Allele-specific Expression Analysis Research Articles

Stabilizing selection and adaptation shape cis and trans gene expression variation in C. elegans.

An outstanding question in the evolution of gene expression is the relative influence of neutral processes versus natural selection, including adaptive change driven by directional selection as well as stabilizing selection, which may include compensatory dynamics. These forces shape patterns of gene expression variation within and between species, including the regulatory mechanisms governing expression in cis and trans . In this study, we interrogate intraspecific gene expression variation among seven wild C. elegans strains, with varying degrees of genomic divergence from the reference strain N2, leveraging this system's unique advantages to comprehensively evaluate gene expression evolution. By capturing allele-specific and between-strain changes in expression, we characterize the regulatory architecture and inheritance mode of gene expression variation within C. elegans and assess their relationship to nucleotide diversity, genome evolutionary history, gene essentiality, and other biological factors. We conclude that stabilizing selection is a dominant influence in maintaining expression phenotypes within the species, and the discovery that genes with higher overall expression tend to exhibit fewer expression differences supports this conclusion, as do widespread instances of cis differences compensated in trans . Moreover, analyses of human expression data replicate our finding that higher expression genes have less variable expression. We also observe evidence for directional selection driving expression divergence, and that expression divergence accelerates with increasing genomic divergence. To provide community access to the data from this first analysis of allele-specific expression in C. elegans , we introduce an interactive web application, where users can submit gene-specific queries to view expression, regulatory pattern, inheritance mode, and other information: https://wildworm.biosci.gatech.edu/ase/ .

Characterizing the allele-specific gene expression landscape in high hyperdiploid acute lymphoblastic leukemia with BASE.

Somatic copy number variations (CNVs), including abnormal chromosome numbers and structural changes leading to gain or loss of genetic material, play a crucial role in initiation and progression of cancer. CNVs are believed to cause gene dosage imbalances and modify cis-regulatory elements, leading to allelic expression imbalances in genes that influence cell division and thereby contribute to cancer development. However, the impact of CNVs on allelic gene expression in cancer remains unclear. Allele-specific expression (ASE) analysis, a potent method for investigating genome-wide allelic imbalance profiles in tumors, assesses the relative expression of two alleles using high-throughput sequencing data. However, many existing methods for gene-level ASE detection rely on only RNA sequencing data, which present challenges in interpreting the genetic mechanisms underlying ASE in cancer. To address this issue, we developed a robust framework that integrates allele-specific copy number calls into ASE calling algorithms by leveraging paired genome and transcriptome data from the same sample. This integration enhances the interpretability of the genetic mechanisms driving ASE, thereby facilitating the identification of driver events triggered by CNVs in cancer. In this study, we utilized BASE to conduct a comprehensive analysis of ASE in high hyperdiploid acute lymphoblastic leukemia (HeH ALL), a prevalent childhood malignancy characterized by gains of chromosomes X, 4, 6, 10, 14, 17, 18, and 21. Our analysis unveiled the comprehensive ASE landscape in HeH ALL. Through a multi-perspective examination of HeH ASEs, we offer a systematic understanding of how CNVs impact ASE in HeH, providing valuable insights to guide ASE studies in cancer.

PRKACB is a novel imprinted gene in marsupials

BackgroundGenomic imprinting results in parent-of-origin-specific gene expression and, among vertebrates, is found only in therian mammals: marsupials and eutherians. A differentially methylated region (DMR), in which the methylation status of CpG dinucleotides differs between the two alleles, can mark the parental identity of imprinted genes. We developed a computational pipeline that detected CpG islands (CGIs) marked by both methylated and unmethylated signals in whole genome bisulfite sequencing data. This approach identified candidate marsupial DMRs in a publicly available koala methylome. One of these candidate DMRs was associated with PRKACB, a gene encoding the protein kinase A catalytic subunit beta. Nothing is known about the imprinting status of PRKACB in eutherian mammals although mutations of this gene are associated with endocrine neoplasia and other developmental disorders.ResultsIn the tammar wallaby and brushtail possum there was parent-of-origin-specific DNA methylation in the PRKACB DMR in which the maternal allele was methylated and the paternal allele was unmethylated. There were multiple RNAs transcribed from this locus. Allele-specific expression analysis identified paternal expression of a PRKACB lncRNA and an mRNA isoform. Comparison of the PRKACB gene start site between marsupials and eutherians demonstrated that the CGI is longer in marsupials. The PRKACB gene product functions in the same signalling pathway as the guanine nucleotide-binding protein alpha subunit encoded at the GNAS locus, a known eutherian imprinted gene. In a mouse methylome Gnas had three differentially methylated CGIs, while in the koala methylome the GNAS locus had two unmethylated CGIs.ConclusionsWe conclude that PRKACB is a novel, DMR-associated marsupial imprinted gene. Imprinting of PRKACB in marsupials and GNAS in eutherians may indicate a conserved selection pressure for imprinting of the protein kinase A signalling pathway in therians with the two lineages adapting by imprinting different genes.

