Detection Of Allele-specific Expression Research Articles

Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles

COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.

Investigation of allele specific expression in various tissues of broiler chickens using the detection tool VADT

Differential abundance of allelic transcripts in a diploid organism, commonly referred to as allele specific expression (ASE), is a biologically significant phenomenon and can be examined using single nucleotide polymorphisms (SNPs) from RNA-seq. Quantifying ASE aids in our ability to identify and understand cis-regulatory mechanisms that influence gene expression, and thereby assist in identifying causal mutations. This study examines ASE in breast muscle, abdominal fat, and liver of commercial broiler chickens using variants called from a large sub-set of the samples (n = 68). ASE analysis was performed using a custom software called VCF ASE Detection Tool (VADT), which detects ASE of biallelic SNPs using a binomial test. On average ~ 174,000 SNPs in each tissue passed our filtering criteria and were considered informative, of which ~ 24,000 (~ 14%) showed ASE. Of all ASE SNPs, only 3.7% exhibited ASE in all three tissues, with ~ 83% showing ASE specific to a single tissue. When ASE genes (genes containing ASE SNPs) were compared between tissues, the overlap among all three tissues increased to 20.1%. Our results indicate that ASE genes show tissue-specific enrichment patterns, but all three tissues showed enrichment for pathways involved in translation.

ASEP: Gene-based detection of allele-specific expression across individuals in a population by RNA sequencing.

Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie phenotypic differences among individuals. Existing methods for gene-level ASE detection analyze one individual at a time, therefore failing to account for shared information across individuals. Failure to accommodate such shared information not only reduces power, but also makes it difficult to interpret results across individuals. However, when only RNA sequencing (RNA-seq) data are available, ASE detection across individuals is challenging because the data often include individuals that are either heterozygous or homozygous for the unobserved cis-regulatory SNP, leading to sample heterogeneity as only those heterozygous individuals are informative for ASE, whereas those homozygous individuals have balanced expression. To simultaneously model multi-individual information and account for such heterogeneity, we developed ASEP, a mixture model with subject-specific random effect to account for multi-SNP correlations within the same gene. ASEP only requires RNA-seq data, and is able to detect gene-level ASE under one condition and differential ASE between two conditions (e.g., pre- versus post-treatment). Extensive simulations demonstrated the convincing performance of ASEP under a wide range of scenarios. We applied ASEP to a human kidney RNA-seq dataset, identified ASE genes and validated our results with two published eQTL studies. We further applied ASEP to a human macrophage RNA-seq dataset, identified genes showing evidence of differential ASE between M0 and M1 macrophages, and confirmed our findings by results from cardiometabolic trait-relevant genome-wide association studies. To the best of our knowledge, ASEP is the first method for gene-level ASE detection at the population level that only requires the use of RNA-seq data. With the growing adoption of RNA-seq, we believe ASEP will be well-suited for various ASE studies for human diseases.

Genome-wide analysis of spatiotemporal allele-specific expression in F1 hybrids of meat- and egg-type chickens.

In diploid organisms, each gene locus is composed of two parental alleles, which would interact with each other for determining the phenotypic variation. Better understanding of the allele-specific expression (ASE) in farm animals is much important to explore the genetic basis underlying economically important traits, which have been poorly understood yet. In this study, genome-wide analysis was applied to explore the spatiotemporal pattern of ASE in the F1 hybrids of chicken. First, meat- and egg-type chickens were selected for producing a full-sib F1 hybrid population (n=57). Then, genome resequencing of two parents and 38 offspring were performed and liver and breast muscle samples (n=38) were subjected to strand-specific RNA sequencing (ssRNA-seq) for ASE detection at 1, 28, and 56days of age, respectively. The results accurately identified a total of 465 informative genes that could be distinguished with respect to their parental origins. There were 0.4% - 4.1% of informative genes showing ASE, and 57 of them were found across different tissues and time points. Besides, most ASE genes in chickens were tissue-specific, and no matter what the time-point pattern of one ASE gene, the same parental allele of this gene almost showed consistently higher or lower expression across all time points in the same type tissue. In conclusion, this study indicated that most of ASE genes were tissue-specific and time-dependent.

Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated by Cis Noncoding Mutations.

