DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates

Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).

Downregulated LRRK2 gene expression inhibits proliferation and migration while promoting the apoptosis of thyroid cancer cells by inhibiting activation of the JNK signaling pathway

Emerging studies have indicated that leucine-rich repeat kinase 2 (LRRK2) is associated with thyroid cancer (TC). The present study investigated the effect of LRRK2 on the cell cycle and apoptosis in TC, and examined the underlying mechanisms in vitro. To screen TC-associated differentially expressed genes, gene expression microarray analysis was conducted. Retrieval of pathways associated with TC from the Kyoto Encyclopedia of Genes and Genomes database indicated that the c-Jun N-terminal kinase (JNK) signaling pathway serves an essential role in TC. SW579, IHH-4, TFC-133, TPC-1 and Nthy-ori3-1 cell lines were used to screen cell lines with the highest and lowest LRRK2 expression for subsequent experiments. The two selected cell lines were transfected with pcDNA-LRRK2, or small interfering RNA against LRRK2 or SP600125 (a JNK inhibitor). Subsequently, flow cytometry, terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling, a 5-ethynyl-2′-deoxyuridine assay and a scratch test was conducted to detect the cell cycle distribution, apoptosis, proliferation and migration, respectively, in each group. The LRRK2 gene was determined to be elevated in TC based on the microarray data of the GSE3678 dataset. The SW579 cell line was identified to exhibit the highest LRRK2 expression, while IHH-4 cells exhibited the lowest LRRK2 expression. LRRK2 silencing, through inhibiting the activation of the JNK signaling pathway, increased the expression levels of genes and proteins associated with cell cycle arrest and apoptosis in TC cells, promoted cell cycle arrest and apoptosis, and inhibited cell migration and proliferation in TC cells, indicating that LRRK2 repression could exert beneficial effects through the JNK signaling pathway on TC cells. These observations demonstrate that LRRK2 silencing promotes TC cell growth inhibition, and facilitates apoptosis and cell cycle arrest. The JNK signaling pathway may serve a crucial role in mediating the anti-carcinogenic activities of downregulated LRRK2 in TC.

Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

Background

Automatic recognition of medical concepts in unstructured text is an important component of many clinical and research applications, and its accuracy has a large impact on electronic health record analysis. The mining of medical concepts is complicated by the broad use of synonyms and nonstandard terms in medical documents.

Objective

We present a machine learning model for concept recognition in large unstructured text, which optimizes the use of ontological structures and can identify previously unobserved synonyms for concepts in the ontology.

Methods

We present a neural dictionary model that can be used to predict if a phrase is synonymous to a concept in a reference ontology. Our model, called the Neural Concept Recognizer (NCR), uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space. It uses the hierarchical structure provided by the biomedical ontology as an implicit prior embedding to better learn embedding of various terms. We trained our model on two biomedical ontologies—the Human Phenotype Ontology (HPO) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT).

Results

We tested our model trained on HPO by using two different data sets: 288 annotated PubMed abstracts and 39 clinical reports. We achieved 1.7%-3% higher F1-scores than those for our strongest manually engineered rule-based baselines (P=.003). We also tested our model trained on the SNOMED-CT by using 2000 Intensive Care Unit discharge summaries from MIMIC (Multiparameter Intelligent Monitoring in Intensive Care) and achieved 0.9%-1.3% higher F1-scores than those of our baseline. The results of our experiments show high accuracy of our model as well as the value of using the taxonomy structure of the ontology in concept recognition.

Conclusion

Most popular medical concept recognizers rely on rule-based models, which cannot generalize well to unseen synonyms. In addition, most machine learning methods typically require large corpora of annotated text that cover all classes of concepts, which can be extremely difficult to obtain for biomedical ontologies. Without relying on large-scale labeled training data or requiring any custom training, our model can be efficiently generalized to new synonyms and performs as well or better than state-of-the-art methods custom built for specific ontologies.

Characterization of the molecular changes associated with the overexpression of a novel epithelial cadherin splice variant mRNA in a breast cancer model using proteomics and bioinformatics approaches: identification of changes in cell metabolism and an increased expression of lactate dehydrogenase B

Background

Breast cancer (BC) is the most common female cancer and the leading cause of cancer death in women worldwide. Alterations in epithelial cadherin (E-cadherin) expression and functions are associated to BC, but the underlying molecular mechanisms have not been fully elucidated. We have previously reported a novel human E-cadherin splice variant (E-cadherin variant) mRNA. Stable transfectants in MCF-7 human BC cells (MCF7Ecadvar) depicted fibroblast-like cell morphology, E-cadherin wild-type downregulation, and other molecular changes characteristic of the epithelial-to-mesenchymal transition process, reduced cell-cell adhesion, and increased cell migration and invasion. In this study, a two-dimensional differential gel electrophoresis (2D-DIGE) combined with mass spectrometry (MS) protein identification and bioinformatics analyses were done to characterize biological processes and canonical pathways affected by E-cadherin variant expression.

Results

By 2D-DIGE and MS analysis, 50 proteins were found differentially expressed (≥ Δ1.5) in MCF7Ecadvar compared to control cells. Validation of transcript expression was done in the ten most overexpressed and underexpressed proteins. Bioinformatics analyses revealed that 39 of the 50 proteins identified had been previously associated to BC. Moreover, metabolic processes were the most affected, and glycolysis the canonical pathway most altered. The lactate dehydrogenase B (LDHB) was the highest overexpressed protein, and transcript levels were higher in MCF7Ecadvar than in control cells. In agreement with these findings, MCF7Ecadvar conditioned media had lower glucose and higher lactate levels than control cells. MCF7Ecadvar cell treatment with 5 mM of the glycolytic inhibitor 2-deoxy-glucose led to decreased cell viability, and modulation of LDHB expression in MCF7Ecadvar cells with a specific small interfering RNA resulted in decreased cell proliferation. Finally, a positive association between expression levels of the E-cadherin variant and LDHB transcripts was demonstrated in 21 human breast tumor tissues, and breast tumor samples with higher Ki67 expression showed higher LDHB mRNA levels.

Conclusions

Results from this investigation contributed to further characterize molecular changes associated to the novel E-cadherin splice variant expression in BC cells. They also revealed an association between expression of the novel variant and changes related to BC progression and aggressiveness, in particular those associated to cell metabolism.

