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Plant protein–protein interaction (PPI) networks provide valuable information for the understanding of plant biological processes. In this study, we summarized and constructed the nuclear-protein interactome of Arabidopsis based on PPIs, and preliminarily analyzed the relationships among the main pathways involving these protein interactions. These proteins are involved mainly in the plant hormone signal transduction and plant-pathogen interaction, and these pathways are linked closely by co-pathway and nodal proteins. The key nodal proteins play important roles in communication among hormonal pathways. Further analysis revealed that all of these key nodal proteins were localized in multiple organelles. High-frequency protein multi-localization analysis suggested that the multi-location proteins may play key roles in organelle communication and plant growth. Thus, the screening and study of multi-location proteins may be an important research direction. Our findings can inform new research based on protein interactions and provide novel directions for plant proteome research.

Purpose: To study the mechanism involved in the anti-cholecystitis effect the Tibetan medicine “Dida”, using network pharmacology-integrated molecular docking simulationsMethods: In this investigation, the bioactive compounds of Dida were collected, network pharmacology methods to predict their targets, and networks were constructed through GO and KEGG pathway analyses. The potential binding between the bioactive compounds and the targets were demonstrated using molecular docking simulations.Results: A total of 12 bioactive compounds and 50 key targets of Dida were identified. Two networks, namely, protein–protein interaction (PPI) network of cholecystitis targets, and compound–target– pathway network, were established. Network analysis showed that 10 targets (GAPDH, AKT1, CASP3, EGFR, TNF, MAPK3, MAPK1, HSP90AA1, STAT3, and BCL2L1) may be the therapeutic targets of Dida in cholecystitis. Analysis of the KEGG pathway indicated that the anti-cholecystitis effect of Dida may its regulation of a few crucial pathways, such as apoptosis, as well as toll-like receptor, T cell receptor, NOD-like receptor, and MAPK signaling pathways. Furthermore, molecular docking simulation revealed that CASP3, CAPDH, HSP90AA1, MAPK3, MAPK1, and STAT3 had well-characterized interactions with the corresponding compounds.Conclusion: The mechanism underlying the anti-cholecystitis effect of Dida has been successfully predicted and verified using a combination of network pharmacology and molecular docking simulation. This provides a firm basis for the experimental verification of the use of Dida in the treatment of cholecystitis, and enhances its rational application in clinical practice. Keywords: Tibetan medicine, Dida, Cholecystitis, Mechanism, Network pharmacology, Molecular docking simulation

In present study, multi-target and multi-pathway mechanisms of Jasminum lanceolarium Roxb (JL) on primary dysmenorrhea (PDM) treatment were predicted by the approach of network pharmacology and molecular docking, leading to the obtaining of 22 predicted targets for 69 compounds in JL. The Compound-Target analysis displayed intimate association among targets and compounds. Meanwhile, the Compound-Target-Target revealed PTGS2, OPRD1 and NOS3 were the key targets with intensive interaction. The Compound-Target-Pathway network indicated these pathways were closely related to hormone regulation, central analgesia, spasmolysis and inflammation. The anti-inflammation pathways might be the key mechanism of JL for the treatment of PDM based on KEGG pathway enrichment analysis and pharmacological experiment, and 10-hydroxyoleoside dimethyl ester might be a promising leading compound due to its good molecular docking scores and previous experimental evaluation.

As an extremely prevalent disease worldwide, allergic rhinitis (AR) is a condition characterized by chronic inflammation of the nasal mucosa. To identify the finer molecular mechanisms associated with the AR susceptibility genes, differentially expressed genes (DEGs) in AR were investigated. The DEG expression and clinical data of the GSE19187 data set were used for weighted gene co-expression network analysis (WGCNA). After the modules related to AR had been screened, the genes in the module were extracted for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, whereby the genes enriched in the KEGG pathway were regarded as the pathway-genes. The DEGs in patients with AR were subsequently screened out from GSE19187, and the sensitive genes were identified in GSE18574 in connection with the allergen challenge. Two kinds of genes were compared with the pathway-genes in order to screen the AR susceptibility genes. Receiver operating characteristic (ROC) curve was plotted to evaluate the capability of the susceptibility genes to distinguish the AR state. Based on the WGCNA in the GSE19187 data set, 10 co-expression network modules were identified. The correlation analyses revealed that the yellow module was positively correlated with the disease state of AR. A total of 89 genes were found to be involved in the enrichment of the yellow module pathway. Four genes (CST1, SH2D1B, DPP4, and SLC5A5) were upregulated in AR and sensitive to allergen challenge, whose potentials were further confirmed by ROC curve. Taken together, CST1, SH2D1B, DPP4, and SLC5A5 are susceptibility genes to AR.

Alzheimer’s disease (AD) is the most common cause of dementia, characterized by progressive cognitive decline and neurodegenerative disorder. Abnormal aggregations of intracellular neurofibrillary tangles (NFTs) and unusual accumulations of extracellular amyloid-β (Aβ) peptides are two important pathological features in AD brains. However, in spite of large-scale clinical studies and computational simulations, the molecular mechanisms of AD development and progression are still unclear. In this study, we divided all of the samples into two groups: early stage (Braak score I–III) and later stage (Braak score IV–VI). By big database mining, the candidate genetic and epigenetic networks (GEN) have been constructed. In order to find out the real GENs for two stages of AD, we performed systems identification and system order detection scheme to prune false positives with the help of corresponding microarray data. Applying the principal network projection (PNP) method, core GENs were extracted from real GENs based on the projection values. By the annotation of KEGG pathway, we could obtain core pathways from core GENs and investigate pathogenetic mechanisms for the early and later stage of AD, respectively. Consequently, according to pathogenetic mechanisms, several potential biomarkers are identified as drug targets for multiple-molecule drug design in the treatment of AD.

