The Comparative Toxicogenomics Database's 10th year anniversary: update 2015

SADR: Self-Supervised Graph Learning With Adaptive Denoising for Drug Repositioning

Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected. At the same time, these methods have poor robustness to noise in the dataset. In response to the above challenges, we propose a drug repositioning model based on self-supervised graph learning with adptive denoising, called SADR. SADR uses data augmentation and contrastive learning strategies to learn feature representations of nodes, which can effectively solve the problems caused by sparse datasets. SADR includes an adaptive denoising training (ADT) component that can effectively identify noisy data during the training process and remove the impact of noise on the model. We have conducted comprehensive experiments on three datasets and have achieved better prediction accuracy compared to multiple baseline models. At the same time, we propose the top 10 new predictive approved drugs for treating two diseases. This demonstrates the ability of our model to identify potential drug candidates for disease indications.

Identifying novel chemical-related susceptibility genes for five psychiatric disorders through integrating genome-wide association study and tissue-specific 3'aQTL annotation datasets

The establishment of 3'aQTLs comprehensive database provides an opportunity to help explore the functional interpretation from the genome-wide association study (GWAS) data of psychiatric disorders. In this study, we aim to search novel susceptibility genes, pathways, and related chemicals of five psychiatric disorders via GWAS and 3'aQTLs datasets. The GWAS datasets of five psychiatric disorders were collected from the open platform of Psychiatric Genomics Consortium (PGC, https://www.med.unc.edu/pgc/ ) and iPSYCH ( https://ipsych.dk/ ) (Demontis et al. in Nat Genet 51(1):63-75, 2019; Grove et al. in Nat Genet 51:431-444, 2019; Genomic Dissection of Bipolar Disorder and Schizophrenia in Cell 173: 1705-1715.e1716, 2018; Mullins et al. in Nat Genet 53: 817-829; Howard et al. in Nat Neurosci 22: 343-352, 2019). The 3'untranslated region (3'UTR) alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) summary datasets of 12 brain regions were obtained from another public platform ( https://wlcb.oit.uci.edu/3aQTLatlas/ ) (Cui et al. in Nucleic Acids Res 50: D39-D45, 2022). First, we aligned the GWAS-associated SNPs of psychiatric disorders and datasets of 3'aQTLs, and then, the GWAS-associated 3'aQTLs were identified from the overlap. Second, gene ontology (GO) and pathway analysis was applied to investigate the potential biological functions of matching genes based on the methods provided by MAGMA. Finally, chemical-related gene-set analysis (GSA) was also conducted by MAGMA to explore the potential interaction of GWAS-associated 3'aQTLs and multiple chemicals in the mechanism of psychiatric disorders. A number of susceptibility genes with 3'aQTLs were found to be associated with psychiatric disorders and some of them had brain-region specificity. For schizophrenia (SCZ), HLA-A showed associated with psychiatric disorders in all 12 brain regions, such as cerebellar hemisphere (P = 1.58 × 10-36) and cortex (P = 1.58 × 10-36). GO and pathway analysis identified several associated pathways, such as Phenylpropanoid Metabolic Process (GO:0009698, P = 6.24 × 10-7 for SCZ). Chemical-related GSA detected several chemical-related gene sets associated with psychiatric disorders. For example, gene sets of Ferulic Acid (P = 6.24 × 10-7), Morin (P = 4.47 × 10-2) and Vanillic Acid (P = 6.24 × 10-7) were found to be associated with SCZ. By integrating the functional information from 3'aQTLs, we identified several susceptibility genes and associated pathways especially chemical-related gene sets for five psychiatric disorders. Our results provided new insights to understand the etiology and mechanism of psychiatric disorders.

Antagonizing Effects of Chromium Against Iron-Decreased Glucose Uptake by Regulating ROS-Mediated PI3K/Akt/GLUT4 Signaling Pathway in C2C12

To investigate the effect of chromium and iron on glucose metabolism via the PI3K/Akt/GLUT4 signaling pathway. Skeletal muscle gene microarray data in T2DM (GSE7014) was selected using Gene Expression Omnibus database. Element-gene interaction datasets of chromium and iron were extracted from comparative toxicogenomics database (CTD). Gene ontology (GO)and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using DAVID online tool. Cell viability, insulin-stimulated glucose uptake, intracellular reactive oxygen species (ROS) level, and protein expression level were measured in C2C12 cells. The bioinformatics research indicated that PI3K/Akt signaling pathway participated in the effects of chromium and iron associated with T2DM. Insulin-stimulated glucose uptake level was significantly higher in chromium picolinate (Cr group) and lower in ammonium iron citrate (FA group) than that for the control group (P < 0.05); chromium picolinate + ammonium iron citrate (Cr + FA group) glucose uptake level was higher than that for the FA group (P < 0.05). Intracellular ROS level was significantly higher in the FAC group than that for the control group (P < 0.05), and that for the Cr + FA group was lower than that for the FA group (P < 0.05). p-PI3K/PI3K, p-Akt/Akt, and GLUT4 levels were significantly lower in the FA group than that for the control group (P < 0.05), and the Cr + FA group had higher levels than the FA group (P < 0.05). Chromium might have a protective effect on iron-induced glucose metabolism abnormalities through the ROS-mediated PI3K/Akt/GLUT4 signaling pathway.

Involvement of toxic metals and PCBs mixture in the thyroid and male reproductive toxicity: In silico toxicogenomic data mining

Toxicological research is mostly limited to considering the effects of a single substance, even though the real exposure of people is reflected in their daily exposure to many different chemical substances in low-doses. This in silico toxicogenomic study aims to provide evidence for the selected environmental (organo)metals (lead, cadmium, methyl mercury) + polychlorinated biphenyls mixture involvement in the possible alteration of thyroid, and male reproductive system function, and furthermore to predict the possible toxic mechanisms of the environmental cocktail. The Comparative Toxicogenomic Database, GeneMANIA online software, and ToppGene Suite portal were used as the main tools for toxicogenomic data mining and gene ontology analysis. The results show that 35 annotated common genes between selected chemicals and endocrine system diseases can interact on the co-expression level. Our study highlighted the disruption of the cytokines, the cell's response to oxidative stress, and the influence of the transcription factors as the potential core of toxicological mechanisms of the discussed mixture's effects. The connected toxicological effects of the tested mixture were abnormal sperm cells, a disrupted level of testosterone, and thyroid hormones. The core mechanisms of these effects were inflammation, oxidative stress, disruption of androgen receptor signaling, and the alteration of the FOXO3-Keap-1/NRF2-HMOX1-NQO1 pathway signaling most likely controlled by the co-expression of overlapped genes among used chemicals. This in silico research can be used as a potential core for the determination of biomarkers that can be monitored in future further in vitro and in vivo experiments.

CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health

The molecular mechanisms connecting environmental exposures to adverse endpoints are often unknown, reflecting knowledge gaps. At the Comparative Toxicogenomics Database (CTD), we developed a bioinformatics approach that integrates manually curated, literature-based interactions from CTD to generate a "CGPD-tetramer": a 4-unit block of information organized as a step-wise molecular mechanism linking an initiating Chemical, an interacting Gene, a Phenotype, and a Disease outcome. Here, we describe a novel, user-friendly tool called CTD Tetramers that generates these evidence-based CGPD-tetramers for any curated chemical, gene, phenotype, or disease of interest. Tetramers offer potential solutions for the unknown underlying mechanisms and intermediary phenotypes connecting a chemical exposure to a disease. Additionally, multiple tetramers can be assembled to construct detailed modes-of-action for chemical-induced disease pathways. As well, tetramers can help inform environmental influences on adverse outcome pathways (AOPs). We demonstrate the tool's utility with relevant use cases for a variety of environmental chemicals (eg, perfluoroalkyl substances, bisphenol A), phenotypes (eg, apoptosis, spermatogenesis, inflammatory response), and diseases (eg, asthma, obesity, male infertility). Finally, we map AOP adverse outcome terms to corresponding CTD terms, allowing users to query for tetramers that can help augment AOP pathways with additional stressors, genes, and phenotypes, as well as formulate potential AOP disease networks (eg, liver cirrhosis and prostate cancer). This novel tool, as part of the complete suite of tools offered at CTD, provides users with computational datasets and their supporting evidence to potentially fill exposure knowledge gaps and develop testable hypotheses about environmental health.

A drug repurposing method based on inhibition effect on gene regulatory network

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

Altered offspring neurodevelopment in an L-NAME-induced preeclampsia rat model

Introduction

To investigate the mechanism underlying the increased risk of subsequent neurodevelopmental disorders in children born to mothers with preeclampsia, we evaluated the neurodevelopment of offspring of a preeclampsia rat model induced by the administration of N-nitro-L-arginine methyl ester (L-NAME) and identified unique protein signatures in the offspring cerebrospinal fluid.

Methods

Pregnant rats received an intraperitoneal injection of L-NAME (250 mg/kg/day) during gestational days 15-20 to establish a preeclampsia model. Behavioral experiments (negative geotaxis, open-field, rotarod treadmill, and active avoidance tests), immunohistochemistry [anti-neuronal nuclei (NeuN) staining in the hippocampal dentate gyrus and cerebral cortex on postnatal day 70], and proteome analysis of the cerebrospinal fluid on postnatal day 5 were performed on male offspring.

Results

Offspring of the preeclampsia dam exhibited increased growth restriction at birth (52.5%), but showed postnatal catch-up growth on postnatal day 14. Several behavioral abnormalities including motor development and vestibular function (negative geotaxis test: p < 0.01) in the neonatal period; motor coordination and learning skills (rotarod treadmill test: p = 0.01); and memory skills (active avoidance test: p < 0.01) in the juvenile period were observed. NeuN-positive cells in preeclampsia rats were significantly reduced in both the hippocampal dentate gyrus and cerebral cortex (p < 0.01, p < 0.01, respectively). Among the 1270 proteins in the cerebrospinal fluid identified using liquid chromatography-tandem mass spectrometry, 32 were differentially expressed. Principal component analysis showed that most cerebrospinal fluid samples achieved clear separation between preeclampsia and control rats. Pathway analysis revealed that differentially expressed proteins were associated with endoplasmic reticulum translocation, Rab proteins, and ribosomal proteins, which are involved in various nervous system disorders including autism spectrum disorders, schizophrenia, and Alzheimer's disease.

Conclusion

The offspring of the L-NAME-induced preeclampsia model rats exhibited key features of neurodevelopmental abnormalities on behavioral and pathological examinations similar to humans. We found altered cerebrospinal fluid protein profiling in this preeclampsia rat, and the unique protein signatures related to endoplasmic reticulum translocation, Rab proteins, and ribosomal proteins may be associated with subsequent adverse neurodevelopment in the offspring.

In silico assessment of mixture toxicity mechanisms involved in the pathogenesis of thyroid diseases: the combination of toxic metal(oid)s and decabrominated diphenyl ether

The current study aimed to assess the connection between the mixture of lead (Pb), cadmium (Cd), arsenic (As), methylmercury (MeHg) and decabrominated diphenyl ether (decaBDE) and thyroid function, by using in silico toxicogenomic data-mining approach. To obtain the linkage between investigated toxic mixture and thyroid diseases (TDs), the Comparative Toxicogenomics Database (CTD) was used, while gene ontology (GO) enrichment analysis was performed by ToppGeneSuite portal. The analysis has shown 10 genes connected to all chemicals present in the mixture and TDs (CAT, GSR, IFNG, IL1B, IL4, IL6, MAPK1, SOD2, TGFB1, TNF), most of which were in co-expression (45.68%), or belonged to the same pathway (30.47%). Top 5 biological processes and molecular functions affected by the investigated mixture emphasized the role of two common mechanisms - oxidative stress and inflammation. Cytokines and inflammatory response was listed as the main molecular pathway that may be triggered by simultaneous exposure to toxic metal(oid)s and decaBDE and connected to TDs. The direct relations between Pb/decaBDE and redox status impairment in thyroid tissue was confirmed by our chemical-phenotype interaction analysis, while the strongest linkage between Pb, As and decaBDE and thyroid disorders was found. The obtained results provide better understanding of molecular mechanisms involved in the thyrotoxicity of the investigated mixture, and can be used to direct further research.

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration

BACKGROUND

Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis.

METHODS

The datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program.

RESULTS

Module-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib.

CONCLUSIONS

We identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D.

Integrative Analysis of Proteome-wide Association Studies and Functional Enrichment Analysis to Identify Genes and Chemicals Associated with Alcohol Dependence

OBJECTIVES

Alcohol dependence accounts for a large proportion of the global burden of disease and disability. This study aims to investigate the candidate genes and chemicals associated with alcohol dependence.

