scholarly journals Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance

2021 ◽  
Vol 17 (3) ◽  
pp. e1008772
Author(s):  
Anton J. M. Larsson ◽  
Christoph Ziegenhain ◽  
Michael Hagemann-Jensen ◽  
Björn Reinius ◽  
Tina Jacob ◽  
...  
Keyword(s):  
Allelic Imbalance ◽  
Burst Size ◽  
Single Cells ◽  
Expression Patterns ◽  
Allelic Expression ◽  
Monoallelic Expression ◽  
Burst Frequency ◽  
Biallelic Expression ◽  
High Consistency ◽  
Transcriptional Bursting

Transcriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of allelic expression resulting from transcriptional bursting and how it compared to the amount biallelic, monoallelic and allele-biased expression observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain the allelic expression patterns observed in single cells, including the frequent observations of autosomal monoallelic gene expression. Importantly, we identified that the burst frequency largely determined the fraction of cells with monoallelic expression, whereas the burst size had little effect on monoallelic observations. The high consistency between the bursting model predictions and scRNA-seq observations made it possible to assess the heterogeneity of a group of cells as their deviation in allelic observations from the expected. Finally, both burst frequency and size contributed to allelic imbalance observations and reinforced that studies of allelic imbalance can be confounded from the inherent noise in transcriptional bursting. Altogether, we demonstrate that allele-level transcriptional bursting renders widespread, although predictable, amounts of monoallelic and biallelic expression in single cells and cell populations.

scholarly journals Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance

2019 ◽  
Author(s):  
Anton J.M. Larsson ◽  
Björn Reinius ◽  
Tina Jacob ◽  
Tim Dalessandri ◽  
Gert-Jan Hendriks ◽  
...  
Keyword(s):  
Allelic Imbalance ◽  
Expression Patterns ◽  
Monoallelic Expression ◽  
Biological Noise ◽  
Burst Frequency ◽  
Large Numbers ◽  
Single Cell Rna Sequencing ◽  
Transcriptional Bursting ◽  
Burst Kinetics ◽  
Shed Light

AbstractTranscriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of monoallelic expression resulting from transcriptional bursting and how it compared to the amounts of monoallelic expression of autosomal genes observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain frequent observations of autosomal monoallelic gene expression in cells. Importantly, the burst frequency largely determined the fraction of cells with monoallelic expression, whereas both burst frequency and size contributed to allelic imbalance. Allelic observations deviate from the expected when analysed across heterogeneous groups of cells, suggesting that allelic modelling can provide an unbiased assessment of heterogeneity within cells. Finally, large numbers of cells are required for analyses of allelic imbalance to avoid confounding observations from transcriptional bursting. Altogether, our results shed light on the implications of transcriptional burst kinetics on allelic expression patterns and phenotypic variation between cells.

scholarly journals Transcriptional bursting explains the noise-versus-mean relationship in mRNA and protein levels

2016 ◽  
Author(s):  
Roy D. Dar ◽  
Sydney M. Schaffer ◽  
Siddarth S. Dey ◽  
Jonathan E. Foley ◽  
Abhyudai Singh ◽  
...  
Keyword(s):  
Conflict Of Interest ◽  
Burst Size ◽  
Inverse Correlation ◽  
Recent Analysis ◽  
Mrna Levels ◽  
Burst Frequency ◽  
Expression Variability ◽  
Protein Levels ◽  
Transcriptional Bursting ◽  
Cell Expression

Recent analysis (Dey et al, 2015), demonstrates that the HIV-1 Long Terminal Repeat (HIV LTR) promoter exhibits a range of possible transcriptional burst sizes and frequencies for any mean-expression level. However, these results have also been interpreted as demonstrating that cell-to-cell expression variability (noise) and mean are uncorrelated, a significant deviation from previous results. Here, we re-examine the available mRNA and protein abundance data for the HIV LTR and find that noise in mRNA and protein expression scales inversely with the mean along analytically predicted transcriptional burst-size manifolds. We then experimentally perturb transcriptional activity to test a prediction of the multiple burst-size model: that increasing burst frequency will cause mRNA noise to decrease along given burst-size lines as mRNA levels increase. The data show that mRNA and protein noise decrease as mean expression increases, supporting the canonical inverse correlation between noise and mean.Conflict of InterestThe authors declare that they have no conflict of interest.