Tissue microenvironments are rich in signaling molecules. However, factors in the tissue matrix that can serve as tissue-specific cues for engineering pancreatic tissues have not been thoroughly identified. In this study, we performed a comprehensive proteomic analysis of porcine decellularized pancreatic extracellular matrix (dpECM). By profiling dpECM collected from subjects of different ages and genders, we showed that the detergent-free decellularization method developed in this study permits the preservation of approximately 62.4% more proteins than a detergent-based method. In addition, we demonstrated that dpECM prepared from young pigs contained approximately 68.5% more extracellular matrix proteins than those prepared from adult pigs. Furthermore, we categorized dpECM proteins by biological process, molecular function, and cellular component through gene ontology analysis. Our study results also suggested that the protein composition of dpECM is significantly different between male and female animals while a KEGG enrichment pathway analysis revealed that dpECM protein profiling varies significantly depending on age. This study provides the proteome of pancreatic decellularized ECM in different animal ages and genders, which will help identify the bioactive molecules that are pivotal in creating tissue-specific cues for engineering tissues in vitro.

Noncoding RNAs have been known to contribute to a variety of fundamental life processes, such as development, metabolism, and circadian rhythms. However, much remains unrevealed in the huge noncoding RNA datasets, which require further bioinformatic analysis and experimental investigation—and in particular, the coding potential of lncRNAs and the functions of lncRNA-encoded peptides have not been comprehensively studied to date. Through integrating the time-course experimentation with state-of-the-art computational techniques, we studied tens of thousands of zebrafish lncRNAs from our own experiments and from a published study including time-series transcriptome analyses of the testis and the pineal gland. Rhythmicity analysis of these data revealed approximately 700 rhythmically expressed lncRNAs from the pineal gland and the testis, and their GO, COG, and KEGG pathway functions were analyzed. Comparative and conservative analyses determined 14 rhythmically expressed lncRNAs shared between both the pineal gland and the testis, and 15 pineal gland lncRNAs as well as 3 testis lncRNAs conserved among zebrafish, mice, and humans. Further, we computationally analyzed the conserved lncRNA-encoded peptides, and revealed three pineal gland and one testis lncRNA-encoded peptides conserved among these three species, which were further investigated for their three-dimensional (3D) structures and potential functions. Our computational findings provided novel annotations and regulatory mechanisms for hundreds of rhythmically expressed pineal gland and testis lncRNAs in zebrafish, and set the stage for their experimental studies in the near future.

Objective. The purpose of our research is to systematically explore the multiple mechanisms of Hemerocallis fulva Flowers (HF) on depressive disorder (DD). Methods. The components of HF were searched from the literature. The targets of components were obtained from PharmMapper. After that, Cytoscape software was used to build a component-target network. The targets of DD were collected from DisGeNET, PharmGKB, TTD, and OMIM. Protein-protein interactions (PPIs) among the DD targets were executed to screen the key targets. Afterward, the GO and KEGG pathway enrichment analysis were performed by the KOBAS database. A compound-target-KEGG pathway network was built to analyze the key compounds and targets. Finally, the potential active substances and targets were validated by molecular docking. Results. A total of 55 active compounds in HF, 646 compound-related targets, and 527 DD-related targets were identified from public databases. After treated with PPI, 219 key targets of DD were acquired. The gene enrichment analysis suggested that HF probably benefits DD patients by modulating pathways related to the nervous system, endocrine system, amino acid metabolism, and signal transduction. The network analysis showed the critical components and targets of HF on DD. Results of molecular docking increased the reliability of this study. Conclusions. It predicted and verified the pharmacological and molecular mechanism of HF against DD from a holistic perspective, which will also lay a foundation for further experimental research and rational clinical application of DD.

Background Rotator cuff tears are one of the most frequent upper extremity injuries and lead to pain and disability. Recent studies have implicated fatty infiltration in rotator cuff is a key failure element with the higher re-tear rates and poorer functional prognosis. Therefore, we investigated the differential expression of key genes in each stage of rotator cuff tear. Methods A published expression profile was downloaded from the Gene Expression Omnibus database and analyzed using the Linear Models for Microarray Data (LIMMA) package in R language to identify differentially expressed genes (DEGs) in different stages of injured rotator cuff muscles. Gene ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the function of the DEGs. Finally, PPI network and module analysis were used to identify hub genes. Results A total of 1089 fatty infiltration-related DEGs were identified, including 733 upregulated and 356 downregulated genes, and GO analyses confirmed that fatty infiltration was strongly associated with inflammatory response, aging, response to lipopolysaccharide, and immune response. Significantly enriched KEGG pathways associated with these DEGs included the phagosome, cell adhesion molecules, tuberculosis, and osteoclast differentiation. Further analyses via a PPI network and module analysis identified a total of 259 hub genes. Among these, Tmprss11d, Ptprc, Itgam, Mmp9, Tlr2, Il1b, Il18, Ccl5, Cxcl10, and Ccr7 were the top ten hub genes. Conclusions Our findings indicated the potential key genes and pathways involved in fatty degeneration in the development of fatty infiltration and supplied underlying therapeutic targets in the future. Electronic supplementary material The online version of this article (10.1186/s13018-019-1182-1) contains supplementary material, which is available to authorized users.

