Protein-protein Network Research Articles

Integrated analysis of a competing endogenous RNA network reveals an 11-lncRNA prognostic signature in ovarian cancer.

Long noncoding RNA (lncRNA) can function as a competing endogenous RNA (ceRNA) involved in tumor initiation and progression. However, the prognostic roles of lncRNAs in the integrated analysis of the ceRNA network in ovarian cancer (OVC) are still lacking. This study aimed to identify lncRNAs associated with the prognosis of OVC. Differential expression analysis and WGCNA were used to screen OVC-specific RNAs. A lncRNA-miRNA-mRNA regulatory network consisting of 201 lncRNAs, 85 miRNA and 146 mRNAs was constructed, and functional enrichment and protein-protein network analyses were performed. Then, the OVC-specific RNAs were submitted to Cox regression analysis. Twelve differentially expressed lncRNAs and mRNAs were identified as significantly associated with OS of OVC patients. Meanwhile, 11 lncRNAs (including C4A-AS1, LINC02408, LINC00488) were established as prognostic risk formulas. The low-risk group had better OS and DFS than the high-risk group (P <0.01). Univariate and multivariate Cox regression analyses revealed the 11-lncRNA risk score as an independent prognostic factor. A prognostic nomogram was developed based on independent prognostic factors. Our data provide evidence that the 11-lncRNA signature could serve as an independent prognostic indicator. This study also suggests that these 11 lncRNAs potentially participate in the progression of OVC.

Integrated multi-omics profiling of nonfunctioning pituitary adenomas.

Genetic and epigenetic alterations are involved in pituitary adenoma pathogenesis, however the molecular basis of proliferative nonfunctioning pituitary adenomas (NFPAs) remains unclear. Here, we analyzed integrated multi-omics profiling including copy number variation (CNV), DNA methylation and gene expression of 8 NFPAs. We collected 4 highly proliferative (hpNFPA, Ki-67 ≥ 3) and 4 lowly proliferative (Ki-67 ≤ 1) NFPAs, and comprehensively assessed CNV, DNA methylation, and gene expression by Illumina HumanMethylation450 BeadChip and Affymetrix GeneChip PrimeView Human Gene Expression Array. We performed Ingenuity Pathway Analysis (IPA) for differentially expressed genes to illustrate aberrant pathways and delineated protein-protein networks of selected key genes in dysregulated pathways. Aberrant arm level CNV, dysregulated DNA methylation, and associated impacts on gene expressions were observed in 2 early occurring hpNFPAs. Chromosomal losses were associated with attenuated expression of DNA methyltransferases, further altering global methylation in these 2 samples. Correlation analysis between DNA methylation and gene expression in 8 NFPAs indicates methylation in promoter and gene body regions are both involved in gene regulation. IPA showed PPARα/RXRα, dopamine receptor signaling, cAMP-mediated signaling, and calcium signaling were all activated, while p38 MAPK and ERK5 signaling were inhibited in hpNFPAs. Moreover, selected key gene networks in hpNFPAs exhibited concurrent methylation status and expression levels of adenylate cyclase genes, G protein subunits, HLA genes, CXCL12, and CCL2. This study presents comprehensive multi-omics views of CNV, DNA methylation, and gene expression in 8 NFPAs. Pathway analysis and network maps of key genes provide clues to elucidate the molecular basis of hpNFPA.

The expression of seven key genes can predict distant metastasis of colorectal cancer to the liver or lung.