Identify the Key Active Ingredients and Pharmacological Mechanisms of Compound XiongShao Capsule in Treating Diabetic Peripheral Neuropathy by Network Pharmacology Approach

Compound XiongShao Capsule (CXSC), a traditional herb mixture, has shown significant clinical efficacy against diabetic peripheral neuropathy (DPN). However, its multicomponent and multitarget features cause difficulty in deciphering its molecular mechanisms. Our study aimed to identify the key active ingredients and potential pharmacological mechanisms of CXSC in treating DPN by network pharmacology and provide scientific evidence of its clinical efficacy. CXSC active ingredients were identified from both the Traditional Chinese Medicine Systems Pharmacology database, with parameters of oral bioavailability ≥ 30% and drug-likeness ≥ 0.18, and the Herbal Ingredients' Targets (HIT) database. The targets of those active ingredients were identified using ChemMapper based on 3D-structure similarity and using HIT database. DPN-related genes were acquired from microarray dataset GSE95849 and five widely used databases (TTD, Drugbank, KEGG, DisGeNET, and OMIM). Next, we obtained candidate targets with therapeutic effects against DPN by mapping active ingredient targets and DPN-related genes and identifying the proteins interacting with those candidate targets using STITCH 5.0. We constructed an “active ingredients-candidate targets-proteins” network using Cytoscape 3.61 and identified key active ingredients and key targets in the network. We identified 172 active ingredients in CXSC, 898 targets of the active ingredients, 110 DPN-related genes, and 38 candidate targets with therapeutic effects against DPN. Three key active ingredients, namely, quercetin, kaempferol, and baicalein, and 25 key targets were identified. Next, we input all key targets into ClueGO plugin for KEGG enrichment and molecular function analyses. The AGE-RAGE signaling pathway in diabetic complications and MAP kinase activity were determined as the main KEGG pathway and molecular function involved, respectively. We determined quercetin, kaempferol, and baicalein as the key active ingredients of CXSC and the AGE-RAGE signaling pathway and MAP kinase activity as the main pharmacological mechanisms of CXSC against DPN, proving the clinical efficacy of CXSC against DPN.

OsteoporosAtlas: a human osteoporosis-related gene database

Background

Osteoporosis is a common, complex disease of bone with a strong heritable component, characterized by low bone mineral density, microarchitectural deterioration of bone tissue and an increased risk of fracture. Due to limited drug selection for osteoporosis and increasing morbidity, mortality of osteoporotic fractures, osteoporosis has become a major health burden in aging societies. Current researches for identifying specific loci or genes involved in osteoporosis contribute to a greater understanding of the pathogenesis of osteoporosis and the development of better diagnosis, prevention and treatment strategies. However, little is known about how most causal genes work and interact to influence osteoporosis. Therefore, it is greatly significant to collect and analyze the studies involved in osteoporosis-related genes. Unfortunately, the information about all these osteoporosis-related genes is scattered in a large amount of extensive literature. Currently, there is no specialized database for easily accessing relevant information about osteoporosis-related genes and miRNAs.

Methods

We extracted data from literature abstracts in PubMed by text-mining and manual curation. Moreover, a local MySQL database containing all the data was developed with PHP on a Windows server.

Results

OsteoporosAtlas (http://biokb.ncpsb.org/osteoporosis/), the first specialized database for easily accessing relevant information such as osteoporosis-related genes and miRNAs, was constructed and served for researchers. OsteoporosAtlas enables users to retrieve, browse and download osteoporosis-related genes and miRNAs. Gene ontology and pathway analyses were integrated into OsteoporosAtlas. It currently includes 617 human encoding genes, 131 human non-coding miRNAs, and 128 functional roles. We think that OsteoporosAtlas will be an important bioinformatics resource to facilitate a better understanding of the pathogenesis of osteoporosis and developing better diagnosis, prevention and treatment strategies.

Broad range metabolomics coupled with network analysis for explaining possible mechanisms of Er-Zhi-Wan in treating liver-kidney Yin deficiency syndrome of Traditional Chinese medicine

ETHNOPHARMACOLOGICAL RELEVANCE

Er-Zhi-Wan (EZW), a famous traditional Chinese formulation, is used to prevent, or to treat, various liver and kidney diseases for its actions of replenishing liver and kidney. However, the mechanisms of treating Liver-kidney Yin deficiency syndrome (LKYDS) of EZW have not been comprehensively investigated.

AIM OF THE STUDY

In this study, a broad range metabolomics strategy coupled with network analysis was established to investigate possible mechanisms of EZW in treating LKYDS.

MATERIALS AND METHOD

The rat models of LKYDS were established using the mixture of thyroxine and reserpine, and the changes of biochemical indices in serum and histopathology were detected to explore the effects of EZW. Next, a broad range metabolomics strategy based on RPLC-Q-TOF/MS and HILIC-Q-TOF/MS has been developed to find the possible significant metabolites in the serum and urine of LKYDS rats. Then, network analysis was applied to visualize the relationships between identified serum and urine metabolites and in detail to find hub metabolites, which might be responsible for the effect of EZW on rats of LKYDS. Furthermore, the shortest path of "disease gene-pathway protein-metabolite" was built to investigate the possible intervention path of EZW from the systematic perspective.

RESULTS

Five hub metabolites, namely, arachidonic acid, L-arginine, testosterone, taurine and oxoglutaric acid, were screened out and could be adjusted to recover by EZW. After that, the shortest path starting from disease genes and ending in metabolites were identified and disclosed, and the genes of aging such as CAV1 and ACO1 were selected to explain the pathological mechanism of LKYDS.

CONCLUSION

Broad range metabolomics coupled with network analysis could provide another perspective on systematically investigating the molecular mechanism of EZW in treating LKYDS at metabolomics level. In addition, EZW might prevent the pathological process of LKYDS through regulating the disturbed metabolic pathway and the aging genes such as CAV1 and ACO1, which may be potential targets for EZW in the treatment of LKYDS.

HumCFS: a database of fragile sites in human chromosomes

Background

Fragile sites are the chromosomal regions that are susceptible to breakage, and their frequency varies among the human population. Based on the frequency of fragile site induction, they are categorized as common and rare fragile sites. Common fragile sites are sensitive to replication stress and often rearranged in cancer. Rare fragile sites are the archetypal trinucleotide repeats. Fragile sites are known to be involved in chromosomal rearrangements in tumors. Human miRNA genes are also present at fragile sites. A better understanding of genes and miRNAs lying in the fragile site regions and their association with disease progression is required.

Result

HumCFS is a manually curated database of human chromosomal fragile sites. HumCFS provides useful information on fragile sites such as coordinates on the chromosome, cytoband, their chemical inducers and frequency of fragile site (rare or common), genes and miRNAs lying in fragile sites. Protein coding genes in the fragile sites were identified by mapping the coordinates of fragile sites with human genome Ensembl (GRCh38/hg38). Genes present in fragile sites were further mapped to DisGenNET database, to understand their possible link with human diseases. Human miRNAs from miRBase was also mapped on fragile site coordinates. In brief, HumCFS provides useful information about 125 human chromosomal fragile sites and their association with 4921 human protein-coding genes and 917 human miRNA’s.

Conclusion

User-friendly web-interface of HumCFS and hyper-linking with other resources will help researchers to search for genes, miRNAs efficiently and to intersect the relationship among them. For easy data retrieval and analysis, we have integrated standard web-based tools, such as JBrowse, BLAST etc. Also, the user can download the data in various file formats such as text files, gff3 files and Bed-format files which can be used on UCSC browser.

Database URL:http://webs.iiitd.edu.in/raghava/humcfs/

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5330-5) contains supplementary material, which is available to authorized users.

PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

Background

Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant – drug – adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks “clinical counterpart” or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources.

Results

In this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and levels of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2701), the literature (65,720) and from discoveries reported in EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD (PGx Linked Open Data) the resulting knowledge base that represents and reconciles knowledge units of those various origins.

Conclusions

The proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2693-9) contains supplementary material, which is available to authorized users.

Exomic and transcriptomic alterations of hereditary gingival fibromatosis

OBJECTIVE

Hereditary gingival fibromatosis (HGF) is a rare oral disease characterized by either localized or generalized gradual, benign, non-hemorrhagic enlargement of gingivae. Although several genetic causes of HGF are known, the genetic etiology of HGF as a non-syndromic and idiopathic entity remains uncertain.