Earthworms are useful indicator organisms of soil health and Eisenia fetida have been extensively used as test organisms in ecotoxicological studies. In order to gain insight into the gene expression profiles associated with physiological functions of earthworms, a full‑length enriched cDNA library of the Eisenia fetida genome was successfully constructed using Switching Mechanism at 5'End of RNA Template technology. Construction of a cDNA library and analysis of Expressed Sequence Tags (ESTs) are efficient approaches for collecting genomic information and identifying genes important for a given biological process. Furthermore, analysis of the expression abundance of ESTs was performed with the aim of providing genetic and transcriptomic information on the development and regenerative process of earthworms. Phrep and Crossmatch were used to process EST data and a total of 1,140 high‑quality EST sequences were determined by sequencing random cDNA clones from the library. Clustering analysis of sequences revealed a total of 593 unique sequences including 225 contiguous and 368 singleton sequences. Basic Local Alignment Search Tool analysis against the Kyoto Encyclopedia of Genes and Genomes database resulted in 593 significant hits (P‑value

Background: The differentially expressed proteins (DEPs) involved in the effect of hydrogen-rich water on myocardial ischemia reperfusion injury (MIRI) and their biological processes and signaling pathway were analyzed. Methods: 20 Wistar rats were randomly and equally divided into a control and a hydrogen-rich group. Hearts were removed and fixed in a Langendorff device. The control group was perfused with K-R solution, and the hydrogen-rich water group was perfused with K-R solution + hydrogen-rich water. Protein was extracted from the ventricular tissues, and GSR-CAA-67 was used to identify the DEPs between two groups. DEPs were analyzed through bioinformatic methods. Results: Compared with the control group, in the treatment group, the expression of 25 proteins was obviously decreased (P

PurposeThis study aimed to investigate the profiles of messenger RNAs (mRNAs) and long noncoding RNAs (lncRNAs) in peripheral blood samples collected from polycystic ovary syndrome (PCOS) patients. In addition, an in-depth bioinformatics analysis regarding the lncRNA-mRNA co-expression network was performed.MethodsHigh-throughput sequencing was used to measure the profiles of mRNAs and lncRNAs expressed in the peripheral blood samples isolated from six patients (three patients with PCOS and three normal women). In addition, five differentially expressed lncRNAs were chosen to validate the results of high-throughput sequencing by quantitative RT-PCR (qRT-PCR). Furthermore, a lncRNA-mRNA co-expression network was constructed using the Cytoscape software.ResultsA total of 14,276 differentially expressed mRNAs and 4,048 differentially expressed lncRNAs were obtained from the RNA-seq analysis of PCOS patients and healthy controls (adjusted q-value < 0.05, Fold change >2.0).The qRT-PCR results were consistent with the data obtained through high-throughput sequencing. Gene ontology (GO) and KEGG pathway analyses showed that the differentially expressed mRNAs were enriched in the chemokine signaling pathway. In addition, the analysis of the lncRNA-mRNA co-expression network of the chemokine signaling pathway showed the involvement of 6 mRNAs and 42 lncRNAs.ConclusionClusters of mRNAs and lncRNAs were aberrantly expressed in the peripheral blood of PCOS patients compared with the controls. In addition, several pairs of lncRNA-mRNAs in the chemokine signaling pathway may be related to PCOS genetically.

Background Shenqisherong pill (SQSRP) has been used clinically to treat cervical spondylotic myelopathy (CSM) with satisfactory results; however, its active ingredients and mechanisms are unclear. The present study aimed to explore the active ingredients and molecular mechanisms of SQSRP against CSM using network pharmacology and molecular docking. Methods The compounds in SQSRP were obtained from public databases and related literature, and oral bioavailability (≥30%) and drug-likeness (≥0.18) were screened using absorption, distribution, metabolism, and excretion (ADME) criteria. Compounds-related and CSM-related target genes were identified using public databases, and the overlapping genes between compounds and CSM target genes were identified using a Venn diagram. Cytoscape and STRING were used to construct, visualize, and analyze the interaction network between these overlapping targets. Gene Ontology (GO) and KEGG pathway enrichment analysis of overlapping targets used Omicshare tools and constructed a compound-overlapping targets network, target-pathway network, and compound-target-pathway network using Cytoscape. Finally, molecular docking software was used to verify the targets. Results A total of 447 compounds in SQSRP were identified, and ADME screening identified 96 compounds as potentially active ingredients. A total of 249 compound-related genes and 280 CSMrelated genes were identified using public databases, and 53 overlapping genes were identified. The results of compound targets and protein-protein interaction network analysis showed that the pharmacological effects of SQSRP against CSM involved 56 compounds and 53 genes. The results of GO and KEGG pathway enrichment analysis suggested that the therapeutic effects of SQSRP against CSM were exerted by reducing inflammation, inhibiting apoptosis, and protecting neurons. The molecular mechanisms may be strongly associated with PI3K-Akt, MAPK, IL-17, and TNF, which might be pivotal signaling pathways. Conclusions The active ingredients and mechanisms of SQSRP against CSM were investigated using network pharmacology. The findings proved that the pill could treat CSM through multi-component, multitarget, and multi-pathway synergy and provide a theoretical basis for the subsequent extraction of active ingredients from SQSRP.