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes.

A major goal in evolutionary biology and biomedicine is to understand the complex interactions between genetic variants, the epigenome, and gene expression. However, the causal relationships between these factors remain poorly understood. mSTARR-seq, a methylation-sensitive massively parallel reporter assay, is capable of identifying methylation-dependent regulatory activity at many thousands of genomic regions simultaneously, and allows for the testing of causal relationships between DNA methylation and gene expression on a region-by-region basis. Here, we developed a multiplexed mSTARR-seq protocol to assay naturally occurring human genetic variation from 25 individuals sampled from 10 localities in Europe and Africa. We identified 6,957 regulatory elements in either the unmethylated or methylated state, and this set was enriched for enhancer and promoter annotations, as expected. The expression of 58% of these regulatory elements was modulated by methylation, which was generally associated with decreased RNA expression. Within our set of regulatory elements, we used allele-specific expression analyses to identify 8,020 sites with genetic effects on gene regulation; further, we found that 42.3% of these genetic effects varied between methylated and unmethylated states. Sites exhibiting methylation-dependent genetic effects were enriched for GWAS and EWAS annotations, implicating them in human disease. Compared to datasets that assay DNA from a single European individual, our multiplexed assay uncovers dramatically more genetic effects and methylation-dependent genetic effects, highlighting the importance of including diverse individuals in assays which aim to understand gene regulatory processes.

Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data.

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

Bayesian Estimation of Allele-Specific Expression in the Presence of Phasing Uncertainty.

Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors. We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways. The software is freely available from https://github.com/x811zou/BEASTIE . BEASTIE is available as Python source code and as a Docker image. Additional information is available online.

Biparental graph strategy to represent and analyze hybrid plant genomes.

Hybrid plants are found extensively in the wild, and they often demonstrate superior performance of complex traits over their parents and other selfing plants. This phenomenon, known as heterosis, has been extensively applied in plant breeding for decades. However, the process of decoding hybrid plant genomes has seriously lagged due to the challenges associated with genome assembly and the lack of appropriate methodologies for their subsequent representation and analysis. Here, we present the assembly and analysis of 2 hybrids, an intraspecific hybrid between 2 maize (Zea mays ssp. mays) inbred lines and an interspecific hybrid between maize and its wild relative teosinte (Z. mays ssp. parviglumis), utilizing a combination of PacBio High Fidelity sequencing and chromatin conformation capture sequencing data. The haplotypic assemblies are well phased at chromosomal scale, successfully resolving the complex loci with extensive parental structural variations (SVs). By integrating into a biparental genome graph, the haplotypic assemblies can facilitate downstream short-read-based SV calling and allele-specific gene expression analysis, demonstrating outstanding advantages over a single linear genome. Our work offers a comprehensive workflow that aims to facilitate the decoding of numerous hybrid plant genomes, particularly those with unknown or inaccessible parentage, thereby enhancing our understanding of genome evolution and heterosis.

The structure of the TH/INS locus and the parental allele expressed are not conserved between mammals

Parent-of-origin-specific expression of imprinted genes is critical for successful mammalian growth and development. Insulin, coded by the INS gene, is an important growth factor expressed from the paternal allele in the yolk sac placenta of therian mammals. The tyrosine hydroxylase gene TH encodes an enzyme involved in dopamine synthesis. TH and INS are closely associated in most vertebrates, but the mouse orthologues, Th and Ins2, are separated by repeated DNA. In mice, Th is expressed from the maternal allele, but the parental origin of expression is not known for any other mammal so it is unclear whether the maternal expression observed in the mouse represents an evolutionary divergence or an ancestral condition. We compared the length of the DNA segment between TH and INS across species and show that separation of these genes occurred in the rodent lineage with an accumulation of repeated DNA. We found that the region containing TH and INS in the tammar wallaby produces at least five distinct RNA transcripts: TH, TH-INS1, TH-INS2, lncINS and INS. Using allele-specific expression analysis, we show that the TH/INS locus is expressed from the paternal allele in pre- and postnatal tammar wallaby tissues. Determining the imprinting pattern of TH/INS in other mammals might clarify if paternal expression is the ancestral condition which has been flipped to maternal expression in rodents by the accumulation of repeat sequences.