SUBJECTS AND METHODS

We performed exome and RNA-seq of idiopathic HGF patients and controls, and then devised a computational framework that specifies exomic/transcriptomic alterations interconnected by a regulatory network to unravel genetic etiology of HGF. Moreover, given the lack of animal model or large-scale cohort data of HGF, we developed a strategy to cross-check their clinical relevance through in silico gene-phenotype mapping with biomedical literature mining and semantic analysis of disease phenotype similarities.

RESULTS

Exomic variants and differentially expressed genes of HGF were connected by members of TGF-β/SMAD signaling pathway and craniofacial development processes, accounting for the molecular mechanism of fibroblast overgrowth mimicking HGF. Our cross-check supports that genes derived from the regulatory network analysis have pathogenic roles in fibromatosis-related diseases.

CONCLUSIONS

The computational approach of connecting exomic and transcriptomic alterations through regulatory networks is applicable in the clinical interpretation of genetic variants in HGF patients.

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs

Background

Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments.

Methods

In this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity based on mutual information, and add miRNA family and cluster information to predict human disease-related miRNAs (FCMDAP). This method not only depends on known miRNA-diseases associations but also accurately measures miRNA and disease similarity and resolves the problem of overestimation. FCMDAP uses the k most similar neighbor recommendation algorithm to predict the association score between miRNA and disease. Information about miRNA cluster is also used to improve prediction accuracy.

Result

FCMDAP achieves an average AUC of 0.9165 based on leave-one-out cross validation. Results confirm the 100, 98 and 96% of the top 50 predicted miRNAs reported in case studies on colorectal, lung, and pancreatic neoplasms. FCMDAP also exhibits satisfactory performance in predicting diseases without any related miRNAs and miRNAs without any related diseases.

Conclusions

In this study, we present a computational method FCMDAP to improve the prediction accuracy of disease related miRNAs. FCMDAP could be an effective tool for further biological experiments.

Electronic supplementary material

The online version of this article (10.1186/s12918-019-0696-9) contains supplementary material, which is available to authorized users.

Comparative Hepatotoxicity of Novel PFOA Alternatives (Perfluoropolyether Carboxylic Acids) on Male Mice

As novel alternatives to perfluorooctanoic acid (PFOA), perfluoropolyether carboxylic acids (multiether PFECAs, CF₃(OCF₂) _nCOO^-, n = 2-4) have been detected in various environmental matrices; however, public information regarding their toxicities remains unavailable. To compare the hepatotoxicity of multiether PFECAs (e.g., PFO2HxA, PFO3OA, and PFO4DA) with PFOA, male mice were exposed to 0.4, 2, or 10 mg/kg/d of each chemical for 28 d, respectively. Results demonstrated that PFO2HxA and PFO3OA exposure did not induce marked increases in relative liver weight; whereas 2 and 10 mg/kg/d of PFO4DA significantly increased relative liver weight. Furthermore, PFO2HxA and PFO3OA demonstrated almost no accumulation in the liver or serum; whereas PFO4DA was accumulated but with weaker potential than PFOA. Exposure to 10 mg/kg/d of PFO4DA led to 198 differentially expressed liver genes (56 down-regulated, 142 up-regulated), with bioinformatics analysis highlighting the urea cycle disorder. Like PFOA, 10 mg/kg/d of PFO4DA decreased the urea cycle-related enzyme protein levels (e.g., carbamoyl phosphate synthetase 1) and serum ammonia content in a dose-dependent manner. Both PFOA and PFO4DA treatment (highest concentration) caused a decrease in glutamate content and increase in both glutamine synthetase activity and aquaporin protein levels in the brain. Thus, we concluded that PFO4DA caused hepatotoxicity, as indicated by hepatomegaly and karyolysis, though to a lesser degree than PFOA, and induced urea cycle disorder, which may contribute to the observed toxic effects.

Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case-control approaches

Deciphering the underlying genetic basis behind pancreatic cancer (PC) and its associated multimorbidities will enhance our knowledge toward PC control. The study investigated the common genetic background of PC and different morbidities through a computational approach and further evaluated the less explored association between PC and autoimmune diseases (AIDs) through an epidemiological analysis. Gene-disease associations (GDAs) of 26 morbidities of interest and PC were obtained using the DisGeNET public discovery platform. The association between AIDs and PC pointed by the computational analysis was confirmed through multivariable logistic regression models in the PanGen European case-control study population of 1,705 PC cases and 1,084 controls. Fifteen morbidities shared at least one gene with PC in the DisGeNET database. Based on common genes, several AIDs were genetically associated with PC pointing to a potential link between them. An epidemiologic analysis confirmed that having any of the nine AIDs studied was significantly associated with a reduced risk of PC (Odds Ratio (OR) = 0.74, 95% confidence interval (CI) 0.58-0.93) which decreased in subjects having ≥2 AIDs (OR = 0.39, 95%CI 0.21-0.73). In independent analyses, polymyalgia rheumatica, and rheumatoid arthritis were significantly associated with low PC risk (OR = 0.40, 95%CI 0.19-0.89, and OR = 0.73, 95%CI 0.53-1.00, respectively). Several inflammatory-related morbidities shared a common genetic component with PC based on public databases. These molecular links could shed light into the molecular mechanisms underlying PC development and simultaneously generate novel hypotheses. In our study, we report sound findings pointing to an association between AIDs and a reduced risk of PC.

In silico Characterization of Human Prion-Like Proteins: Beyond Neurological Diseases

Prion-like behavior has been in the spotlight since it was first associated with the onset of mammalian neurodegenerative diseases. However, a growing body of evidence suggests that this mechanism could be behind the regulation of processes such as transcription and translation in multiple species. Here, we perform a stringent computational survey to identify prion-like proteins in the human proteome. We detected 242 candidate polypeptides and computationally assessed their function, protein–protein interaction networks, tissular expression, and their link to disease. Human prion-like proteins constitute a subset of modular polypeptides broadly expressed across different cell types and tissues, significantly associated with disease, embedded in highly connected interaction networks, and involved in the flow of genetic information in the cell. Our analysis suggests that these proteins might play a relevant role not only in neurological disorders, but also in different types of cancer and viral infections.

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures

Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.

MorCVD: A Unified Database for Host-Pathogen Protein-Protein Interactions of Cardiovascular Diseases Related to Microbes

Microbe induced cardiovascular diseases (CVDs) are less studied at present. Host-pathogen interactions (HPIs) between human proteins and microbial proteins associated with CVD can be found dispersed in existing molecular interaction databases. MorCVD database is a curated resource that combines 23,377 protein interactions between human host and 432 unique pathogens involved in CVDs in a single intuitive web application. It covers endocarditis, myocarditis, pericarditis and 16 other microbe induced CVDs. The HPI information has been compiled, curated, and presented in a freely accessible web interface (http://morcvd.sblab-nsit.net/About). Apart from organization, enrichment of the HPI data was done by adding hyperlinked protein ID, PubMed, gene ontology records. For each protein in the database, drug target and interactors (same as well as different species) information has been provided. The database can be searched by disease, protein ID, pathogen name or interaction detection method. Interactions detected by more than one method can also be listed. The information can be presented in tabular form or downloaded. A comprehensive help file has been developed to explain the various options available. Hence, MorCVD acts as a unified resource for retrieval of HPI data for researchers in CVD and microbiology.