As one of the most important poultry worldwide, ducks (Anas platyrhynchos) are raised mainly for meat and egg products, and muscle development in ducks is important for meat production. Therefore, an investigation of gene expression in duck skeletal muscle would significantly contribute to our understanding of muscle development. In this study, twenty-four cDNA libraries were constructed from breast and leg muscles of Hanzhong Ma ducks at day 17, 21, 27 of the embryo and postnatal at 6-month-old. High-throughput sequencing and bioinformatics were used to determine the abundances and characteristics of transcripts. A total of 632,172,628 (average 52,681,052) and 637,213,938 (average 53,101,162) reads were obtained from the sequencing data of breast and leg muscles, respectively. Over 71.63% and 77.36% of the reads could be mapped to the Anas platyrhynchos genome. In the skeletal muscle of Hanzhong duck, intron variant (INTRON), synonymous variant (SYNONYMOUS_CODING), and prime 3′ UTR variant (UTR_3_PRIME) were the main single nucleotide polymorphisms (SNP) annotation information, and “INTRON”, “UTR_3_PRIME”, and downstream-gene variant (DOWNSTREAM) were the main insertion-deletion (InDel) annotation information. The predicted number of alternative splicing (AS) in all samples were mainly alternative 5′ first exon (transcription start site)-the first exon splicing (TSS) and alternative 3′ last exon (transcription terminal site)-the last exon splicing (TTS). Besides, there were 292 to 2801 annotated differentially expressed genes (DEGs) in breast muscle and 304 to 1950 annotated DEGs in leg muscle from different databases. It is worth noting that 75 DEGs in breast muscle and 49 DEGs in leg muscle were co-expressed at all developmental points of comparison, respectively. The RNA-Seq data were confirmed to be reliable by qPCR. The identified DEGs, such as CREBL2, RHEB, GDF6, SHISA2, MYLK2, ACTN3, RYR3, and STMN1, were specially highlighted, indicating their strong associations with muscle development in the Hanzhong Ma duck. KEGG pathway analysis suggested that regulation of actin cytoskeleton, oxidative phosphorylation, and focal adhesion were involved in the development of skeletal muscle. The findings from this study can contribute to future investigations of the growth and development mechanism in duck skeletal muscle.

The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.

The aim of this study was to generate and analyze the atlas of the loggerhead turtle blood transcriptome by RNA-seq, as well as identify and characterize thioredoxin (Tnxs) and peroxiredoxin (Prdxs) antioxidant enzymes of the greatest interest in the control of peroxide levels and other biological functions. The transcriptome of loggerhead turtle was sequenced using the Illumina Hiseq 2000 platform and de novo assembly was performed using the Trinity pipeline. The assembly comprised 515,597 contigs with an N50 of 2,631 bp. Contigs were analyzed with CD-Hit obtaining 374,545 unigenes, of which 165,676 had ORFs encoding putative proteins longer than 100 amino acids. A total of 52,147 (31.5%) of these transcripts had significant homology matches in at least one of the five databases used. From the enrichment of GO terms, 180 proteins with antioxidant activity were identified, among these 28 Prdxs and 50 putative Tnxs. The putative proteins of loggerhead turtles encoded by the genes Prdx1, Prdx3, Prdx5, Prdx6, Txn and Txnip were predicted and characterized in silico. When comparing Prdxs and Txns of loggerhead turtle with homologous human proteins, they showed 18 (9%), 52 (18%) 94 (43%), 36 (16%), 35 (33%) and 74 (19%) amino acid mutations respectively. However, they showed high conservation in active sites and structural motifs (98%), with few specific modifications. Of these, Prdx1, Prdx3, Prdx5, Prdx6, Txn and Txnip presented 0, 25, 18, three, six and two deleterious changes. This study provides a high quality blood transcriptome and functional annotation of loggerhead sea turtles.

Genome-wide analysis of miRNA molecules can reveal important information for understanding the biology of cancer. Typically, miRNAs are used as features in statistical learning methods in order to train learning models to predict cancer. This motivates us to propose a method that integrates clustering and classification techniques for diverse cancer types with survival analysis via regression to identify miRNAs that can potentially play a crucial role in the prediction of different types of tumors. Our method has two parts. The first part is a feature selection procedure, called the stochastic covariance evolutionary strategy with forward selection (SCES-FS), which is developed by integrating stochastic neighbor embedding (SNE), the covariance matrix adaptation evolutionary strategy (CMA-ES), and classifiers, with the primary objective of selecting biomarkers. SNE is used to reorder the features by performing an implicit clustering with highly correlated neighboring features. A subset of features is selected heuristically to perform multi-class classification for diverse cancer types. In the second part of our method, the most important features identified in the first part are used to perform survival analysis via Cox regression, primarily to examine the effectiveness of the selected features. For this purpose, we have analyzed next generation sequencing data from The Cancer Genome Atlas in form of miRNA expression of 1,707 samples of 10 different cancer types and 333 normal samples. The SCES-FS method is compared with well-known feature selection methods and it is found to perform better in multi-class classification for the 17 selected miRNAs, achieving an accuracy of 96%. Moreover, the biological significance of the selected miRNAs is demonstrated with the help of network analysis, expression analysis using hierarchical clustering, KEGG pathway analysis, GO enrichment analysis, and protein-protein interaction analysis. Overall, the results indicate that the 17 selected miRNAs are associated with many key cancer regulators, such as MYC, VEGFA, AKT1, CDKN1A, RHOA, and PTEN, through their targets. Therefore the selected miRNAs can be regarded as putative biomarkers for 10 types of cancer.

