13273 research outputs found
Sort by
3D genome architecture coordinates trans and cis regulation of differentially expressed ear and tassel genes in maize.
Get PDFBACKGROUND: Maize ears and tassels are two separate types of inflorescence which are initiated by similar developmental processes but gradually develop distinct architectures. However, coordinated trans and cis regulation of differentially expressed genes determining ear and tassel architecture within the 3D genome context is largely unknown. RESULTS: We identify 56,055 and 52,633 open chromatin regions (OCRs) in developing maize ear and tassel primordia using ATAC-seq and characterize combinatorial epigenome features around these OCRs using ChIP-seq, Bisulfite-seq, and RNA-seq datasets. Our integrative analysis of coordinated epigenetic modification and transcription factor binding to OCRs highlights the cis and trans regulation of differentially expressed genes in ear and tassel controlling inflorescence architecture. We further systematically map chromatin interactions at high-resolution in corresponding tissues using in situ digestion-ligation-only Hi-C (DLO Hi-C). The extensive chromatin loops connecting OCRs and genes provide a 3D view on cis- and trans-regulatory modules responsible for ear- and tassel-specific gene expression. We find that intergenic SNPs tend to locate in distal OCRs, and our chromatin interaction maps provide a potential mechanism for trait-associated intergenic SNPs that may contribute to phenotypic variation by influencing target gene expression through chromatin loops. CONCLUSIONS: Our comprehensive epigenome annotations and 3D genome maps serve as valuable resource and provide a deep understanding of the complex regulatory mechanisms of genes underlying developmental and morphological diversities between maize ear and tassel
Genome and time-of-day transcriptome of Wolffia australiana link morphological minimization with gene loss and less growth control.
Get PDFRootless plants in the genus Wolffia are some of the fastest growing known plants on Earth. Wolffia have a reduced body plan, primarily multiplying through a budding type of asexual reproduction. Here, we generated draft reference genomes for Wolffia australiana (Benth.) Hartog & Plas, which has the smallest genome size in the genus at 357 Mb and has a reduced set of predicted protein-coding genes at about 15,000. Comparison between multiple high-quality draft genome sequences from W. australiana clones confirmed loss of several hundred genes that are highly conserved among flowering plants, including genes involved in root developmental and light signaling pathways. Wolffia has also lost most of the conserved nucleotide-binding leucine-rich repeat (NLR) genes that are known to be involved in innate immunity, as well as those involved in terpene biosynthesis, while having a significant overrepresentation of genes in the sphingolipid pathways that may signify an alternative defense system. Diurnal expression analysis revealed that only 13% of Wolffia genes are expressed in a time-of-day (TOD) fashion, which is less than the typical ∼40% found in several model plants under the same condition. In contrast to the model plants Arabidopsis and rice, many of the pathways associated with multicellular and developmental processes are not under TOD control in W. australiana, where genes that cycle the conditions tested predominantly have carbon processing and chloroplast-related functions. The Wolffia genome and TOD expression data set thus provide insight into the interplay between a streamlined plant body plan and optimized growth
KrasP34R and KrasT58I mutations induce distinct RASopathy phenotypes in mice.
Get PDFSomatic KRAS mutations are highly prevalent in many cancers. In addition, a distinct spectrum of germline KRAS mutations causes developmental disorders called RASopathies. The mutant proteins encoded by these germline KRAS mutations are less biochemically and functionally activated than those in cancer. We generated mice harboring conditional KrasLSL-P34Rand KrasLSL-T58I knock-in alleles and characterized the consequences of each mutation in vivo. Embryonic expression of KrasT58I resulted in craniofacial abnormalities reminiscent of those seen in RASopathy disorders, and these mice exhibited hyperplastic growth of multiple organs, modest alterations in cardiac valvulogenesis, myocardial hypertrophy, and myeloproliferation. By contrast, embryonic KrasP34R expression resulted in early perinatal lethality from respiratory failure due to defective lung sacculation, which was associated with aberrant ERK activity in lung epithelial cells. Somatic Mx1-Cre-mediated activation in the hematopoietic compartment showed that KrasP34R and KrasT58I expression had distinct signaling effects, despite causing a similar spectrum of hematologic diseases. These potentially novel strains are robust models for investigating the consequences of expressing endogenous levels of hyperactive K-Ras in different developing and adult tissues, for comparing how oncogenic and germline K-Ras proteins perturb signaling networks and cell fate decisions, and for performing preclinical therapeutic trials
Improving replicability in single-cell RNA-Seq cell type discovery with Dune
Get PDFSingle-cell transcriptome sequencing (scRNA-Seq) has allowed many new types of investigations at unprecedented and unique levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into potentially novel cell types. Many approaches build on the existing clustering literature to develop tools specific to single-cell applications. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters identified. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist to select these tuning parameters, most of them are quite ad hoc. In general, there is little assurance that any given set of parameters will represent an optimal choice in the ever-present trade-off between cluster resolution and replicability. For instance, it may be the case that another set of parameters will result in more clusters that are also more replicable, or in fewer clusters that are also less replicable. Here, we propose a new method called Dune for optimizing the trade-off between the resolution of the clusters and their replicability across datasets. Our method takes as input a set of clustering results on a single dataset, derived from any set of clustering algorithms and associated tuning parameters, and iteratively merges clusters within partitions in order to maximize their concordance between partitions. As demonstrated on a variety of scRNA-Seq datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters. It provides an objective approach for identifying replicable consensus clusters most likely to represent common biological features across multiple datasets
Improving representations of genomic sequence motifs in convolutional networks with exponential activations.
