Precision of Tissue Patterning is Controlled by Dynamical Properties of Gene Regulatory Networks, bioRxiv, 2019-08-01

AbstractDuring development, gene regulatory networks allocate cell fates by partitioning tissues into spatially organised domains of gene expression. How the sharp boundaries that delineate these gene expression patterns arise, despite the stochasticity associated with gene regulation, is poorly understood. We show, in the vertebrate neural tube, using perturbations of coding and regulatory regions, that the structure of the regulatory network contributes to boundary precision. This is achieved, not by reducing noise in individual genes, but by the configuration of the network modulating the ability of stochastic fluctuations to initiate gene expression changes. We use a computational screen to identify the properties of a network that influence boundary precision, revealing two dynamical mechanisms by which small gene circuits attenuate the effect of noise to increase patterning precision. These results establish design principles of gene regulatory networks that produce precise patterns of gene expression.

biorxiv developmental-biology 0-100-users 2019

Graphmap2 - splice-aware RNA-seq mapper for long reads, bioRxiv, 2019-07-31

AbstractIn this paper we present Graphmap2, a splice-aware mapper built on our previously developed DNA mapper Graphmap. Graphmap2 is tailored for long reads produced by Pacific Biosciences and Oxford Nanopore devices. It uses several newly developed algorithms which enable higher precision and recall of correctly detected transcripts and exon boundaries. We compared its performance with the state-of-the-art tools Minimap2 and Gmap. On both simulated and real datasets Graphmap2 achieves higher mappability and more correctly recognized exons and their ends. In addition we present an analysis of potential of splice aware mappers and long reads for the identification of previously unknown isoforms and even genes. The Graphmap2 tool is publicly available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comlbcb-scigraphmap2>httpsgithub.comlbcb-scigraphmap2<jatsext-link>.

biorxiv bioinformatics 0-100-users 2019

The histone chaperone FACT induces Cas9 multi-turnover behavior and modifies genome manipulation in human cells, bioRxiv, 2019-07-24

SummaryCas9 is a prokaryotic RNA-guided DNA endonuclease that binds substrates tightly in vitro but turns over rapidly when used to manipulate genomes in eukaryotic cells. Little is known about the factors responsible for dislodging Cas9 or how they influence genome engineering. Using a proximity labeling system for unbiased detection of transient protein interactions in cell-free Xenopus laevis egg extract, we identified the dimeric histone chaperone FACT as an interactor of substrate-bound Cas9. Immunodepletion of FACT subunits from extract potently inhibits Cas9 unloading and converts Cas9’s activity from multi-turnover to single-turnover. In human cells, depletion of FACT delays genome editing and alters the balance between indel formation and homology directed repair. Depletion of FACT also increases epigenetic marking by dCas9-based transcriptional effectors with concomitant enhancement of transcriptional modulation. FACT thus shapes the intrinsic cellular response to Cas9-based genome manipulation most likely by determining Cas9 residence times.

biorxiv cell-biology 0-100-users 2019

Evidence that APP gene copy number changes reflect recombinant vector contamination, bioRxiv, 2019-07-23

AbstractMutations that occur in cells of the body, called somatic mutations, cause human diseases including cancer and some neurological disorders1. In a recent study published in Nature, Lee et al.2 (hereafter “the Lee study”) reported somatic copy number gains of the APP gene, a known risk locus of Alzheimer’s disease (AD), in the neurons of AD-patients and controls (69% vs 25% of neurons with at least one APP copy gain on average). The authors argue that the mechanism of these copy number gains was somatic integration of APP mRNA into the genome, creating what they called genomic cDNA (gencDNA). We reanalyzed the data from the Lee study, revealing evidence that APP gencDNA originates mainly from contamination by exogenous APP recombinant vectors, rather from true somatic retrotransposition of endogenous APP. Our reanalysis of two recent whole exome sequencing (WES) datasets—one by the authors of the Lee study3 and the other by Park et al.4—revealed that reads claimed to support APP gencDNA in AD samples resulted from contamination by PCR products and mRNA, respectively. Lastly, we present our own single-cell whole genome sequencing (scWGS) data that show no evidence for somatic APP retrotransposition in AD neurons or in neurons from normal individuals of various ages.

biorxiv genomics 0-100-users 2019

Differential gene expression and gene variants drive color and pattern development in divergent color morphs of a mimetic poison frog, bioRxiv, 2019-07-22

AbstractEvolutionary biologists have long investigated the ecological contexts, evolutionary forces, and proximate mechanisms that produce the diversity of animal coloration we see in the natural world. In aposematic species, color and pattern is directly tied to survival and thus understanding the origin of the phenotype has been a focus of both theoretical and empirical inquiry. In order to better understand this diversity, we examined gene expression in skin tissue during development in four different color morphs of the aposematic mimic poison frog, Ranitomeya imitator. We identified a suite of candidate color-related genes a priori and identified the pattern of expression in these genes over time, differences in expression of these genes between the mimetic morphs, and genetic variants that differ between color morphs. We identified several candidate color genes that are differentially expressed over time or across populations, as well as a number of color genes with fixed genetic variants between color morphs. Many of the color genes we discovered in our dataset are involved in the canonical Wnt signaling pathway, including several fixed SNPs between color morphs. Further, many genes in this pathway were differentially expressed at different points in development (e.g., lef1, tyr, tyrp1). Importantly, Wnt signaling pathway genes are overrepresented relative to expression in Xenopus tropicalis. Taken together, this provides evidence that the Wnt signaling pathway is contributing to color pattern production in R. imitator, and is an excellent candidate for producing some of the differences in color pattern between morphs. In addition, we found evidence that sepiapterin reductase is likely important in the production of yellow-green coloration in this adaptive radiation. Finally, two iridophore genes (arfap1, gart) draw a strong parallel to previous work in another dendrobatid, indicating that these genes are also strong candidates for differential color production. We have used high throughput sequencing throughout development to examine the evolution of coloration in a rapid mimetic adaptive radiation and found that these divergent color patterns are likely to be affected by a combination of developmental patterns of gene expression, color morph-specific gene expression, and color morph-specific gene variants.

biorxiv evolutionary-biology 0-100-users 2019

RootNav 2.0 Deep Learning for Automatic Navigation of Complex Plant Root Architectures, bioRxiv, 2019-07-20

AbstractWe present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and first and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a final time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httprootsystemml.github.io>httprootsystemml.github.io<jatsext-link>), as well as segmentation masks compatible with other automated measurement tools.

biorxiv bioinformatics 0-100-users 2019