Background Due to the fact that pulmonary tuberculosis (PTB) is a highly infectious respiratory disease characterized by high herd susceptibility and hard to be treated, this study aimed to search novel effective biomarkers to improve the prognosis and treatment of PTB patients. Methods Firstly, bioinformatics analysis was performed to identify PTB-related differentially expressed genes (DEGs) from GEO database, which were then subjected to GO annotation and KEGG pathway enrichment analysis to initially describe their functions. Afterwards, clustering analysis was conducted to identify PTB-related gene clusters and relevant PPI networks were established using the STRING database. Results Based on the further differential and clustering analyses, 10 DEGs decreased during PTB development were identified and considered as candidate hub genes. Besides, we retrospectively analyzed some relevant studies and found that 7 genes (CCL20, PTGS2, ICAM1, TIMP1, MMP9, CXCL8 and IL6) presented an intimate correlation with PTB development and had the potential serving as biomarkers. Conclusions Overall, this study provides a theoretical basis for research on novel biomarkers of PTB, and helps to estimate PTB prognosis as well as probe into targeted molecular treatment.

Background. Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF. Methods. The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the “SZF-key targets-DKD” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools. Results. We obtained 182 potential targets and 30 key targets. Furthermore, a “SZF-key targets-DKD” network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list. Conclusion. This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the biological knowledge about the data in the process of gene selection and machine learning. Integrative gene selection enables incorporation of biological domain knowledge from external biological resources. In this study, we propose a new computational approach named CogNet that is an integrative gene selection tool that exploits biological knowledge for grouping the genes for the computational modeling tasks of ranking and classification. In CogNet, the pathfindR serves as the biological grouping tool to allow the main algorithm to rank active-subnetwork-oriented KEGG pathway enrichment analysis results to build a biologically relevant model. CogNet provides a list of significant KEGG pathways that can classify the data with a very high accuracy. The list also provides the genes belonging to these pathways that are differentially expressed that are used as features in the classification problem. The list facilitates deep analysis and better interpretability of the role of KEGG pathways in classification of the data thus better establishing the biological relevance of these differentially expressed genes. Even though the main aim of our study is not to improve the accuracy of any existing tool, the performance of the CogNet outperforms a similar approach called maTE while obtaining similar performance compared to other similar tools including SVM-RCE. CogNet was tested on 13 gene expression datasets concerning a variety of diseases.

Using an allergic rhinitis (AR) model, we evaluated the pharmacological effects of novel peptide drugs (P-ONE and P-TWO) at the small RNA (sRNA) level. Using high-throughput sequencing, we assessed the sRNA expression profile of the negative control, AR antagonist (positive control), P-ONE, and P-TWO groups. By functional clustering and Gene Ontology and KEGG pathway analyses, we found that sRNA target genes have a specific enrichment pattern and may contribute to the effects of the novel peptides. Small RNA sequencing confirmed the biological foundations of novel and traditional AR treatments and suggested unique pharmacological effects. Our findings will facilitate evaluation of the pathogenesis of AR and of the pharmacological mechanisms of novel peptide drugs.

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.

Cancer has been a major public health problem worldwide for many centuries. Cancer is a complex disease associated with accumulative genetic mutations, epigenetic aberrations, chromosomal instability, and expression alteration. Increasing lines of evidence suggest that many non-coding transcripts, which are termed as non-coding RNAs, have important regulatory roles in cancer. In particular, long non-coding RNAs (lncRNAs) play crucial roles in tumorigenesis. Cancer-related lncRNAs serve as oncogenic factors or tumor suppressors. Although many lncRNAs are identified as potential regulators in tumorigenesis by using traditional experimental methods, they are time consuming and expensive considering the tremendous amount of lncRNAs needed. Thus, effective and fast approaches to recognize tumor-related lncRNAs should be developed. The proposed approach should help us understand not only the mechanisms of lncRNAs that participate in tumorigenesis but also their satisfactory performance in distinguishing cancer-related lncRNAs. In this study, we utilized a decision tree (DT), a type of rule learning algorithm, to investigate cancer-related lncRNAs with functional annotation contents [gene ontology (GO) terms and KEGG pathways] of their co-expressed genes. Cancer-related and other lncRNAs encoded by the key enrichment features of GO and KEGG filtered by feature selection methods were used to build an informative DT, which further induced several decision rules. The rules provided not only a new tool for identifying cancer-related lncRNAs but also connected the lncRNAs and cancers with the combinations of GO terms. Results provided new directions for understanding cancer-related lncRNAs.

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.

This research was carried out to reveal specific hub genes involved in diabetic heart failure, as well as remarkable pathways that hub genes locate. The GSE26887 dataset from the GEO website was downloaded. The gene co-expression network was generated and central modules were analyzed to identify key genes using the WGCNA method. Functional analyses were conducted on genes of the clinical interest modules via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment, associated with protein–protein interaction (PPI) network construction in a sequence. Centrality parameters of the PPI network were determined using the CentiScape plugin in Cytoscape. Key genes, defined as genes in the ≥95% percentile of the degree distribution of significantly perturbed networks, were identified. Twenty gene co-expression modules were detected by WGCNA analysis. The module marked in light yellow exhibited the most significant association with diabetes (P=0.08). Genes involved in this module were primarily located in immune response, plasma membrane and receptor binding, as shown by the GO analysis. These genes were primarily assembled in endocytosis and phagosomes for KEGG pathway enrichment. Three key genes, STK39, HLA-DPB1 and RAB5C, which may be key genes for diabetic heart failure, were identified. To our knowledge, our study is the first to have constructed the co-expression network involved in diabetic heart failure using the WGCNA method. The results of the present study have provided better understanding the molecular mechanism of diabetic heart failure.

