It's relatively easy to show RNA is in a circular form once you've found it, but finding an RNA circle amongst the cell's transcriptional noise isn't easy. While several researchers adopted bioinformatic strategies, Sharpless preferred a wet-bench approach. “I'm a molecular biologist at heart,” he says.
He considered a gel-trap assay, in which an RNA sample is embedded in a polymer and then loaded on a gel. Linear molecules can migrate through such a matrix, but circles cannot, thereby enriching the population. But the gel trap is a complicated “old-school” assay. Sharples opted for a simpler approach: RNA exonuclease resistance. Since circles have no ends, they should be resistant to digestion with the exonuclease RNase R.
William Jeck, a graduate student in Sharpless’ lab, digested total cellular RNA, tweaking conditions to optimize cANRIL recovery as a positive control. “Then we just took that gamish of [exonuclease-enriched] RNA and next-gen sequenced it,” Sharpless says. That was when things got surprising.
Instead of a “couple of circles”, they found more than 25,000. In many cases, circles were far more abundant than related linear RNAs—perhaps not surprising, given their higher nuclease stability. Circular RNAs were detected in both mice and man, suggesting these molecules are not molecular mistakes, but evolutionarily conserved functional elements.
It was then that Jeck and Sharpless tried to publish their findings. The pair sent their manuscript to a “famous high-profile journal. Rejected. Sent it to the next branch on the journal tree, same deal: Got reviewed, can't be right. And worked our way down until we finally got reviewers who really knew RNA biology. And they immediately said, you know, this is really interesting, we should publish it right away.”
All told, it took the team 14 months to publish their findings. “If you really had something that assaulted peoples’ worldview, that was really different from the way they were thinking, then they're not gonna go down without a fight.”
To be fair, Sharpless concedes, that viewpoint, at least in part, is based on well established, but incorrect, assumptions. Early RNA-Seq efforts used reads that were too short to reliably detect circular transcripts. Thus, they missed circular signatures that newer sequencing data sets, with their longer reads, might not. But even more importantly, he says, until recently most alignment algorithms assumed exons could only appear in increasing order. That is, exon 2 must always precede exon 3, or (by alternative splicing) 4 or 5. “If they found a read that said exon 3 came before exon 2, they would throw it out as a non-mapping read.”
It took a new, less biased alignment program called MapSplice, created by University of North Carolina researcher Jan Prins and University of Kentucky researcher Jinze Liu, to finally break out of that intellectual box.
The reviewers, he says, “didn't realize that their mapping algorithm had made an important choice for them not to look for these circles, and so I think that's why there was confusion.”From professor to post-doc
Julia Salzman had an easier time with her circular RNA story. But Salzman, a postdoctoral researcher in Patrick Brown's lab at Stanford, didn't set out to be a transcriptome biologist. In fact, she wasn't, technically speaking, a biologist at all.
With a bachelor's degree in math and a PhD in statistics, Salzman was an assistant professor of statistics at Columbia University when she decided she needed a change. “I loved biology so much that I really wanted to get a better understanding of it and have experience as an experimentalist,” she says.
In Brown's lab, Salzman started looking for evidence of genomic rearrangement in cancer, not at the DNA level but in the transcriptome. To do that, she wrote software to probe RNA-Seq data sets for so-called “scrambled” exons. “I don't have a degree in computer science,” she concedes, “but I know enough to make trouble.” To her surprise, she found quite a bit.
“Thousands of genes had evidence of [scrambling],” she says, but not only in the cancer samples; exons were rearranged in normal controls too. She found the same thing in her own RNA-Seq data sets.
At first, she attributed these scrambled genes to genomic rearrangements, such as tandem duplications (eg, 1, 2, 3, 2, 3, 4). But the statistical models she was developing didn't support that theory.