It is unclear how primary colorectal cancer (CRC) cells select to metastasize to the liver or lungs, the most frequent distant metastasis of CRC. We aimed to identify the key genes and pathways that may predict the distant metastasis of CRC to these sites. Three gene expression array datasets from the Gene Expression Omnibus were analyzed. Protein-protein network analyses, best subsets regressions and backward stepwise regression analyses were used to screen the key genes and their expressions were used to construct a predictive logistic regression model. Expression data from local clinical samples were used as a validation dataset. The receiver operating characteristic (ROC) curve was used to test the performance of the predictive model. In total, 59 differentially expressed genes (DEG) related to liver-metastasis, 90 related to lung metastasis and 45 related to both liver and lung metastasis were identified. The KEGG pathways and gene oncology (GO) terms that were enriched in liver and lung metastasis were identified. A predictive logistic regression model consisted of SPARC, COL1A2, MMP9, COL11A1, COL3A1, CXCL12 and THBS2 was established. The area under the ROC curve reached 0.839 in predicting liver and lung metastasis, using our clinical samples as the validation dataset. Seven key genes capable of predicting liver and lung metastasis of colorectal cancer were identified, which provides clues for exploring the mechanism of selecting target organs during the metastatic process in CRC and inspires the researches for new potential targets for therapy to inhibit metastasis.

Genome-wide analysis of PHD finger gene family and identification of potential miRNA and their PHD finger gene specific targets in Oryza sativa indica.

Rice (Oryza sativa L.) is one of the most important cereal crops for one third of the world population. However, the grain quality as well as yield of rice is severely affected by various abiotic stresses. Environmental stresses affect the expression of various microRNAs (miRNAs) which in turn negatively regulate gene expression at the post-transcriptional level either by degrading the target mRNA genes or suppressing translation in plants. Plant homeo-domain (PHD) finger proteins are known to be involved in the plant response to salinity stress. In the present study, we identified 44 putative OsPHD finger genes in Oryza sativa Indica, using Ensembl Plants Database. Using computational approach, potential miRNAs that target OsPHD finger genes were identified. Out of the 44 OsPHD finger genes only three OsPHD finger genes i.e., OsPHD2, OsPHD35 and OsPHD11, were found to be targeted by five newly identified putative miRNAs i.e., ath-miRf10010-akr, ath-miRf10110-akr, osa-miR1857–3p, osa-miRf10863-akr, and osa-miRf11806-akr. This is the first report of these five identified miRNAs on targeting PHD finger in Oryza sativa Indica. Further, expression analysis of 44 PHD finger genes under salinity was also performed using quantitative Real-Time PCR. The expression profile of 8 genes were found to be differentially regulated, among them two genes were significantly up regulated i.e., OsPHD6 and OsPHD12. In silico protein-protein interaction analysis using STRING database showed interaction of the OsPHD finger proteins with other protein partners that are directly or indirectly involved in development and abiotic stress tolerance.

DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks.

Eukaryotic cells contain numerous components, which are known as subcellular compartments or subcellular organelles. Proteins must be sorted to proper subcellular compartments to carry out their molecular functions. Mis-localized proteins are related to various cancers. Identifying mis-localized proteins is important in understanding the pathology of cancers and in developing therapies. However, experimental methods, which are used to determine protein subcellular locations, are always costly and time-consuming. We tried to identify cancer-related mis-localized proteins in three different cancers using computational approaches. By integrating gene expression profiles and dynamic protein-protein interaction networks, we established DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine), a predictive model using the SVM classifier with diffusion kernels. With this predictive model, we identified a number of mis-localized proteins. Since we introduced the dynamic protein-protein network, which has never been considered in existing works, our model is capable of identifying more mis-localized proteins than existing studies. As far as we know, this is the first study to incorporate dynamic protein-protein interaction network in identifying mis-localized proteins in cancers.