Identifying cancer-relevant mutations in noncoding regions is challenging due to the large numbers of such mutations, their low levels of recurrence, and difficulties in interpreting their functional impact. To uncover genes that are dysregulated due to somatic mutations in cis, we build upon the concept of differential allele-specific expression (ASE) and introduce methods to identify genes within an individual's cancer whose ASE differs from what is found in matched normal tissue. When applied to breast cancer tumor samples, our methods detect the known allele-specific effects of copy number variation and nonsense-mediated decay. Further, genes that are found to recurrently exhibit differential ASE across samples are cancer relevant. Genes with cis mutations are enriched for differential ASE, and we find 147 potentially functional noncoding mutations cis to genes that exhibit significant differential ASE. We conclude that differential ASE is a promising means for discovering gene dysregulation due to cis noncoding mutations.

RPASE: Individual-based allele-specific expression detection without prior knowledge of haplotype phase.

Variation in gene expression is believed to make a significant contribution to phenotypic diversity and divergence. The analysis of allele-specific expression (ASE) can reveal important insights into gene expression regulation. We developed a novel method called RPASE (Read-backed Phasing-based ASE detection) to test for genes that show ASE. With mapped RNA-seq data from a single individual and a list of SNPs from the same individual as the only input, RPASE is capable of aggregating information across multiple dependent SNPs and producing individual-based gene-level tests for ASE. RPASE performs well in simulations and comparisons. We applied RPASE to multiple bird species and found a potentially rich landscape of ASE.

A haplotype-based normalization technique for the analysis and detection of allele specific expression

BackgroundAllele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects.ResultsHere, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking.ConclusionsThe correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1238-8) contains supplementary material, which is available to authorized users.

RNA Sequencing and Analysis.

RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell. Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome. Beyond quantifying gene expression, the data generated by RNA-Seq facilitate the discovery of novel transcripts, identification of alternatively spliced genes, and detection of allele-specific expression. Recent advances in the RNA-Seq workflow, from sample preparation to library construction to data analysis, have enabled researchers to further elucidate the functional complexity of the transcription. In addition to polyadenylated messenger RNA (mRNA) transcripts, RNA-Seq can be applied to investigate different populations of RNA, including total RNA, pre-mRNA, and noncoding RNA, such as microRNA and long ncRNA. This article provides an introduction to RNA-Seq methods, including applications, experimental design, and technical challenges.

High-resolution transcriptome analysis with long-read RNA sequencing.

RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2×75 bp and 2×262 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals.

139 TRANSCRIPTOME ANALYSIS OF SINGLE BOVINE EMBRYOS BY RNA-Seq

Transcriptome sequencing by high-throughput technologies provides global gene expression levels as well as gene structure information. Moreover, analysis of samples from single individuals allows the detection of allele-specific expression. We investigated the possibility of RNA-Seq analysis using single bovine blastocysts. Embryos were in vitro produced using abattoir-derived, in vitro- matured oocytes, TALP-based fertilization and KSOM embryo culture medium. On Day 7 of culture, 5 expanded blastocysts were collected and stored in RNA extraction buffer at –80°C. Total RNA was extracted from each individual embryo using the Arcturus PicoPure RNA isolation kit including DNAse treatment. Approximately 1.5 ng of high-quality total RNA was obtained per embryo. The RNA was amplified using the SPIA-based Ovation RNA-Seq kit (NuGen, San Carlos, CA, USA). After amplification, 6 μg of cDNA was obtained and directly used for library construction with the NuGen Encore NGS Library I kit. Libraries were submitted to the University of California Davis Genome Center for 40-bp single read sequencing on an Illumina GAIIx apparatus. Data analysis was performed using CLC Genomics Workbench. On average, 38 094 173 good-quality reads were produced from each sample. Removing 9 bp from the 5′ end of the sequences greatly improved read alignment. Mapping of trimmed reads to BTAU 4.0 allowing up to 2 mismatches per read resulted in 88.9 ± 0.3% of reads aligning to the genome. Using an SNP discovery algorithm, a total of 31 993 unique SNP were detected with an average of 12 530 ± 496 SNP per sample, 50% of which were heterozygous. Of the total, 45, 21, 14 and 9% were common to at least 2, 3, 4 and 5 samples, respectively. Allelic expression imbalance, defined as 75% of reads corresponding to one allele of a heterozygous SNP with coverage ≥50, was observed in 22% of SNP among those common to at least 2 samples. Mapping the reads to the transcriptome resulted in 71.8 ± 0.4% of reads aligning to genes present in Ensembl. Among those mapping to RefSeq transcripts, 64.4% corresponded to exons, 7.2% to exon-exon boundaries and 0.3% to exon-intron boundaries, with the remainder mapping to introns. An average of 9746 ± 122 genes with RPKM greater than 0.3 were detected in each sample, with 7982 genes expressed commonly among all 5 embryos. The correlation for RPKM between sample pairs was between 0.978 and 0.993. Genes known to be almost exclusively expressed by pre-implantation embryos were present, including OCT4, NANOG and CDX2 among others. Gene ontology analysis of gene groups, divided into quintiles by level of expression, indicated that the most highly expressed genes are enriched in ribosomal proteins and oxidative phosphorylation. Genes with medium-high levels of expression were enriched in structural components including organelles and the cytoskeleton. Genes at the medium level of expression represented nuclear and chromatin proteins, whereas medium-low and low were related to regulation of transcription and DNA metabolism. We conclude that RNA-Seq from a single bovine blastocyst is possible and represents a powerful tool for understanding the biology and pathologies of pre-implantation embryo development.