METHODS

Using data from published alcohol dependence genome-wide association studies, we first conducted a proteome-wide association study of alcohol dependence by integrating alcohol dependence genome-wide association studies with 2 human brain reference proteomes of dorsolateral prefrontal cortex from the Religious Order Study and Rush Memory and Aging Project and the Banner Sun Health Research Institute. Then, based on the identified genes in proteome-wide association study, we conducted functional enrichment analysis and chemical-related functional enrichment analysis to detect the related Gene Ontology terms and chemicals.

RESULTS

Proteome-wide association study identified several potential candidate genes for alcohol dependence, such as GOT2 (P = 7.59 × 10-6) and C3orf33 (P = 5.00 × 10-3). Furthermore, functional enrichment analysis identified multiple candidate Gene Ontology terms associated with alcohol dependence, such as glyoxylate metabolic process (adjusted P = 2.99 × 10-6) and oxoglutarate metabolic process (adjusted P = 9.95 × 10-6). Chemical-related functional enrichment analysis detected several alcohol dependence-related candidate chemicals, such as pitavastatin (P = 2.00 × 10-4), cannabinoids (P = 4.00 × 10-4), 11-nor-Δ(9)-tetrahydrocannabinol-9-carboxylic acid (P = 4.00 × 10-4), and gabapentin (P = 2.00 × 10-3).

CONCLUSIONS

Our study reports multiple candidate genes and chemicals associated with alcohol dependence, providing novel clues for understanding the biological mechanism of alcohol dependence.

CXCR4 and TYROBP mediate the development of atrial fibrillation via inflammation

Atrial fibrillation (AF) is a rapid supraventricular arrhythmia. However, the pathogenesis of atrial fibrillation remains controversial. We obtained transcriptome expression profiles GSE41177, GSE115574 and GSE79768 from GEO database. WGCNA was performed, DEGs were screened, PPI network was constructed using STRING database. CTD database was used to identify the reference score of hub genes associated with cardiovascular diseases. Prediction of miRNAs of hub genes was performed by TargetScan. DIANA‐miRPath v3.0 was applied to make functional annotation of miRNA. The animal model of atrial fibrillation was constructed, RT‐PCR was used to verify the expression of hub genes. Immunofluorescence assay for THBS2 and VCAN was made to identify molecular. Design of BP neural network was made to explore the prediction relationship of CXCR4 and TYROBP on AF. The merged datasets contained 104 up‐regulated and 34 down‐regulated genes. GO and KEGG enrichment analysis results of DEGs showed they were mainly enriched in ‘regulation of release of sequestered calcium ion into cytosol’, ‘actin cytoskeleton organization’ and ‘focal adhesion’. The hub genes were CXCR4, SNAI2, S100A4, IGFBP3, CSNK2A1, CHGB, VCAN, APOE, C1QC and TYROBP, which were up‐regulated expression in the AF compared with control tissues. There was strong correlation among the CXCR4, TYROBP and AF based on the BP neural network. Through training, best training performance is 9.6474e‐05 at epoch 14, and the relativity was 0.99998. CXCR4 and TYROBP might be involved in the development of atrial fibrillation by affecting inflammation‐related signalling pathways and may serve as targets for early diagnosis and preventive treatment.

GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions

MOTIVATION

Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network.

RESULTS

In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale.

AVAILABILITY

Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.

M2PP: a novel computational model for predicting drug-targeted pathogenic proteins

BACKGROUND

Detecting pathogenic proteins is the origin way to understand the mechanism and resist the invasion of diseases, making pathogenic protein prediction develop into an urgent problem to be solved. Prediction for genome-wide proteins may be not necessarily conducive to rapidly cure diseases as developing new drugs specifically for the predicted pathogenic protein always need major expenditures on time and cost. In order to facilitate disease treatment, computational method to predict pathogenic proteins which are targeted by existing drugs should be exploited.

RESULTS

In this study, we proposed a novel computational model to predict drug-targeted pathogenic proteins, named as M2PP. Three types of features were presented on our constructed heterogeneous network (including target proteins, diseases and drugs), which were based on the neighborhood similarity information, drug-inferred information and path information. Then, a random forest regression model was trained to score unconfirmed target-disease pairs. Five-fold cross-validation experiment was implemented to evaluate model's prediction performance, where M2PP achieved advantageous results compared with other state-of-the-art methods. In addition, M2PP accurately predicted high ranked pathogenic proteins for common diseases with public biomedical literature as supporting evidence, indicating its excellent ability.

CONCLUSIONS

M2PP is an effective and accurate model to predict drug-targeted pathogenic proteins, which could provide convenience for the future biological researches.

A Text Mining Protocol for Extracting Drug-Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease

Drug-drug interactions (DDIs) and adverse drug reactions (ADR) are experienced by many patients, especially by elderly population due to their multiple comorbidities and polypharmacy. Databases such as PubMed contain hundreds of abstracts with DDI and ADR information. PubMed is being updated every day with thousands of abstracts. Therefore, manually retrieving the data and extracting the relevant information is tedious task. Hence, automated text mining approaches are required to retrieve DDI and ADR information from PubMed. Recently we developed a hybrid approach for predicting DDI and ADR information from PubMed. There are many other existing approaches for retrieving DDI and ADR information from PubMed. However, none of the approaches are meant for retrieving DDI and ADR specific to patient population, gender, pharmacokinetics, and pharmacodynamics. Here, we present a text mining protocol which is based on our recent work for retrieving DDI and ADR information specific to patient population, gender, pharmacokinetics, and pharmacodynamics from PubMed.

A Text Mining Protocol for Predicting Drug-Drug Interaction and Adverse Drug Reactions from PubMed Articles

Drug-drug interactions (DDIs) and adverse drug reactions (ADRs) occur during the pharmacotherapy of multiple comorbidities and in susceptible individuals. DDIs and ADRs limit the therapeutic outcomes in pharmacotherapy. DDIs and ADRs have significant impact on patients' life and health care cost. Hence, knowledge of DDI and ADRs is required for providing better clinical outcomes to patients. Various approaches are developed by the scientific community to document and report the occurrences of DDIs and ADRs through scientific publications. Due to the enormously increasing number of publications and the requirement of updated information on DDIs and ADRs, manual retrieval of data is time consuming and laborious. Various automated techniques are developed to get information on DDIs and ADRs. One such technique is text mining of DDIs and ADRs from published biomedical literature in PubMed. Here, we present a recently developed text mining protocol for predicting DDIs and ADRs from PubMed abstracts.

