Biology subjects

Brendel, V. P.

Publications and source records attributed to Brendel, V. P..

5 recordsLinked to original sources

bioRxiv2024

A PRDM16-CtBP1/2 Complex Interacts with HDAC1/2 to Regulate Transcriptional Programs of Neurogenesis and Guide Cortical Neuron Migration

Chromatin regulation of transcriptional enhancers plays a central role in cell fate specification and differentiation. However, how the coordinated activity of transcription factors and chromatin-modifying enzymes regulates enhancers in neural stem cells (NSCs) and dictates subsequent stages of neuronal differentiation and migration is not well understood. The histone methyltransferase PRDM16 is expressed in NSCs of the developing mouse and human cerebral cortex and is essential for determining the position of upper-layer cortical neurons. Here, we report that PRDM16 interacts with C-terminal binding protein 1 (CtBP1) and CtBP2 to control the transcriptional programs of cortical neurogenesis and regulate upper-layer neuron migration. PRDM16 and CtBP1/2 co-regulate enhancers by interacting with histone deacetylase 1 (HDAC1) and HDAC2, and lysine-specific demethylase 1 (LSD1). In addition, our results suggest that the CCCTC-binding factor CTCF plays a key role in recruiting CtBP1/2 to cortical enhancers. These findings underscore that reduced interactions between PRDM16 and ubiquitous chromatin regulators may contribute to neurodevelopmental deficits in patients with PRDM16 haploinsufficiency.

developmental biology↗

bioRxiv2021

iLoci: Robust evaluation of genome content and organization for provisional and mature genome assemblies

BackgroundThe rate at which new draft genome assemblies and corresponding annotation versions are being produced has long outpaced the scientific communitys capacity to refine these drafts into "finished," reference-quality data resources to a standard typically expected from dedicated efforts of model organism research communities. Nonetheless, scientists must be able to evaluate newly sequenced genomes in the context of previously published data, requiring summaries of genome content and organization that can be quickly computed, updated, and meaningfully compared. As annotation quality will necessarily vary within and across data sets, the ability to select subsets of only those data that are well supported is critical for distinguishing technical artifacts from biological effects in genome-wide analyses. ResultsWe introduce a new framework for genome analyses based on parsing an annotated genome assembly into distinct interval loci (iLoci), available as open source software as part of the AEGeAn Toolkit (https://github.com/BrendelGroup/AEGeAn). We demonstrate that iLoci provide an alternative coordinate system that is robust to changes in assembly and annotation versions and facilitates granular quality control of genome data. We discuss how statistics computed on iLoci reflect various characteristics of genome content and organization and illustrate how these statistics can be used to establish a baseline for assessment of the completeness and accuracy of the data. We also introduce a well-defined measure of relative genome compactness and compute other iLocus statistics that reveal genome-wide characteristics of gene arrangements in the whole genome context. ConclusionsWe present a coherent computational framework that calculates informative statistics from genome assembly/annotation data input. Given the fast pace of assembly/annotation updates, our AEGeAn Toolkit fills a niche in computational genomics based on deriving persistent and species-specific genome statistics. Gene structure model centric iLoci provide a precisely defined coordinate system that can be used to store assembly/annotation updates that reflect either stable or changed assessments. Large-scale application of the approach revealed species and clade specific genome organization in precisely defined computational terms, promising intriguing forays into the forces of shaping genome structure as more and more genome assemblies are being deposited.

genomics↗

bioRxiv2021

Tools and applications for integrative analysis of DNA methylation in social insects

DNA methylation is a common epigenetic signaling tool and an important biological process which is widely studied in a large array of species. The presence, level, and function of DNA methylation varies greatly across species. In insects, DNA methylation systems are reduced, and methylation rates are often low. Low methylation levels probed by whole genome bisulfite sequencing require great care with respect to data quality control and interpretation. Here we introduce BWASP/R, a complete workflow that allows efficient, scalable, and entirely reproducible analyses of raw DNA methylation sequencing data. Consistent application of quality control filters and analysis parameters provides fair comparisons among different studies and an integrated view of all experiments on one species. We describe the capabilities of the BWASP/R workflow by re-analyzing several publicly available social insect WGBS data sets, comprising 70 samples and cumulatively 147 replicates from four different species. We show that the CpG methylome comprises only about 1.5% of CpG sites in the honeybee genome and that the cumulative data are consistent with genetic signatures of site accessibility and physiological control of methylation levels. Significance StatementDNA methylation in the honeybee genome occurs almost entirely at CpG sites. Methylation rates are small compared to rates in mammalian or plant genomes. De novo analysis of all published honeybee methylation studies and statistical modeling suggests that the CpG methylome consists of about only 300,000 sites. The development of a fully reproducible, scalable, portable workflow allows for easy accessible updates of integrative views of all current experiments. The integrated results for the honeybee are consistent with genetic determination of methylation site accessibility by yet uncharacterized sequence features and physiological control of methylation levels at those sites.

bioinformatics↗

bioRxiv2021

Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR

Heterogeneity in transcription initiation has important consequences for transcript stability and translation, and shifts in transcription start site (TSS) usage are prevalent in various disease and developmental contexts. Accordingly, numerous methods for global TSS profiling have been developed, including our recently published Survey of TRanscription Initiation at Promoter Elements with high-throughput sequencing (STRIPE-seq), a method to profile transcription start sites (TSSs) on a genome-wide scale with minimal cost and time. In parallel to our development of STRIPE-seq, we built TSRexploreR, an R package for end-to-end analysis of TSS mapping data. TSRexploreR provides functions for TSS and TSR detection, normalization, correlation, visualization, and differential TSS/TSR analysis. TSRexploreR is highly interoperable, accepting the data structures of TSS and TSR sets generated by several existing tools for processing and alignment of TSS mapping data, such as CAGEr for Cap Analysis of Gene Expression (CAGE) data. Lastly, TSRexploreR implements a novel approach for the detection of shifts in TSS distribution.

bioinformatics↗

bioRxiv2020

Simple and efficient measurement of transcription initiation and transcript levels with STRIPE-seq

Accurate mapping of transcription start sites (TSSs) is key for understanding transcriptional regulation. However, current protocols for genome-wide TSS profiling are laborious and/or expensive. We present Survey of TRanscription Initiation at Promoter Elements with high-throughput sequencing (STRIPE-seq), a simple, rapid, and cost-effective protocol for sequencing capped RNA 5 ends from as little as 50 ng total RNA. Including depletion of uncapped RNA and SPRI bead cleanups, a STRIPE-seq library can be constructed in about five hours. We demonstrate application of STRIPE-seq to TSS profiling in yeast and human cells and show that it can also be effectively used for measuring transcript levels and differential gene expression analysis. In conjunction with our ready-to-use computational analysis workflows, STRIPE-seq is a straightforward, efficient means by which to probe the landscape of transcriptional initiation.

genomics↗