A Common Binding Motif in the ET Domain of BRD3 Forms Polymorphic Structural Interfaces with Host and Viral Proteins

The extra-terminal (ET) domain of BRD3 is conserved among BET proteins (BRD2, BRD3, BRD4), interacting with multiple host and viral protein-protein networks. Solution NMR structures of complexes formed between BRD3-ET domain with either the 79-residue murine leukemia virus integrase (IN) C-terminal domain (IN329-408), or its 22-residue IN tail peptide (TP) (IN386-407) alone, reveal similar intermolecular three-stranded β-sheet formation. 15N relaxation studies reveal a 10-residue linker region (IN379-388) tethering the SH3 domain (IN329-378) to the ET-binding motif (IN389-405)-ET complex. This linker has restricted flexibility, impacting its potential range of orientations in the IN - nucleosome complex. The complex of the ET-binding peptide of host NSD3 protein (NSD3148-184) and BRD3-ET domain includes a similar three-stranded b-sheet interaction, but the orientation of the b-hairpin is flipped compared to the two IN : ET complexes. These studies expand our understanding of molecular recognition polymorphism in complexes of ET-binding motifs with viral and host proteins.

An analysis of tissue-specific alternative splicing at the protein level.

The role of alternative splicing is one of the great unanswered questions in cellular biology. There is strong evidence for alternative splicing at the transcript level, and transcriptomics experiments show that many splice events are tissue specific. It has been suggested that alternative splicing evolved in order to remodel tissue-specific protein-protein networks. Here we investigated the evidence for tissue-specific splicing among splice isoforms detected in a large-scale proteomics analysis. Although the data supporting alternative splicing is limited at the protein level, clear patterns emerged among the small numbers of alternative splice events that we could detect in the proteomics data. More than a third of these splice events were tissue-specific and most were ancient: over 95% of splice events that were tissue-specific in both proteomics and RNAseq analyses evolved prior to the ancestors of lobe-finned fish, at least 400 million years ago. By way of contrast, three in four alternative exons in the human gene set arose in the primate lineage, so our results cannot be extrapolated to the whole genome. Tissue-specific alternative protein forms in the proteomics analysis were particularly abundant in nervous and muscle tissues and their genes had roles related to the cytoskeleton and either the structure of muscle fibres or cell-cell connections. Our results suggest that this conserved tissue-specific alternative splicing may have played a role in the development of the vertebrate brain and heart.

Comprehensive bioinformatics analysis of the TP53 signaling pathway in Wilms' tumor.

BackgroundDifferential expression of tumor protein 53 (TP53, or p53) has been observed in multiple cancers. However, the expression levels and prognostic role of TP53 signaling pathway genes in Wilms’ tumor (WT) have yet to be fully explored.MethodsThe expression levels of TP53 signaling pathway genes including TP53, mouse double minute 2 (MDM2), mouse double minute 4 (MDM4), cyclin-dependent kinase 2A (CDKN2A), cyclin-dependent kinase 2B (CDKN2B), and tumor suppressor p53-binding protein 1 (TP53BP1) in WT were analyzed using the Oncomine database. Aberration types, co-mutations, mutation locations, signaling pathways, and the prognostic role of TP53 in WT were investigated using cBioPortal. MicroRNA (miRNA) and transcription factor (TF) targets were identified with miRTarBase, miWalk, and ChIP-X Enrichment Analysis 3 (CheA3), respectively. A protein-protein network was constructed using GeneMANIA. The expression of TP53 signaling genes were confirmed in WT samples and normal kidney tissues using the Human Protein Atlas (HPA). Cancer Therapeutics Response Portal (CTRP) was used to analyze the small molecules potentially targeting TP53.ResultsTP53 was significantly expressed in the Cutcliffe Renal (P=0.010), but not in the Yusenko Renal (P=0.094). Meanwhile, MDM2 was significantly overexpressed in the Yusenko Renal (P=0.058), but not in the Cutcliffe Renal (P=0.058). The expression levels of MDM4 no significant difference between the tumor and normal tissue samples. The most common TP53 alteration was missense and the proportion of TP53 pathway-related mutations was 2.3%. Co-expressed genes included ZNF609 (zinc finger protein 609), WRAP53 (WD40-encoding RNA antisense to p53), CNOT2 (CC chemokine receptor 4-negative regulator of transcription 2), and CDH13 (cadherin 13). TP53 alterations indicated poor prognosis of WT (P=1.051e-4). The regulators of the TP53 pathway included miR-485-5p and TFs NR2F2 and KDM5B. The functions of TP53 signaling pathway were signal transduction in response to DNA damage and regulate the cell cycle. The small molecules targeting TP53 included PRIMA-1, RITA, SJ-172550, and SCH-529074.ConclusionsTP53 was found to be differentially expressed in WT tissues. TP53 mutations indicated poor outcomes of WT. Therefore, pifithrin-mu, PRIMA-1, RITA, SJ-172550, and SCH-529074 could be used in combination with traditional chemotherapy to treat WT.