In Silico Prediction of Potential Drug Combinations for Type 2 Diabetes Mellitus by an Integrated Network and Transcriptome Analysis

Type 2 diabetes mellitus (T2DM) is a heterogeneous disorder, so achieving the desired therapeutic efficacy through monotherapy is tricky. Drug combinations play a vital role in treating multiple complex diseases by providing increased efficacy and reduced toxicity. Here, we adopted a computational framework to discover potential drugs and drug pairs for T2DM. Firstly, we collected T2DM-associated genes and constructed the disease module for T2DM. Then, by quantifying the proximity between drugs and the disease module, we found out potential drugs. Based on the drug-induced gene expression profiles, we further performed Gene Set Enrichment Analysis (GSEA) on these drugs and identified several potential candidates. In addition, through network-based separation, potential drug combinations for T2DM were predicted. Results from this study could provide insights for anti-T2DM drug discovery and rational drug use of existing agents. As a useful computational framework, our approach could also be applied in drug research for other complex diseases.

DocR-BERT: Document-level R-BERT for Chemical-induced Disease Relation Extraction via Gaussian Probability Distribution

Chemical-induced disease (CID) relation extraction from biomedical articles plays an important role in disease treatment and drug development. Existing methods are insufficient for capturing complete document level semantic information due to ignoring semantic information of entities in different sentences. In this work, we proposed an effective document-level relation extraction model to automatically extract intra-/inter-sentential CID relations from articles. Firstly, our model employed BERT to generate contextual semantic representations of the title, abstract and shortest dependency paths (SDPs). Secondly, to enhance the semantic representation of the whole document, cross attention with self-attention (named cross2self-attention) between abstract, title and SDPs was proposed to learn the mutual semantic information. Thirdly, to distinguish the importance of the target entity in different sentences, the Gaussian probability distribution was utilized to compute the weights of the co-occurrence sentence and its adjacent entity sentences. More complete semantic information of the target entity is collected from all entities occurring in the document via our presented document-level R-BERT (DocR-BERT). Finally, the related representations were concatenated and fed into the softmax function to extract CIDs. We evaluated the model on the CDR corpus provided by BioCreative V. The proposed model without external resources is superior in performance as compared with other state-of-the-art models (our model achieves 53.5%, 70%, and 63.7% of the F1-score on inter-/intra-sentential and overall CDR dataset). The experimental results indicate that cross2self-attention, the Gaussian probability distribution and DocR-BERT can effectively improve the CID extraction performance. Furthermore, the mutual semantic information learned by the cross self-attention from abstract towards title can significantly influence the extraction performance of document-level biomedical relation extraction tasks.

ELMO1 signaling is a promoter of osteoclast function and bone loss

Osteoporosis affects millions worldwide and is often caused by osteoclast induced bone loss. Here, we identify the cytoplasmic protein ELMO1 as an important ‘signaling node’ in osteoclasts. We note that ELMO1 SNPs associate with bone abnormalities in humans, and that ELMO1 deletion in mice reduces bone loss in four in vivo models: osteoprotegerin deficiency, ovariectomy, and two types of inflammatory arthritis. Our transcriptomic analyses coupled with CRISPR/Cas9 genetic deletion identify Elmo1 associated regulators of osteoclast function, including cathepsin G and myeloperoxidase. Further, we define the ‘ELMO1 interactome’ in osteoclasts via proteomics and reveal proteins required for bone degradation. ELMO1 also contributes to osteoclast sealing zone on bone-like surfaces and distribution of osteoclast-specific proteases. Finally, a 3D structure-based ELMO1 inhibitory peptide reduces bone resorption in wild type osteoclasts. Collectively, we identify ELMO1 as a signaling hub that regulates osteoclast function and bone loss, with relevance to osteoporosis and arthritis.

Osteoporosis and bone fractures affect millions of patients worldwide and are often due to increased bone resorption. Here the authors identify the cytoplasmic protein ELMO1 as an important ‘signaling node’ promoting the bone resorption function of osteoclasts.

Polymorphisms at site 469 of B-RAF protein associated with skin melanoma may be correlated with dabrafenib resistance: An in silico study

Melanoma is a type of skin cancer. Numerous genes and their proteins are strongly associated with melanoma susceptibility. This study aims to use an in silico method to identify genetic variants in the melanoma susceptibility gene. The COSMIC database was queried for genes and cross-referenced with three environment-gene interaction databases (EGP, SeattleSNPs and CTD) to identify shared genes. The majority of approved skin melanoma drugs were found to act on the protein serine/threonine-protein kinase (B-RAF) encoded by the BRAF gene, which was also present in all three referenced databases. Comprehensive computational analysis was performed to predict deleterious genetic variants associated with skin melanoma, and the nsSNPs G469V and G469E were prioritized based on their predicted deleterious effects. Molecular dynamic simulation analysis of the B-RAF protein mutants G469V and G469E reveals that variations in the amino acid conformation at the drug binding site result in inconsistency in drug interaction. Additionally, this analysis showed that the G469V and G469E mutants have lower binding energy for dabrafenib than the wild type. The population with the highest frequency of each deleterious and pathogenic variant has been determined. The study's findings would support the development of more effective treatment strategies for skin melanoma. Communicated by Ramaswamy H. Sarma.

Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins

Infectious diseases in humans appear to be one of the most primary public health issues. Identification of novel disease-associated proteins will furnish an efficient recognition of the novel therapeutic targets. Here, we develop a Graph Convolutional Network (GCN)-based model called PINDeL to identify the disease-associated host proteins by integrating the human Protein Locality Graph and its corresponding topological features. Because of the amalgamation of GCN with the protein interaction network, PINDeL achieves the highest accuracy of 83.45% while AUROC and AUPRC values are 0.90 and 0.88, respectively. With high accuracy, recall, F1-score, specificity, AUROC, and AUPRC, PINDeL outperforms other existing machine-learning and deep-learning techniques for disease gene/protein identification in humans. Application of PINDeL on an independent dataset of 24320 proteins, which are not used for training, validation, or testing purposes, predicts 6448 new disease-protein associations of which we verify 3196 disease-proteins through experimental evidence like disease ontology, Gene Ontology, and KEGG pathway enrichment analyses. Our investigation informs that experimentally-verified 748 proteins are indeed responsible for pathogen-host protein interactions of which 22 disease-proteins share their association with multiple diseases such as cancer, aging, chem-dependency, pharmacogenomics, normal variation, infection, and immune-related diseases. This unique Graph Convolution Network-based prediction model is of utmost use in large-scale disease-protein association prediction and hence, will provide crucial insights on disease pathogenesis and will further aid in developing novel therapeutics.