Identification and analysis of the human sex-biased genes

Tremendous differences between human sexes are universally observed. Therefore, identifying and analyzing the sex-biased genes are becoming basically important for uncovering the mystery of sex differences and personalized medicine. Here, we presented a computational method to identify sex-biased genes from public gene expression databases. We obtained 1407 female-biased genes (FGs) and 1096 male-biased genes (MGs) across 14 different tissues. Bioinformatics analysis revealed that compared with MGs, FGs have higher evolutionary rate, higher single-nucleotide polymorphism density, less homologous gene numbers and smaller phyletic age. FGs have lower expression level, higher tissue specificity and later expressed stage in body development. Moreover, FGs are highly involved in immune-related functions, whereas MGs are more enriched in metabolic process. In addition, cellular network analysis revealed that MGs have higher degree, more cellular activating signaling and tend to be located in cellular inner space, whereas FGs have lower degree, more cellular repressing signaling and tend to be located in cellular outer space. Finally, the identified sex-biased genes and the discovered biological insights together can be a valuable resource helpful for investigating sex-biased physiology and medicine, for example sex-biased disease diagnosis and therapy, which represents one important aspect of personalized and precision medicine.

Association between DNA methylation in obesity-related genes and body mass index percentile in adolescents

Childhood obesity remains an epidemic in the U.S. and worldwide. However, little is understood regarding the epigenetic basis of obesity in adolescents. To investigate the cross-sectional association between DNA methylation level in obesity-related genes and body mass index (BMI) percentile, data from 263 adolescents in the population-based Penn State Child Cohort follow-up exam was analysed. Using DNA extracted from peripheral leukocytes, epigenome-wide single nucleotide resolution of DNA methylation in cytosine-phosphate-guanine (CpG) sites and surrounding regions was obtained. We used multivariable-adjusted linear regression models to assess the association between site-specific methylation level and age- and sex-specific BMI percentile. Hypergeometric and permutation tests were used to determine if obesity-related genes were significantly enriched among all intragenic sites that achieved a p < 0.05 throughout the epigenome. Among the 5,669 sites related to BMI percentile with p < 0.05, 28 were identified within obesity-related genes. Obesity-related genes were significantly enriched among 103,466 intragenic sites (P_{hypergeometric} = 0.006; P_permutation = 0.006). Moreover, increased methylation on one site within SIM1 was significantly related to higher BMI percentile (P = 4.2E-05). If externally validated, our data would suggest that DNA methylation in obesity-related genes may relate to obesity risk in adolescents.

Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method

Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered.

NeuroMuscleDB: a Database of Genes Associated with Muscle Development, Neuromuscular Diseases, Ageing, and Neurodegeneration

Skeletal muscle is a highly complex, heterogeneous tissue that serves a multitude of biological functions in living organisms. With the advent of methods, such as microarrays, transcriptome analysis, and proteomics, studies have been performed at the genome level to gain insight of changes in the expression profiles of genes during different stages of muscle development and of associated diseases. In the present study, a database was conceived for the straightforward retrieval of information on genes involved in skeletal muscle formation, neuromuscular diseases (NMDs), ageing, and neurodegenerative disorders (NDs). The resulting database named NeuroMuscleDB ( http://yu-mbl-muscledb.com/NeuroMuscleDB ) is the result of a wide literature survey, database searches, and data curation. NeuroMuscleDB contains information of genes in Homo sapiens, Mus musculus, and Bos Taurus, and their promoter sequences and specified roles at different stages of muscle development and in associated myopathies. The database contains information on ~ 1102 genes, 6030 mRNAs, and 5687 proteins, and embedded analytical tools that can be used to perform tasks related to gene sequence usage. The authors believe NeuroMuscleDB provides a platform for obtaining desired information on genes related to myogenesis and their associations with various diseases (NMDs, ageing, and NDs). NeuroMuscleDB is freely available on the web at http://yu-mbl-muscledb.com/NeuroMuscleDB and supports all major browsers.

Risk gene-set and pathways in 22q11.2 deletion-related schizophrenia: a genealogical molecular approach

The 22q11.2 deletion is a strong, but insufficient, "first hit" genetic risk factor for schizophrenia (SZ). We attempted to identify "second hits" from the entire genome in a unique multiplex 22q11.2 deletion syndrome (DS) family. Bioinformatic analysis of whole-exome sequencing and comparative-genomic hybridization array identified de novo and inherited, rare and damaging variants, including copy number variations, outside the 22q11.2 region. A specific 22q11.2-haplotype was associated with psychosis. The interaction of the identified "second hits" with the 22q11.2 haploinsufficiency may affect neurodevelopmental processes, including neuron projection, cytoskeleton activity, and histone modification in 22q11.2DS-ralated psychosis. A larger load of variants, involved in neurodevelopment, in combination with additional molecular events that affect sensory perception, olfactory transduction and G-protein-coupled receptor signaling may account for the development of 22q11.2DS-related SZ. Comprehensive analysis of multiplex families is a promising approach to the elucidation of the molecular pathophysiology of 22q11.2DS-related SZ with potential relevance to treatment.

Global pathway view analysis of microRNA clusters in myasthenia gravis

The significant roles of microRNAs (miRNAs) in the pathogenesis of myasthenia gravis (MG) have been observed in numerous previous studies. The impact of miRNA clusters on immunity has been demonstrated in previous years; however, the regulation of miRNA clusters in MG remains to be elucidated. In the present study, 245 MG risk genes were collected and 99 MG risk pathways enriched by these genes were identified. A catalog of 126 MG risk miRNAs was then created; the MG risk miRNAs were located on each chromosome and a miRNA cluster was defined as a number of miRNAs with a relative distance of <6 kb on the same sub‑band, same band, same region and same chromosome. Furthermore, enrichment analyses were performed using the target genes of the MG risk miRNA clusters, and a number of risk pathways of each miRNA clusters were identified. As a result, 15 significant miRNA clusters associated with MG were identified. Additionally, the most significant pathways of the miRNA clusters were identified to be enriched on chromosomes 9, 19 and 22, characterized by immunity, infection and carcinoma, suggesting that the mechanism of MG may be associated with certain abnormalities of miRNA clusters on chromosomes 9, 19 and 22. The present study provides novel insight into a global pathway view of miRNA clusters in the pathogenesis of MG.

HACER: an atlas of human active enhancers to interpret regulatory variants

Recent studies have shown that disease-susceptibility variants frequently lie in cell-type-specific enhancer elements. To identify, interpret, and prioritize such risk variants, we must identify the enhancers active in disease-relevant cell types, their upstream transcription factor (TF) binding, and their downstream target genes. To address this need, we built HACER (http://bioinfo.vanderbilt.edu/AE/HACER/), an atlas of Human ACtive Enhancers to interpret Regulatory variants. The HACER atlas catalogues and annotates in-vivo transcribed cell-type-specific enhancers, as well as placing enhancers within transcriptional regulatory networks by integrating ENCODE TF ChIP-Seq and predicted/validated chromatin interaction data. We demonstrate the utility of HACER in (i) offering a mechanistic hypothesis to explain the association of SNP rs614367 with ER-positive breast cancer risk, (ii) exploring tumor-specific enhancers in selective MYC dysregulation and (iii) prioritizing/annotating non-coding regulatory regions targeting CCND1. HACER provides a valuable resource for studies of GWAS, non-coding variants, and enhancer-mediated regulation.