PPARγ Cistrome Repression during Activation of Lung Monocyte-Macrophages in Severe COVID-19.

SummaryThe molecular mechanisms of cytokine storm in patients with severe COVID-19 infections are poorly understood. To uncover these events, we performed transcriptome analyses of lung biopsies from patients with COVID-19, revealing a gene enrichment pattern similar to that of PPARγ-knockout macrophages. Single-cell gene expression analysis of bronchoalveolar lavage fluids revealed a characteristic trajectory of PPARγ-related disturbance in the CD14+/CD16+ cells. We identified a correlation with the disease severity and the reduced expression of several members of the PPARγ complex such as EP300, RXRA, RARA, SUMO1, NR3C1, and CCDC88A. ChIP-seq analyses confirmed repression of the PPARγ-RXRA-NR3C1 cistrome in COVID-19 lung samples. Further analysis of protein-protein networks highlighted an interaction between the PPARγ-associated protein SUMO1 and a nucleoprotein of the SARS virus. Overall, these results demonstrate for the first time the involvement of the PPARγ complex in severe COVID-19 lung disease and suggest strongly its role in the major monocyte/macrophage-mediated inflammatory storm.

Identification of candidate genes for reproductive traits in cattle using a functional interaction network approach

Reproduction is a key element in cattle production systems. Systems biology approaches, including those involving gene networks, have been applied to genetic dissection complex phenotypes in cattle. A set of 385 genes associated with reproductive traits in cattle were included in a protein-protein network analysis to identify and prioritize candidate genes related to phenotypic differences in cattle reproduction. Genes belonging to the ubiquitin family - Ubiquitin C ( Ubc , Gene ID: 444874) and Ubiquitin B ( Ubb , Gene ID: 281370) -had the highest probability of being associated with these traits in cattle. Both proteins were identified as important hubs in a protein-protein interaction network, each having 3,775 interactions of 3,856 possible. Resequencing of the Ubb gene coding region to evaluate the presence of SNPs in a discovery population identified the G/T (rs110366695) transversion. This causes emergence of a stop codon and a protein truncated by 287 aa. The allelic frequency distributions found in two beef cattle breeds highlight the promise of further research into the effects of protein truncation and the potential of these proteins as molecular markers for semen quality.

Prospects of circadian clock in joint cartilage development.

Altering the food intake, exercise, and sleep patterns have a great influence on the homeostasis of the biological clock. This leads to accelerated aging of the articular cartilage, susceptibility to arthropathy and other aspects. Deficiency or overexpression of certain circadian clock-related genes accelerates the cartilage deterioration and leads to phenotypic variation in different joints. The process of joint cartilage development includes the formation of joint site, interzone, joint cavitation, epiphyseal ossification center, and cartilage maturation. The mechanism by which, biological clock regulates the cell-cycle, growth, metabolism, and other biological processes of chondrocytes is poorly understood. Here, we summarized the interaction between biological clock proteins and developmental pathways in chondrogenesis and provided the evidence from other tissues that further predicts the molecular patterns of these protein-protein networks in activation, proliferation, and differentiation. The purpose of this review is to gain deeper understanding of the evolution of cartilage and its irreversibility seen in damage and aging.

