Transcriptomic prediction of pig Leg1s functions in HepG2 cells
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA606768 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The newly identified liver-enriched gene 1 (Leg1) encodes a protein with characteristic Domain of Unknown Function 781 (DUF781/LEG1) domain constituting a protein family with only one member. Functional study in zebrafish suggested that Leg1 genes were involved in the liver development, while the platypus MLP homolog that was enriched in mammary gland and milk acts as an anti-bacterial substance. However, no functional study on eutherian Leg1s has been published at present. Thus, we first report here a functional prediction research in a cellular model. As previously reported, eutherian Leg1s could be classified into three paralogous groups. Pig has all three Leg1 genes, while human and mouse only have retained Leg1a. Hence, pig is an ideal model to study the gene function. RNA-seq was then performed by overexpression of pig Leg1s and platypus MLP in the HepG2 cells. Enrichment analysis showed that pLeg1a and pLeg1b might be of little function in the liver cell; however, pLeg1c was probably involved in the ER stress response and protein folding. Additionally, gene set enrichment analysis revealed that platypus MLP has anti-bacterial activity confirming the functional study in the platypus. Therefore, our study, from the transcriptomic perspective of view, concluded that the mammalian Leg1s have different functions in the liver cells due to subfunctionalization of the paralogous genes.
2020-05-01 | GSE145329 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE36242: Transcriptomic response to benzo[a]pyrene treatment in HepG2 cells (RNA-Seq) GSE36243: Transcriptomic response to benzo[a]pyrene treatment in HepG2 cells (Affymetrix) Refer to individual Series
2012-11-16 | E-GEOD-36244 | biostudies-arrayexpress
Project description: Not available
| S-EPMC7230230 | biostudies-literature
Project description:Assessing the potential carcinogenicity of human toxins represents an ongoing challenge. Chronic rodent bioassays predict human cancer risk with limited reliability, and are expensive and time-consuming. To identify alternative prediction methods, we evaluated a transcriptomics-based human in vitro model to classify carcinogens by their modes of action. The aim of this study was to determine the transcriptomic response and identify specific molecular signatures of polycyclic aromatic hydrocarbons (PAHs), which can be used as predictors of carcinogenicity of environmental toxins in human in vitro systems. We found that characteristic molecular signatures facilitate identification and prediction of carcinogens. To evaluate the change in gene expression levels, human hepatocellular carcinoma (HepG2) cells were exposed to nine different PAHs (benzo[a]pyrene, dibenzo[a,h]anthracene, 3-methylcholanthrene, naphthalene, chrysene, phenanthrene, benzo[a]anthracene, benzo[k]fluoranthene, and indeno[1,2,3-c,d]pyrene) for 48 h. Gene expression analysis was conducted using a 44K whole human genome microarray (Agilent Technologies, USA).
2012-04-17 | E-GEOD-37326 | biostudies-arrayexpress
Project description:Myocardial infarction (MI) is the leading cause for hear failure (HF). Following MI, the non-infarcted region of left ventricle (LV) is critical for maintaining heart function, and disruption of the LV contributes greatly to post-MI HF. Transcriptomic profiling by high-throughput sequencing was performed in a chronic HF pig model, to explore the molecular changes in the post-MI LV related to cardiovascular deterioration. Samples were taken from heart tissue of MI-induced pigs and from control pigs not subjected to MI. Regions of the heart where samples were taken included the site of ischemia (LV ischemia), area bordering ischemia (LV border), area remote to ischemia (LV remote) and the right ventricle (RV).
2021-10-04 | E-MTAB-8856 | biostudies-arrayexpress
Project description:Transcriptomic profiling of chronic infarcted pig hearts
| PRJEB37123 | ENA
Project description:Transcriptomic alteration after miR-6126 tranfection into HepG2-NTCP
| PRJNA792405 | ENA
Project description:A transcriptomic analysis of EDN1 overexpression in HepG2 cells.
| PRJNA738355 | ENA
Project description:The goal of this study is to examine transcriptomic changes in the left ventricles during the transition from a regenerative to a non-regenerative state in the pig neonatal heart. RNA was isolated from pig left ventricular tissue at postnatal day (P)0, P7, and P15, to compare the regeneration-capable P0 cardiac transcriptomic environment to the non-regenerative timepoints of P7 and P15, in pig hearts.
2021-02-15 | GSE145346 | GEO
Project description:Rationale: Ivacaftor is a recently FDA-approved drug for the treatment of cystic fibrosis (CF) patients with at least one copy of the G511D mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The transcriptomic effect of Ivacaftor in CF patients remains unclear. Objectives: We aim to examine if and how the transcriptome of patients is influenced by Ivacaftor treatment and to determine if these data allow prediction of Ivacaftor responsiveness. Methods: We performed RNA-sequencing (RNA-seq) on PBMCs from CF patients and compared the transcriptomic changes before and after Ivacaftor treatments. Consensus clustering method is employed to stratify patients into sub-groups based on clinical responses post treatment, and determined differences in baseline gene expression. A random forest model is built to predict Ivacaftor responsiveness. Measurement and Main Results: We identified 239 genes that were significantly influenced by Ivacaftor in PBMC. The functions of these genes relate to cell differentiation, microbial infection, inflammation, Toll-like receptor signaling, and metabolism. We classified patients into “good” and “moderate” responders based on clinical response to Ivacraftor. We identified a panel of signature genes and built a statistical model for predicting CFTR modulator responsiveness. Despite a limited sample size, adequate prediction performance was achieved with an accuracy of 0.92. Conclusions: For the first time, the present study demonstrates profound transcriptomic impacts of Ivafactor in CF patients PBMCs and successfully built a statistical model for predicting the clinical responsiveness to Ivacaftor prior to treatment.
2019-08-27 | GSE128723 | GEO