Bioinformatics integrated analysis to investigate candidate biomarkers and associated metabolites in osteosarcoma

BACKGROUND

This study hoped to explore the potential biomarkers and associated metabolites during osteosarcoma (OS) progression based on bioinformatics integrated analysis.

METHODS

Gene expression profiles of GSE28424, including 19 human OS cell lines (OS group) and 4 human normal long bone tissue samples (control group), were downloaded. The differentially expressed genes (DEGs) in OS vs. control were investigated. The enrichment investigation was performed based on DEGs, followed by protein-protein interaction network analysis. Then, the feature genes associated with OS were explored, followed by survival analysis to reveal prognostic genes. The qRT-PCR assay was performed to test the expression of these genes. Finally, the OS-associated metabolites and disease-metabolic network were further investigated.

RESULTS

Totally, 357 DEGs were revealed between the OS vs. control groups. These DEGs, such as CXCL12, were mainly involved in functions like leukocyte migration. Then, totally, 38 feature genes were explored, of which 8 genes showed significant associations with the survival of patients. High expression of CXCL12, CEBPA, SPARCL1, CAT, TUBA1A, and ALDH1A1 was associated with longer survival time, while high expression of CFLAR and STC2 was associated with poor survival. Finally, a disease-metabolic network was constructed with 25 nodes including two disease-associated metabolites cyclophosphamide and bisphenol A (BPA). BPA showed interactions with multiple prognosis-related genes, such as CXCL12 and STC2.

CONCLUSION

We identified 8 prognosis-related genes in OS. CXCL12 might participate in OS progression via leukocyte migration function. BPA might be an important metabolite interacting with multiple prognosis-related genes.

Hepatotoxic evaluation of toosendanin via biomarker quantification and pathway mapping of large-scale chemical proteomics

Drug-induced liver injury (DILI) is a major side effect, sometimes can't be exactly evaluated by current approaches partly as the covalent modification of drug or its reactive metabolites (RMs) with proteins is a possible reason. In this study, we developed a rapid, sensitive, and specific analytical method to assess the hepatotoxicity induced by drug covalently modified proteins based on the quantification of the modified amino acids using toosendanin (TSN), a hepatotoxic chemical, as an example. TSN RM-protein adducts both in rat liver and blood showed good correlation with the severity of hepatotoxicity. Thus, TSN RM-protein adducts in serum can potentially serve as minimally invasive biomarkers of hepatotoxicity. Meanwhile, large-scale chemical proteomics analysis showed that at least 84 proteins were modified by TSN RMs in rat liver, and the bioinformatics analysis revealed that TSN might induce hepatotoxicity through multi-target protein-protein interaction especially involved in energy metabolism. These findings suggest that our approach may serve as a valuable tool to evaluate DILI and investigate the possible mechanism, especially for complex compounds.

Clinical Bioinformatics in Precise Diagnosis of Mitochondrial Disease

Clinical bioinformatics system is well-established for diagnosing genetic disease based on next-generation sequencing, but requires special considerations when being adapted for the next-generation sequencing-based genetic diagnosis of mitochondrial diseases. Challenges are caused by the involvement of mitochondrial DNA genome in disease etiology. Heteroplasmy and haplogroup are key factors in interpreting mitochondrial DNA variant effects. Data resources and tools for analyzing variant and sequencing data are available at MSeqDR, MitoMap, and HmtDB. Revised specifications of the American College of Medical Genetics/Association of Molecular Pathology standards and guidelines for mitochondrial DNA variant interpretation are proposed by the MSeqDr Consortium and community experts.

Predicting Drug-Disease Association Based on Ensemble Strategy

Drug repositioning is used to find new uses for existing drugs, effectively shortening the drug research and development cycle and reducing costs and risks. A new model of drug repositioning based on ensemble learning is proposed. This work develops a novel computational drug repositioning approach called CMAF to discover potential drug-disease associations. First, for new drugs and diseases or unknown drug-disease pairs, based on their known neighbor information, an association probability can be obtained by implementing the weighted K nearest known neighbors (WKNKN) method and improving the drug-disease association information. Then, a new drug similarity network and new disease similarity network can be constructed. Three prediction models are applied and ensembled to enable the final association of drug-disease pairs based on improved drug-disease association information and the constructed similarity network. The experimental results demonstrate that the developed approach outperforms recent state-of-the-art prediction models. Case studies further confirm the predictive ability of the proposed method. Our proposed method can effectively improve the prediction results.

Identifying insomnia-related chemicals through integrative analysis of genome-wide association studies and chemical-genes interaction information

STUDY OBJECTIVES

Insomnia is a common sleep disorder and constitutes a major issue in modern society. We provide new clues for revealing the association between environmental chemicals and insomnia.

METHODS

Three genome-wide association studies (GWAS) summary datasets of insomnia (n = 113,006, n = 1,331,010, and n = 453,379, respectively) were driven from the UK Biobank, 23andMe, and deCODE. The chemical-gene interaction dataset was downloaded from the Comparative Toxicogenomics Database. First, we conducted a meta-analysis of the three datasets of insomnia using the METAL software. Using the result of meta-analysis, transcriptome-wide association studies were performed to calculate the expression association testing statistics of insomnia. Then chemical-related gene set enrichment analysis (GSEA) was used to explore the association between chemicals and insomnia.

RESULTS

For GWAS meta-analysis dataset of insomnia, we identified 42 chemicals associated with insomnia in brain tissue (p < 0.05) by GSEA. We detected five important chemicals such as pinosylvin (p = 0.0128), bromobenzene (p = 0.0134), clonidine (p = 0.0372), gabapentin (p = 0.0372), and melatonin (p = 0.0404) which are directly associated with insomnia.

CONCLUSION

Our study results provide new clues for revealing the roles of environmental chemicals in the development of insomnia.

Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics

Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.

