Protein-protein Interaction Network Prediction Research Articles

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits.

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Structure-based prediction of protein-protein interaction network in rice.

Comprehensive protein-protein interaction (PPI) maps are critical for understanding the functional organization of the proteome, but challenging to produce experimentally. Here, we developed a computational method for predicting PPIs based on protein docking. Evaluation of performance on benchmark sets demonstrated the ability of the docking-based method to accurately identify PPIs using predicted protein structures. By employing the docking-based method, we constructed a structurally resolved PPI network consisting of 24,653 interactions between 2,131 proteins, which greatly extends the current knowledge on the rice protein-protein interactome. Moreover, we mapped the trait-associated single nucleotide polymorphisms (SNPs) to the structural interactome, and computationally identified 14 SNPs that had significant consequences on PPI network. The protein structural interactome map provided a resource to facilitate functional investigation of PPI-perturbing alleles associated with agronomically important traits in rice.

MYC integrates FSH signalling networks in cumulus cells during bovine oocyte maturation.

Follicle-stimulating hormone (FSH) contributes to the acquisition of oocyte competence by modulating signalling pathways in cumulus cells (CCs), albeit much less is known about transcription factors (TFs) that orchestrate the downstream transcriptional changes. This work allowed to prospect TFs involved in FSH-mediated signalling during oocyte in vitro maturation (IVM). Bovine cumulus-oocyte complexes underwent IVM with FSH (FSH+) or without FSH (control/CTL) for 22 h, and CCs were subjected to gene expression profiling. Five software identified reference genes for RT-qPCR (ATP1A1, UBB, and YWHAZ). The transcript levels of FSH-responsive genes HAS2 and PTGS2 (COX2) validated the experimental design. Among candidate TFs, MYC was down-regulated (0.35-fold; P < 0.0001), and THAP11 (RONIN) was up-regulated (1.47-fold; P = 0.016) under FSH+ conditions. In silico analyses predicted binding motifs at MYC and THAP11 genes for previously known FSH-responsive TFs. Signalling pathways (EGFR, ERK, GSK3, PKA, and P38) may execute post-translational regulation due to potential phosphorylation sites in MYC and THAP11 proteins. Prediction of protein-protein interaction networks showed MYC as a core component of FSH signalling, albeit THAP11 acts independently. Hence, MYC integrates FSH signalling networks and may assist in exploring genome-wide transcriptional changes associated with the acquisition of oocyte competence.

Prediction of protein-protein interaction networks and druggable genes associated with parkinson’s disease

Parkinson’s disease (PD) affects about 2-3% of the global population over 65 years of age and hence, it is the second most common neurodegenerative disorder in the world. This study explored the key genes and miRNA involved in PD. Microarray dataset (accession number GSE19587) comprising of two regions of medulla: dorsal motor nucleus of vagus (DMNV) and inferior olivary nucleus (ION) was downloaded from Gene Expression Omnibus (GEO) database. A total of 697 DEGs from ION (605 up-regulated genes and 92 down-regulated genes) and 663 DEGs from DMNV (638 up-regulated genes and 25 down-regulated genes) were screened. These DEGs were found to be enriched in 46 (DMNV) and 24 (ION) pathways common in DAVID and Comparative Toxicogenomics Database. In PPI network analysis, IGF1 and CD44 were identified as hub genes in DMNV whereas for ION, the hub genes identified were CSF2 and CD44. In TF-miRNA-target gene networks, an aggregate of 11 transcription factors and 46 miRNA were observed to influence the target genes. In drug-gene interaction studies, CYP3A5 and ESR1 had higher connective degrees and hence, they might be novel druggable targets for Parkinson’s disease.

Potential Networks Regulated by MSCs in Acute-On-Chronic Liver Failure: Exosomal miRNAs and Intracellular Target Genes.