scholarly journals scHLAcount: Allele-specific HLA expression from single-cell gene expression data

2019 ◽  
Author(s):  
Charlotte A. Darby ◽  
Michael J. T. Stubbington ◽  
Patrick J. Marks ◽  
Álvaro Martínez Barrio ◽  
Ian T. Fiddes
Keyword(s):  
Single Cells ◽  
Expression Patterns ◽  
Allelic Expression ◽  
Loss Of Function ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Type Specificity ◽  
Specific Expression ◽  
Hla Genes ◽  
Allele Specific

AbstractStudies in bulk RNA sequencing data suggest cell-type and allele-specific expression of the human leukocyte antigen (HLA) genes. These loci are extremely diverse and they function as part of the major histocompatibility complex (MHC) which is responsible for antigen presentation. Mutation and or misregulation of expression of HLA genes has implications in diseases, especially cancer. Immune responses to tumor cells can be evaded through HLA loss of function. However, bulk RNA-seq does not fully disentangle cell type specificity and allelic expression. Here we present scHLAcount, a workflow for computing allele-specific molecule counts of the HLA genes in single cells an individualized reference. We demonstrate that scHLAcount can be used to find cell-type specific allelic expression of HLA genes in blood cells, and detect different allelic expression patterns between tumor and normal cells in patient biopsies. scHLAcount is available at https://github.com/10XGenomics/scHLAcount.

Tlr4 Is Monoallelically Expressed in Human BFU-E and CFU-E and May Have Application as a Clonality Marker in Hematological Disorders Due to Somatic Mutations in Both Males and Females.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3619-3619
Author(s):  
George L. Chen ◽  
Enli Liu ◽  
Sabina Swierczek ◽  
Josef T. Prchal
Keyword(s):  
Polycythemia Vera ◽  
X Chromosome ◽  
Whole Blood ◽  
Jak2 V617f ◽  
Allelic Expression ◽  
Monoallelic Expression ◽  
Tlr4 Expression ◽  
Biallelic Expression ◽  
Normal Human ◽  
Males And Females

Abstract Tlr4 is monoallelically expressed in a tissue specific manner in mice (Peireira JP, Girard R, Chaby R, et al. Nat Immunol 4(5): 464–70, 2003). We characterized Tlr4 allelic expression in human BFU-E, CFU-E, and reticulocytes. The alleles were distinguished by a single nucleotide polymorphism (SNP) that resulted in creation of a new Bcc1 restriction enzyme cutting site. Individuals were genotyped for the SNP and only individuals heterozygous for the SNP were used in experiments. Polycythemia vera patients were diagnosed based on clinical presentation, the presence of the JAK2 V617F mutation, and clonality by X chromosome inactivation methods when available. Individual human BFU-E and CFU-E were grown from whole blood mononuclear cell fractions on methylcellulose media. Allelic expression of Tlr4 was assayed after reverse transcription of mRNA, nested PCR amplification of cDNA, and Bcc1 digestion of PCR product. Normal human BFU-E and CFU-E colonies demonstrated monoallelic expression of both alleles as well as biallelic expression (n=1). Polycythemia vera patient BFU-E and CFU-E colonies demonstrated a similar pattern (n=2). Tlr4 allelic expression in reticulocyte RNA fractions was assayed as outlined above and by quantitative reverse transcription PCR methods. Normal human reticulocyte RNA fractions isolated from whole blood demonstrated biallelic expression (n=4). 3 of 5 polycythemia vera patient reticulocyte RNA fractions isolated from whole blood demonstrated skewed biallelic expression, and 2 of 5 polycythemia vera patients demonstrated monoallelic expression of either allele. Tlr4 is located on chromosome 9 in humans. The finding of monoallelic expression in the reticulocyte RNA of 2 of 5 PV patients suggests thatTlr4 may be used as a phenotypic clonality marker in males and females, anda subset of polycythemia vera patients can be identified within the diagnostic population defined by clinical history and the JAK2 V617F mutation.If the assumption that clonal proliferation follows somatic mutation is true, then at least 2 mutations are implied in the pathogenesis of polycythemia vera: one by the original demonstration of clonality by X-chromosome inactivation methods, and the other by the demonstration of acquired monoallelic Tlr4 expression in the BFU-E/CFU-E to reticulocyte transition. These data are now followed by a prospective study of Tlr4 expression in a large number of female and male patients to be correlated with quantitative JAK2 V617F measurements.