Long non-coding RNAs (lncRNAs) emerge as critical regulators with various biological functions in living organisms. However, to date, no systematic characterization of lncRNAs has been investigated in the ectoparasitic mite Varroa destructor, the most severe biotic threat to honey bees worldwide. Here, we performed an initial genome-wide identification of lncRNAs in V. destructor via high-throughput sequencing technology and reported, for the first time, the transcriptomic landscape of lncRNAs in the devastating parasite. By means of a lncRNA identification pipeline, 6,645 novel lncRNA transcripts, encoded by 3,897 gene loci, were identified, including 2,066 sense lncRNAs, 2,772 lincRNAs, and 1,807 lncNATs. Compared with protein-coding mRNAs, V. destructor lncRNAs are shorter in terms of full length, as well as of the ORF length, contain less exons, and express at lower level. GO term and KEGG pathway enrichment analyses of the lncRNA target genes demonstrated that these predicted lncRNAs are likely to play key roles in cellular processes, genetic information processing and environmental responses. To our knowledge, this is the first catalog of lncRNA profile in the parasitiformes species, providing a valuable resource for genetic and genomic studies. Understanding the characteristics and features of lncRNAs in V. destructor would promote sustainable pest control.

Small intestinal neuroendocrine tumors (SI-NETs) remain the most common subset in gastrointestinal neuroendocrine tumors and featured by aggressiveness. However, the molecular feature of SI-NETs remains largely unclear with key genes and pathways yet to be identified. The gene expression profile GSE65286 was retrieved for analysis. Artificial neural networks (ANNs) were constructed for the hub genes network models. A total of 613 differentially expressed genes (DEGs) were identified between normal (N) and primary tumor (T) groups, whereas 61 DEGs were identified between T and liver metastases (LM) groups. The top Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for the DEGs of N versus T were fat digestion and absorption pathway. For T versus LM the top KEGG pathways were complement and coagulation. In gene set enrichment analysis (GSEA), five gene sets, including Notch signaling, inflammatory response, coagulation, KRAS signaling, and allograft rejection were significantly enriched in the T group. The hub genes in the DEGs of T versus LM included albumin, fibrinogen gamma chain, alpha 2-HS glycoprotein, transferrin and GC, vitamin D binding protein. A distinct correlational alteration of hub genes was observed between T and LM groups. In ANN analysis, ALB and TF were the top predictors of metastasis. Moreover, the expression of ALB≤ showed the highest support to T whereas ALB>15.97 supports LM. TF≤7.54 showed the highest negative correlation to the T. This bioinformatics analysis provided insights on potential key pathways and genes networks involved in SI-NETs and established an ANN-based hub gene model for metastatic prediction.

Jia-Wei-Qi-Fu-Yin (JWQFY) is a newly developed anti-Alzheimer's disease (AD) prescription modified from a classical traditional Chinese medicine formula, Qi-Fu-Yin (QFY). However, a systematic understanding of its chemical constituents and molecular mechanisms is still elusive. To address this problem, comprehensive chemical profiling followed by network pharmacology-based analysis of JWQFY was performed. Firstly, a total of 136 compounds were characterized by high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (HPLC-QTOF MS), 17 of them were specifically identified in JWQFY comparing with QFY. Seventy compounds were further quantified via a validated HPLC coupled with triple quadrupole tandem mass spectrometry (QQQ MS) method. Then the protein targets of the seventy compounds were gathered from public databases for network construction. As a result, fifty-seven compounds were filtered, which interacted with 655 targets. Thirty-four of them were mapped into the KEGG pathway of AD, indicating JWQFY might exert anti-AD effects by anti-inflammation, neuronal apoptosis intervening, Aβ production inhibition and phosphorylating tau protein moderating. Furthermore, in the compound-target-AD network, a list of hub compounds and hub targets was identified based on their topological features, including the degree, node betweenness and closeness. Four of the hub compounds were specifically originated from JWQFY, supporting the modification rationality of this formula. This study provided a scientific basis for understanding the bioactive compounds and the multi-target mechanism of JWQFY.

Although osteoporosis is one of the most common chronic age-related diseases, there is currently no gold standard for treatment. Evidence suggests resveratrol, a natural polyphenolic compound, may be helpful in the treatment of osteoporosis and other diseases. However, the molecular mechanisms underlying the anti-osteoporotic effects of resveratrol remain largely unknown. In the present study, KEGG pathway enrichment analysis of resveratrol-targeted genes identified 33 associated pathways, 12 of which were also involved in osteoporosis. In particular, the MDM2/p53 signaling pathway was identified as a potential key pathway among the shared pathways. In vitro experiments indicated that MDM2-mediated p53 degradation induced osteoblast differentiation, and resveratrol could partially reverse p53-dependent inhibition of osteogenic differentiation. These findings suggest resveratrol may alleviate osteoporosis at least in part by modulating the MDM2/p53 signaling pathway.