Acute-on-chronic liver failure (ACLF) is a severe syndrome associated with high mortality. Alterations in the liver microenvironment are one of the vital causes of immune damage and liver dysfunction. Human bone marrow mesenchymal stem cells (hBMSCs) have been reported to alleviate liver injury via exosome-mediated signaling; of note, miRNAs are one of the most important cargoes in exosomes. Importantly, the miRNAs within exosomes in the hepatic microenvironment may mediate the mesenchymal stem cell (MSC)-derived regulation of liver function. This study investigated the hepatocyte exosomal miRNAs which are regulated by MSCs and the target genes which have potential in the treatment of liver failure. Briefly, ACLF was induced in mice using carbon tetrachloride and primary hepatocytes were isolated and co-cultured (or not) with MSCs under serum-free conditions. Exosomes were then collected, and the expression of exosomal miRNAs was assessed using next-generation sequencing; a comparison was performed between liver cells from healthy versus ACLF animals. Additionally, to identify the intracellular targets of exosomal miRNAs in humans, we focused on previously published data, i.e., microarray data and mass spectrometry data in liver samples from ACLF patients. The biological functions and signaling pathways associated with differentially expressed genes were predicted using gene ontology and Kyoto Encyclopedia of Genes and Genomics enrichment analyses; hub genes were also screened based on pathway analysis and the prediction of protein-protein interaction networks. Finally, we constructed the hub gene-miRNA network and performed correlation analysis and qPCR validation. Importantly, our data revealed that MSCs could regulate the miRNA content within exosomes in the hepatic microenvironment. MiR-20a-5p was down-regulated in ACLF hepatocytes and their exosomes, while the levels of chemokine C-X-C Motif Chemokine Ligand 8 (CXCL8; interleukin 8) were increased in hepatocytes. Importantly, co-culture with hBMSCs resulted in up-regulated expression of miR-20a-5p in exosomes and hepatocytes, and down-regulated expression of CXCL8 in hepatocytes. Altogether, our data suggest that the exosomal miR-20a-5p/intracellular CXCL8 axis may play an important role in the reduction of liver inflammation in ACLF in the context of MSC-based therapies and highlights CXCL8 as a potential target for alleviating liver injury.

Protein-protein interaction network and potential drug target candidates of Streptococcus suis.

This study aimed to explore potential drug targets of Streptococcus suis at the system level. A homologous protein mapping method was used in the construction of a protein-protein interaction (PPI) network of S. suis, which presented 1147 non-redundant interaction pairs among 286 proteins. The parameters of PPI networks were calculated and showed scale-free network properties. In all, 41 possibly essential proteins identified from 47 highly connected proteins were selected as potential drug target candidates. Of these proteins, 30 were already regarded as drug targets in other bacterial species. Six transporters with high connections to other functional proteins were identified as probably not essential but important functional proteins. Afterward, the subnetwork centred with cell division protein FtsZ was used in confirming the PPI network through bacterial two-hybrid analysis. The predicted PPI network covers 13·04% of the proteome in S. suis. The selected 41 potential drug target candidates are conserved between S. suis and several model bacteria. The predictions included proteins known to be drug targets, and a verifying experiment confirmed the reliability of predicted interactions. This work is the first to present systematic computational PPI data for S. suis and provides potential drug targets, which are valuable in exploring novel anti-streptococcus drugs.

Mreža protein-protein interakcija i protein-ligand doking - trenutno stanje i perspektive

Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.

Correlation and integration of circulating miRNA and peripheral whole blood gene expression profiles in patients with venous thromboembolism

ABSTRACT The main aim of this work was to evaluate differential expression and biological functions of circulating miRNA and whole peripheral blood (PB) genes in patients affected by venous thromboembolism (VTE) and in healthy subjects. Circulating miRNA sequences and PB expression profiles were obtained from GEO datasets. Ten miRNAs with the most significant differential expression rate (dif-miRNA) were subjected to miRbase to confirm their identity. Dif-miRNA targets were predicted by TargetScan and aligned with differentially expressed genes to obtain overlapping co-genes. Biological functions of co-genes were analyzed by Gene Ontology and KEGG analysis. Interaction network of dif-miRNAs, co-genes, and their downstream pathways were studied by analyzing protein–protein interaction (PPI) clusters (STRING) and determining the crucial hubs (Cytoscape). MiR-522-3p and miR-134 dif-miRNAs are involved in protein translation and apoptosis by regulating their respective co-genes in PB. Co-genes are present in nucleolus and extracellular exosomes and are involved in oxidative phosphorylation and ribosome/poly(A)-RNA organization. The predicted PPI network covered 107 clustered genes and 220 marginal joints, where ten hub genes participating in PPIs were found. All these hub genes were down-regulated in VTE patients. Our study identifies new miRNAs as potential biological markers and therapeutic targets for VTE.