Network Pharmacology and Molecular Docking Suggest the Mechanism for Biological Activity of Rosmarinic Acid

Rosmarinic acid (RosA) is a natural phenolic acid compound, which is mainly extracted from Labiatae and Arnebia. At present, there is no systematic analysis of its mechanism. Therefore, we used the method of network pharmacology to analyze the mechanism of RosA. In our study, PubChem database was used to search for the chemical formula and the Chemical Abstracts Service (CAS) number of RosA. Then, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to evaluate the pharmacodynamics of RosA, and the Comparative Toxicogenomics Database (CTD) was used to identify the potential target genes of RosA. In addition, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were carried out by using the web-based gene set analysis toolkit (WebGestalt). At the same time, we uploaded the targets to the STRING database to obtain the protein interaction network. Then, we carried out a molecular docking about targets and RosA. Finally, we used Cytoscape to establish a visual protein-protein interaction network and drug-target-pathway network and analyze these networks. Our data showed that RosA has good biological activity and drug utilization. There are 55 target genes that have been identified. Then, the bioinformatics analysis and network analysis found that these target genes are closely related to inflammatory response, tumor occurrence and development, and other biological processes. These results demonstrated that RosA can act on a variety of proteins and pathways to form a systematic pharmacological network, which has good value in drug development and utilization.

Ontology-driven weak supervision for clinical entity classification in electronic health records

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.

CTD Anatomy: analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies

The Comparative Toxicogenomics Database (CTD) is a freely available public resource that curates and interrelates chemical, gene/protein, phenotype, disease, organism, and exposure data. CTD can be used to address toxicological mechanisms for environmental chemicals and facilitate the generation of testable hypotheses about how exposures affect human health. At CTD, manually curated interactions for chemical-induced phenotypes are enhanced with anatomy terms (tissues, fluids, and cell types) to describe the physiological system of the reported event. These same anatomy terms are used to annotate the human media (e.g., urine, hair, nail, blood, etc.) in which an environmental chemical was assayed for exposure. Currently, CTD uses more than 880 unique anatomy terms to contextualize over 255,000 chemical-phenotype interactions and 167,000 exposure statements. These annotations allow chemical-phenotype interactions and exposure data to be explored from a novel, anatomical perspective. Here, we describe CTD's anatomy curation process (including the construction of a controlled, interoperable vocabulary) and new anatomy webpages (that coalesce and organize the curated chemical-phenotype and exposure data sets). We also provide examples that demonstrate how this feature can be used to identify system- and cell-specific chemical-induced toxicities, help inform exposure data, prioritize phenotypes for environmental diseases, survey tissue and pregnancy exposomes, and facilitate data connections with external resources. Anatomy annotations advance understanding of environmental health by providing new ways to explore and survey chemical-induced events and exposure studies in the CTD framework.

Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways

Hepatocellular carcinoma (HCC) is a significant health problem worldwide with poor prognosis. Drug repositioning represents a profitable strategy to accelerate drug discovery in the treatment of HCC. In this study, we developed a new approach for predicting therapeutic drugs for HCC based on tissue-specific pathways and identified three newly predicted drugs that are likely to be therapeutic drugs for the treatment of HCC. We validated these predicted drugs by analyzing their overlapping drug indications reported in PubMed literature. By using the cancer cell line data in the database, we constructed a Connectivity Map (CMap) profile similarity analysis and KEGG enrichment analysis on their related genes. By experimental validation, we found securinine and ajmaline significantly inhibited cell viability of HCC cells and induced apoptosis. Among them, securinine has lower toxicity to normal liver cell line, which is worthy of further research. Our results suggested that the proposed approach was effective and accurate for discovering novel therapeutic options for HCC. This method also could be used to indicate unmarked drug-disease associations in the Comparative Toxicogenomics Database. Meanwhile, our method could also be applied to predict the potential drugs for other types of tumors by changing the database.

Comparative Toxicogenomics Database (CTD): update 2021

The public Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is an innovative digital ecosystem that relates toxicological information for chemicals, genes, phenotypes, diseases, and exposures to advance understanding about human health. Literature-based, manually curated interactions are integrated to create a knowledgebase that harmonizes cross-species heterogeneous data for chemical exposures and their biological repercussions. In this biennial update, we report a 20% increase in CTD curated content and now provide 45 million toxicogenomic relationships for over 16 300 chemicals, 51 300 genes, 5500 phenotypes, 7200 diseases and 163 000 exposure events, from 600 comparative species. Furthermore, we increase the functionality of chemical-phenotype content with new data-tabs on CTD Disease pages (to help fill in knowledge gaps for environmental health) and new phenotype search parameters (for Batch Query and Venn analysis tools). As well, we introduce new CTD Anatomy pages that allow users to uniquely explore and analyze chemical-phenotype interactions from an anatomical perspective. Finally, we have enhanced CTD Chemical pages with new literature-based chemical synonyms (to improve querying) and added 1600 amino acid-based compounds (to increase chemical landscape). Together, these updates continue to augment CTD as a powerful resource for generating testable hypotheses about the etiologies and molecular mechanisms underlying environmentally influenced diseases.

'In silico' toxicology methods in drug safety assessment

While experimental animal investigation has historically been the most conventional approach conducted to assess drug safety and is currently considered the main method for determining drug toxicity, these studies are constricted by cost, time, and ethical approvals. Over the last 20 years, there have been significant advances in computational sciences and computer data processing, while knowledge of alternative techniques and their application has developed into a valuable skill in toxicology. Thus, the application of in silico methods in drug safety assessment is constantly increasing. They are very complex and are grounded on accumulated knowledge from toxicology, bioinformatics, biochemistry, statistics, mathematics, as well as molecular biology. This review will summarize current state-of-the-art scientific data on the use of in silico methods in toxicity testing, taking into account their shortcomings, and highlighting the strategies that should deliver consistent results, while covering the applications of in silico methods in preclinical trials and drug impurities toxicity testing.

Systematic Analysis of Protein-Protein and Gene-Environment Interactions to Decipher the Cognitive Mechanisms of Autism Spectrum Disorder

Autism spectrum disorder (ASD), a heterogeneous neurodevelopmental disorder resulting from both genetic and environmental risk factors, is manifested by deficits in cognitive function. Elucidating the cognitive disorder-relevant biological mechanisms may open up promising therapeutic approaches. In this work, we mined ASD cognitive phenotype proteins to construct and analyze protein-protein and gene-environment interaction networks. Incorporating the protein-protein interaction (PPI), human cognition proteins, and connections of autism-cognition proteins enabled us to generate an autism-cognition network (ACN). With the topological analysis of ACN, important proteins, highly clustered modules, and 3-node motifs were identified. Moreover, the impact of environmental exposures in cognitive impairment was investigated through chemicals that target the cognition-related proteins. Functional enrichment analysis of the ACN-associated modules and chemical targets revealed biological processes involved in the cognitive deficits of ASD. Among the 17 identified hub-bottlenecks in the ACN, PSD-95 was recognized as an important protein through analyzing the module and motif interactions. PSD-95 and its interacting partners constructed a cognitive-specific module. This hub-bottleneck interacted with the 89 cognition-related 3-node motifs. The identification of gene-environment interactions indicated that most of the cognitive-related proteins interact with bisphenol A (BPA) and valproic acid (VPA). Moreover, we detected significant expression changes of 56 cognitive-specific genes using four ASD microarray datasets in the GEO database, including GSE28521, GSE26415, GSE18123 and GSE29691. Our outcomes suggest future endeavors for dissecting the PSD-95 function in ASD and evaluating the various environmental conditions to discover possible mechanisms of the different levels of cognitive impairment.