Comprehensive analysis of genomic alterations, clinical outcomes, putative functions and potential therapeutic value of MMP11 in human breast cancer

The aim of the current study is to comprehend the prognostic and therapeutic value of matrix metalloproteinase (MMP11) in breast cancer using computational analyses. Gene expression-based Outcome for Breast cancer Online (GOBO) analysis revealed that MMP11 expression was increased in tumor when compared to normal. Among the subtypes, human epidermal growth factor receptor 2 (HER2) enriched and estrogen receptor (ER) positive showed higher expression of MMP11. The results were independently validated with cBioPortal and Oncomine databases. Tumors with high MMP11 expression showed poor overall survival and distant metastasis free survival. Further, protein-protein interactome network was constructed and molecular pathway analysis using Reactome database identified its putative role in diverse cellular processes. In addition, UALCAN web portal revealed hypomethylation in the promoter region of MMP11 in breast tumors. Molecular docking using iGEMDOCK and Autodock Vina were performed to identify natural compounds and repurposed drugs interaction with MMP11. Our study confirms the prognostic significance of MMP11 in breast cancer and their intrinsic subtypes. Twelve bio-compounds which were predominantly flavonoids and anti-psychotic drug pimozide showed significant docking scores with MMP11. Further, potential of these compounds in inhibiting MMP11 related molecular pathways and in targeting breast cancer cells are yet to be studied.

Whole Genome Sequence Data Provides Novel Insights Into the Genetic Architecture of Meat Quality Traits in Beef.

Tenderness is a major quality attribute for fresh beef steaks in the United States, and meat quality traits in general are suitable candidates for genomic research. The objectives of the present analysis were to (1) perform genome-wide association (GWA) analysis for marbling, Warner-Bratzler shear force (WBSF), tenderness, and connective tissue using whole-genome data in an Angus population, (2) identify enriched pathways in each GWA analysis; (3) construct a protein–protein interaction network using the associated genes and (4) perform a μ-calpain proteolysis assessment for associated structural proteins. An Angus-sired population of 2,285 individuals was assessed. Animals were transported to a commercial packing plant and harvested at an average age of 457 ± 46 days. After 48 h postmortem, marbling was recorded by graders’ visual appraisal. Two 2.54-cm steaks were sampled from each muscle for recording of WBSF, and tenderness, and connective tissue by a sensory panel. The relevance of additive effects on marbling, WBSF, tenderness, and connective tissue was evaluated on a genome-wide scale using a two-step mixed model-based approach in single-trait analysis. A tissue-restricted gene enrichment was performed for each GWA where all polymorphisms with an association p-value lower than 1 × 10–3 were included. The genes identified as associated were included in a protein–protein interaction network and a candidate structural protein assessment of proteolysis analyses. A total of 1,867, 3,181, 3,926, and 3,678 polymorphisms were significantly associated with marbling, WBSF, tenderness, and connective tissue, respectively. The associate region on BTA29 (36,432,655–44,313,046 bp) harbors 13 highly significant markers for meat quality traits. Enrichment for the GO term GO:0005634 (Nucleus), which includes transcription factors, was evident. The final protein–protein network included 431 interations between 349 genes. The 42 most important genes based on significance that encode structural proteins were included in a proteolysis analysis, and 81% of these proteins were potential μ-Calpain substrates. Overall, this comprehensive study unraveled genetic variants, genes and mechanisms of action responsible for the variation in meat quality traits. Our findings can provide opportunities for improving meat quality in beef cattle via marker-assisted selection.

An integrated analysis to predict micro-RNAs targeting both stemness and metastasis in human gastric cancer.

