amyotrophic Lateral Sclerosis Through the Lens of Interaction Network Modules

Background: Despite the discovery of familial cases with mutations in Cu/Zn-superoxide dismutase (SOD1), Guanine nucleotide exchange C9orf72, TAR DNA-binding protein 43 (TARDBP) and RNA-binding protein FUS as well as a number of other genes linked to Amyotrophic Lateral Sclerosis (ALS), the etiology and molecular pathogenesis of this devastating disease is still not understood. As proteins do not act alone, conducting an analysis of ALS at the system level may provide new insights into the molecular biology of ALS and put it into relationship to other neurological diseases.Methods: A set of ALS-associated genes/proteins were collected from publicly available databases and text mining of scientific literature. We used these as seed proteins to build protein-protein interaction (PPI) networks serving as a scaffold for further analyses. From the collection of networks, a set of core modules enriched in seed proteins were identified. The molecular biology of the core modules was investigated, as were their associations to other diseases. To assess the core modules’ ability to describe unknown or less well-studied ALS biology, they were queried for proteins more recently associated to ALS and not involved in the primary analysis.Results: We describe a set of 26 ALS core modules enriched in ALS-associated proteins. We show that these ALS core modules not only capture most of the current knowledge about ALS, but they also allow us to suggest biological interdependencies. In addition, new associations of ALS networks with other neurodegenerative diseases, e.g. Alzheimer’s, Huntington’s and Parkinson’s disease were found. A follow-up analysis of 140 ALS-associated proteins identified since 2014 reveals a significant overrepresentation of new ALS proteins in these 26 disease modules.Conclusions: Using protein-protein interaction networks offers a relevant approach for broadening the understanding of the biological context of known ALS-associated genes. Using a bottom-up approach for the analysis of protein-protein interaction networks is a useful method to avoid bias caused by over-connected proteins. Our ALS-enriched modules cover most known biological functions associated with ALS. The presence of recently identified ALS-associated proteins in the core modules highlights the potential for using these as a scaffold for identification of novel ALS disease mechanisms.

Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

Background Osteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis. Methods We performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways. Results Eighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein–protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein–protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial–mesenchymal transition. Conclusion We identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

Pharmacologically controlling protein-protein interactions through epichaperomes for therapeutic vulnerability in cancer

Cancer cell plasticity due to the dynamic architecture of interactome networks provides a vexing outlet for therapy evasion. Here, through chemical biology approaches for systems level exploration of protein connectivity changes applied to pancreatic cancer cell lines, patient biospecimens, and cell- and patient-derived xenografts in mice, we demonstrate interactomes can be re-engineered for vulnerability. By manipulating epichaperomes pharmacologically, we control and anticipate how thousands of proteins interact in real-time within tumours. Further, we can essentially force tumours into interactome hyperconnectivity and maximal protein-protein interaction capacity, a state whereby no rebound pathways can be deployed and where alternative signalling is supressed. This approach therefore primes interactomes to enhance vulnerability and improve treatment efficacy, enabling therapeutics with traditionally poor performance to become highly efficacious. These findings provide proof-of-principle for a paradigm to overcome drug resistance through pharmacologic manipulation of proteome-wide protein-protein interaction networks.