Tyler Weirick

ANGIOGENES: knowledge database for protein-coding and noncoding RNA genes in endothelial cells

Increasing evidence indicates the presence of long noncoding RNAs (lncRNAs) is specific to various cell types. Although lncRNAs are speculated to be more numerous than protein-coding genes, the annotations of lncRNAs remain primitive due to the lack of well-structured schemes for their identification and description. Here, we introduce a new knowledge database “ANGIOGENES” (http://angiogenes.uni-frankfurt.de) to allow for in silico screening of protein-coding genes and lncRNAs expressed in various types of endothelial cells, which are present in all tissues. Using the latest annotations of protein-coding genes and lncRNAs, publicly-available RNA-seq data was analyzed to identify transcripts that are expressed in endothelial cells of human, mouse and zebrafish. The analyzed data were incorporated into ANGIOGENES to provide a one-stop-shop for transcriptomics data to facilitate further biological validation. ANGIOGENES is an intuitive and easy-to-use database to allow in silico screening of expressed, enriched and/or specific endothelial transcripts under various conditions. We anticipate that ANGIOGENES serves as a starting point for functional studies to elucidate the roles of protein-coding genes and lncRNAs in angiogenesis.

Airn Regulates Igf2bp2 Translation in Cardiomyocytes

Rationale: Increasing evidence indicates the presence of lncRNAs in various cell types. Airn is an imprinting gene transcribed from the paternal chromosome. It is in antisense orientation to the imprinted, but maternally derived, Igf2r gene, on which Airn exerts its regulation in cis. Although Airn is highly expressed in the heart, functions aside from imprinting remain unknown. Objective: Here, we studied the functions of Airn in the heart, especially cardiomyocytes. Methods and Results: Silencing of Airn via siRNAs augmented cell death, vulnerability to cellular stress, and reduced cell migration. To find the cause of such phenotypes, the potential binding partners of Airn were identified via RNA pull-down followed by mass spectrometry, which indicated Igf2bp2 (insulin-like growth factor 2 mRNA-binding protein 2) and Rpa1 (replication protein A1) as potential binding partners. Further experiments showed that Airn binds to Igf2bp2 to control the translation of several genes. Moreover, silencing of Airn caused less binding of Igf2bp2 to other mRNAs and reduced translation of Igf2bp2 protein. Conclusions: Our study uncovers a new function of Airn and demonstrates that Airn is important for the physiology of cardiomyocytes.

Long Non-coding RNAs in Endothelial Biology

In recent years, the role of RNA has expanded to the extent that protein-coding RNAs are now the minority with a variety of non-coding RNAs (ncRNAs) now comprising the majority of RNAs in higher organisms. A major contributor to this shift in understanding is RNA sequencing (RNA-seq), which allows a largely unconstrained method for monitoring the status of RNA from whole organisms down to a single cell. This observational power presents both challenges and new opportunities, which require specialized bioinformatics tools to extract knowledge from the data and the ability to reuse data for multiple studies. In this review, we summarize the current status of long non-coding RNA (lncRNA) research in endothelial biology. Then, we will cover computational methods for identifying, annotating, and characterizing lncRNAs in the heart, especially endothelial cells.

Increasing workflow development speed and reproducibility with Vectools

Despite advances in bioinformatics, custom scripts remain a source of difficulty, slowing workflow development and hampering reproducibility. Here, we introduce Vectools, a command-line tool-suite to reduce reliance on custom scripts and improve reproducibility by offering a wide range of common easy-to-use functions for table and vector manipulation. Vectools also offers a number of vector related functions to speed up workflow development, such as simple machine learning and common statistics functions.