Differential Allelic Expression among Long Non-Coding RNAs

Long non-coding RNAs (lncRNA) comprise a diverse group of non-protein-coding RNAs >200 bp in length that are involved in various normal cellular processes and disease states, and can affect coding gene expression through mechanisms in cis or in trans. Since the discovery of the first functional lncRNAs transcribed by RNA Polymerase II, H19 and Xist, many others have been identified and noted for their unusual transcriptional pattern, whereby expression from one chromosome homolog is strongly favored over the other, also known as mono-allelic or differential allelic expression. lncRNAs with differential allelic expression have been observed to play critical roles in developmental gene regulation, chromosome structure, and disease. Here, we will focus on known examples of differential allelic expression of lncRNAs and highlight recent research describing functional lncRNAs expressed from both imprinted and random mono-allelic expression domains.

Transcriptome Reveals Allele Contribution to Heterosis in Maize

Heterosis, which has greatly increased maize yields, is associated with gene expression patterns during key developmental stages that enhance hybrid phenotypes relative to parental phenotypes. Before heterosis can be more effectively used for crop improvement, hybrid maize developmental gene expression patterns must be better understood. Here, six maize hybrids, including the popular hybrid Zhengdan958 (ZC) from China, were studied. Maize hybrids created in-house were generated using an incomplete diallel cross (NCII)-based strategy from four elite inbred parental lines. Differential gene expression (DEG) profiles corresponding to three developmental stages revealed that hybrid partial expression patterns exhibited complementarity of expression of certain parental genes, with parental allelic expression patterns varying both qualitatively and quantitatively in hybrids. Single-parent expression (SPE) and parent-specific expression (PSE) types of qualitative variation were most prevalent, 43.73 and 41.07% of variation, respectively. Meanwhile, negative super-dominance (NSD) and positive super-dominance (PSD) types of quantitative variation were most prevalent, 31.06 and 24.30% of variation, respectively. During the early reproductive growth stage, the gene expression pattern differed markedly from other developmental stage patterns, with allelic expression patterns during seed development skewed toward low-value parental alleles in hybrid seeds exhibiting significant quantitative variation-associated superiority. Comparisons of qualitative gene expression variation rates between ZC and other hybrids revealed proportions of SPE-DEGs (41.36%) in ZC seed DEGs that significantly exceeded the average proportion of SPE-DEGs found in seeds of other hybrids (28.36%). Importantly, quantitative gene expression variation rate comparisons between ZC and hybrids, except for transgressive expression, revealed that the ZC rate exceeded the average rate for other hybrids, highlighting the importance of partial gene expression in heterosis. Moreover, enriched ZC DEGs exhibiting distinct tissue-specific expression patterns belonged to four biological pathways, including photosynthesis, plant hormone signal transduction, biology metabolism and biosynthesis. These results provide valuable technical insights for creating hybrids exhibiting strong heterosis.

Locus specific epigenetic modalities of random allelic expression imbalance

Most autosomal genes are thought to be expressed from both alleles, with some notable exceptions, including imprinted genes and genes showing random monoallelic expression (RME). The extent and nature of RME has been the subject of debate. Here we investigate the expression of several candidate RME genes in F1 hybrid mouse cells before and after differentiation, to define how they become persistently, monoallelically expressed. Clonal monoallelic expression is not present in embryonic stem cells, but we observe high frequencies of monoallelism in neuronal progenitor cells by assessing expression status in more than 200 clones. We uncover unforeseen modes of allelic expression that appear to be gene-specific and epigenetically regulated. This non-canonical allelic regulation has important implications for development and disease, including autosomal dominant disorders and opens up therapeutic perspectives.