Prediction and analysis of human‐herpes simplex virus type 1 protein‐protein interactions by integrating multiple methods

BackgroundHerpes simplex virus type 1 (HSV‐1) is a ubiquitous infectious pathogen that widely affects human health. To decipher the complicated human‐HSV‐1 interactions, a comprehensive protein‐protein interaction (PPI) network between human and HSV‐1 is highly demanded.MethodsTo complement the experimental identification of human‐HSV‐1 PPIs, an integrative strategy to predict proteome‐wide PPIs between human and HSV‐1 was developed. For each human‐HSV‐1 protein pair, four popular PPI inference methods, including interolog mapping, the domain‐domain interaction‐based method, the domain‐motif interaction‐based method, and the machine learning‐based method, were optimally implemented to generate four interaction probability scores, which were further integrated into a final probability score.ResultsAs a result, a comprehensive high‐confidence PPI network between human and HSV‐1 was established, covering 10,432 interactions between 4,546 human proteins and 72 HSV‐1 proteins. Functional and network analyses of the HSV‐1 targeting proteins in the context of human interactome can recapitulate the known knowledge regarding the HSV‐1 replication cycle, supporting the overall reliability of the predicted PPI network. Considering that HSV‐1 infections are implicated in encephalitis and neurodegenerative diseases, we focused on exploring the biological significance of the brain‐specific human‐HSV‐1 PPIs. In particular, the predicted interactions between HSV‐1 proteins and Alzheimer’s‐disease‐related proteins were intensively investigated.ConclusionThe current work can provide testable hypotheses to assist in the mechanistic understanding of the human‐HSV‐1 relationship and the anti‐HSV‐1 pharmaceutical target discovery. To make the predicted PPI network and the datasets freely accessible to the scientific community, a user‐friendly database browser was released at http://www.zzdlab.com/HintHSV/index.php.

Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network.

Protein-protein interactions are the foundations of cellular life activities. At present, the already known protein-protein interactions only account for a small part of the total. With the development of experimental and computing technology, more and more PPI data are mined, PPI networks are more and more dense. It is possible to predict protein-protein interaction from the perspective of network structure. Although there are many high-throughput experimental methods to detect protein-protein interactions, the cost of experiments is high, time-consuming, and there is a certain error rate meanwhile. Network-based approaches can provide candidates of protein pairs for high-throughput experiments and improve the accuracy rate. This paper presents a new link prediction approach “Sim” for PPI networks from the perspectives of proteins' complementary interfaces and gene duplication. By integrating our approach “Sim” with the state-of-art network-based approach “L3,” the prediction accuracy and robustness are improved.

In silico Prediction of Protein-Protein Interaction Network Induced by Manganese II in Meyerozyma guilliermondii.