IFN-γ enhances cell-mediated cytotoxicity against keratinocytes via JAK2/STAT1 in lichen planus

Lichen planus (LP) is a chronic debilitating inflammatory disease of unknown etiology affecting the skin, nails, and mucosa with no current FDA-approved treatments. It is histologically characterized by dense infiltration of T cells and epidermal keratinocyte apoptosis. Using global transcriptomic profiling of patient skin samples, we demonstrate that LP is characterized by a type II interferon (IFN) inflammatory response. The type II IFN, IFN-γ, is demonstrated to prime keratinocytes and increase their susceptibility to CD8⁺ T cell-mediated cytotoxic responses through MHC class I induction in a coculture model. We show that this process is dependent on Janus kinase 2 (JAK2) and signal transducer and activator of transcription 1 (STAT1), but not JAK1 or STAT2 signaling. Last, using drug prediction algorithms, we identify JAK inhibitors as promising therapeutic agents in LP and demonstrate that the JAK1/2 inhibitor baricitinib fully protects keratinocytes against cell-mediated cytotoxic responses in vitro. In summary, this work elucidates the role and mechanisms of IFN-γ in LP pathogenesis and provides evidence for the therapeutic use of JAK inhibitors to limit cell-mediated cytotoxicity in patients with LP.

Environmental Impact on Male (In)Fertility via Epigenetic Route

In the last 40 years, male reproductive health-which is very sensitive to both environmental exposure and metabolic status-has deteriorated and the poor sperm quality observed has been suggested to affect offspring development and its health in adult life. In this scenario, evidence now suggests that epigenetics shapes endocrine functions, linking genetics and environment. During fertilization, spermatozoa share with the oocyte their epigenome, along with their haploid genome, in order to orchestrate embryo development. The epigenetic signature of spermatozoa is the result of a dynamic modulation of the epigenetic marks occurring, firstly, in the testis-during germ cell progression-then, along the epididymis, where spermatozoa still receive molecules, conveyed by epididymosomes. Paternal lifestyle, including nutrition and exposure to hazardous substances, alters the phenotype of the next generations, through the remodeling of a sperm epigenetic blueprint that dynamically reacts to a wide range of environmental and lifestyle stressors. With that in mind, this review will summarize and discuss insights into germline epigenetic plasticity caused by environmental stimuli and diet and how spermatozoa may be carriers of induced epimutations across generations through a mechanism known as paternal transgenerational epigenetic inheritance.

Ontology-driven weak supervision for clinical entity classification in electronic health records

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.

A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions

Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.

A systematic review of microRNA expression studies with exposure to bisphenol A

Bisphenol A (BPA), as a common industrial component, is generally consumed in the synthesis of polymeric materials. To gain a deeper understanding of the detrimental effects of BPA, BPA-induced microRNA (miRNA) alterations were investigated. A systematic search was performed in the PubMed, SCOPUS and Web of Science databases to evoke relevant published data up to August 10, 2019. We identified altered miRNAs that have been repeated in at least three studies. Moreover, miRNA homology analysis between human and nonhuman species was performed to determine the toxicity signatures of BPA in human exposure. In addition, to reflect the effects of environmental exposure levels of BPA, the study designs were categorized into two groups, including low and high doses according to the previous definitions. In total, 28 studies encountered our criteria and 17 miRNAs were identified that were differentially expressed in at least three independent studies. Upregulating miR-146a and downregulating miR-192, miR-134, miR-27b and miR-324 were found in three studies. MiR-122 and miR-29a were upregulated in four studies after BPA exposure, and miR-21 was upregulated in six studies. The results indicate that BPA at low-level exposures can also alter miRNA expression in response to toxicity. Finally, the miRNA-related pathways showed that BPA seriously can affect human health through various cell signaling pathways, which were predictable and consistent with existing studies. Overall, our findings suggest that further studies should be conducted to examine the role of miRNA level changes in human BPA exposure.

Bioinformatic Analysis Reveals Phosphodiesterase 4D-Interacting Protein as a Key Frontal Cortex Dementia Switch Gene

The mechanisms that initiate dementia are poorly understood and there are currently no treatments that can slow their progression. The identification of key genes and molecular pathways that may trigger dementia should help reveal potential therapeutic reagents. In this study, SWItch Miner software was used to identify phosphodiesterase 4D-interacting protein as a key factor that may lead to the development of Alzheimer’s disease, vascular dementia, and frontotemporal dementia. Inflammation, PI3K-AKT, and ubiquitin-mediated proteolysis were identified as the main pathways that are dysregulated in these dementias. All of these dementias are regulated by 12 shared transcription factors. Protein–chemical interaction network analysis of dementia switch genes revealed that valproic acid may be neuroprotective for these dementias. Collectively, we identified shared and unique dysregulated gene expression, pathways and regulatory factors among dementias. New key mechanisms that lead to the development of dementia were revealed and it is expected that these data will advance personalized medicine for patients.

Multimodal network diffusion predicts future disease-gene-chemical associations

MOTIVATION

Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction.

RESULTS

We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies.

AVAILABILITY AND IMPLEMENTATION

Code and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Comparative Toxicogenomics Database: update 2019

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a premier public resource for literature-based, manually curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. In this biennial update, we present our new chemical–phenotype module that codes chemical-induced effects on phenotypes, curated using controlled vocabularies for chemicals, phenotypes, taxa, and anatomical descriptors; this module provides unique opportunities to explore cellular and system-level phenotypes of the pre-disease state and allows users to construct predictive adverse outcome pathways (linking chemical–gene molecular initiating events with phenotypic key events, diseases, and population-level health outcomes). We also report a 46% increase in CTD manually curated content, which when integrated with other datasets yields more than 38 million toxicogenomic relationships. We describe new querying and display features for our enhanced chemical–exposure science module, providing greater scope of content and utility. As well, we discuss an updated MEDIC disease vocabulary with over 1700 new terms and accession identifiers. To accommodate these increases in data content and functionality, CTD has upgraded its computational infrastructure. These updates continue to improve CTD and help inform new testable hypotheses about the etiology and mechanisms underlying environmentally influenced diseases.