Allele-specific DNA methylation and gene expression during shoot organogenesis in tissue culture of hybrid poplar.

Plant tissue regeneration is critical for genetic transformation and genome editing techniques. During the regeneration process, changes in epigenetic modifications accompany the cell fate transition. However, how allele-specific DNA methylation in two haplotypes contributes to the transcriptional dynamics during regeneration remains elusive. Here we applied an inter-species hybrid poplar (Populus alba × P. glandulosa cv. 84K) as a system to characterize the DNA methylation landscape during de novo shoot organogenesis at allele level. Both direct and indirect shoot organogenesis showed a reduction in genome-wide DNA methylation. At gene level, non-expressed genes were hypermethylated in comparison with expressed genes. Among the genes exhibiting significant correlations between levels of DNA methylation and gene expression, the expression patterns of 75% of genes were negatively correlated with DNA methylation in the CG context, whereas the correlation patterns in the CHH context were the reverse. The allele-biased DNA methylation was consistent during shoot organogenesis, with fewer than one-thousandth of allele-specific methylation regions shifted. Analysis of allele-specific expression revealed that there were only 1909 genes showing phase-dependent allele-biased expression in the regeneration process, among which the allele pairs with greater differences in transcription factor binding sites at promoter regions exhibited greater differences in allele expression. Our results indicated a relatively independent transcriptional regulation in two subgenomes during shoot organogenesis, which was contributed by cis-acting genomic and epigenomic variations.

Abstract C050: Allele-specific expression analysis identifies novel dysregulated genes in high-risk prostate tumors from African-American men

Abstract African-American (AA) patients are twice as likely to die from prostate cancer (PCa) compared to European American (EA) patients. Racism, socioeconomic status, access to healthcare, and tumor biology have been attributed to this mortality gap. Tumor biology has not been fully characterized in AA patients, particularly the landscape of gene-regulatory alterations and how they may contribute to tumor aggressiveness. We use allele specific expression (ASE) analysis, which detects genes showing imbalanced expression of alleles often due to altered gene regulation on one allele, to comprehensively identify dysregulated genes in high-risk PCa tumors from AA patients. We develop a framework for identifying candidate cancer genes by integrating ASE data, clinical data, and epigenetics. Our approach identifies genes that recurrently undergo ASE across AA patient tumors. Further, we describe genes that more frequently undergo ASE in AA patient tumors compared to EA patient tumors, some of which are implicated in hormone therapy resistance, aggressive disease, and worse disease-free survival. Our work demonstrates the potential for ASE to uncover dysregulated genes that may not have been identified through RNAseq alone. Ultimately, this work increases our understanding of the effects of regulatory alterations in tumors and how they may contribute to disparities in PCa. Citation Format: Margaret Tsui, Kevin Hu, Hanbing Song, Franklin W. Huang. Allele-specific expression analysis identifies novel dysregulated genes in high-risk prostate tumors from African-American men [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C050.

Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects

Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.

Transcriptome-wide identification and characterization of genes exhibit allele-specific imprinting in maize embryo and endosperm

BackgroundGenomic imprinting refers to a subset of genes that are expressed from only one parental allele during seed development in plants. Studies on genomic imprinting have revealed that intraspecific variations in genomic imprinting expression exist in naturally genetic varieties. However, there have been few studies on the functional analysis of allele-specific imprinted genes.ResultsHere, we generated three reciprocal crosses among the B73, Mo17 and CAU5 inbred lines. Based on the transcriptome-wide analysis of allele-specific expression using RNA sequencing technology, 305 allele-specific imprinting genes (ASIGs) were identified in embryos, and 655 ASIGs were identified in endosperms from three maize F1 hybrids. Of these ASIGs, most did not show consistent maternal or paternal bias between the same tissue from different hybrids or different tissues from one hybrid cross. By gene ontology (GO) analysis, five and eight categories of GO exhibited significantly higher functional enrichments for ASIGs identified in embryo and endosperm, respectively. These functional categories indicated that ASIGs are involved in intercellular nutrient transport, signaling pathways, and transcriptional regulation of kernel development. Finally, the mutation and overexpression of one ASIG (Zm305) affected the length and width of the kernel.ConclusionIn this study, our data will be helpful in gaining further knowledge of genes exhibiting allele-specific imprinting patterns in seeds. The gain- and loss-of-function phenotypes of ASIGs associated with agronomically important seed traits provide compelling evidence for ASIGs as crucial targets to optimize seed traits in crop plants.