Recently, there has been an increasing interest in the use of yeast to produce biosorbent materials, because yeast is economical to use, adaptable to a variety of conditions, and amenable to morphological manipulations to yield better raw biomaterials. Previous studies from our laboratory have shown that Meyerozyma guilliermondii, a non-pathogenic haploid yeast (ascomycete), exhibits excellent biosorption capacity for Mn2+, as demonstrated by kinetic analyses. Shotgun/bottom-up analyses of soluble fractions revealed a total of 1257 identified molecules, with 117 proteins expressed in the absence of Mn2+ and 69 expressed only in the presence of Mn2+. In this article, we describe the first in silico prediction and screening of protein–protein interactions (PPIs) in M. guilliermondii using experimental data from shotgun/bottom-up analyses. We also present the categorization of biological processes (BPs), molecular functions (MFs), and metabolic pathways of 71 proteins upregulated in the M. guilliermondii proteome in response to stress caused by an excess of Mn2+ ions. Most of the annotated proteins were related to oxidation–reduction processes, metabolism, and response to oxidative stress. We identified seven functional enrichments and 42 metabolic pathways; most proteins belonged to pathways related to metabolic pathways (19 proteins) followed by the biosynthesis of secondary metabolites (10 proteins) in the presence of Mn2+. Using our data, it is possible to infer that defense mechanisms minimize the impact of Mn2+ via the expression of antioxidant proteins, thus allowing adjustment during the defense response. Previous studies have not considered protein interactions in this genus in a manner that permits comparisons. Consequently, the findings of the current study are innovative, highly relevant, and provide a description of interactive complexes and networks that yield insight into the cellular processes of M. guilliermondii. Collectively, our data will allow researchers to explore the biotechnological potential of M. guilliermondii in future bioremediation processes.

Exploring the Mechanism of Action Compound-Xueshuantong Capsule in Diabetic Retinopathy Treatment Based on Network Pharmacology.

Materials and Methods The components with oral bioavailability ≥30% and drug similarity ≥0.18 were screened by the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), and the effective grouping of Compound-Xueshuantong Capsule was obtained. At the same time, the targets of each drug active component in the Compound-Xueshuantong Capsule were obtained by searching the TCMSP. The effective components and targets of the Compound-Xueshuantong Capsule were annotated by the UniProt database, and the disease treatment targets were searched by the GeneCards database. The disease treatment target is intersected with the drug target and the Wayne diagram is drawn by VennDiagram. The active ingredient targets of the intersection and Compound-Xueshuantong Capsule were inputted into Cytoscape 3.7.2 software to construct the active ingredient-target-disease interaction network. The above targets were inputted into the String database for protein-protein interaction network prediction. Finally, by using the DAVID database, GO and KEGG enrichment analysis was carried out to reveal the potential signal pathway of the Compound-Xueshuantong Capsule in diabetic retinopathy treatment. Results 93 active components of the Compound-Xueshuantong Capsule and 92 targets for treating diabetic retinopathy were screened. The main active components of the Compound-Xueshuantong Capsule in treating diabetic retinopathy were quercetin, luteolin, kaempferol, beta-sitosterol, isorhamnetin, and tanshinone IIa. The effect of the Compound-Xueshuantong Capsule on diabetic retinopathy may be related to IL6, EFGR, CASP3, and VEGFA. In addition, the treatment of diabetic retinopathy mainly involves in the regulation of nuclear receptors and transcription factors in vivo. The target of the Compound-Xueshuantong Capsule in diabetic retinopathy treatment is significantly enriched in the AGE-RAGE signal pathway, TNF signal pathway, HIF-1 signal pathway, and VEGF signal pathway in diabetic complications. Conclusion Compound-Xueshuantong Capsule can treat diabetic retinopathy through multitarget, multipathway, and multipathway regulation of the biomolecular network. The potential biological mechanism of the Compound-Xueshuantong Capsule in diabetic retinopathy treatment may be related to the AGE-RAGE signal pathway, TNF signal pathway, HIF-1 signal pathway, and VEGF signal pathway in diabetic complications, but these findings still need to be confirmed by further clinical research.