LncRNAs plays an important role in the regulation of gene expression. Identification of cancer-related lncRNAs GO terms and KEGG pathways is great helpful for revealing cancer-related functional biological processes. Therefore, in this study, we proposed a computational method to identify novel cancer-related lncRNAs GO terms and KEGG pathways. By using existing lncRNA database and Max-relevance Min-redundancy (mRMR) method, GO terms and KEGG pathways were evaluated based on their importance on distinguishing cancer-related and non-cancer-related lncRNAs. Finally, GO terms and KEGG pathways with high importance were presented and analyzed. Our literature reviewing showed that the top 10 ranked GO terms and pathways were really related to interpretable tumorigenesis according to recent publications.

Background Danhong injection (DHI), which is one of the most well-known Traditional Chinese Medicine (TCM) injections, widely used to treat unstable angina (UA). However, its underlying pharmacological mechanisms need to be further clarified. Methods In the present study, network pharmacology was adopted. Firstly, the relative compounds were obtained by a wide-scaled literatures-mining and potential targets of these compounds by target fishing were collected. Then, we built the UA target database by DisGeNET, DigSee, TTD, OMIM. Based on data, protein-protein interaction (PPI) analysis, GO and KEGG pathway enrichment analysis were performed and screen the hub targets by topology. Furthermore, evaluation of the binding potential of key targets and compounds through molecular docking. Results The results showed that 12 ingredients of DHI and 27 putative known therapeutic targets were picked out. By systematic analysis, identified 4 hub targets (TNF, TLR4, NFKB1 and SERPINE1) mainly involved in the complex treating effects associated with coagulation and hemostasis, cell membrane region, platelet alpha granule, NF-kappa B signaling pathway and TNF signaling pathway. Conclusion The results of this study preliminarily explained the potential targets and signaling pathways of DHI in the treatment of UA, which may help to laid a good foundation for experimental research and further clinical application.

Cancer is one of the serious disease that causes several human deaths every year. Up to now, we have spent lots of time and money to investigate this disease, thereby designing effective treatments. Previous studies mainly focus on studying genetic background of different subtypes of cancer and neglect another important factor, environmental factor. Carcinogenic chemical is one of the type of environmental factor, exposure of such chemical may definitely initiate and promote the tumorigenesis. In this study, we tried to partly describe the differences between carcinogenic and non-carcinogenic chemicals using gene ontology (GO) terms and KEGG pathways. The carcinogenic and non-carcinogenic chemicals that were retrieved from Carcinogenic Potency Database (CPDB) were encoded into numeric vectors using the enrichment theories of GO terms and KEGG pathways. Then, the minimal redundancy maximal relevance (mRMR) method was adopted to analyze all features, resulting in some important GO terms and KEGG pathways. The extensive analysis of the identified GO terms and KEGG pathways indicate that they all play roles during the tumorigenesis, inducing that they can be key indicator for identification of carcinogenic chemicals.

Protein-protein interactions (PPIs) describe the direct physical contact of two proteins that usually results in specific biological functions or regulatory processes. The characterization and study of PPIs through the investigation of their pattern and principle have remained a question in biological studies. Various experimental and computational methods have been used for PPI studies, but most of them are based on the sequence similarity with current validated PPI participators or cellular localization patterns. Most methods ignore the fact that PPIs are defined by their specific biological functions. In this study, we constructed a novel rule-based computational method using gene ontology and KEGG pathway annotation of PPI participators that correspond to the complicated biological effects of PPIs. Our newly presented computational method identified a group of biological functions that are tightly associated with PPIs and provided a new function-based tool for PPI studies in a rule manner.

Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information. Availability : http://biohealth.snu.ac.kr/software/MIDAS/

Carthamus tinctorius L. is widely used in traditional Chinese medicines for the treatment of cardiovascular disease. However, our current understanding of the molecular mechanisms supporting its clinical application still lags behind. In this study, a systems pharmacology approach integrating drug-likeness evaluation, oral bioavailability prediction, target exploration, GO enrichment analysis, KEGG pathway performance and network construction was adopted to explore its therapeutic mechanism. A total of 21 active ingredients contained in Carthamus tinctorius L. and 113 major proteins were screened out as effective players in the treatment of cardiovascular disease through some related pathways. And the association among the active ingredients, major hubs and main pathways was investigated, implying the potential biological progression of Carthamus tinctorius L. acting on cardiovascular disease. Importantly, the majority of hubs and pathways were found to be highly related with platelet activation process. Core genes that can be regulated by Carthamus tinctorius L. in platelet activation pathway were PRKACA, PIK3R1, MAPK1, PPP1CC, PIK3CA and SYK, and they may play a central role in suppressing platelet aggregation. The systems pharmacology approach used in this study may provide a feasible tool to clarify the mechanism of traditional Chinese medicines and further develop their therapeutic potentials.

