Rattus norvegicus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA345636 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR4374820.fastq.gz | Fastqsanger.gz | |||
| SRR4374830.fastq.gz | Fastqsanger.gz | |||
| SRR4374840.fastq.gz | Fastqsanger.gz | |||
| SRR4374821.fastq.gz | Fastqsanger.gz | |||
| SRR4374831.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 25 |
Similar Datasets
Project description:Toxicogenomic module associations with pathogenesis: A network based approach to understanding drug toxicity
| PRJNA345586 | ENA
Project description:Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome (the ‘TXG-MAP’). Accounting for the overall transcriptional activity resulting from treatment, we explain mechanisms of toxicity and predict distinct toxicity phenotypes using module associations. We demonstrate that early network responses compliment traditional histology-based assessment in predicting outcomes for longer studies and identify a novel mechanism of hepatotoxicity involving endoplasmic reticulum stress and Nrf2 activation. Module-based molecular subtypes of cholestatic injury derived using rat translate to human. Moreover, compared to gene-level analysis alone, combining module and gene-level analysis performed in sequence identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.
2017-04-12 | GSE87730 | GEO
Project description:Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome (the ‘TXG-MAP’). Accounting for the overall transcriptional activity resulting from treatment, we explain mechanisms of toxicity and predict distinct toxicity phenotypes using module associations. We demonstrate that early network responses compliment traditional histology-based assessment in predicting outcomes for longer studies and identify a novel mechanism of hepatotoxicity involving endoplasmic reticulum stress and Nrf2 activation. Module-based molecular subtypes of cholestatic injury derived using rat translate to human. Moreover, compared to gene-level analysis alone, combining module and gene-level analysis performed in sequence identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.
2017-04-12 | GSE87696 | GEO
Project description:There is a lack of comprehensive toxicity studies of QACs associated with pulmonary toxicity. Therefore, we aimed to elucidate the potential pulmonary toxic effects of QACs based on toxicogenomic approach. We conducted transcriptome analysis using RNA sequencing to identify alterations in gene expression associated with QACs exposure.
2024-04-08 | GSE263314 | GEO
Project description:A toxicogenomic approach was used to assess the sex-specific transcriptomic changes in kidney tissue of rats from a one generation reproductive toxicity study. Transcriptomic data from both F0 and F1, male and female rats were assessed.
2022-04-10 | GSE200428 | GEO
Project description:Genome-wide association studies (GWASs) for bone mineral density (BMD), one of the most significant predictors of osteoporotic fracture, have identified over 1100 independent associations; however, few of the causal genes have been identified. Recently, the “omnigenic” model of the genetic architecture of complex traits proposed two general categories of causal genes, core and peripheral. Core genes play a direct role in regulating traits; thus, their identification is key to revealing critical regulators and potential therapeutic targets. Here, we identified a co-expression module enriched for genes exhibiting properties consistent with core genes for BMD by analyzing GWAS data through the lens of a cell-type and timepoint-specific gene co-expression network for mineralizing osteoblasts. We identified multiple co-expression modules enriched for genes implicated by BMD GWAS and prioritized modules based on their enrichment for genes with core-like properties. Only one module, the purple module, was enriched for genes correlated with in vitro mineralization (r = 0.49; FDR = 0.012), with known roles in skeletal development (P <2.2 x 10-16), that when perturbed produce a bone phenotype in mice (OR = 4.1; P = 2.14 x 10-9), and are monogenic bone disease genes in humans (OR=21.3; P=6.94 x 10-9). Furthermore, the purple module contained genes from two distinct transcriptional profiles with regards to osteoblast differentiation, one of which, termed the late differentiation cluster (LDC), was more highly enriched for genes with core-like properties. Within the LDC, we found that the most highly connected genes were more likely to overlap a BMD GWAS association and associations that contained LDC genes overlapped enhancers and promoters in osteoblasts. Finally, we identified four LDC genes (B4GALNT3, CADM1, DOCK9, and GPR133) with colocalizing expression quantitative trait loci (eQTL) and altered BMD in mouse knockouts. Our network-based approach identified a “core” module for BMD and has provided a resource for expanding our understanding of the genetics of bone mass.
2019-07-11 | GSE134081 | GEO
Project description:Selecting the right immunosuppressant to ensure rejection-free outcomes poses unique challenges in pediatric liver transplant (LT) recipients. A molecular predictor can comprehensively address these challenges. Although early acute cellular rejection (ACR) is mediated by cytotoxic T-cells, late rejection also includes antibody-mediated damage in addition to cell-mediated injury. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients either pre- or post- transplant. Here, we discover and validate separate pre- and post- transplant molecular signatures of LT outcome from whole blood transcriptomes. Using an integrative machine learning approach, we combine transcriptomic data with the high-quality reference human protein interactome network to identify differentially regulated functional sub-components of the network, or “network module signatures”, which drive ACR. Unlike gene signatures, our approach is inherently multivariate, more robust to replication and captures the structure of the underlying molecular network, encapsulating additive effects. We also identify, in a patient-specific manner, network module signatures that can be targeted by current anti-rejection drugs and other mechanisms that can be repurposed. Overall, our approach can enable personalized adjustment of drug regimens for the dominant targetable pathways in pre- and post- LT in children.
2022-04-11 | GSE200340 | GEO
Project description:Toxicogenomic Study of Pentamethylchromanol (PMCol)
| PRJNA141309 | ENA
Project description:Understanding the bacterial compositional network associations between oral and gut microbiome within healthy Koreans
| PRJNA940351 | ENA