A novel prognostic nomogram based on 5 long non-coding RNAs in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common and invasive histological subtype of all kidney malignancies, with high levels of incidence and mortality. In the present study, long non-coding (lnc)RNA expression profiles of patients with ccRCC from The Cancer Genome Atlas database were comprehensively analyzed to identify differentially expressed lncRNAs (DElncRNAs). The patients with ccRCC were then divided into training and validation cohorts. Univariate and LASSO regression analyses were performed to select the most significant survival-associated candidate DElncRNAs in the training cohort. Multivariate Cox regression analysis was then performed to develop a risk score formula and a prognostic nomogram for predicting 3- and 5-year overall survival (OS). The accuracies of the nomogram predictions were evaluated by determining the area under the receiver operating characteristic curve (AUC) and a calibration plot. Finally, functional enrichment analysis and protein-protein interaction network prediction were implemented to predict the functions and molecular mechanisms of the candidate DElncRNAs in ccRCC. A total of 1,553 DElncRNAs were identified, and 5 candidate DElncRNAs (AC026992.2, AC245041.2, LINC00524, LINC01956 and LINC02080) were included in the nomogram. The AUC values for 3- and 5-year overall survival in the training cohort were 0.768 and 0.814, respectively, which were increased compared with that based on the clinical index (0.760 and 0.694, respectively). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that the 521 mRNAs highly associated with 5 DElncRNAs were primarily involved in 17 terms and 25 pathways, respectively. Based on the 5 DElncRNAs, a novel and convenient prognostic nomogram for predicting 3- and 5-year OS for patients with ccRCC was developed. The results of the present study may be conducive to the development of a precise predictive tool for the prognosis of ccRCC and may provide information regarding the molecular mechanisms of ccRCC. However, additional experimental in vitro and in vivo studies investigating lncRNAs may be required.

Abstract 363: Systems Network Genomic Analysis Reveals the Role Of MURC/Cavin-4 in Cardiac Ischemia/reperfusion Injury

Background: Ischemia/reperfusion (I/R) injury is a critical issue in the development of treatment strategies for ischemic heart disease. Muscle-restricted coiled-coil protein (MURC)/Cavin-4, which is a component of caveolae, is involved in the pathophysiology of dilated cardiomyopathy and cardiac hypertrophy. However, the role of MURC in cardiac I/R injury remains unknown. Objective: To elucidate the role of MURC in cardiac I/R injury. Methods and Results: The systems network genomic analysis based on PC-corr network inference on microarray data between wild-type and MURC knockout (KO) mouse hearts described a network of discriminating genes associated with reactive oxygen species (ROS). To demonstrate the prediction, we investigated the effect of MURC deletion in cardiac I/R injury. MURC deletion in IR-injured mouse hearts decreased infarct size and preserved heart contraction with the inhibition of ROS production and ROS-related gene expressions such as EGR1 and DDIT4 as well as EGR1 protein level. PC-corr network inference integrated with a protein-protein interaction network prediction showed that MURC is also involved in the apoptotic pathway. We confirmed the upregulated activity of STAT3, which is a transcription factor of anti-apoptotic signaling, with the increase of BCL2 mRNA expression and protein level and the decrease of cleaved Caspase 3 protein level in MURC KO compared with WT mouse hearts after I/R. TUNEL assay showed that MURC modulates the apoptosis in cardiomyocytes exposed to hydrogen peroxide. STAT3 inhibitor cancelled the cardioprotective effect of MURC deletion in I/R-injured heart and the anti-apoptotic effect of MURC knockdown in cardiomyocytes. Conclusions: Our findings suggest that MURC plays a pivotal role in the regulation of ROS-induced cell death and STAT3-meditated anti-apoptotic signaling in cardiac I/R injury. MURC may be a therapeutic target for cardiac I/R injury.

Gallbladder cancer integrated bioinformatics analysis of protein profile data.

Identifying the critical genes that differentiate gall bladder cancer from a normal gall bladder and the related biological terms was the aim of this study. The molecular mechanism underlying gall bladder cancer (GBC) trigger and development still requires investigations. Potential therapeutic biomarkers can be identified through protein-protein interaction network prediction of proteome as a complementary study. Here, a literature review of proteomics studies of gall bladder cancer from 2010 to 2019 was undertaken to screen differentially expressed proteins in this cancer. A network of 27 differentially expressed proteins (DEPs) via Cytoscape 3.7.1 and its plug-ins was constructed and analyzed. Ten proteins were introduced as hub-bottlenecks among which four were from DEPs. The gene ontology analysis also indicated that positive regulation of multi-organism process and regulation of response to biotic stimulus are the most disrupted biological processes of GBC considering their relationships with the DEPs. ACTG, ALB, GGH, and DYNC1H1, and relative biological terms were introduced as drug targets and possible diagnostic biomarkers.