Network Pharmacology Integrated Molecular Docking Reveals the Antiosteosarcoma Mechanism of Biochanin A

Background

As the malignant tumor with the highest incidence in teenagers, osteosarcoma has become a major problem in oncology research. In addition to surgical management, the pharmacotherapeutic strategy for osteosarcoma treatment is an attractive way to explore. It has been demonstrated that biochanin A has an antitumor capacity on multiple kinds of solid tumor, including osteosarcoma. But the precise mechanism of biochanin A against osteosarcoma is still needed to be discovered.

Objective

To identify the potential therapeutic targets of biochanin A in treating osteosarcoma.

Methods

In present study, an integrated approach including network pharmacology and molecular docking technique was conducted, which mainly comprises target prediction, network construction, gene ontology, and pathway enrichment. CCK8 test was employed to evaluate the cell viability of MG63 cells. Western-blot was used to verify the target proteins of biochanin A.

Results

Ninety-six and 114 proteins were obtained as the targets of biochanin A and osteosarcoma, respectively. TP53, IGF1, JUN, BGLAP, ATM, MAPK1, ATF3, H2AFX, BAX, CDKN2A, and EGF were identified as the potential targets of biochanin A against osteosarcoma. Based on the western-blot detection, the expression of BGLAP, BAX, and ATF3 in MG63 cell line changed under the treatment of biochanin A.

Conclusion

Biochanin A can effectively suppress the proliferation of osteosarcoma and regulate the expression of BGLAP, BAX, and ATF3, which may act as the potential therapeutic targets of osteosarcoma.

Epigallocatechin‑3‑gallate modulates long non‑coding RNA and mRNA expression profiles in lung cancer cells

(−)-Epigallocatechin-3-gallate (EGCG), a major constituent of green tea, is a potential anticancer agent, but the molecular mechanisms of its effects are not well-understood. The present study was conducted to examine the mechanism of EGCG in lung cancer cells. Alterations in long non-coding RNAs (lncRNAs) and mRNAs were investigated in lung cancer cells treated with EGCG by lncRNA microarray analysis. Furthermore, the functions and signaling pathways regulated by EGCG were predicted by bioinformatics analysis. A total of 960 lncRNAs and 1,434 mRNAs were significantly altered following EGCG treatment. These lncRNAs were distributed across nearly all human chromosomes and the mRNAs were involved in the cell cycle and the mitotic cell cycle process. Through a combination of microarray and bioinformatics analysis, 20 mRNAs predicted to serve a key role in the EGCG regulation were identified, and certain regulatory networks involving EGCG-regulated lncRNAs were predicted. In conclusion, EGCG affects the expression of various lncRNAs and mRNAs in the cells, therefore affecting cell functions. The results of the present study provide an insight into the mechanism of EGCG, which may be useful for therapeutic development.

Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods

N6-methyladenosine (m6A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m6A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m6A levels are controlled and whether and how regulation of m6A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m6A-regulated genes and m6A-associated disease, which includes Deep-m6A, the first model for detecting condition-specific m6A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m6A, a new network-based pipeline that prioritizes functional significant m6A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m6A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m6A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m6A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m6A regulatory functions and its roles in diseases.

DNRLMF-MDA:Predicting microRNA-Disease Associations Based on Similarities of microRNAs and Diseases

MicroRNAs (miRNAs) are a class of non-coding RNAs about  ∼ 22nt nucleotides. Studies have proven that miRNAs play key roles in many human complex diseases. Therefore, discovering miRNA-disease associations is beneficial to understanding disease mechanisms, developing drugs, and treating complex diseases. It is well known that it is a time-consuming and expensive process to discover the miRNA-disease associations via biological experiments. Alternatively, computational models could provide a low-cost and high-efficiency way for predicting miRNA-disease associations. In this study, we propose a method (called DNRLMF-MDA) to predict miRNA-disease associations based on dynamic neighborhood regularized logistic matrix factorization. DNRLMF-MDA integrates known miRNA-disease associations, functional similarity and Gaussian Interaction Profile (GIP) kernel similarity of miRNAs, and functional similarity and GIP kernel similarity of diseases. Especially, positive observations (known miRNA-disease associations) are assigned higher importance levels than negative observations (unknown miRNA-disease associations).DNRLMF-MDA computes the probability that a miRNA would interact with a disease by a logistic matrix factorization method, where latent vectors of miRNAs and diseases represent the properties of miRNAs and diseases, respectively, and further improve prediction performance via dynamic neighborhood regularized. The 5-fold cross validation is adopted to assess the performance of our DNRLMF-MDA, as well as other competing methods for comparison. The computational experiments show that DNRLMF-MDA outperforms the state-of-art method PBMDA. The AUC values of DNRLMF-MDA on three datasets are 0.9357, 0.9411, and 0.9416, respectively, which are superior to the PBMDA's results of 0.9218, 0.9187, and 0.9262. The average computation times per 5-fold cross validation of DNRLMF-MDA on three datasets are 38, 46, and 50 seconds, which are shorter than the PBMDA's average computation times of 10869, 916, and 8448 seconds, respectively. DNRLMF-MDA also can predict potential diseases for new miRNAs. Furthermore, case studies illustrate that DNRLMF-MDA is an effective method to predict miRNA-disease associations.

Integrating Biological Networks for Drug Target Prediction and Prioritization

Computational prediction of the clinical success or failure of a potential drug target for therapeutic use is a challenging problem. Novel network propagation algorithms that integrate heterogeneous biological networks are proving useful for drug target identification and prioritization. These approaches typically utilize a network describing relationships between targets, a method to disseminate the relevant information through the network, and a method to elucidate new associations between targets and diseases. Here, we utilize one such network propagation-based approach, DTINet, which starts with diffusion component analysis of networks of both potential drug targets and diseases. Then an inductive matrix completion algorithm is applied to identify novel disease targets based on their network topological similarities with known disease targets with successfully launched drugs. DTINet performed well as assessed with area under the precision-recall curve (AUPR = 0.88 ± 0.007) and area under the receiver operating characteristic curve (AUROC = 0.86 ± 0.008). These metrics improved when we combined data from multiple networks in the target space but reduced significantly when we used a more conservative method to define negative controls (AUPR = 0.56 ± 0.007, AUROC = 0.57 ± 0.007). We are optimistic that integration of more relevant and cleaner datasets and networks, careful calibration of model parameters, as well as algorithmic improvements will improve prediction accuracy. However, we also recognize that predicting drug targets that are likely to be successful is an extremely challenging problem due to its complex nature and sparsity of known disease targets.

Differential Connectivity in Colorectal Cancer Gene Expression Network

BACKGROUND

Colorectal cancer (CRC) is one of the challenging types of cancers; thus, exploring effective biomarkers related to colorectal could lead to significant progresses toward the treatment of this disease.

METHODS

In the present study, CRC gene expression datasets have been reanalyzed. Mutual differentially expressed genes across 294 normal mucosa and adjacent tumoral samples were then utilized in order to build two independent transcriptional regulatory networks. By analyzing the networks topologically, genes with differential global connectivity related to cancer state were determined for which the potential transcriptional regulators including transcription factors were identified.

