Efficient CRISPR-rAAV engineering of endogenous genes to study protein function by allele-specific RNAi.

Gene knockout strategies, RNAi and rescue experiments are all employed to study mammalian gene function. However, the disadvantages of these approaches include: loss of function adaptation, reduced viability and gene overexpression that rarely matches endogenous levels. Here, we developed an endogenous gene knockdown/rescue strategy that combines RNAi selectivity with a highly efficient CRISPR directed recombinant Adeno-Associated Virus (rAAV) mediated gene targeting approach to introduce allele-specific mutations plus an allele-selective siRNA Sensitive (siSN) site that allows for studying gene mutations while maintaining endogenous expression and regulation of the gene of interest. CRISPR/Cas9 plus rAAV targeted gene-replacement and introduction of allele-specific RNAi sensitivity mutations in the CDK2 and CDK1 genes resulted in a >85% site-specific recombination of Neo-resistant clones versus ∼8% for rAAV alone. RNAi knockdown of wild type (WT) Cdk2 with siWT in heterozygotic knockin cells resulted in the mutant Cdk2 phenotype cell cycle arrest, whereas allele specific knockdown of mutant CDK2 with siSN resulted in a wild type phenotype. Together, these observations demonstrate the ability of CRISPR plus rAAV to efficiently recombine a genomic locus and tag it with a selective siRNA sequence that allows for allele-selective phenotypic assays of the gene of interest while it remains expressed and regulated under endogenous control mechanisms

PanGEA: Identification of allele specific gene expression using the 454 technology

<p>Abstract</p> <p>Background</p> <p>Next generation sequencing technologies hold great potential for many biological questions. While mainly used for genomic sequencing, they are also very promising for gene expression profiling. Sequencing of cDNA does not only provide an estimate of the absolute expression level, it can also be used for the identification of allele specific gene expression.</p> <p>Results</p> <p>We developed PanGEA, a tool which enables a fast and user-friendly analysis of allele specific gene expression using the 454 technology. PanGEA allows mapping of 454-ESTs to genes or whole genomes, displaying gene expression profiles, identification of SNPs and the quantification of allele specific gene expression. The intuitive GUI of PanGEA facilitates a flexible and interactive analysis of the data. PanGEA additionally implements a modification of the Smith-Waterman algorithm which deals with incorrect estimates of homopolymer length as occuring in the 454 technology</p> <p>Conclusion</p> <p>To our knowledge, PanGEA is the first tool which facilitates the identification of allele specific gene expression. PanGEA is distributed under the Mozilla Public License and available at: <url>http://www.kofler.or.at/bioinformatics/PanGEA</url></p

Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage.

One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11-q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis

Association between the c.*229C>T polymorphism of the topoisomerase IIb binding protein 1 (TopBP1) gene and breast cancer

Topoisomerase IIb binding protein 1 (TopBP1) is involved in cell survival, DNA replication, DNA damage repair and cell cycle checkpoint control. The biological function of TopBP1 and its close relation with BRCA1 prompted us to investigate whether alterations in the TopBP1 gene can influence the risk of breast cancer. The aim of this study was to examine the association between five polymorphisms (rs185903567, rs116645643, rs115160714, rs116195487, and rs112843513) located in the 30UTR region of the TopBP1 gene and breast cancer risk as well as allele-specific gene expression. Five hundred thirty-four breast cancer patients and 556 population controls were genotyped for these SNPs. Allele-specific Top- BP1 mRNA and protein expressions were determined by using real time PCR and western blotting methods, respectively. Only one SNP (rs115160714) showed an association with breast cancer. Compared to homozygous common allele carriers, heterozygous and homozygous for the T variant had significantly increased risk of breast cancer (adjusted odds ratio = 3.81, 95 % confidence interval: 1.63–8.34, p = 0.001). Mean TopBP1 mRNA and protein expression were higher in the individuals with the CT or TT genotype. There was a significant association between the rs115160714 and tumor grade and stage. Most carriers of minor allele had a high grade (G3) tumors classified as T2-T4N1M0. Our study raises a possibility that a genetic variation of TopBP1 may be implicated in the etiology of breast cancer

Expression of natural killer receptor alleles at different Ly49 loci occurs independently and is regulated by major histocompatibility complex class I molecules.