Get PDFABSTRACT Deep convolutional neural networks (CNNs) trained on regulatory genomic sequences tend to build representations in a distributed manner, making it a challenge to extract learned features that are biologically meaningful, such as sequence motifs. Here we perform a comprehensive analysis on synthetic sequences to investigate the role that CNN activations have on model interpretability. We show that employing an exponential activation to first layer filters consistently leads to interpretable and robust representations of motifs compared to other commonly used activations. Strikingly, we demonstrate that CNNs with better test performance do not necessarily imply more interpretable representations with attribution methods. We find that CNNs with exponential activations significantly improve the efficacy of recovering biologically meaningful representations with attribution methods. We demonstrate these results generalise to real DNA sequences across several in vivo datasets. Together, this work demonstrates how a small modification to existing CNNs, i.e. setting exponential activations in the first layer, can significantly improve the robustness and interpretabilty of learned representations directly in convolutional filters and indirectly with attribution methods
BRICseq Bridges Brain-wide Interregional Connectivity to Neural Activity and Gene Expression in Single Animals.
No full textComprehensive analysis of neuronal networks requires brain-wide measurement of connectivity, activity, and gene expression. Although high-throughput methods are available for mapping brain-wide activity and transcriptomes, comparable methods for mapping region-to-region connectivity remain slow and expensive because they require averaging across hundreds of brains. Here we describe BRICseq (brain-wide individual animal connectome sequencing), which leverages DNA barcoding and sequencing to map connectivity from single individuals in a few weeks and at low cost. Applying BRICseq to the mouse neocortex, we find that region-to-region connectivity provides a simple bridge relating transcriptome to activity: the spatial expression patterns of a few genes predict region-to-region connectivity, and connectivity predicts activity correlations. We also exploited BRICseq to map the mutant BTBR mouse brain, which lacks a corpus callosum, and recapitulated its known connectopathies. BRICseq allows individual laboratories to compare how age, sex, environment, genetics, and species affect neuronal wiring and to integrate these with functional activity and gene expression
Quantitative Imaging Reveals Distinct Contributions of SnRK2 and ABI3 in Plasmodesmatal Permeability in Physcomitrella patens
No full textCell-to-cell communication is tightly regulated in response to environmental stimuli in plants. We previously used a photoconvertible fluorescent protein Dendra2 as a model reporter to study this process. This experiment revealed that macromolecular trafficking between protonemal cells in Physcomitrella patens is suppressed in response to abscisic acid (ABA). However, it remains unknown which ABA signaling components contribute to this suppression and how. Here, we show that ABA signaling components SUCROSE NON-FERMENTING 1-RELATED PROTEIN KINASE 2 (PpSnRK2) and ABA INSENSITIVE 3 (PpABI3) play roles as an essential and promotive factor, respectively, in regulating ABA-induced suppression of Dendra2 diffusion between cells (ASD). Our quantitative imaging analysis revealed that disruption of PpSnRK2 resulted in defective ASD onset itself, whereas disruption of PpABI3 caused an 81-min delay in the initiation of ASD. Live-cell imaging of callose deposition using aniline blue staining showed that, despite this onset delay, callose deposition on cross walls remained constant in the PpABI3 disruptant, suggesting that PpABI3 facilitates ASD in a callose-independent manner. Given that ABA is an important phytohormone to cope with abiotic stresses, we further explored cellular physiological responses. We found that the acquisition of salt stress tolerance is promoted by PpABI3 in a quantitative manner similar to ASD. Our results suggest that PpABI3-mediated ABA signaling may effectively coordinate cell-to-cell communication during the acquisition of salt stress tolerance. This study will accelerate the quantitative study for ABA signaling mechanism and function in response to various abiotic stresses
Ensembl Genomes 2020-enabling non-vertebrate genomic research
No full textEnsembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of interfaces to genomic data across the tree of life, including reference genome sequence, gene models, transcriptional data, genetic variation and comparative analysis. Data may be accessed via our website, online tools platform and programmatic interfaces, with updates made four times per year (in synchrony with Ensembl). Here, we provide an overview of Ensembl Genomes, with a focus on recent developments. These include the continued growth, more robust and reproducible sets of orthologues and paralogues, and enriched views of gene expression and gene function in plants. Finally, we report on our continued deeper integration with the Ensembl project, which forms a key part of our future strategy for dealing with the increasing quantity of available genome-scale data across the tree of life
From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection
No full textMethods to detect signals of natural selection from genomic data have traditionally emphasized the use of simple summary statistics. Here, we review a new generation of methods that consider combinations of conventional summary statistics and/or richer features derived from inferred gene trees and ancestral recombination graphs (ARGs). We also review recent advances in methods for population genetic simulation and ARG reconstruction. Finally, we describe opportunities for future work on a variety of related topics, including the genetics of speciation, estimation of selection coefficients, and inference of selection on polygenic traits. Together, these emerging methods offer promising new directions in the study of natural selection