RESULTS

The majority of differentially connected genes (DCGs) were up-regulated in colorectal transcriptome experiments. Moreover, a number of these genes have been experimentally validated as cancer or CRC-associated genes. The DCGs, including GART, TGFB1, ITGA2, SLC16A5, SOX9, and MMP7, were investigated across 12 cancer types. Functional enrichment analysis followed by detailed data mining exhibited that these candidate genes could be related to CRC by mediating in metastatic cascade in addition to shared pathways with 12 cancer types by triggering the inflammatory events.

DISCUSSION

Our study uncovered correlated alterations in gene expression related to CRC susceptibility and progression that the potent candidate biomarkers could provide a link to disease.

The human DEPhOsphorylation Database DEPOD: 2019 update

The human Dephosphorylation Database (DEPOD) is a manually curated resource that harbors human phosphatases, their protein and non-protein substrates, dephosphorylation sites and the associated signaling pathways. We report here an update to DEPOD by integrating and/or linking to annotations from 69 other open access databases including disease associations, phosphorylating kinases, protein interactions, and also genome browsers. We also provide tools to visualize protein interactions, protein structures, phosphorylation networks, evolutionary conservation of proteins, dephosphorylation sites, and short linear motifs within various proteins. The updated version of DEPOD contains 254 human phosphatases, 336 protein and 83 non-protein substrates, and 1215 manually curated phosphatase-substrate relationships. In addition, we have improved the data access as all the data in DEPOD can now be easily downloaded in a user-friendly format. With multiple significant improvements, DEPOD continues serving as a key resource for research on phosphatase-kinase networks. Database URL: www.depod.org.

Integrative Gene Selection on Gene Expression Data: Providing Biological Context to Traditional Approaches

The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand complex diseases like cancer, and eventually develop effective measures for diagnosis and treatment. The high dimensionality of gene expression data poses challenges to its computational analysis, which is addressed with measures of gene selection. Traditional gene selection approaches base their findings on statistical analyses of the actual expression levels, which implies several drawbacks when it comes to accurately identifying the underlying biological processes. In turn, integrative approaches include curated information on biological processes from external knowledge bases during gene selection, which promises to lead to better interpretability and improved predictive performance. Our work compares the performance of traditional and integrative gene selection approaches. Moreover, we propose a straightforward approach to integrate external knowledge with traditional gene selection approaches. We introduce a framework enabling the automatic external knowledge integration, gene selection, and evaluation. Evaluation results prove our framework to be a useful tool for evaluation and show that integration of external knowledge improves overall analysis results.

The Importance of Biologic Knowledge and Gene Expression Context for Genomic Data Interpretation

Background: Genomic sequencing, including whole exome sequencing (WES), is enabling a higher resolution for defining diseases, understand mechanisms, and improving the practice of clinical care. However, WES routinely identifies genomic variants with uncertain functional effects. Furthering uncertainty in WES data interpretation is that many genes can express multiple transcripts and their relative expression may differ by body tissue. In order to interpret WES data, we not only need to understand which transcript is most relevant, but what tissue is most relevant. Methods: In this work, we quantify how frequently differences in transcript and tissue expression affect WES data interpretation at gene, pathway, disease, and biologic network levels. We combined and analyzed multiple large and publically available datasets to inform genomic data interpretation. Results: Across well-established biologic pathways and genes with pathogenic disease variants, 54 and 40% have a different protein coding effect by transcript selection for, respectively, 25 and 50% of the genes contained. Additionally, strong differences in human tissue expression levels affect 33 and 19% of the same set of pathways and diseases for, respectively, 25 and 50% of the genes contained. Conclusion: Whole exome sequencing identifies genomic variants, but to interpret the functional effects of those variants in high-resolution, we recommend building transcript selection and cross-tissue gene expression levels into hypotheses and analyses. Using current large-scale data, we show how extensively interpretation of genomic variants may differ according to transcript and tissue, across most pathways and disease. Thus, their inclusion is necessary for WES data interpretation.

Construction of a long non‑coding RNA-mediated competitive endogenous RNA network reveals global patterns and regulatory markers in gestational diabetes

Gestational diabetes mellitus (GDM) is a common disease affecting pregnant women. Recent studies have suggested that competing endogenous RNAs (ceRNAs), which compete with long non‑coding RNAs (lncRNAs) for microRNA (miRNA or miR) binding and indirectly regulate miRNA targets through competing interactions, play a critical role in disease. In this study, we present a computationally integrated approach with which to construct a lncRNA‑mediated ceRNA network (LCEN) in GDM by integrating RNA interactions and expression data. lncRNAs exhibited specific features and played critical roles in GDM‑associated LCEN. The construction of a global functional score profile revealed that ceRNAs had a high activity in GDM. We extracted several ceRNA modules and demonstrated that these modules had increased close interactions. We further discovered that these ceRNA modules may be utilized as specific and effective circulating biomarkers for GDM. Finally, functional analyses demonstrated that the GDM‑associated ceRNAs participated in the regulation of irisin and the thyroid hormone signaling pathway. It was suggested that there were close associations between the thyroid hormone and GDM. Collectively, ceRNAs may accelerate biomarker discovery and therapeutic development in GDM.

Plasma Protein Biomarkers for the Prediction of CSF Amyloid and Tau and [18F]-Flutemetamol PET Scan Result

Background: Blood biomarkers may aid in recruitment to clinical trials of Alzheimer's disease (AD) modifying therapeutics by triaging potential trials participants for amyloid positron emission tomography (PET) or cerebrospinal fluid (CSF) Aβ and tau tests. Objective: To discover a plasma proteomic signature associated with CSF and PET measures of AD pathology. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics were performed in plasma from participants with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD, recruited to the Amsterdam Dementia Cohort, stratified by CSF Tau/Aβ42 (n = 50). Technical replication and independent validation were performed by immunoassay in plasma from SCD, MCI, and AD participants recruited to the Amsterdam Dementia Cohort with CSF measures (n = 100), MCI participants enrolled in the GE067-005 study with [18F]-Flutemetamol PET amyloid measures (n = 173), and AD, MCI and cognitively healthy participants from the EMIF 500 study with CSF Aβ42 measurements (n = 494). Results: 25 discovery proteins were nominally associated with CSF Tau/Aβ42 (P < 0.05) with associations of ficolin-2 (FCN2), apolipoprotein C-IV and fibrinogen β chain confirmed by immunoassay (P < 0.05). In the GE067-005 cohort, FCN2 was nominally associated with PET amyloid (P < 0.05) replicating the association with CSF Tau/Aβ42. There were nominally significant associations of complement component 3 with PET amyloid, and apolipoprotein(a), apolipoprotein A-I, ceruloplasmin, and PPY with MCI conversion to AD (all P < 0.05). In the EMIF 500 cohort FCN2 was trending toward a significant relationship with CSF Aβ42 (P ≈ 0.05), while both A1AT and clusterin were nominally significantly associated with CSF Aβ42 (both P < 0.05). Conclusion: Associations of plasma proteins with multiple measures of AD pathology and progression are demonstrated. To our knowledge this is the first study to report an association of FCN2 with AD pathology. Further testing of the proteins in larger independent cohorts will be important.

