“Junk” intron RNA, long thought to be discarded in the process of cell formation, appears instead to play a critical role in the process, according to a new statistical modeling system created by researchers at the USC Viterbi School of Engineering.
The method offers potential insight into the complex and critical "splicing" operations that take place when the genetic message carried in genes is expressed as cell-building proteins. It may ultimately have applications in areas ranging from the treatment of hereditary conditions and metabolic diseases to cancer therapy.
Ph.D. student Jing Zhang authors paper in top journal
The inherited genetic code, the genome, is just a blueprint. The actual expressions of the code are the complex proteins and other molecules that organize into cells, which can then organize themselves into multicellular (“eukaryotic”) organisms.
The basic mechanism has been known for decades: the DNA genomic blueprint is replicated in chemical mirror form as RNA. Then RNA sections that code the actual proteins expressed, the exons, are carved out of the raw RNA sequence, edited, and reassembled into finished messenger RNA, after sizable sections of the original RNA, the introns, are sliced out.
What has become clear, though puzzling, is that the messenger RNA created by the slicing and recombination of exons does not guide a single, simple synthesis. It instead plays multiple roles, interacting with a number of other long-mysterious transient substances called Splicing Regulatory Elements (SREs). The process is known as "alternative splicing", and is ubiquitous, particularly in big eukaryotic organisms such as humans, where it occurs in as much as 95 percent of protein syntheses. But the origins and precise functions of the SREs have long been little understood.
The new USC research indicates that some SREs are actually located in the sliced out parts of 
the RNA – in the introns.
In a paper published in the July 2012 issue of the Journal of Computational Biology, the USC group described a new analytic tool they call VERSE, an acronym for "Varying Effects Regression Model on Splicing Elements". It builds on a large volume of recent work done in other laboratories identifying SREs affiliated with specific exons. “A boost of genomic data due to the recent development in deep sequencing techniques and new splicing events,” reads the paper.
The researchers used VERSE to analyze these volumes of new data for factors that might predict what SREs the original exon-sliced RNA might produce.
The team consisted of Professor C.C. Jay Kuo and Ph.D. student Jing Zhang of the Ming Hsieh Department of Electrical Engineering and Assistant Professor Liang Chen of USC Dornsife's Department of Molecular and Computational Biology. Zhang is the lead author.
The group applied VERSE to the intron sequences surrounding the genetically active exon, to the areas removed in exon formation in 16 different human tissues, both the exons explicitly associated with the tissue proteins and the introns around it. The results indicate that the intron RNA carried icoding that, according to the long-established nucleotide/amino acid connection, corresponded with the SREs that researchers have found in the creation of proteins in question. That is, the RNA portion that had been thought to play no role in the synthesis actually seems to play an active and vital role.
Some 1,562 potential SREs were identified in the introns of RNA genes of the 16 human tissues analyzed. Many of these potential SREs are already known to be actual SREs that have been proven to be involved in cell synthesis.
An illustration of an alternative splicing event in which two proteins are generated from the same genetic codes.
"Alternative splicing is a key biological process in higher eukaryotes to generate multiple transcript isoforms from a single gene, and thus promotes protein diversity with functional or structural differences," the authors write.
"VERSE may serve as a powerful tool not only to discover the splicing elements by incorporating additional informative signals, but also to precisely quantify their varying contributions under different biological context."
