Project description:We sequenced the transcriptome of gentamicin induced renal regeneration in adult zebrafish. Specifically, zebrafish kidney tissues in the first, third, fifth and seventh days of kidney injury and the control group were selected. Each sample contains three kidneys. Three samples were taken in each period, and then the total RNA of the kidney was extracted for second-generation sequencing.
Project description:Adverse effects associated with exposure to dioxin-like compounds (DLCs) are mediated primarily through activation of the aryl hydrocarbon receptor (AHR). However, little is known about the cascades of events that link activation of the AHR to apical adverse effects. Therefore, this study used high-throughput, next-generation molecular tools to investigate similarities and differences in whole transcriptome and whole proteome responses to equipotent concentrations of three agonists of the AHR, 2,3,7,8-TCDD, PCB 77, and benzo[a]pyrene, in livers of a non-model fish, the white sturgeon (Acipenser transmontanus). A total of 926 and 658 unique transcripts were up- and down-regulated, respectively, by one or more of the three chemicals. Of the transcripts shared by responses to all three chemicals, 85% of up-regulated transcripts and 75% of down-regulated transcripts had the same magnitude of response. A total of 290 and 110 unique proteins were up- and down-regulated, respectively, by one or more of the three chemicals. Of the proteins shared by responses to all three chemicals, 70% of up-regulated proteins and 48% of down-regulated proteins had the same magnitude of response. Among treatments there was 68% similarity between the global transcriptome and global proteome. Pathway analysis revealed that perturbed physiological processes were indistinguishable between equipotent concentrations of the three chemicals. The results of this study contribute towards more completely describing adverse outcome pathways associated with activation of the AHR.
Project description:This work highlights similarities and differences between three platforms (next-generation sequencing, microarray and NanoString) for detecting miRNAs and compares their strengths and weaknesses.
Project description:This work highlights similarities and differences between three platforms (next-generation sequencing, microarray and NanoString) for detecting miRNAs and compares their strengths and weaknesses.
Project description:This work highlights similarities and differences between three platforms (next-generation sequencing, microarray and NanoString) for detecting miRNAs and compares their strengths and weaknesses.