Analysis of subcellular RNA fractions demonstrates significant genetic regulation of gene expression in human brain post-transcriptionally

Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale.

Analysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library. Here, we develop a spike-in approach which is highly accurate at only a small fraction of the cost. We show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and Caenorhabditis elegans. Our new approach, controlFreq, enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ∼5%. Analysis pipeline for this approach is available at GitHub as R package controlFreq (github.com/gimelbrantlab/controlFreq).

Dynamic Changes in the Global Transcriptome of Postnatal Skeletal Muscle in Different Sheep.

Sheep growth performance, mainly skeletal muscle growth, provides direct economic benefits to the animal husbandry industry. However, the underlying genetic mechanisms of different breeds remain unclear. We found that the cross-sectional area (CSA) of skeletal muscle in Dorper (D) and binary cross-breeding (HD) was higher than that in Hu sheep (H) from 3 months to 12 months after birth. The transcriptomic analysis of 42 quadriceps femoris samples showed that a total of 5053 differential expression genes (DEGs) were identified. The differences in the global gene expression patterns, the dynamic transcriptome of skeletal muscle development, and the transcriptome of the transformation of fast and slow muscles were explored using weighted correlation network analysis (WGCNA) and allele-specific expression analysis. Moreover, the gene expression patterns of HD were more similar to D rather than H from 3 months to 12 months, which might be the reason for the difference in muscle growth in the three breeds. Additionally, several genes (GNB2L1, RPL15, DVL1, FBXO31, etc.) were identified as candidates related to skeletal muscle growth. These results should serve as an important resource revealing the molecular basis of muscle growth and development in sheep.

Exploration of genotype-by-environment interactions affecting gene expression responses in porcine immune cells.

As one of the keys to healthy performance, robustness of farm animals is gaining importance, and with this comes increasing interest in genetic dissection of genotype-by-environment interactions (G×E). Changes in gene expression are among the most sensitive responses conveying adaptation to environmental stimuli. Environmentally responsive regulatory variation thus likely plays a central role in G×E. In the present study, we set out to detect action of environmentally responsive cis-regulatory variation by the analysis of condition-dependent allele specific expression (cd-ASE) in porcine immune cells. For this, we harnessed mRNA-sequencing data of peripheral blood mononuclear cells (PBMCs) stimulated in vitro with lipopolysaccharide, dexamethasone, or their combination. These treatments mimic common challenges such as bacterial infection or stress, and induce vast transcriptome changes. About two thirds of the examined loci showed significant ASE in at least one treatment, and out of those about ten percent exhibited cd-ASE. Most of the ASE variants were not yet reported in the PigGTEx Atlas. Genes showing cd-ASE were enriched in cytokine signaling in immune system and include several key candidates for animal health. In contrast, genes showing no ASE featured cell-cycle related functions. We confirmed LPS-dependent ASE for one of the top candidates, SOD2, which ranks among the major response genes in LPS-stimulated monocytes. The results of the present study demonstrate the potential of in vitro cell models coupled with cd-ASE analysis for the investigation of G×E in farm animals. The identified loci may benefit efforts to unravel the genetic basis of robustness and improvement of health and welfare in pigs.

Characterization of Genes That Exhibit Genotype-Dependent Allele-Specific Expression and Its Implications for the Development of Maize Kernel

Heterosis or hybrid vigor refers to the superior phenotypic traits of hybrids relative to their parental inbred lines. An imbalance between the expression levels of two parental alleles in the F1 hybrid has been suggested as a mechanism of heterosis. Here, based on genome-wide allele-specific expression analysis using RNA sequencing technology, 1689 genes exhibiting genotype-dependent allele-specific expression (genotype-dependent ASEGs) were identified in the embryos, and 1390 genotype-dependent ASEGs in the endosperm, of three maize F1 hybrids. Of these ASEGs, most were consistent in different tissues from one hybrid cross, but nearly 50% showed allele-specific expression from some genotypes but not others. These genotype-dependent ASEGs were mostly enriched in metabolic pathways of substances and energy, including the tricarboxylic acid cycle, aerobic respiration, and energy derivation by oxidation of organic compounds and ADP binding. Mutation and overexpression of one ASEG affected kernel size, which indicates that these genotype-dependent ASEGs may make important contributions to kernel development. Finally, the allele-specific methylation pattern on genotype-dependent ASEGs indicated that DNA methylation plays a potential role in the regulation of allelic expression for some ASEGs. In this study, a detailed analysis of genotype-dependent ASEGs in the embryo and endosperm of three different maize F1 hybrids will provide an index of genes for future research on the genetic and molecular mechanism of heterosis.