GM604 regulates developmental neurogenesis pathways and the expression of genes associated with amyotrophic lateral sclerosis

Background

Amyotrophic lateral sclerosis (ALS) is currently an incurable disease without highly effective pharmacological treatments. The peptide drug GM604 (GM6 or Alirinetide) was developed as a candidate ALS therapy, which has demonstrated safety and good drug-like properties with a favorable pharmacokinetic profile. GM6 is hypothesized to bolster neuron survival through the multi-target regulation of developmental pathways, but mechanisms of action are not fully understood.

Methods

This study used RNA-seq to evaluate transcriptome responses in SH-SY5Y neuroblastoma cells following GM6 treatment (6, 24 and 48 h).

Results

We identified 2867 protein-coding genes with expression significantly altered by GM6 (FDR < 0.10). Early (6 h) responses included up-regulation of Notch and hedgehog signaling components, with increased expression of developmental genes mediating neurogenesis and axon growth. Prolonged GM6 treatment (24 and 48 h) altered the expression of genes contributing to cell adhesion and the extracellular matrix. GM6 further down-regulated the expression of genes associated with mitochondria, inflammatory responses, mRNA processing and chromatin organization. GM6-increased genes were located near GC-rich motifs interacting with C2H2 zinc finger transcription factors, whereas GM6-decreased genes were located near AT-rich motifs associated with helix-turn-helix homeodomain factors. Such motifs interacted with a diverse network of transcription factors encoded by GM6-regulated genes (STAT3, HOXD11, HES7, GLI1). We identified 77 ALS-associated genes with expression significantly altered by GM6 treatment (FDR < 0.10), which were known to function in neurogenesis, axon guidance and the intrinsic apoptosis pathway.

Conclusions

Our findings support the hypothesis that GM6 acts through developmental-stage pathways to influence neuron survival. Gene expression responses were consistent with neurotrophic effects, ECM modulation, and activation of the Notch and hedgehog neurodevelopmental pathways. This multifaceted mechanism of action is unique among existing ALS drug candidates and may be applicable to multiple neurodegenerative diseases.

Electronic supplementary material

The online version of this article (10.1186/s40035-018-0135-7) contains supplementary material, which is available to authorized users.

PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

Biomedical text mining for research rigor and integrity: tasks, challenges, directions

An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise.

Drug repositioning using drug-disease vectors based on an integrated network

BACKGROUND

Diverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network.

RESULTS

We developed a novel method for identifying candidate indications of existing drugs considering types of interactions between biomolecules based on known drug-disease associations. To obtain associations between drug and disease genes, we constructed a directed network using protein interaction and gene regulation data obtained from various public databases providing diverse biological pathways. The network includes three types of edges depending on relationships between biomolecules. To quantify the association between a target gene and a disease gene, we explored the shortest paths from the target gene to the disease gene and calculated the types and weights of the shortest paths. For each drug-disease pair, we built a vector consisting of values for each disease gene influenced by the drug. Using the vectors and known drug-disease associations, we constructed classifiers to identify novel drugs for each disease.

CONCLUSION

We propose a method for exploring candidate drugs of diseases using associations between drugs and disease genes derived from a directed gene network instead of gene regulation data obtained from gene expression profiles. Compared to existing methods that require information on gene relationships and gene expression data, our method can be applied to a greater number of drugs and diseases. Furthermore, to validate our predictions, we compared the predictions with drug-disease pairs in clinical trials using the hypergeometric test, which showed significant results. Our method also showed better performance compared to existing methods for the area under the receiver operating characteristic curve (AUC).

Low expression of PDK1 inhibits renal cell carcinoma cell proliferation, migration, invasion and epithelial mesenchymal transition through inhibition of the PI3K-PDK1-Akt pathway

As the most commonly occurring form of primary renal tumor, renal cell carcinoma (RCC) is a malignancy accompanied by a high mortality rate. 3-phosphoinositide-dependent protein kinase 1 (PDK1) has been established as a protein target and generated considerable interest in both the pharmaceutical and academia industry. The aim of the current study was to investigate the effect of si-PDK1 on the RCC cell apoptosis, proliferation, migration, invasion and epithelial mesenchymal transition (EMT) in connection with the PI3K-PDK1-Akt pathway. Microarray analysis from the GEO database was adopted to identify differentially expressed genes (DEGs) related to RCC, after which the positive expression of the PDK1 protein in tissue was determined accordingly. The optimal silencing si-RNA was subsequently selected and RCC cell lines 786-O and A498 were selected and transfected with either a si-PDK1 or activator of the PI3K-PDK1-Akt pathway for grouping purposes. The mRNA and protein expressions of PDK1, the PI3K-PDK1-Akt pathway-, EMT- and apoptosis-related genes were then evaluated. The effect of si-PDK1 on cell proliferation, apoptosis, invasion and migration was then analyzed. Through microarray analysis of GSE6344, GSE53757, GSE14762 and GSE781, PDK1 was examined. PDK1 was determined to be highly expressed in RCC tissues. Si-PDK1 exhibited marked reductions in relation to the mRNA and protein expression of PDK1, PI3K, AKT as well as Vimentin while elevated mRNA and protein expressions of E-cadherin were detected, which ultimately suggested that cell migration, proliferation and invasion had been inhibited coupled with enhanced levels of cell apoptosis. While a notable observation was made highlighting that the PI3K-PDK1-Akt pathway antagonized the effect of PDK1 silencing. Taken together, the key observations of this study provide evidence suggesting that high expressions of PDK1 are found in RCC, while highlighting that silencing PDK1 could inhibit RCC cell proliferation, migration, invasion and EMT by repressing the PI3K-PDK1-Akt pathway.

C/VDdb: A multi-omics expression profiling database for a knowledge-driven approach in cardiovascular disease (CVD)

The cardiovascular disease (C/VD) database is an integrated and clustered information resource that covers multi-omic studies (microRNA, genomics, proteomics and metabolomics) of cardiovascular-related traits with special emphasis on coronary artery disease (CAD). This resource was built by mining existing literature and public databases and thereafter manual biocuration was performed. To enable integration of omic data from distinct platforms and species, a specific ontology was applied to tie together and harmonise multi-level omic studies based on gene and protein clusters (CluSO) and mapping of orthologous genes (OMAP) across species. CAD continues to be a leading cause of death in the population worldwide, and it is generally thought to be an age-related disease. However, CAD incidence rates are now known to be highly influenced by environmental factors and interactions, in addition to genetic determinants. With the complexity of CAD aetiology, there is a difficulty in research studies to elucidate general elements compared to other cardiovascular diseases. Data from 92 studies, covering 13945 molecular entries (4353 unique molecules) is described, including data descriptors for experimental setup, study design, discovery-validation sample size and associated fold-changes of the differentially expressed molecular features (p-value<0.05). A dedicated interactive web interface, equipped with a multi-parametric search engine, data export and indexing menus are provided for a user-accessible browsing experience. The main aim of this work was the development of a data repository linking clinical information and molecular differential expression in several CVD-related traits from multi-omics studies (genomics, transcriptomics, proteomics and metabolomics). As an example case of how to query and identify data sets within the database framework and concomitantly demonstrate the database utility, we queried CAD-associated studies and performed a systems-level integrative analysis. URL: www.padb.org/cvd

Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis

Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored.

