Dynamic ASXL1 Exon Skipping and Alternative Circular Splicing in Single Human Cells

<div><p>Circular RNAs comprise a poorly understood new class of noncoding RNA. In this study, we used a combination of targeted deletion, high-resolution splicing detection, and single-cell sequencing to deeply probe ASXL1 circular splicing. We found that efficient circular splicing required the canonical transcriptional start site and inverted AluSx elements. Sequencing-based interrogation of isoforms after ASXL1 overexpression identified promiscuous linear splicing between all exons, with the two most abundant non-canonical linear products skipping the exons that produced the circular isoforms. Single-cell sequencing revealed a strong preference for either the linear or circular ASXL1 isoforms in each cell, and found the predominant exon skipping product is frequently co-expressed with its reciprocal circular isoform. Finally, absolute quantification of ASXL1 isoforms confirmed our findings and suggests that standard methods overestimate circRNA abundance. Taken together, these data reveal a dynamic new view of circRNA genesis, providing additional framework for studying their roles in cellular biology.</p></div

Determination of Sequence Requirements for ASXL1 Circularization.

<p>A) Truncating deletions of the ASLX1 gene identify a region (deletion 7) upstream of the branch site that includes an Alu Sx element and produces a significant increase in the ratio of linear to circular RNA formation B) Deletion internal to the 5’UTR identify the same region as an important regulator of preferential linear to circular splicing.</p

ASXL1 Circular Isoforms.

<p>A) Overview of ASXL1 isoforms B) RNAse R resistance assay show degradation of linear isoform (blue arrow) and resistance of the known circular isoform (red arrow). In addition, a second RNAse R resistant isoform was detected in the circular PCR that was confirmed to contain an unannotated exon in intron 3 (exon 3*) by Sanger sequencing C) Quantitation of RNAse R resistance by qPCR showing increased Ct for both linear isoforms after RNAse R treatment but a decreased Ct for the circular isoform D) PCR between exons 1 and 4 for short or long isoforms detect the expected band that corresponds to exons 1 through 4 (green arrow). A band that would have resulted from the removal of exons 2 and 3 through exon skipping was not clearly identified (red arrow).</p

Evaluating Forward versus Back Splicing of ASXL1.

<p>A) Schematic showing approach for detecting forward versus back splicing. B) Fold induction of short, long, and circular ASXL1 isoforms after transfection of plasmids with full length gene, plasmid with 5’ UTR removed, or plasmid after deletion of the CMV promoter.</p

Global Identification of ASXL1 Splice Products after Overexpression of the ASXL1 Locus.

<p>A) Exons and introns are depicted around the plot. Locations where a breakpoint was identified are represented by connecting lines, where the color of the line is proportional to the number of reads corresponding to that junction. Unknown junctions were almost exclusively restricted to exon-exon boundaries. Only breakpoints with greater 1% of reads supporting the junction were mapped. B) The preference of exon 1 to splice forward to exon 2, 3, 3*, or 4 were quantitated using primers that amplified from exon 1 to exon 4. The 3’ splicing junction was then quantitated for reads that spliced from exon 1 to 2 on the 5’ end. Most reads corresponded to the expected consecutive junctions, but a significant minority of reads corresponded to non-canonical junctions. C) Quantification of the circular isoforms that either included or excluded exon 3*. Of note, the abundance of the 3–2 circular isoform correlated with the abundance of the reciprocal exon skipping product from exon 1 to exon 3*.</p

Proposed Model for ASXL1 Circularization.

<p>Our single cell data provides evidence that the ASXL1 locus undergoes transcriptional bursts. The resulting transcripts undergo canonical linear splicing or create alternatively spliced products that skip exons 2 and 3, which may occur in a cell context-specific manner. Either the linear or circular products include exon 3* within intron 3 (blue or orange path). The resulting lariats then undergo further processing to create the detected circular isoforms, which is facilitated by the inverted Alu Sx repeats (red arrows). The initial circular splicing occurs at a low level, but the circular isoforms accumulate over time due to their increased stability as a result of exonuclease resistance.</p

Quantification of ASXL1 Isoforms in Individual Human Cells.

<p>1) Overview of strategy for single cell ASXL1 isoform quantification. Targeted reverse transcription adds a common sequence, Nextera sequences, and a unique molecular identifier. Products then undergo a series of amplifications to produce full length amplicons that are tagged with a UMI. B) Quantification of each of the isoforms in each cell. Either the canonical linear isoform or 3–2 circular isoform comprise the majority of the reads for each cell that expresses ASXL1. Cells also tend to have either high or little to no ASXL1 expression. C) The 1–3* junction reads co-occur in the same cell as the reciprocal circular isoform in contrast to the canonical linear isoform, which is not to be highly expressed in the same cell as the circular isoform. Most cells that only express one of the isoforms express the circular isoform, which may be due to increased stability. D) Absolute quantification of ASXL1 isoforms confirms a preference for expressing either the linear or circular isoform and estimate an average of 11 circular isoform molecules per cell compared to about 64 linear molecules.</p

Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype

<div><p>Quantification of cell-free DNA (cfDNA) in circulating blood derived from a transplanted organ is a powerful approach to monitoring post-transplant injury. Genome transplant dynamics (GTD) quantifies donor-derived cfDNA (dd-cfDNA) by taking advantage of single-nucleotide polymorphisms (SNPs) distributed across the genome to discriminate donor and recipient DNA molecules. In its current implementation, GTD requires genotyping of both the transplant recipient and donor. However, in practice, donor genotype information is often unavailable. Here, we address this issue by developing an algorithm that estimates dd-cfDNA levels in the absence of a donor genotype. Our algorithm predicts heart and lung allograft rejection with an accuracy that is similar to conventional GTD. We furthermore refined the algorithm to handle closely related recipients and donors, a scenario that is common in bone marrow and kidney transplantation. We show that it is possible to estimate dd-cfDNA in bone marrow transplant patients that are unrelated or that are siblings of the donors, using a hidden Markov model (HMM) of identity-by-descent (IBD) states along the genome. Last, we demonstrate that comparing dd-cfDNA to the proportion of donor DNA in white blood cells can differentiate between relapse and the onset of graft-versus-host disease (GVHD). These methods alleviate some of the barriers to the implementation of GTD, which will further widen its clinical application.</p></div

Probabilities of donor genotypes of a bi-allelic SNP conditioning on recipient genotype, donor population and IBD between two haploid pairs of donor-recipient genomes.

<p>The table shows for a bi-allelic SNP_i, which has two possible alleles: A and B that are occur with frequency <i>f</i>_<i>A</i> and <i>f</i>_<i>B</i> in <i>Pop</i>_<i>m</i> respectively.</p

Comparison of predicted levels of dd-cfDNA by one- and two-genomes methods in heart and lung transplant recipients.

<p>(a) and (b) Comparison between levels of dd-cfDNA predicted by the two-genomes method (x-axis) and the one-genome method (y-axis). (c) and (d) show a comparison of one- and two-genomes methods predictability of organ rejection. Each bar shows the area under the curve (AUC) of discriminating between two rejection states as measured using biopsies using dd-cfDNA fraction estimates [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005629#pcbi.1005629.ref008" target="_blank">8</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005629#pcbi.1005629.ref009" target="_blank">9</a>]. Error bars marks AUC 95% confidence interval. The significance of the difference between corresponding receiver operating characteristic (ROC) of the one-genome and two-genomes was evaluated using the DeLong two-sided test [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005629#pcbi.1005629.ref031" target="_blank">31</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005629#pcbi.1005629.ref032" target="_blank">32</a>].</p