Cancer stem cells (CSCs), a subpopulation of tumor cells, assess the capacity of self-renewal, metastasis, and therapeutic resistance. Regulation of CSCs and their epithelial to mesenchymal transition (EMT) potential is one of the promising strategies to eliminate cancer or to inhibit metastasis. Micro-RNAs (miRNAs) as regulators of several cell properties, such as self-renewal, metastasis, and resistance to the drug, could be proper targets in cancer diagnosis and therapy. The aim of the present study is to select common miRNAs targeting both self-renewal and metastasis in gastric cancer. Stemness-related and EMT-related genes were selected by literature mining. The common miRNAs targeting genes were chosen using different databases and r programming language. The expression pattern of selected miRNAs and genes was evaluated in gastrospheres-as a gastric CSC model-and gastric tumor biopsies. Based on the integrated analysis, six miRNAs common to both stemness and metastasis were identified. miR-200c-3p and miR-520c-3p overexpressed in MKN-45 gastrospheres and grade III tumors. In AGS spheres, however, miR-520c-3p and miR-200c-3p upregulation and miR-34a-5p downregulation were similar to grade II tumors. Interestingly, miR-200c-3p and miR-520c-3p indicated a positive correlation with OCT4 and NOTCH1 expression in grade III tumors and MKN-45 spheres. Protein-protein network revealed that the EMT acquisition can be induced by stemness activation through intermediated core-regulatory genes, including CTNNB1, CTNND1, MAML1, KAT2A, and MAML3. The upregulation of mir-200c-3p and mir-520c-3p could effect on stemness and metastasis in gastric cancer as well as gastric CSCs. Therefore, they can be used as diagnosis and prognostic factors.

Gene network analysis of efflux pump proteins in Shigella spp.

The prevalence of multi-drug resistant bacteria is a global concern and addressing this issue is the need of the hour. Efflux mediated resistance is one of the major mechanisms of drug resistance in bacteria and is mainly attributed to the class of Resistance Nodulation Division pumps. In our study, a network of directly interacting proteins with the components of the AcrAB-TolC efflux system was generated. Functional enrichment analysis was performed for the concerned genes. The protein-protein association network was grouped in clusters and topological parameters were analysed. From the study, we sorted out genes that have no direct connection to the efflux system from those which can act as drug targets.

Grad-seq shines light on unrecognized RNA and protein complexes in the model bacterium Escherichiacoli.

Stable protein complexes, including those formed with RNA, are major building blocks of every living cell. Escherichia coli has been the leading bacterial organism with respect to global protein-protein networks. Yet, there has been no global census of RNA/protein complexes in this model species of microbiology. Here, we performed Grad-seq to establish an RNA/protein complexome, reconstructing sedimentation profiles in a glycerol gradient for ∼85% of all E. coli transcripts and ∼49% of the proteins. These include the majority of small noncoding RNAs (sRNAs) detectable in this bacterium as well as the general sRNA-binding proteins, CsrA, Hfq and ProQ. In presenting use cases for utilization of these RNA and protein maps, we show that a stable association of RyeG with 30S ribosomes gives this seemingly noncoding RNA of prophage origin away as an mRNA of a toxic small protein. Similarly, we show that the broadly conserved uncharacterized protein YggL is a 50S subunit factor in assembled 70S ribosomes. Overall, this study crucially extends our knowledge about the cellular interactome of the primary model bacterium E. coli through providing global RNA/protein complexome information and should facilitate functional discovery in this and related species.

Characterization and molecular evolution of claudin genes in the Pungitius sinensis.

Claudins are a family of integrated membrane-bound proteins involving in paracellular tightness, barrier forming, ion permeability, and substrate selection at tight junctions of chordate epithelial and endothelial cells. Here, 39 putative claudin genes were identified in the Pungitius sinensis based on the high throughput RNA-seq. Conservative motif distribution in each group suggested functional relevance. Divergence of duplicated genes implied the species' adaptation to the environment. In addition, selective pressure analyses identified one site, which may accelerate functional divergence in this protein family. Pesticides cause environmental pollution and have a serious impact on aquatic organisms when entering the water. The expression pattern of most claudin genes was affected by organophosphorus pesticide, indicating that they may be involved in the immune regulation of organisms and the detoxification of xenobiotics. Protein-protein network analyses also exhibited 439 interactions, which implied the functional diversity. It will provide some references for the functional study on claudin genes.