Red-TE Homozygous Alleles of MdMYB10 Confer Full-Red Apple Fruit Skin in a High-Temperature Region

Apple is a major fruit crop grown worldwide and provides humans with an essential diet and health benefits. One of the health benefits is related to the accumulation of fruit anthocyanin, which also provides fruit with an attractive red colour. It is known that an MdMYB10 allele containing a transposable element (TE) insertion in its promoter (termed Red-TE allele) underlies anthocyanin accumulation and red colouration in the fruit skin of cultivated apples. However, the distribution of this Red-TE allele in wider Malus germplasm accessions is not clear. In this study, we showed that MdMYB10 RNA in fruit skin was specifically expressed from the Red-TE allele by using allele-specific expression analysis of transcriptome data. Apple cultivars and hybrids with homozygous Red-TE alleles showed stronger red colour than those with heterozygous alleles after analysing 65 cultivars and 337 hybrids. Furthermore, both hetero- and homozygous plants growing in the same high-temperature conditions had different colourations. However, the Red-TE allele was not detected in 16 wild apple accessions showing red skin, indicating that the red skin colour of these wild apples was not conferred by the Red-TE allele. These findings provide guidance for selecting cultivars able to develop consistent red colouration under high growth temperature conditions and open the opportunity for identifying novel genetic variants underpinning fruit red colouration in wild apple species.

Differential haplotype expression in class I MHC genes during SARS-CoV-2 infection of human lung cell lines.

Cell entry of SARS-CoV-2 causes genome-wide disruption of the transcriptional profiles of genes and biological pathways involved in the pathogenesis of COVID-19. Expression allelic imbalance is characterized by a deviation from the Mendelian expected 1:1 expression ratio and is an important source of allele-specific heterogeneity. Expression allelic imbalance can be measured by allele-specific expression analysis (ASE) across heterozygous informative expressed single nucleotide variants (eSNVs). ASE reflects many regulatory biological phenomena that can be assessed by combining genome and transcriptome information. ASE contributes to the interindividual variability associated with the disease. We aim to estimate the transcriptome-wide impact of SARS-CoV-2 infection by analyzing eSNVs. We compared ASE profiles in the human lung cell lines Calu-3, A459, and H522 before and after infection with SARS-CoV-2 using RNA-Seq experiments. We identified 34 differential ASE (DASE) sites in 13 genes (HLA-A, HLA-B, HLA-C, BRD2, EHD2, GFM2, GSPT1, HAVCR1, MAT2A, NQO2, SUPT6H, TNFRSF11A, UMPS), all of which are enriched in protein binding functions and play a role in COVID-19. Most DASE sites were assigned to the MHC class I locus and were predominantly upregulated upon infection. DASE sites in the MHC class I locus also occur in iPSC-derived airway epithelium basal cells infected with SARS-CoV-2. Using an RNA-Seq haplotype reconstruction approach, we found DASE sites and adjacent eSNVs in phase (i.e., predicted on the same DNA strand), demonstrating differential haplotype expression upon infection. We found a bias towards the expression of the HLA alleles with a higher binding affinity to SARS-CoV-2 epitopes. Independent of gene expression compensation, SARS-CoV-2 infection of human lung cell lines induces transcriptional allelic switching at the MHC loci. This suggests a response mechanism to SARS-CoV-2 infection that swaps HLA alleles with poor epitope binding affinity, an expectation supported by publicly available proteome data.

Citrus genomic resources unravel putative genetic determinants of Huanglongbing pathogenicity

Citrus HLB caused by Candidatus Liberibacter asiaticus is a pathogen-triggered immune disease. Here, we identified putative genetic determinants of HLB pathogenicity by integrating citrus genomic resources to characterize the pan-genome of accessions that differ in their response to HLB. Genome-wide association mapping and analysis of allele-specific expression between susceptible, tolerant, and resistant accessions further refined candidates underlying the response to HLB. We first developed a phased diploid assembly of Citrus sinensis 'Newhall' genome and produced resequencing data for 91 citrus accessions that differ in their response to HLB. These data were combined with previous resequencing data from 356 accessions for genome-wide association mapping of the HLB response. Genes determinants for HLB pathogenicity were associated with host immune response, ROS production, and antioxidants. Overall, this study has provided a significant resource of citrus genomic data and identified candidate genes to be further explored to understand the genetic determinants of HLB pathogenicity.