Background Cleft palate (CP) is the second most common congenital birth defect; however, the relationship between CP-associated genes and epigenetic regulation remains largely unknown. In this study, we investigated the contribution of microRNAs (miRNAs) to cell proliferation and regulation of genes involved in CP development. Methods In order to identify all genes for which mutations or association/linkage have been found in individuals with CP, we conducted a systematic literature search, followed by bioinformatics analyses for these genes. We validated the bioinformatics results experimentally by conducting cell proliferation assays and miRNA-gene regulatory analyses in cultured human palatal mesenchymal cells treated with each miRNA mimic. Results We identified 131 CP-associated genes in the systematic review. The bioinformatics analysis indicated that the CP genes were associated with signaling pathways, microRNAs (miRNAs), metabolic pathways, and cell proliferation. A total 17 miRNAs were recognized as potential modifiers of human CP genes. To validate miRNA function in cell proliferation, a main cause of CP, we conducted cell proliferation/viability assays for the top 11 candidate miRNAs from our bioinformatics analysis. Overexpression of miR-133b, miR-374a-5p, and miR-4680-3p resulted in a more than 30% reduction in cell proliferation activity in human palatal mesenchymal cell cultures. We found that several downstream target CP genes predicted by the bioinformatics analyses were significantly downregulated through induction of these miRNAs (FGFR1, GCH1, PAX7, SMC2, and SUMO1 by miR-133b; ARNT, BMP2, CRISPLD1, FGFR2, JARID2, MSX1, NOG, RHPN2, RUNX2, WNT5A and ZNF236 by miR-374a-5p; and ERBB2, JADE1, MTHFD1 and WNT5A by miR-4680-3p) in cultured cells. Conclusions Our results indicate that miR-374a-5p, miR-4680-3p, and miR-133b regulate expression of genes that are involved in the etiology of human CP, providing insight into the association between CP-associated genes and potential targets of miRNAs in palate development. Electronic supplementary material The online version of this article (10.1186/s12920-019-0546-z) contains supplementary material, which is available to authorized users.

KEGG is a reference knowledge base for biological interpretation of large-scale molecular datasets, such as genome and metagenome sequences. It accumulates experimental knowledge about high-level functions of the cell and the organism represented in terms of KEGG molecular networks, including KEGG pathway maps, BRITE hierarchies, and KEGG modules. By the process called KEGG mapping, a set of protein coding genes in the genome, for example, can be converted to KEGG molecular networks enabling interpretation of cellular functions and other high-level features. Here we report a new version of KEGG Mapper, a suite of KEGG mapping tools available at the KEGG website (https://www.kegg.jp/ or https://www.genome.jp/kegg/), together with the KOALA family tools for automatic assignment of KO (KEGG Orthology) identifiers used in the mapping.

MotivationReinforcement of HMMER search for secondary metabolism-specific Pfam domains should contribute to discover novel biosynthetic machinery of clinically important natural products.ResultsHere we provide a Python-based command line tool, named as KPHMMER, to extract the Pfam domains that are specific in the user-defined set of pathways in the user-defined set of organisms registered in the KEGG database. KPHMMER outperformed the previous study in detecting secondary metabolism-specific Pfam domain set. Furthermore, it was proven that KPHMMER helps reduce the computational cost compared with the case using the whole Pfam-A HMM file. We believe that KPHMMER is a powerful tool enabling to deal with many other genome-sequenced species for more general purpose.AvailabilityKPHMMER is implemented as a Python package freely available via the package management system “pip” and also at https://github.com/suecharo/KPHMMERContactmaskot@chemsys.t.u-tokyo.ac.jp

Background Osteoarthritis (OA) is one of the most important age-related degenerative diseases, and the leading cause of disability and chronic pain in the aging population. Recent studies have identified several lncRNA-associated functions involved in the development of OA. Because age is a key risk factor for OA, we investigated the differential expression of age-related lncRNAs in each stage of OA. Methods Two gene expression profiles were downloaded from the GEO database and differentially expressed genes (DEGs) were identified across each of the different developmental stages of OA. Next, gene ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the function of the DEGs. Finally, a lncRNA-targeted DEG network was used to identify hub-lncRNAs. Results A total of 174 age-related DEGs were identified. GO analyses confirmed that age-related degradation was strongly associated with cell adhesion, endodermal cell differentiation and collagen fibril organization. Significantly enriched KEGG pathways associated with these DEGs included the PI3K–Akt signaling pathway, focal adhesion, and ECM–receptor interaction. Further analyses via a protein–protein interaction (PPI) network identified two hub lncRNAs, CRNDE and LINC00152, involved in the process of age-related degeneration of articular cartilage. Our findings suggest that lncRNAs may play active roles in the development of OA. Investigation of the gene expression profiles in different development stages may supply a new target for OA treatment.

Background Major depressive disorder (MDD) is a serious mental health problem in modern society, which is difficult to identify and diagnose in the early stages. Despite strong evidence supporting the heritability of MDD, progresses in large‐scale and individual genetic studies remain preliminary. Methods In this study, a multi‐data source‐based prioritization (MDSP) method was proposed, and an appropriate threshold was determined for the optimization of depression‐related genes (DEPgenes). Analyses on Gene Ontology biological processes, KEGG pathway and the specific pathway crosstalk network were further proposed. Results A total of 143 DEPgenes were identified and the MDD‐specific network was constructed for the pathogenesis investigation and therapeutic methods development of MDD. Comparing with existing research strategies, the genetic optimization and analysis results were confirmed to be reliable. Finally, the pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. The related core pathways of neuroactive ligand‐receptor interaction and dopaminergic synapse supported the neuropathology hypothesis of MDD. And the pathways of serotonergic synapse and morphine addiction indicated the mechanism of drug addiction caused by serotonin used in the treatment. Conclusions This work provided a reference for the study of MDD, although future validation by extensive experimentation is still required.