The GWAS Risk Genes for Depression May Be Actively Involved in Alzheimer's Disease.

Depression is one of the most frequent psychiatric symptoms observed in people during the development of Alzheimer's disease (AD). We hypothesized that genetic factors conferring risk of depression might affect AD development. In this study, we screened 31 genes, which were located in 19 risk loci for major depressive disorder (MDD) identified by two recent large genome-wide association studies (GWAS), in AD patients at the genomic and transcriptomic levels. Association analysis of common variants was performed by using summary statistics of the International Genomics of Alzheimer's Project (IGAP), and association analysis of rare variants was conducted by sequencing the entire coding region of the 31 MDD risk genes in 107 Han Chinese patients with early-onset and/or familial AD. We also quantified the mRNA expression alterations of these MDD risk genes in brain tissues of AD patients and AD mouse models, followed by protein-protein interaction network prediction to show their potential effects in AD pathways. We found that common and rare variants of L3MBTL2 were significantly associated with AD. mRNA expression levels of 18 MDD risk genes, in particular SORCS3 and OAT, were differentially expressed in AD brain tissues. 13 MDD risk genes were predicted to physically interact with core AD genes. The involvement of HACE1, NEGR1, and SLC6A15 in AD was supported by convergent lines of evidence. Taken together, our results showed that MDD risk genes might play an active role in AD pathology and supported the notion that depression might be the "common cold" of psychiatry.

Identification of methylated genes and miRNA signatures in nasopharyngeal carcinoma by bioinformatics analysis.

Nasopharyngeal carcinoma (NPC) is prevalent in several regions, including. Southern China and Southeast Asia, with high mortality. The present study aimed to explore the epigenetic mechanisms of NPC and to provide novel biomarkers for prognosis. Two methylation data sets (GSE52068 and GSE62336) were downloaded from the Gene Expression Omnibus database. Following pretreatment of the raw data, differentially methylated regions (DMRs) and differentially methylated CpG islands (DMCs) were identified between the NPC samples and normal tissue controls using COHCAP software. The overlapped DMRs and DMCs in the two data sets were extracted and associated to relevant genes. Enrichment analysis and protein-protein interaction (PPI) network analyses were performed on the identified genes using Database for Annotation, Visualization and Integration Discovery and Cytoscape, respectively. MicroRNAs (miRNAs) targeting the overlapped genes were identified based on the miRWalk database. NPC-related genes were analyzed with the Comparative Toxicogenomics Database. Multiple overlapping DMRs between the two data sets were identified and were associated with 1,854 hypermethylated and 18 hypomethylated genes, which were revealed to be enriched in certain pathways, including the mitogen-activated protein kinase (MAPK) signaling pathway and the phosphatidylinositol 3-kinase (PI3K)/AKT signaling pathway. Several nodes in the predicted PPI network were highlighted, including proto-oncogene tyrosine-protein kinase SRC, SMAD family member 3 (SMAD3), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein ζ (YWHAZ) and Heat shock protein family A member 4 (HSPA4), all of which were hypomethylated. A total of 14 miRNAs were identified that correlated with the overlapped genes such as miRNA (miR)-148a-3p, which was predicted to target of HSPA4; and 17 genes were identified as related to NPC, including SMAD3 and SRC. miR129-2 was hypermethylated. Several novel methylated genes or miRNAs were suggested as biomarkers for NPC prognosis: Hypomethylation of SRC, SMAD3, YWHAZ and HSPA4, and hypermethylation of miR129-2 may be linked to poor prognosis of NPC.