The Role of Overexpressed Apolipoprotein AV in Insulin-Resistant Hepatocytes

In this paper, we sought to explore the relationship between apolipoprotein AV (APOAV) overexpression and insulin resistance in hepatocytes. The insulin-resistant HepG2 cell model was constructed, and then, APOAV-overexpressed HepG2 cells (B-M) were induced by infecting with a recombinant adenovirus vector. Microarray data were developed from B-M samples compared with negative controls (A-con), and the microarray data were analyzed by bioinformatic methods. APOAV-overexpression induced 313 upregulated genes and 563 downregulated ones in B-M sample. The differentially expressed genes (DEGs) were significantly classified in fat digestion and absorption pathway. Protein-protein interaction network was constructed, and AGTR1 (angiotensin II receptor type 1) and P2RY2 (purinergic receptor P2Y, G-protein coupled 2) were found to be the significant nodes closely related with G-protein related signaling. Additionally, overexpression of APOAV could change the expression of Glut4 and release the insulin resistance of hepatic cells. Thus, APOAV overexpression may prevent the insulin resistance in liver cells by mediating the genes such as AGTR1 and P2RY2.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.

Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

Hepatocellular carcinoma (HCC) is the fourth most common primary liver tumor and is an important medical problem worldwide. However, the use of current therapies for HCC is no possible to be cured, and despite numerous attempts and clinical trials, there are not so many approved targeted treatments for HCC. So, it is necessary to identify additional treatment strategies to prevent the growth of HCC tumors. We are looking for a systematic drug repositioning bioinformatics method to identify new drug candidates for the treatment of HCC, which considers not only aberrant genomic information, but also the changes of transcriptional landscapes. First, we screen the collection of HCC feature genes, i.e., kernel genes, which frequently mutated in most samples of HCC based on human mutation data. Then, the gene expression data of HCC in TCGA are combined to classify the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is calculated based on the kolmogorov-smirnov statistical method. Using this strategy, we identify five drugs that associated with HCC, including three drugs that could treat HCC and two drugs that might have side-effect on HCC. In addition, we also make Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. All these findings suggest that our approach is effective for accurate predicting novel therapeutic options for HCC and easily to be extended to other tumors.

CYP1B1, VEGFA, BCL2, and CDKN1A Affect the Development of Chronic Obstructive Pulmonary Disease

Purpose

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by poor airflow. The purpose of this study was to explore the mechanisms involved in the development of COPD.

Patients and Methods

The mRNA expression profile GSE100281, consisting of 79 COPD and 16 healthy samples, was acquired from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between COPD samples and healthy samples were analyzed using the limma package. Functional enrichment analysis for the DEGs was carried out using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Furthermore, DEG-compound pairs were predicted using the Comparative Toxicogenomics Database. The KEGG metabolite IDs corresponding to the compounds were also obtained through the MetaboAnalyst pipeline. Based on the diffusion algorithm, the metabolite network was constructed. Finally, the expression levels of key genes were determined using quantitative PCR (qPCR).

Results

There were 594 DEGs identified between the COPD and healthy samples, including 242 upregulated and 352 downregulated genes. A total of 696 DEG-compound pairs, such as BCL2-C00469 (ethanol) and BCL2-C00389 (quercetin) pairs, were predicted. CYP1B1, VEGFA, BCL2, and CDKN1A were included in the top 10 DEG-compound pairs. Additionally, 57 metabolites were obtained. In particular, hsa04750 (inflammatory mediator regulation of TRP channels)-C00469 (ethanol) and hsa04152 (AMPK signaling pathway)-C00389 (quercetin) pairs were found in the metabolite network. The results of qPCR showed that the expression of CYP1B1, VEGFA, BCL2, and CDKN1A was consistent with that predicted using bioinformatic analysis.

Conclusion

CYP1B1, VEGFA, BCL2, and CDKN1A may play important functions in the development and progression of COPD.

Target identification among known drugs by deep learning from heterogeneous networks

Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. Here, we develop deepDTnet, a deep learning methodology for new target identification and drug repurposing in a heterogeneous drug-gene-disease network embedding 15 types of chemical, genomic, phenotypic, and cellular network profiles. Trained on 732 U.S. Food and Drug Administration-approved small molecule drugs, deepDTnet shows high accuracy (the area under the receiver operating characteristic curve = 0.963) in identifying novel molecular targets for known drugs, outperforming previously published state-of-the-art methodologies. We then experimentally validate that deepDTnet-predicted topotecan (an approved topoisomerase inhibitor) is a new, direct inhibitor (IC50 = 0.43 μM) of human retinoic-acid-receptor-related orphan receptor-gamma t (ROR-γt). Furthermore, by specifically targeting ROR-γt, topotecan reveals a potential therapeutic effect in a mouse model of multiple sclerosis. In summary, deepDTnet offers a powerful network-based deep learning methodology for target identification to accelerate drug repurposing and minimize the translational gap in drug development.

Systematic Analysis of Environmental Chemicals That Dysregulate Critical Period Plasticity-Related Gene Expression Reveals Common Pathways That Mimic Immune Response to Pathogen

The tens of thousands of industrial and synthetic chemicals released into the environment have an unknown but potentially significant capacity to interfere with neurodevelopment. Consequently, there is an urgent need for systematic approaches that can identify disruptive chemicals. Little is known about the impact of environmental chemicals on critical periods of developmental neuroplasticity, in large part, due to the challenge of screening thousands of chemicals. Using an integrative bioinformatics approach, we systematically scanned 2001 environmental chemicals and identified 50 chemicals that consistently dysregulate two transcriptional signatures of critical period plasticity. These chemicals included pesticides (e.g., pyridaben), antimicrobials (e.g., bacitracin), metals (e.g., mercury), anesthetics (e.g., halothane), and other chemicals and mixtures (e.g., vehicle emissions). Application of a chemogenomic enrichment analysis and hierarchical clustering across these diverse chemicals identified two clusters of chemicals with one that mimicked an immune response to pathogen, implicating inflammatory pathways and microglia as a common chemically induced neuropathological process. Thus, we established an integrative bioinformatics approach to systematically scan thousands of environmental chemicals for their ability to dysregulate molecular signatures relevant to critical periods of development.