The Comparative Toxicogenomics Database: update 2019

Davis, Allan Peter; Grondin, Cynthia; Johnson, Robin J.; Sciaky, Daniela; McMorran, Roy; Wiegers, Jolene; Wiegers, Thomas C.; Mattingly, Carolyn J.

doi:10.1093/nar/gky868

Cited by 811 publications

(598 citation statements)

References 43 publications

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“…Finally, we analyzed the trends in genes known to be involved in nociception, pain and neuronal plasticity. Genes with | SSMD | > 2.0 between conditions, and known to be associated with pain from the Human Pain Genetics Database, and the Pain – Gene association geneset (from the Comparative Toxicogenomics Database in Harmonizome [15; 49]), as well as from the literature, are underlined in Table 2. They identify pain-associated genes in these pharmacologically relevant families that change in expression between acutely dissected and cultured DRGs.…”

Section: Resultsmentioning

confidence: 99%

Transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia focused on pharmacological targets

Wangzhou

McIlvried²,

Paige

et al. 2019

Preprint

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words)Dorsal root ganglion (DRG) neurons detect sensory inputs and are crucial for pain processing.They are often studied in vitro as dissociated cell cultures with the assumption that this reasonably represents in vivo conditions. However, to our knowledge, no study has ever directly compared genome-wide transcriptomes of DRG tissue in vivo versus in vitro, or between different labs and culturing protocols. We extracted bilateral lumbar DRG from C57BL6/J mice and human organ donors, and acutely froze one side and processed the other side as a dissociated cell culture, which was then maintained in vitro for 4 days. RNA was extracted and sequenced using the NextSeq Illumina platform. Comparing native to cultured human or mouse DRG, we found that the overall expression level of many ion channels and GPCRs specifically expressed in neurons is markedly lower in culture, but still expressed. This suggests that most pharmacological targets expressed in vivo are present in culture conditions. However, there are changes in expression levels for these genes. The reduced relative expression for neuronal genes in human DRG cultures is likely accounted for by increased expression of genes in fibroblast-like and other proliferating cells, consistent with the mitotic status of many cells in these cultures. We did find a subset of genes that are typically neuronally expressed, increased in human and mouse DRG cultures, including genes associated with nerve injury and/or inflammation in preclinical models such as BDNF, MMP9, GAL, and ATF3. We also found a striking upregulation of a number of inflammation-associated genes in DRG cultures, although many were different between mouse and human. Our findings suggest an injury-like phenotype in DRG cultures that has important implications for the use of this model system for pain drug discovery. Methods Experimental DesignBecause genetic variation can be a possible contributor to transcriptome level differences in nervous system samples from human populations [39; 41], we chose a study design wherein we cultured lumbar DRGs from one side in human donors and immediately froze the opposite side from the same donor for RNA sequencing. Although we used an inbred mouse strain (C57BL/6) for parallel mouse studies, we used a similar culturing design where cultures were done in two independent laboratories to look for variability across labs. RNA sequencing was performed at 4 days in vitro (DIV) to stay within the electrophysiologically relevant range of 1 -7 DIV for human DRG and the biochemical assay range of 4 -7 DIV for both human and mouse DRG. AnimalsPrice Lab: All procedures were approved by the Institutional Animal Care and Use Committee of University of Texas at Dallas and were in strict accordance with the US National Institute of Health (NIH) Guide for the Care and Use of Laboratory Animals. Adult C57Bl/6 mice (8-15 weeks of age) were bred in house, and were originally obtained from The Jackson Laboratory. Animals were housed in the University of Texas at Dallas animal facilities o...

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Section: Resultsmentioning

confidence: 99%

Transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia focused on pharmacological targets

Wangzhou

McIlvried²,

Paige

et al. 2019

Preprint

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“…mRNAs implicated in epilepsy were identified using an in-house database collating information from CARPEDB [http://carpedb.ua.edu/], epiGAD 67 , Wang et al 68 and curated epilepsy-genes from the Comparative Toxicogenomics Database (CTD) [http://ctdbase.org/ (Jan, 2019)]. 69 Pathway analysis was performed on Reactome pathways containing 10-500 genes by applying the cumulative hypergeometric distribution for p-value comparison 70 . Pathways with corrected p-values < 0.05 (Benjamini-Hochberg) were considered significantly enriched.…”

Section: Methodsmentioning

confidence: 99%

Ago2-seq identifies new microRNA targets for seizure control

Reschke

Morris

Connolly

et al. 2019

Preprint

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36MicroRNAs (miRNAs) are short noncoding RNAs that shape the gene expression landscape, 37 including during the pathogenesis of temporal lobe epilepsy (TLE). In order to provide a full 38 catalog of the miRNA changes that happen during experimental TLE, we sequenced 39Argonaute 2-loaded miRNAs in the hippocampus of three different animal models at regular 40 intervals between the time of the initial precipitating insult to the establishment of 41 spontaneous recurrent seizures. The commonly upregulated miRNAs were selected for a 42 functional in vivo screen using oligonucleotide inhibitors. This revealed anti-seizure 43 phenotypes upon inhibition of miR-10a-5p, miR-21a-5p and miR-142a-5p as well as 44 neuroprotection-only effects for inhibition of miR-27a-3p and miR-431-5p. Proteomic data 45 and pathway analysis on predicted and validated targets of these miRNAs indicated a role for 46TGFβ signaling in a shared seizure-modifying mechanism. Together, these results identify 47 functional miRNAs in the hippocampus and a pipeline of new targets for seizure control in 48 epilepsy. 49 50

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“…For the purpose of assessment, we used the Comparative Toxicogenomics Database (CTD) (Davis et al, 2019) to classify diseases into 8 classes based on their Disease Ontology (DO) terms (Schriml et al, 2012) where diseases are represented in the MeSH vocabulary (Rogers, 1963…”

Section: Disease Classificationmentioning

confidence: 99%

Network-principled deep generative models for designing drug combinations as graph sets

Karimi

Hasanzadeh

Shen

2020

Preprint

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Motivation:Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results: We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer. First, we have developed Hierarchical Variational Graph Auto-Encoders (HVGAE) trained end-to-end to jointly embed gene-gene, gene-disease, and disease-disease networks. Novel attentional pooling is introduced here for learning disease-representations from associated genes' representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse molecules with distributions similar to known drugs. We have also designed a network principle-based reward for drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, HVGAE learns more informative and generalizable disease representations. Results also show that the deep generative models generate drug combinations following the principle across diseases. Case studies on four diseases show that network-principled drug combinations tend to have low toxicity. The generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could potentially suggest novel systems-pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space.

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The Comparative Toxicogenomics Database: update 2019

Cited by 811 publications

References 43 publications

Transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia focused on pharmacological targets

Transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia focused on pharmacological targets

Ago2-seq identifies new microRNA targets for seizure control

Network-principled deep generative models for designing drug combinations as graph sets

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