Exploring the gonad transcriptome of two extreme male pigs with RNA-seq.

BackgroundAlthough RNA-seq greatly advances our understanding of complex transcriptome landscapes, such as those found in mammals, complete RNA-seq studies in livestock and in particular in the pig are still lacking. Here, we used high-throughput RNA sequencing to gain insight into the characterization of the poly-A RNA fraction expressed in pig male gonads. An expression analysis comparing different mapping approaches and detection of allele specific expression is also discussed in this study.ResultsBy sequencing testicle mRNA of two phenotypically extreme pigs, one Iberian and one Large White, we identified hundreds of unannotated protein-coding genes (PcGs) in intergenic regions, some of them presenting orthology with closely related species. Interestingly, we also detected 2047 putative long non-coding RNA (lncRNA), including 469 with human homologues. Two methods, DEGseq and Cufflinks, were used for analyzing expression. DEGseq identified 15% less expressed genes than Cufflinks, because DEGseq utilizes only unambiguously mapped reads. Moreover, a large fraction of the transcriptome is made up of transposable elements (14500 elements encountered), as has been reported in previous studies. Gene expression results between microarray and RNA-seq technologies were relatively well correlated (r = 0.71 across individuals). Differentially expressed genes between Large White and Iberian showed a significant overrepresentation of gamete production and lipid metabolism gene ontology categories. Finally, allelic imbalance was detected in ~ 4% of heterozygous sites.ConclusionsRNA-seq is a powerful tool to gain insight into complex transcriptomes. In addition to uncovering many unnanotated genes, our study allowed us to determine that a considerable fraction is made up of long non-coding transcripts and transposable elements. Their biological roles remain to be determined in future studies. In terms of differences in expression between Large White and Iberian pigs, these were largest for genes involved in spermatogenesis and lipid metabolism, which is consistent with phenotypic extreme differences in prolificacy and fat deposition between these two breeds.

Microarrays, deep sequencing and the true measure of the transcriptome

Microarrays first made the analysis of the transcriptome possible, and have produced much important information. Today, however, researchers are increasingly turning to direct high-throughput sequencing - RNA-Seq - which has considerable advantages for examining transcriptome fine structure - for example in the detection of allele-specific expression and splice junctions. In this article, we discuss the relative merits of the two techniques, the inherent biases in each, and whether all of the vast body of array work needs to be revisited using the newer technology. We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.