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 Genome-wide analysis of allele-specific expression of genes in the model diatom Phaeodactylum tricornutum

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Antoine Hoguin ◽  
Achal Rastogi ◽  
Chris Bowler ◽  
Leila Tirichine
Keyword(s):  
Phaeodactylum Tricornutum ◽  
Protein Transport ◽  
Natural Hybridization ◽  
Allelic Expression ◽  
Monoallelic Expression ◽  
Sequencing Data ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Biallelic Expression ◽  
Allele Specific

AbstractRecent advances in next generation sequencing technologies have allowed the discovery of widespread autosomal allele-specific expression (aASE) in mammals and plants with potential phenotypic effects. Extensive numbers of genes with allele-specific expression have been described in the diatom Fragilariopsis cylindrus in association with adaptation to external cues, as well as in Fistulifera solaris in the context of natural hybridization. However, the role of aASE and its extent in diatoms remain elusive. In this study, we investigate allele-specific expression in the model diatom Phaeodactylum tricornutum by the re-analysis of previously published whole genome RNA sequencing data and polymorphism calling. We found that 22% of P. tricornutum genes show moderate bias in allelic expression while 1% show nearly complete monoallelic expression. Biallelic expression associates with genes encoding components of protein metabolism while moderately biased genes associate with functions in catabolism and protein transport. We validated candidate genes by pyrosequencing and found that moderate biases in allelic expression were less stable than monoallelically expressed genes that showed consistent bias upon experimental validations at the population level and in subcloning experiments. Our approach provides the basis for the analysis of aASE in P. tricornutum and could be routinely implemented to test for variations in allele expression under different environmental conditions.

scholarly journals Transcriptional bursting shape autosomal dynamic random monoallelic expression in pre-gastrulation embryos

2020 ◽  
Author(s):  
C H Naik ◽  
D Chandel ◽  
S Mandal ◽  
S Gayen
Keyword(s):  
Monoallelic Expression ◽  
Wide Spread ◽  
Mammalian Development ◽  
Burst Frequency ◽  
Genome Wide ◽  
Allele Specific ◽  
Transcriptional Bursting ◽  
Burst Kinetics ◽  
Cell Expression ◽  
Early Mammalian Development

AbstractRecent years, allele-specific single cell RNA-seq (scRNA-seq) analysis have demonstrated wide-spread dynamic random monoallelic expression of autosomal genes (aRME). However, the origin of dynamic aRME remains poorly understood. It is believed that dynamic aRME is originated from discrete transcriptional burst of two alleles. Here, for the first time, we have profiled genome-wide pattern of dynamic aRME and allele-specific burst kinetics in mouse pre-gastrulation embryos. We found wide-spread dynamic aRME across the different lineages of pre-gastrulation embryos and which is linked to the allelic burst kinetics. Specially, we found that expression level and burst frequency are the key determinants of dynamic aRME. Altogether, our study provides significant insight about the origin of prevalent dynamic aRME and cell to cell expression heterogeneity during the early mammalian development.