Single-cell reconstitution reveals persistence of clonal heterogeneity in the murine hematopoietic system

The persistence of patterns of monoallelic expression is a controversial matter. We report a genome-wide in vivo transcriptomics approach based on allelic expression imbalance to evaluate whether the transcriptional allelic patterns of single murine hematopoietic stem cells (HSC) are still present in the respective differentiated clonal B-cell populations. For 14 genes, we show conclusive evidence for a remarkable persistence in HSC-derived B clonal cells of allele-specific autosomal transcriptional states already present in HSCs. In a striking contrast to the frequency of genes with clonal allelic expression differences in clones expanded without differentiation (up to 10%), we find that clones that have undergone multiple differentiation steps in vivo are more similar to each other. These data suggest that most of the random allele-specific stable transcriptional states on autosomal chromosomes are established de novo during cell lineage differentiation. Given that allele-specific transcriptional states are more stable in cells not undergoing extensive differentiation than in the clones we assessed after full lineage differentiation in vivo, we introduce the "Punctuated Disequilibria" model: random allelic expression biases are stable if the cells are not undergoing differentiation, but may change during differentiation between developmental stages and reach a new stable equilibrium that will only be challenged if the cell engages in further differentiation. Thus, the transcriptional allelic states may not be a stable feature of the differentiating clone, but phenotypic diversity between clones of a population at any given stage of the cell lineage is still ensured.

Clinical implementation of RNA sequencing for Mendelian disease diagnostics

Lack of functional evidence hampers variant interpretation, leaving a large proportion of cases with a suspected Mendelian disorder without genetic diagnosis after genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies, and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA-sequencing (RNA-seq) in routine diagnostics. To address these issues, we implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease. We detected on average 12,500 genes per sample including around 60% disease genes - a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than one week from sample preparation to result reporting and provided a median of eight disease genes per patient for inspection. A genetic diagnosis was established for 16% of the WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions. Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.

Targeted Double-Stranded cDNA Sequencing-Based Phase Analysis to Identify Compound Heterozygous Mutations and Differential Allelic Expression

There are two combinations of heterozygous mutation, i.e., in trans, which carries mutations on different alleles, and in cis, which carries mutations on the same allele. Because only in trans compound heterozygous mutations have been implicated in autosomal recessive diseases, it is important to distinguish them for clinical diagnosis. However, conventional phase analysis is limited because of the large target size of genomic DNA. Here, we performed a genetic analysis on a patient with Wilson disease, and we detected two heterozygous mutations chr13:51958362;G>GG (NM_000053.4:c.2304dup r.2304dup p.Met769HisfsTer26) and chr13:51964900;C>T (NM_000053.4:c.1841G>A r.1841g>a p.Gly614Asp) in the causative gene ATP7B. The distance between the two mutations was 6.5 kb in genomic DNA but 464 bp in mRNA. Targeted double-stranded cDNA sequencing-based phase analysis was performed using direct adapter ligation library preparation and paired-end sequencing, and we elucidated they are in trans compound heterozygous mutations. Trio analysis showed that the mutation (chr13:51964900;C>T) derived from the father and the other mutation from the mother, validating that the mutations are in trans composition. Furthermore, targeted double-stranded cDNA sequencing-based phase analysis detected the differential allelic expression, suggesting that the mutation (chr13:51958362;G>GG) caused downregulation of expression by nonsense-mediated mRNA decay. Our results indicate that targeted double-stranded cDNA sequencing-based phase analysis is useful for determining compound heterozygous mutations and confers information on allelic expression.

Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance

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.

Preferential allelic expression of PIK3CA mutations is frequent in breast cancer and is prognostically significant

Genomic allelic imbalances of mutations have been widely associated with tumor evolution and treatment response. However, allelic imbalances generated at the transcriptomic level by germline and somatic regulatory variants have been less studied. We found widespread allelic expression imbalance between PIK3CA missense mutant and wild-type alleles in breast cancers, predominantly towards the preferential expression of the mutant allele. Expression imbalance was more frequently due to regulatory variation in cis, although contribution of copy number allelic imbalances at the DNA level showed a greater effect. Preferential expression of one allele due to cis-regulatory variant effects was associated with poor prognosis, particularly identifying a poorer prognosis subgroup in ER+, PR+ and Her2− tumors that expressed the mutant allele at extremely low levels. This knowledge challenges the benefit of treating these patients with PI3Kα inhibitors. Overall, our work raises the clinical importance of PIK3CA mutations by demonstrating that their transcriptional allelic imbalance is prognostic in breast tumor biology and by presenting compelling evidence that allelic expression levels of mutations should be taken into consideration during patient clinical management.