Ly49 receptor genes are expressed by subsets of natural killer (NK) cells in an overlapping fashion, accounting for the capacity of NK subsets to attack host cells that have selectively downregulated self-major histocompatibility complex (MHC) class I molecules. It was shown previously that most NK cells express only one or the other allele of a given Ly49 gene, while a smaller population expresses both alleles. However, the methods used to detect monoallelic and biallelic cells were nonquantitative. Here, new allele-specific antibodies were used to provide the first quantitative examination of biallelic and monoallelic expression of Ly49A and Ly49G2. The results demonstrate conclusively that most Ly49A(+) and Ly49G2(+) NK cells express the corresponding gene in a monoallelic fashion, with a smaller subset expressing both alleles. Unexpectedly, biallelic Ly49A(+) NK cells were more numerous than predicted by completely independent allelic expression, suggesting some heterogeneity among NK progenitors in the potential to express a given Ly49 gene. The data also show that cells expressing one allele of Ly49G2 may express Ly49A from the same or opposite chromosome with equal likelihood, indicating that the expressed allele is chosen independently for different Ly49 genes. Finally, the data demonstrate that biallelic expression of Ly49A or Ly49G2 occurs least frequently in mice that express ligands for these receptors (H-2(d) mice), and most frequently in class I-deficient mice. Thus, biallelic expression of Ly49 genes is regulated by interactions of NK cell progenitors with MHC class I molecules

Fully Bayesian analysis of allele-specific RNA-seq data

Diploid organisms have two copies of each gene, called alleles, that can be separately transcribed. The RNA abundance associated to any particular allele is known as allele-specific expression (ASE). When two alleles have polymorphisms in transcribed regions, ASE can be studied using RNA-seq read count data. ASE has characteristics different from the regular RNA-seq expression: ASE cannot be assessed for every gene, measures of ASE can be biased towards one of the alleles (reference allele), and ASE provides two measures of expression for a single gene for each biological samples with leads to additional complications for single-gene models. We present statistical methods for modeling ASE and detecting genes with differential allelic expression. We propose a hierarchical, overdispersed, count regression model to deal with ASE counts. The model accommodates gene-specific overdispersion, has an internal measure of the reference allele bias, and uses random effects to model the gene-specific regression parameters. Fully Bayesian inference is obtained using the fbseq package that implements a parallel strategy to make the computational times reasonable. Simulation and real data analysis suggest the proposed model is a practical and powerful tool for the study of differential ASE

The DNA Methylome of Human Peripheral Blood Mononuclear Cells

Analysis across the genome of patterns of DNA methylation reveals a rich landscape of allele-specific epigenetic modification and consequent effects on allele-specific gene expression

Simultaneous quantitative and allele-specific expression analysis with real competitive PCR

Background: For a diploid organism such as human, the two alleles of a particular gene can be expressed at different levels due to X chromosome inactivation, gene imprinting, different local promoter activity, or mRNA stability. Recently, imbalanced allelic expression was found to be common in human and can follow Mendelian inheritance. Here we present a method that employs real competitive PCR for allele-specific expression analysis. Results: A transcribed mutation such as a single nucleotide polymorphism ( SNP) is used as the marker for allele-specific expression analysis. A synthetic mutation created in the competitor is close to a natural mutation site in the cDNA sequence. PCR is used to amplify the two cDNA sequences from the two alleles and the competitor. A base extension reaction with a mixture of ddNTPs/ dNTP is used to generate three oligonucleotides for the two cDNAs and the competitor. The three products are identified and their ratios are calculated based on their peak areas in the MALDI-TOF mass spectrum. Several examples are given to illustrate how allele-specific gene expression can be applied in different biological studies. Conclusions: This technique can quantify the absolute expression level of each individual allele of a gene with high precision and throughput

Allele specific expression analysis identifies regulatory variation associated with stress-related genes in the Mexican highland maize landrace Palomero Toluqueño.

BackgroundGene regulatory variation has been proposed to play an important role in the adaptation of plants to environmental stress. In the central highlands of Mexico, farmer selection has generated a unique group of maize landraces adapted to the challenges of the highland niche. In this study, gene expression in Mexican highland maize and a reference maize breeding line were compared to identify evidence of regulatory variation in stress-related genes. It was hypothesised that local adaptation in Mexican highland maize would be associated with a transcriptional signature observable even under benign conditions.MethodsAllele specific expression analysis was performed using the seedling-leaf transcriptome of an F1 individual generated from the cross between the highland adapted Mexican landrace Palomero Toluqueño and the reference line B73, grown under benign conditions. Results were compared with a published dataset describing the transcriptional response of B73 seedlings to cold, heat, salt and UV treatments.ResultsA total of 2,386 genes were identified to show allele specific expression. Of these, 277 showed an expression difference between Palomero Toluqueño and B73 alleles under benign conditions that anticipated the response of B73 cold, heat, salt and/or UV treatments, and, as such, were considered to display a prior stress response. Prior stress response candidates included genes associated with plant hormone signaling and a number of transcription factors. Construction of a gene co-expression network revealed further signaling and stress-related genes to be among the potential targets of the transcription factors candidates.DiscussionPrior activation of responses may represent the best strategy when stresses are severe but predictable. Expression differences observed here between Palomero Toluqueño and B73 alleles indicate the presence of cis-acting regulatory variation linked to stress-related genes in Palomero Toluqueño. Considered alongside gene annotation and population data, allele specific expression analysis of plants grown under benign conditions provides an attractive strategy to identify functional variation potentially linked to local adaptation