Assigning chemoreceptors to chemosensory pathways in Pseudomonas aeruginosa

In contrast to Escherichia coli, a model organism for chemotaxis that has 5 chemoreceptors and a single chemosensory pathway, Pseudomonas aeruginosa PAO1 has a much more complex chemosensory network, which consists of 26 chemoreceptors feeding into four chemosensory pathways. While several chemoreceptors were rigorously linked to specific pathways in a series of experimental studies, for most of them this information is not available. Thus, we addressed the problem computationally. Protein-protein interaction network prediction, coexpression data mining, and phylogenetic profiling all produced incomplete and uncertain assignments of chemoreceptors to pathways. However, comparative sequence analysis specifically targeting chemoreceptor regions involved in pathway interactions revealed conserved sequence patterns that enabled us to unambiguously link all 26 chemoreceptors to four pathways. Placing computational evidence in the context of experimental data allowed us to conclude that three chemosensory pathways in P. aeruginosa utilize one chemoreceptor per pathway, whereas the fourth pathway, which is the main system controlling chemotaxis, utilizes the other 23 chemoreceptors. Our results show that while only a very few amino acid positions in receptors, kinases, and adaptors determine their pathway specificity, assigning receptors to pathways computationally is possible. This requires substantial knowledge about interacting partners on a molecular level and focusing comparative sequence analysis on the pathway-specific regions. This general principle should be applicable to resolving many other receptor-pathway interactions.

Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space.

Prediction of protein-protein interaction (PPI) remains a central task in systems biology. With more PPIs identified, forming PPI networks, it has become feasible and also imperative to study PPIs at the network level, such as evolutionary analysis of the networks, for better understanding of PPI networks and for more accurate prediction of pairwise PPIs by leveraging the information gained at the network level. In this work we developed a novel method that enables us to incorporate evolutionary information into geometric space to improve PPI prediction, which in turn can be used to select and evaluate various evolutionary models. The method is tested with cross-validation using human PPI network and yeast PPI network data. The results show that the accuracy of PPI prediction measured by ROC score is increased by up to 14.6%, as compared to a baseline without using evolutionary information. The results also indicate that our modified evolutionary model DANEOsf—combining a gene duplication/neofunctionalization model and scale-free model—has a better fitness and prediction efficacy for these two PPI networks. The improved PPI prediction performance may suggest that our DANEOsf evolutionary model can uncover the underlying evolutionary mechanism for these two PPI networks better than other tested models. Consequently, of particular importance is that our method offers an effective way to select evolutionary models that best capture the underlying evolutionary mechanisms, evaluating the fitness of evolutionary models from the perspective of PPI prediction on real PPI networks.

COMPUTATIONAL IDENTIFICATION OF PUTATIVE DRUG TARGETS IN MALASSEZIA GLOBOSA BY SUBTRACTIVE GENOMICS AND PROTEIN CLUSTER NETWORK APPROACH

Objective: Yeast commonly causes superficial mycoses similar to the dermatophytes. Superficial mycoses were reported with an estimated incidence of ∼140,000,000 cases/year worldwide and most frequently caused by Malassezia globosa and Malassezia furfur. Treatment available for these conditions is limited and with side effects. Moreover, termination of the treatment may result in the reoccurrence of the disease. The objective of this research was to identify the putative drug targets using computational approaches.Methods: The analysis of genome sequence improves the understanding of diseases which leads to better treatment. Comparison of the genome of the pathogen with the host at the molecular level is suitable for performing the sequence based prediction of protein-protein interaction network, which also forms the basis of drug target identification leading to the discovery of new drugs for the improved treatment.Results: Out of 100 pathways of M. globosa, 95 were common to the host and 5 were unique to the pathogen. Total common and unique targets from common pathways are 1704 and 300, respectively. A unique target from unique pathways and 147 from common pathways were non-homologous targets. From this, 46 targets were screened out as essential and processed in the next phase to identify the clustered targets which resulted with three clusters based on their biological role and subcellular location.Conclusion: In this study, putative drug targets were identified in M. globosa using in silico approaches of subtractive genomics and cluster network which will help in the next level of drug discovery such as lead identification for the novel targets.