Heterogeneous network embedding for identifying symptom candidate genes

Objective

Investigating the molecular mechanisms of symptoms is a vital task in precision medicine to refine disease taxonomy and improve the personalized management of chronic diseases. Although there are abundant experimental studies and computational efforts to obtain the candidate genes of diseases, the identification of symptom genes is rarely addressed. We curated a high-quality benchmark dataset of symptom-gene associations and proposed a heterogeneous network embedding for identifying symptom genes.

Methods

We proposed a heterogeneous network embedding representation algorithm, which constructed a heterogeneous symptom-related network that integrated symptom-related associations and applied an embedding representation algorithm to obtain the low-dimensional vector representation of nodes. By measuring the relevance between symptoms and genes via calculating the similarities of their vectors, the candidate genes of given symptoms can be obtained.

Results

A benchmark dataset of 18 270 symptom-gene associations between 505 symptoms and 4549 genes was curated. We compared our method to baseline algorithms (FSGER and PRINCE). The experimental results indicated our algorithm achieved a significant improvement over the state-of-the-art method, with precision and recall improved by 66.80% (0.844 vs 0.506) and 53.96% (0.311 vs 0.202), respectively, for TOP@3 and association precision improved by 37.71% (0.723 vs 0.525) over the PRINCE.

Conclusions

The experimental validation of the algorithms and the literature validation of typical symptoms indicated our method achieved excellent performance. Hence, we curated a prediction dataset of 17 479 symptom-candidate genes. The benchmark and prediction datasets have the potential to promote investigations of the molecular mechanisms of symptoms and provide candidate genes for validation in experimental settings.

PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations

CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

Pancreatic cancer is regarded as the most fatal and aggressive malignancy cancer due to its low 5-year survival rate and poor prognosis. The approaches of early diagnosis and treatment are limited, which makes it urgent to identify the complex mechanism of pancreatic oncogenesis. In this study, we used RNA-seq to investigate the transcriptomic (mRNA and miRNA) profiles of pancreatic cancer in paired tumor and normal pancreatic samples from ten patients. More than 1000 differentially expressed genes were identified, nearly half of which were also found to be differentially expressed in the majority of examined patients. Functional enrichment analysis revealed that these genes were significantly enriched in multicellular organismal and metabolic process, secretion, mineral transport, and intercellular communication. In addition, only 24 differentially expressed miRNAs were found, all of which have been reported to be associated with pancreatic cancer. Furthermore, an integrated miRNA-mRNA interaction network was generated using multiple resources. Based on the calculation of disease correlation scores developed here, several genes present in the largest connected subnetwork, such as albumin, ATPase H⁺ /K⁺ exchanging alpha polypeptide and carcinoembryonic antigen-related cell adhesion molecule 1, were considered as novel genes that play important roles in the development of pancreatic cancer. Overall, our data provide new insights into further understanding of key molecular mechanisms underlying pancreatic tumorigenesis.

Tissue-specific pathways and networks underlying sexual dimorphism in non-alcoholic fatty liver disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) encompasses benign steatosis and more severe conditions such as non-alcoholic steatohepatitis (NASH), cirrhosis, and liver cancer. This chronic liver disease has a poorly understood etiology and demonstrates sexual dimorphisms. We aim to examine the molecular mechanisms underlying sexual dimorphisms in NAFLD pathogenesis through a comprehensive multi-omics study. We integrated genomics (DNA variations), transcriptomics of liver and adipose tissue, and phenotypic data of NAFLD derived from female mice of ~ 100 strains included in the hybrid mouse diversity panel (HMDP) and compared the NAFLD molecular pathways and gene networks between sexes.

RESULTS

We identified both shared and sex-specific biological processes for NAFLD. Adaptive immunity, branched chain amino acid metabolism, oxidative phosphorylation, and cell cycle/apoptosis were shared between sexes. Among the sex-specific pathways were vitamins and cofactors metabolism and ion channel transport for females, and phospholipid, lysophospholipid, and phosphatidylinositol metabolism and insulin signaling for males. Additionally, numerous lipid and insulin-related pathways and inflammatory processes in the adipose and liver tissue appeared to show more prominent association with NAFLD in male HMDP. Using data-driven network modeling, we identified plausible sex-specific and tissue-specific regulatory genes as well as those that are shared between sexes. These key regulators orchestrate the NAFLD pathways in a sex- and tissue-specific manner. Gonadectomy experiments support that sex hormones may partially underlie the sexually dimorphic genes and pathways involved in NAFLD.

CONCLUSIONS

Our multi-omics integrative study reveals sex- and tissue-specific genes, processes, and networks underlying sexual dimorphism in NAFLD and may facilitate sex-specific precision medicine.

Identification of 12 novel loci that confer susceptibility to early-onset dyslipidemia

The circulating concentrations of triglycerides, high density lipoprotein (HDL)-cholesterol, and low density lipoprotein (LDL)-cholesterol have a substantial genetic component, and the heritability of early-onset dyslipidemia might be expected to be higher compared with late-onset forms. In the present study, exome-wide association studies (EWASs) were performed for early-onset hypertriglyceridemia, hypo-HDL-cholesterolemia, and hyper-LDL-cholesterolemia, with the aim to identify genetic variants that confer susceptibility to these conditions in the Japanese population. A total of 8,073 individuals aged ≤65 years were enrolled in the study. The EWASs for hypertriglyceridemia (2,664 cases and 5,294 controls), hypo-HDL-cholesterolemia (974 cases and 7,085 controls), and hyper-LDL-cholesterolemia (2,911 cases and 5,111 controls) were performed with Illumina Human Exome-12 v1.2 DNA Analysis BeadChip or Infinium Exome-24 v1.0 BeadChip arrays. The association of allele frequencies for 31,198, 31,133, or 31,175 single nucleotide polymorphisms (SNPs) to hypertriglyceridemia, hypo-HDL-cholesterolemia, or hyper-LDL-cholesterolemia, respectively, was examined with Fisher’s exact test. To compensate for multiple comparisons of genotypes with each of the three conditions, Bonferroni’s correction was applied for statistical significance of association. The results demonstrated that 25, 28 and 65 SNPs were significantly associated with hypertriglyceridemia, hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia, respectively. Multivariable logistic regression analysis with adjustment for age and sex revealed that all 25, 28 and 65 of these SNPs were significantly associated with hypertriglyceridemia, hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia, respectively. Following examination of the association of the identified SNPs to serum concentrations of triglycerides, HDL-cholesterol, or LDL-cholesterol, linkage disequilibrium of the SNPs, and results of previous genome-wide association studies, we newly identified chromosomal region 19p12 as a susceptibility locus for hypertriglyceridemia, eight loci (MOB3C-TMOD4, LPGAT1, EHD3, COL6A3, ZNF860-CACNA1D, COL6A5, DCLRE1C, ZNF77) for hypo-HDL-cholesterolemia, and three loci (KIAA0319-FAM65B, UBD, LOC105375015) for hyper-LDL-cholesterolemia. The present study thus identified 12 novel loci that may confer susceptibility to early-onset dyslipidemia. Determination of genotypes for the SNPs at these loci may prove informative for assessment of genetic risk for hypertriglyceridemia, hypo-HDL-cholesterolemia, or hyper-LDL-cholesterolemia in the Japanese population.