Bioinformatics analysis of microRNA profiles and identification of microRNA-mRNA network and biological markers in intracranial aneurysm.

Intracranial aneurysm (IA) is a kind of cerebrovascular disorder, which may result in the subarachnoid hemorrhage with high lethality and disability. The purpose of this study was to reveal the pathogenesis and identify novel biomarkers in IA.We processed the raw microRNA (miRNA) expression profile data of IA obtained from Gene Expression Omnibus. Then weighted correlation network analysis was performed to identify the hub miRNAs in IA. Target genes of hub miRNAs were predicted using multiR package. In addition, a protein-protein network as well as miRNA-mRNA network was constructed and functional and pathway enrichment analyses were done. Finally, the prediction value of hub miRNAs in IA was tested in validation set.Two modules that had relation with IA were identified and 10 hub miRNAs in each module with higher gene-module association were selected. The protein-protein network and miRNA-mRNA network contained 243 nodes and 1496 edges. Functional and pathway enrichment analyses showed that they were mainly enriched in cell cycle, cell proliferation, and PI3K/Akt signaling pathways. Besides, hsa-miR-191-3p, hsa-miR-423-5p, hsa-miR-424-5p, hsa-miR-425-3p were proven to be valuable in prediction IA occurrence.In a word, this study reveals hub miRNAs, target genes and pathways potentially participating in formation and development of IA and screens out some candidate biomarkers. Our findings provide some new perspectives for research and treatment of IA.

Protein Profile of Human Lung Epithelial Cells (A549) Revealing Deviation in Cytoskeleton Proteins in Response to Zinc Oxide Nanoparticles Exposure

Zinc oxide nanoparticles (ZnO NPs) are widely used in biomedicine and scientific research because of their high dissolution property and bioavailability. On the contrary, this property also increases the intracellular reactivity, accessibility and cytotoxicity. These nano-bio interactions could induce undesirable changes in the proteome of the interacting cells, especially in the lung cells as these are the primary contact site. However, the potential effects of ZnO NPs exposure on proteome remain unclear. Proteomics data will substantiate the detailed mechanism of cellular interactions and modulatory effects of ZnO NPs on cells. Quantitative proteomic profiling was done using MALDI-TOF/TOF and MS/MS to identify differential protein expression on exposure to NPs among non exposed and exposed cells. Twenty-two proteins, with approximately 1.5 fold differential expression in cells exposed to ZnO NPs as compared to control cells were identified. Differentially expressed proteins were further classified using PANTHER software on the basis of functional gene ontology term: molecular function, biological process and cellular component. ToppGene suite was used to study protein-protein interaction and network was enriched with STRING. This study is a systematic analysis of protein modulation of the A549 cells exposed to ZnO NPs indicating alterations in the cytoskeleton.

A Network-Based Embedding Method for Drug-Target Interaction Prediction.

Integration of multi-omics and pharmacological data can help researchers understand the impact of drugs on dynamic biological systems. Network-based approaches to such integration explore the interaction of different cellular components and drugs. However, with ever-increasing amounts of data, processing these high-dimensional biological networks requires powerful tools. We investigate whether network embeddings can address this problem by providing an effective method for dimensionality reduction in drug-related networks. A neural network-based embedding method is employed to encode protein-protein, protein-disease, drug-drug and drug-disease networks for the prediction of novel drug-target interactions. We found that drug-target interaction prediction using embeddings of heterogeneous networks as input features performs comparably to state-of-the-art methods, exhibiting an area under the ROC curve of 84%, outperforming methods such as BLM-NII and NetLapRLS, and coming very close to the best performing network methods such as HNM, CMF and DTINet. These encouraging results suggest that further investigation of this approach is warranted.