RNA Interference in Specific Gene Silencing ('Knockdown')

RNA interference in specific gene silencing ('knockdown')

Jason Carter Christopher Jones

Biocomputing Fall 2005 Overview:

RNA interference (RNAi) appears to be a highly potent and specific process which is carried out by special mechanisms in the cell. While the complete details of how it works are still unknown, it appears that a protein called Dicer is involved in one of two ways.

When Dicer finds a double-stranded RNA molecule (dsRNA), it unwinds the strands and splices them into small interfering RNA (siRNA) 20-25 nucleotides long, which proceeds to degrade single-stranded RNA molecules that are complementary to one of its segments. siRNAs use RNA-degrading enzymes (RNAses) to destroy mRNA transcripts complementary to the siRNA, effectively silencing protein production. The genetic information of many viruses is held in the form of double-stranded RNA, so it is likely that the RNA interference machinery evolved as a defense against these viruses.

Certain parts of the genome are transcribed into micro RNA (miRNA) short RNA 20-25 nucleotide long strand that folds back on itself in a hairpin shape to create a double strand. Dicer detects these and removes the hairpin, releasing the transcribed miRNA which silences matching mRNA either by blocking protein translation or by degradation of the mRNA. This mechanism was first shown in the "JAW microRNA" of Arabidopsis; it is involved in the regulation of several genes that control the plant's shape. The mechanism has also been shown in many other eukaryotes; by now, some 150 microRNAs have been detected in humans.

RNAi Applications in Medicine -- Fight the Cause, Not the Symptoms

A typical mRNA produces approximately 5,000 copies of a protein. Consequently, targeting mRNA rather than the protein itself is potentially a much more efficient approach to block protein function. In RNAi- based therapy, a double-stranded short interfering RNA (siRNA) molecule is engineered to precisely match the protein-encoding nucleotide sequence of the target mRNA to be “silenced.”

Following administration, the siRNA molecule associates with a group of proteins termed the RNA- induced silencing complex (RISC), and directs the RISC to the target mRNA. The siRNA-associated RISC binds to the target mRNA through a base-pairing interaction and degrades it by cleaving the mRNA at the center of the siRNA substrate using an unknown protein dubbed Slicer. The RISC is then capable of degrading additional copies of the targeted mRNA, while the cell uses RNAse to degrade the cleaved mRNA strands preventing translation. siRNA Administration siRNA must be deliverable to affected tissues. In the laboratory, siRNA can be directly injected into individual cells. However simply injecting siRNA into whole animals is not very effective. Intravenous injection is accomplished by placing siRNA inside special lipid enclosures (liposomes) to form a Stable Nucleic Acid Lipid Particles (SNALP). The lipid enclosure is engineered to facilitate cellular uptake and to preserve longevity of the siRNA in vivo.

RNA interference-based therapeutics advantages:

Broad Applicability — Diseases for which an abnormal gene function can be identified as a cause or as an essential contributing factor are potentially treatable with RNA interference-based drugs.

Therapeutic Precision — Some of the side effects associated with traditional drugs may be reduced or avoided by using RNA interference-based drugs designed to inhibit expression of only a disease-associated and targeted gene and not interfere with other genes in the body.

Target RNA Destruction — Compared to most drugs that only temporarily prevent targeted protein function, RNA interference-based drugs are designed to destroy the target RNA and therefore stop the associated undesirable protein production required for disease progression.

Macular Degeneration

The first RNAi therapy to reach patients in clinical trials was aimed at a debilitating eye disease called Macular Degeneration. Biotech firms had set their sights on the disease for many reasons but most importantly because the RNAi drug could be delivered directly to the diseased tissue. Since local delivery is possible, it makes it less likely that the drug will have unanticipated, harmful effects somewhere else in the body.

The disease is caused by a well-known protein called VEGF that promoted blood vessel growth. For patients with Macular Degeneration, too much of the protein is created which leads to sprouting of excess blood vessels behind the retina. The blood vessels leak which cloud the patient’s vision. The new RNAi drugs shut down the genes that produce VEGF.

The first clinical trial of about two dozen patients took place in 2004. The intention of the first trial is to primarily assess safety issues which are showing promising results. Two months after being injected, a quarter of the patients had significantly clearer vision, and the other patients’ vision had at least stabilized. If more trials prove effective, RNAi drugs for this condition could hit Blood vessel reduction in mouse the market by as early as 2009. cornea

As soon as RNA interference was discovered in human cells, scientists began exploring how it could be used to battle HIV. In 2002, scientists at MIT accounted they could interrupt various steps in the HIV life cycle using RNAi. But these and other experiments were studies stopping the virus in cell cultures, not in human patients.

HIV mutates and evolves resistance so rapidly that any single target for an RNAi therapy won’t be enough. Molecular biologists at Colorado State University have engineered an RNAi therapy aiming at multiple HIV genes. Clinical trials may start as early as 2006. Cancer

Cancer usually involves mutant genes that promote uncontrolled cell growth. In the last few years, researchers have silenced more than a dozen known cancer-causing genes with RNAi, but most of this success has been with cell cultures in a lab. Researchers are just at the beginning, delivery poses the key hurdle in moving from the lab to the bedside of patients and how RNAi therapies might reach and penetrate tumors.