scholarly journals 3 ′-5 ′ crosstalk contributes to transcriptional bursting

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Massimo Cavallaro ◽  
Mark D. Walsh ◽  
Matt Jones ◽  
James Teahan ◽  
Simone Tiberi ◽  
...  
Keyword(s):  
Stochastic Process ◽  
Mammalian Cells ◽  
Burst Size ◽  
Noise Analysis ◽  
Cell Populations ◽  
Burst Frequency ◽  
Transcriptional Noise ◽  
Mrna Species ◽  
Genome Wide ◽  
Transcriptional Bursting

Abstract Background Transcription in mammalian cells is a complex stochastic process involving shuttling of polymerase between genes and phase-separated liquid condensates. It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3 ′ and 5 ′ ends of a gene that enable reinitiation of transcription upon termination. Results Using Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment. Conclusions Interactions between the 3 ′ and 5 ′ ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.

scholarly journals 3’-5’ crosstalk contributes to transcriptional bursting

2019 ◽  
Author(s):  
Massimo Cavallaro ◽  
Mark D. Walsh ◽  
Matt Jones ◽  
James Teahan ◽  
Simone Tiberi ◽  
...  
Keyword(s):  
Stochastic Process ◽  
Mammalian Cells ◽  
Burst Size ◽  
Noise Analysis ◽  
Cell Populations ◽  
Burst Frequency ◽  
Transcriptional Noise ◽  
Mrna Species ◽  
Genome Wide ◽  
Transcriptional Bursting

AbstractBackgroundTranscription in mammalian cells is a complex stochastic process involving shuttling of polymerase between genes and phase-separated liquid condensates. It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3’ and 5’ ends of a gene that enable reinitiation of transcription upon termination.ResultsUsing Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment.ConclusionsInteractions between the 3’ and 5’ ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.

scholarly journals Somatotroph Tumors and the Epigenetic Status of the GNAS Locus

2021 ◽  
Vol 22 (14) ◽  
pp. 7570
Author(s):  
Pauline Romanet ◽  
Justine Galluso ◽  
Peter Kamenicky ◽  
Mirella Hage ◽  
Marily Theodoropoulou ◽  
...  
Keyword(s):  
Genomic Analysis ◽  
Somatostatin Analogues ◽  
Differentially Methylated Region ◽  
Monoallelic Expression ◽  
Lower Sensitivity ◽  
Biallelic Expression ◽  
Expression Levels ◽  
Methylation Index ◽  
Significant Difference ◽  
Gsp Oncogene

Background: Forty percent of somatotroph tumors harbor recurrent activating GNAS mutations, historically called the gsp oncogene. In gsp-negative somatotroph tumors, GNAS expression itself is highly variable; those with GNAS overexpression most resemble phenotypically those carrying the gsp oncogene. GNAS is monoallelically expressed in the normal pituitary due to methylation-based imprinting. We hypothesize that changes in GNAS imprinting of gsp-negative tumors affect GNAS expression levels and tumorigenesis. Methods: We characterized the GNAS locus in two independent somatotroph tumor cohorts: one of 23 tumors previously published (PMID: 31883967) and classified by pan-genomic analysis, and a second with 82 tumors. Results: Multi-omics analysis of the first cohort identified a significant difference between gsp-negative and gsp-positive tumors in the methylation index at the known differentially methylated region (DMR) of the GNAS A/B transcript promoter, which was confirmed in the larger series of 82 tumors. GNAS allelic expression was analyzed using a polymorphic Fok1 cleavage site in 32 heterozygous gsp-negative tumors. GNAS expression was significantly reduced in the 14 tumors with relaxed GNAS imprinting and biallelic expression, compared to 18 tumors with monoallelic expression. Tumors with relaxed GNAS imprinting showed significantly lower SSTR2 and AIP expression levels. Conclusion: Altered A/B DMR methylation was found exclusively in gsp-negative somatotroph tumors. 43% of gsp-negative tumors showed GNAS imprinting relaxation, which correlated with lower GNAS, SSTR2 and AIP expression, indicating lower sensitivity to somatostatin analogues and potentially aggressive behavior.

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