239 ALLELE-SPECIFIC EXPRESSION OF X CHROMOSOME-LINKED GENE MAO-A DURING PRE-IMPLANTATION DEVELOPMENT IN BOVINE EMBRYO

The in vitro embryo culture might affect epigenetic mechanisms, which are involved in controlling the expression of genes related to embryonic development and inactivation of X chromosome. Female mammals have 2 X chromosomes, and males have only 1. This has led to a particular mechanism of evolution of dosage compensation, called X-chromosome inactivation, an important epigenetic event that must occur in all mammalian female embryos. During embryogenesis, at the late blastocyst development (Xue F et al. 2002 Nature Genet. 31, 216–220), 1 of the 2 X chromosomes is randomly inactivated in each cell of the inner cell mass and preferentially X paternal in trophoblast. The aim of this study was to characterize the allele-specific expression of the X chromosome-linked gene monoamine oxidase type A (MAO-A) during in vitro pre-implantation embryo development in bovine. For phenotyping of the MAO-A gene, the RT-PCR restriction fragment length polymorphism technique was used. Primers were designed flanking a single nucleotide polymorphism and the sequence of forward inner primer creating a site of restriction to the RsaI enzyme, thus allowing the detection of allele-specific expression (Bos taurus Taurus × Bos taurus indicus). Oocytes were aspirated from 9 Nelore heifers homozygous for theA allele previously genotyped. The oocytes were selected, matured in vitro, and inseminated with X-sorted sperm from a Holstein bull homozygous for the G allele. Two pools of 10 heterozygous in vitro embryos of each developmental stage, 4-cell [44 h post-insemination (p.i.)], 8- to 16-cell (72 h p.i.), morula (144 h p.i.), blastocyst (156 p.i.), and expanded blastocyst (168 h p.i.), were produced and frozen until RNA extraction. Total RNA was extracted using Invisorb® Spin Cell RNA Mini Kit (Invitek, Berlin, Germany) according to the manufacturer’s protocol, and residual genomic DNA was removed with DNase I treatment. cDNA was done using Oligo dT primers (Invitrogen) and superscript III reverse transcriptase (Invitrogen). Nested PCR for each pool was performed and then the amplicons were digested with 10 U of RsaI enzyme (Promega, Madison, WI, USA). The products were separated by electrophoresis on a 3% agarose gel stained with ethidium bromide. The results showed that both alleles were expressionally represented in the 4-cell, 8- to 16-cell, and expanded blastocyst stages, with the X paternal allele disappearing in morula and blastocyst. We can conclude that both, maternal and paternal X chromosomes, are activated in the 2 earliest stages, inactivated in the morula and blastocyst stages, and reactivated in the expanded blastocyst stage. This research was supported by Embrapa Genetic Resources and Biotechnology and the Brazilian National Council for Scientific and Technological Development (CNPq).

Genome-wide screening for cis-regulatory variation using a classical diallel crossing scheme

Large-scale screening studies carried out to date for genetic variants that affect gene regulation are generally limited to descriptions of differences in allele-specific expression (ASE) detected in vivo. Allele-specific differences in gene expression provide evidence for a model whereby cis-acting genetic variation results in differential expression between alleles. Such gene surveys for regulatory variation are a first step in identifying the specific nucleotide changes that govern gene expression differences, but they leave the underlying mechanisms unexplored. Here, we propose a quantitative genetics approach to perform a genome-wide analysis of ASE differences (GASED). The GASED approach is based on a diallel design that is often used in plant breeding programs to estimate general combining abilities (GCA) of specific inbred lines and to identify high-yielding hybrid combinations of parents based on their specific combining abilities (SCAs). In a context of gene expression, the values of GCA and SCA parameters allow cis- and trans-regulatory changes to be distinguished and imbalances in gene expression to be ascribed to cis-regulatory variation. With this approach, a total of 715 genes could be identified that are likely to carry allelic polymorphisms responsible for at least a 1.5-fold allelic expression difference in a total of 10 diploid Arabidopsis thaliana hybrids. The major strength of the GASED approach, compared to other ASE detection methods, is that it is not restricted to genes with allelic transcript variants. Although a false-positive rate of 9/41 was observed, the GASED approach is a valuable pre-screening method that can accelerate systematic surveys of naturally occurring cis-regulatory variation among inbred lines for laboratory species, such as Arabidopsis, mouse, rat and fruitfly, and economically important crop species, such as corn.

Simultaneous genotyping, gene-expression measurement, and detection of allele-specific expression with oligonucleotide arrays

Oligonucleotide microarrays provide a high-throughput method for exploring genomes. In addition to their utility for gene-expression analysis, oligonucleotide-expression arrays have also been used to perform genotyping on genomic DNA. Here, we show that in segregants from a cross between two unrelated strains of Saccharomyces cerevisiae, high-quality genotype data can also be obtained when mRNA is hybridized to an oligonucleotide-expression array. We were able to identify and genotype nearly 1000 polymorphisms at an error rate close to 3% in segregants and at an error rate of 7% in diploid strains, a performance comparable to methods using genomic DNA. In addition, we demonstrate how simultaneous genotyping and gene-expression profiling can reveal cis-regulatory variation by screening hundreds of genes for allele-specific expression. With this method, we discovered 70 ORFs with evidence for preferential expression of one allele in a diploid hybrid of two S. cerevisiae strains.

Detection of allele-specific expression of N-RAS oncogenes in human leukaemia cells.

We report analysis of allele-specific expression of N-RAS transcripts in myeloid leukaemic cells and cell lines. Expression was assessed by an assay of reverse-transcription/PCR combined with differential hybridization with mutation-specific oligonucleotides. In cells from all patients with acute myeloid leukaemia (AML) and in myeloid cell lines HL-60 and THP-1, expression of both the wild-type allele and the abnormal allele altered by a point mutation could be detected, albeit not always at comparable levels. This might be due for instance to allelic exclusion. The assay described provides a means of analysing the degree of expression of dominant oncogenes.