Yue Chen

Very few ribozymes have been found in complex eukaryotes. Here, we developed a novel method, AbRiDGed, for highly accurate and sensitive ab initio self-cleaving ribozyme discovery. Our results indicate the existence of millions of self-cleaving ribozymes in humans and other species. We characterized ∼150 novel classes of self-cleaving ribozymes, including one class which exhibits noncanonical biochemical properties and widespread presence in all domains of life and appears to play specific functions in complex organisms. These results revealed that the true complexity of the “ribozyme-ome” is several orders of magnitude higher than previously estimated. We demonstrate that newly identified self-cleaving ribozymes influence organismal fitness by regulating gene expression and enhancing transcriptome complexity in vivo . Moreover, these ribozymes hold promise as foundational tools for biotechnological applications, such as direct RNA detection technologies. Overall, catalytic RNAs are prevalent and diverse, and may represent a hidden regulatory layer in various organisms.

Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using 7 in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, the performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of tested sequences, suggesting that it might represent the future of RNA structure prediction algorithms.

The human genome encodes a multitude of different noncoding transcripts that have been traditionally separated on the basis of their lengths into long (>200 nt) or small (40%) of unannotated sncRNAs appear to have biological relevance. Furthermore, contrary to the expectation, the potentially functional transcripts are not highly abundant and can be derived from protein-coding mRNAs. These results strongly suggest that the small noncoding transcriptome can harbor multiple functional transcripts that warrant future studies.