scholarly journals Spatially varying cis-regulatory divergence inDrosophilaembryos elucidates cis-regulatory logic

2017 ◽  
Author(s):  
Peter A. Combs ◽  
Hunter B. Fraser
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Raw Material ◽  
Spatial Patterning ◽  
Rna Seq ◽  
Specific Expression ◽  
Complex Interactions ◽  
Allele Specific ◽  
Spatially Varying ◽  
Cis And Trans

AbstractSpatial patterning of gene expression is a key process in development—responsible for the incredible diversity of animal body plans—yet how it evolves is still poorly understood. Both cis- and trans-acting changes could accumulate and participate in complex interactions, so to isolate the cis-regulatory component of patterning evolution, we measured allele-specific spatial gene expression patterns inD. melanogaster×D. simulanshybrid embryos. RNA-seq of cryosectioned slices revealed 55 genes with strong spatially varying allele-specific expression, and several hundred more with weaker but significant spatial divergence. For example, we found thathunchback (hb), a major regulator of developmental patterning, had reduced expression specifically in the anterior tip ofD. simulansembryos. Mathematical modeling ofhbcis-regulation suggested that a mutation in a Bicoid binding site was responsible, which we verified using CRISPR-Cas9 genome editing. In sum, even comparing morphologically near-identical species we identified a substantial amount of spatial variation in gene expression, suggesting that development is robust to many such changes, but also that natural selection may have ample raw material for evolving new body plans via cis-regulatory divergence.

scholarly journals Highly Genotype- and Tissue-specific Single-Parent Expression Drives Dynamic Gene Expression Complementation in Maize Hybrids

2019 ◽  
Author(s):  
Zhi Li ◽  
Peng Zhou ◽  
Rafael Della Coletta ◽  
Tifu Zhang ◽  
Alex B. Brohammer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Single Parent ◽  
Specific Expression ◽  
Data Set ◽  
Tissue Specific ◽  
Allele Specific ◽  
Silent Allele ◽  
Almost All

AbstractMaize exhibits tremendous gene expression variation between different lines. Complementation of diverse gene expression patterns in hybrids could play an important role in the manifestation of heterosis. In this study, we used transcriptome data of five different tissues from 33 maize inbreds and 89 hybrids (430 samples in total) to survey the global gene expression landscape of F1-hybrids relative to their inbred parents. Analysis of this data set revealed that single parent expression (SPE), which is defined as gene expression in only one of the two parents, while commonly observed, is highly genotype- and tissue-specific. Genes that have SPE in at least one pair of inbreds also tend to be tissue-specific. Genes with SPE caused by genomic presence/absence variation (PAV SPE) are much more frequently expressed in hybrids than genes that are present in the genome of both inbreds, but expressed in only a single-parent (non-PAV SPE) (74.7% vs. 59.7%). For non-PAV SPE genes, allele specific expression was used to investigate whether parental alleles not expressed in the inbred line (“silent allele”) can be actively transcribed in the hybrid. We found that expression of the silent allele in the hybrid is relatively rare (∼6.3% of non-PAV SPE genes), but is observed in almost all hybrids and tissues. Non-PAV SPE genes with expression of the silent allele in the hybrid are more likely to exhibit above high-parent expression level in the hybrid than those that do not express the silent allele. Finally, both PAV SPE and non-PAV SPE genes are highly enriched for being classified as non-syntenic, but depleted for curated genes with experimentally determined functions. This study provides a more comprehensive understanding of the potential role of non-PAV SPE and PAV SPE genes in heterosis.

scholarly journals RNA-Seq Analyses Identify Additivity as the Predominant Gene Expression Pattern in F1 Chicken Embryonic Brain and Liver

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 27 ◽  
Author(s):  
Zhu Zhuo ◽  
Susan J. Lamont ◽  
Behnam Abasht
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Pairwise Comparison ◽  
Intermediate Phenotype ◽  
Superior Performance ◽  
Rna Seq ◽  
Specific Expression ◽  
Parental Lines

The superior performance of hybrids to parents, termed heterosis, has been widely utilized in animal and plant breeding programs, but the molecular mechanism underlying heterosis remains an enigma. RNA-Seq provides a novel way to investigate heterosis at the transcriptome-wide level, because gene expression functions as an intermediate phenotype that contributes to observable traits. Here we compared embryonic gene expression between chicken hybrids and their inbred parental lines to identify inheritance patterns of gene expression. Inbred Fayoumi and Leghorn were crossed reciprocally to obtain F1 fertile eggs. RNA-Seq was carried out using 24 brain and liver samples taken from day 12 embryos, and the differentially expressed (DE) genes were identified by pairwise comparison among the hybrids, parental lines, and mid-parent expression values. Our results indicated the expression levels of the majority of the genes in the F1 cross are not significantly different from the mid-parental values, suggesting additivity as the predominant gene expression pattern in the F1. The second and third prevalent gene expression patterns are dominance and over-dominance. Additionally, we found only 7–20% of the DE genes exhibit allele-specific expression in the F1, suggesting that trans regulation is the main driver for differential gene expression and thus contributes to heterosis effect in the F1 crosses.

scholarly journals A Bayesian mixture model for the analysis of allelic expression in single cells

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Kwangbom Choi ◽  
Narayanan Raghupathy ◽  
Gary A. Churchill
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Mixture Model ◽  
Time Course ◽  
Single Cells ◽  
Expression Patterns ◽  
Allelic Expression ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific

AbstractAllele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We demonstrate that discarding multi-mapping reads leads to higher variability in estimates of allelic proportions, an increased frequency of sampling zeros, and can lead to spurious findings of dynamic and monoallelic gene expression. Here, we report a method for ASE analysis from single-cell RNA-Seq data that accurately classifies allelic expression states and improves estimation of allelic proportions by pooling information across cells. We further demonstrate that combining information across cells using a hierarchical mixture model reduces sampling variability without sacrificing cell-to-cell heterogeneity. We applied our approach to re-evaluate the statistical independence of allelic bursting and track changes in the allele-specific expression patterns of cells sampled over a developmental time course.

scholarly journals Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

2017 ◽  
Author(s):  
Garth R. Ilsley ◽  
Ritsuko Suyama ◽  
Takeshi Noda ◽  
Nori Satoh ◽  
Nicholas M. Luscombe
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Stage ◽  
Specific Expression ◽  
Temporal Gene Expression

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

scholarly journals QuASAR: Quantitative Allele Specific Analysis of Reads

2014 ◽  
Author(s):  
Chris Harvey ◽  
Gregory A Moyebrailean ◽  
Omar Davis ◽  
Xiaoquan Wen ◽  
Francesca Luca ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Individual ◽  
Rna Seq ◽  
Specific Expression ◽  
Model Base ◽  
Specific Analysis ◽  
Genotype Information ◽  
Contrast Analysis ◽  
Allele Specific ◽  
Over Dispersion

Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression, enabling a better understanding of the functional role of non-coding sequences. However, eQTL studies are costly, requiring large sample sizes and genome-wide genotyping of each sample. In contrast, analysis of allele specific expression (ASE) is becoming a popular approach to detect the effect of genetic variation on gene expression, even within a single individual. This is typically achieved by counting the number of RNA-seq reads matching each allele at heterozygous sites and testing the null hypothesis of a 1:1 allelic ratio. In principle, when genotype information is not readily available it could be inferred from the RNA-seq reads directly. However, there are currently no existing methods that jointly infer genotypes and conduct ASE inference, while considering uncertainty in the genotype calls. We present QuASAR, Quantitative Allele Specific Analysis of Reads, a novel statistical learning method for jointly detecting heterozygous genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high quality genotypes are available. Results for an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available.

scholarly journals RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species

2021 ◽  
Vol 12 ◽  
Author(s):  
Frédéric Jehl ◽  
Fabien Degalez ◽  
Maria Bernard ◽  
Frédéric Lecerf ◽  
Laetitia Lagoutte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Call Rate ◽  
Rna Seq ◽  
Snp Detection ◽  
Specific Expression ◽  
Genotype Calling ◽  
Allele Specific Expression ◽  
Livestock Species ◽  
Allele Specific ◽  
The Impact

In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

scholarly journals Parent of origin gene expression in a founder population identifies two new imprinted genes at known imprinted regions

2018 ◽  
Author(s):  
Sahar V. Mozaffari ◽  
Michelle M. Stein ◽  
Kevin M. Magnaye ◽  
Dan L. Nicolae ◽  
Carole Ober
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Gene Expression Data ◽  
Imprinted Genes ◽  
Genotype Data ◽  
Expression Data ◽  
Rna Seq ◽  
Specific Expression ◽  
Allele Specific ◽  
Parent Of Origin

AbstractGenomic imprinting is the phenomena that leads to silencing of one copy of a gene inherited from a specific parent. Mutations in imprinted regions have been involved in diseases showing parent of origin effects. Identifying genes with evidence of parent of origin expression patterns in family studies allows the detection of more subtle imprinting. Here, we use allele specific expression in lymphoblastoid cell lines from 306 Hutterites related in a single pedigree to provide formal evidence for parent of origin effects. We take advantage of phased genotype data to assign parent of origin to RNA-seq reads in individuals with gene expression data. Our approach identified known imprinted genes, two putative novel imprinted genes, and 14 genes with asymmetrical parent of origin gene expression. We used gene expression in peripheral blood leukocytes (PBL) to validate our findings, and then confirmed imprinting control regions (ICRs) using DNA methylation levels in the PBLs.Author SummaryLarge scale gene expression studies have identified known and novel imprinted genes through allele specific expression without knowing the parental origins of each allele. Here, we take advantage of phased genotype data to assign parent of origin to RNA-seq reads in 306 individuals with gene expression data. We identified known imprinted genes as well as two novel imprinted genes in lymphoblastoid cell line gene expression. We used gene expression in PBLs to validate our findings, and DNA methylation levels in PBLs to confirm previously characterized imprinting control regions that could regulate these imprinted genes.

scholarly journals Elimination of reference mapping bias reveals robust immune related allele-specific expression in crossbred sheep

2019 ◽  
Author(s):  
Mazdak Salavati ◽  
Stephen J. Bush ◽  
Sergio Palma-Vera ◽  
Mary E. B. McCulloch ◽  
David A. Hume ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Allelic Imbalance ◽  
Cell Types ◽  
Rna Seq ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Gene Expression Atlas ◽  
Expression Atlas ◽  
Allele Specific

AbstractPervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript we describe an unbiased standardised computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open licence. The analysis pipeline we present is designed to minimise reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel x Scottish Blackface sheep, using the sheep gene expression atlas dataset. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited and instead they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programmes for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq datasets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterisation of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling, to provide both a novel analysis of the multi-dimensional sheep gene expression atlas dataset, and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.

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