ABSTRACT
Angiogenesis and vascular remodeling are essential for the establishment of vascular networks during organogenesis. Here we show that the Hippo signaling pathway effectors YAP and TAZ are required, in a gene dosage-dependent manner, for the proliferation and migration of vascular endothelial cells (ECs) during retinal angiogenesis. Intriguingly, nuclear translocation of YAP and TAZ induced by Lats1/2-deletion blocked endothelial migration and phenocopied Yap/Taz-deficient mutants. Furthermore, overexpression of a cytoplasmic form of YAP (YAPS127D) partially rescued the migration defects caused by loss of YAP and TAZ function. Finally, we found that cytoplasmic YAP positively regulated the activity of the small GTPase CDC42, deletion of which caused severe defects in endothelial migration. These findings uncover a previously unrecognized role of cytoplasmic YAP/TAZ in promoting cell migration by activating CDC42 and provide insight into how Hippo signaling in ECs regulates angiogenesis.
Subject(s)
Adaptor Proteins, Signal Transducing/physiology , Cell Movement , Endothelium, Vascular/cytology , Neovascularization, Physiologic , Phosphoproteins/physiology , Transcription Factors/physiology , cdc42 GTP-Binding Protein/physiology , Acyltransferases , Adaptor Proteins, Signal Transducing/genetics , Adaptor Proteins, Signal Transducing/metabolism , Animals , Cell Cycle Proteins , Cell Proliferation , Endothelium, Vascular/physiology , Human Umbilical Vein Endothelial Cells/metabolism , Humans , Mice , Mice, Knockout , Phosphoproteins/genetics , Phosphoproteins/metabolism , Signal Transduction , Transcription Factors/genetics , Transcription Factors/metabolism , YAP-Signaling ProteinsABSTRACT
RNA-Seq is the leading technology for analyzing gene expression on a global scale across a broad spectrum of sample types. However, due to chemical modifications by fixation or degradation due to collection methods, samples often contain an abundance of RNA that is no longer intact, and the capability of current RNA-Seq protocols to accurately quantify such samples is often limited. We have developed an RNA-Seq protocol to address these key issues as well as quantify gene expression from the whole transcriptome. Furthermore, for compatibility with improved sequencing platforms, we use restructured adapter sequences to generate libraries for Illumina HiSeq, MiSeq, and NextSeq platforms. Our protocol utilizes duplex-specific nuclease (DSN) to remove abundant ribosomal RNA sequences while retaining other types of RNA for superior transcriptome profiling from low quantity input. We employ the Illumina sequencing platform, but this method is described in sufficient detail to adapt to other platforms.
Subject(s)
Gene Expression Profiling/methods , High-Throughput Nucleotide Sequencing/methods , RNA, Messenger/genetics , RNA, Ribosomal/genetics , Reverse Transcriptase Polymerase Chain Reaction/methods , Transcriptome , Cell Line, Tumor , Gene Library , Humans , Neoplasms/genetics , Oligonucleotide Probes/chemistry , RNA Cleavage , RNA, Double-Stranded/chemistry , RNA, Double-Stranded/genetics , RNA, Ribosomal/chemistry , Ribonucleases/chemistry , Sequence Analysis, RNAABSTRACT
QuaCRS (Quality Control for RNA-Seq) is an integrated, simplified quality control (QC) system for RNA-seq data that allows easy execution of several open-source QC tools, aggregation of their output, and the ability to quickly identify quality issues by performing meta-analyses on QC metrics across large numbers of samples in different studies. It comprises two main sections. First is the QC Pack wrapper, which executes three QC tools: FastQC, RNA-SeQC, and selected functions from RSeQC. Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool. Second is the QC database, which displays the resulting metrics in a user-friendly web interface. It was designed to allow users with less computational experience to easily generate and view QC information for their data, to investigate individual samples and aggregate reports of sample groups, and to sort and search samples based on quality. The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future. The source code for not-for-profit use and a fully functional sample user interface with mock data are available at http://bioserv.mps.ohio-state.edu/QuaCRS/.