Rather than taking a leading role, some RNAi therapies may help defeat cancers by supporting chemotherapy. RNAi can stop production of the protein, P-glycoprotein, which is guilty of purging cancer- fighting drugs from infected cells. By stopping the production of P-glycoprotein, the patient’s cells will be more sensitive and responsive to existing drugs. siRNA Prediction — Designing gene-specific siRNA

Investigation into siRNA has focused on identifying subsequences in genes that lead to highly effective siRNAs. Consider the solution space for potential siRNAs: 4^22 = 1.759e13 possibilities, while there are only approx. 25,000 genes in the human genome. It seems plausible that an siRNA may exist for the entire genome, though this may not be the case.

Fortunately, siRNA must match its target mRNA, so siRNA design begins with a known gene sequence. However, not just any portion of a protein coding sequence can be selected for use as siRNA. siRNA must take a certain form in order to be recognized by Dicer and other proteins involved in RNAi. Through examination of several mammalian genes by generating many potential siRNAs and testing their efficacy, several rules have been proposed for predicting effective siRNA.

Prediction of siRNA is not usually sufficient. A match to any other gene of as little as 11 residues can lead to off-target silencing whereby other genes are also silenced by the same siRNA. Great care must be taken then in order to design siRNA that targets only a specific gene, and some small portion of the genome may not be uniquely targetable. Unfortunately this requires an expensive search of the entire genome as well as spliced exon overlaps and alternative splicing genetic targets. Studies have shown good specificity when the selected siRNA has at least 3 mismatches in any of its off-target gene alignments. siRNA Prediction Method from: siDirect: highly effective target-specific siRNA design software for mammalian RNA interference, (Naito, Yamada, Ui-Tei, Morishita, Saigo, 2004)

By analyzing many siRNAs derived from several genes, the structure of effective siRNAs has been partially deduced and condensed into four proposed rules:

i. A/U at the 5' end of the antisense strand ii. G/C at the 5' end of the sense strand iii. AU richness in the 5' terminal 1/3rd of the antisense strand iv. the absence of any G/C stretch exceeding 9 bp in length

siRNA Prediction Method from: Rational siRNA design for RNA interference (Reynolds, Leake, Boese, Scaringe, Marshall, Khvorova, 2004)

Effective siRNA sequences can be selected using the following guidelines [Raynolds et al.]. The score is sum of the points for each criterion. At least 7 points are required to be scored as effective siRNA.

i. 30%-52% GC content - Add 1 point for satisfying this criterion. ii. Three or more A/Us at positions 15-19 (sense) - Add 1 point for each A/U for a total up to 5 points. At least 3 points are required. iii. A at position 19 (sense) - Add 1 point for satisfying this criterion. iv. A at position 3 (sense) - Add 1 point for satisfying this criterion. v. U at position 10 (sense) - Add 1 point for satisfying this criterion. vi. No G/C at position 19 (sense) - Subtract 1 point for not satisfying this criterion. vii. No G at position 13 (sense) - Subtract 1 point for not satisfying this criterion. Filtering out off-target siRNAs

For siRNA to be gene-specific, it must not match other genes. This forces us to exhaustively search the entire genome.

Exhaustive search

The Smith-Waterman algorithm can be used for an exhaustive search of local alignments. The algorithm is a dynamic programming algorithm which is guaranteed to find the optimal local alignment with respect to the scoring system being used (including the substitution matrix and gap-scoring). However, the algorithm is demanding of time and memory resources: in order to align two sequences of lengths m and n, O(mn) time and space are required. There are ~25,000 genes coding for proteins comprising 1.5% of approximately three billion base pairs in the human genome. This leaves a minimum of 45 million base pairs that must be searched for off target hits. The problem is magnified by alternative splicing where some genes code for more than one protein via alternate splicing of exons. Thus, each time this filtering step is conducted, at least O(21 X 4.5e6 = 0.945 billion) time and space complexity is required. This is not feasible in practice, for quick results, but would no doubt be undertaken prior to a siRNA drug being used in human trials.

Approximate Search

Fortunately, faster heuristic forms of search are available such as the BLAST algorithm. The acronym stands for Basic Local Alignment Search Tool. BLAST can run over 50 times faster by breaking the search into stages. In the first stage, BLAST searches for short matches of a fixed length W between the query and sequences in the database. For example, given the sequences AGTTAC and ACTTAG and a word length W = 3, BLAST would identify the matching substring TTA that is common to both sequences. In the second stage, BLAST performs an ungapped alignment between the query and database sequence if they share a common word. The ungapped alignment process extends the initial match of length W in each direction in an attempt to boost the alignment score. Insertions and deletions are not considered during this stage. If a high-scoring ungapped alignment is found, the database sequence is passed on to the third stage. In the third stage, BLAST performs a gapped alignment between the query sequence and the database sequence using a variation of the Smith-Waterman algorithm. Statistically significant alignments are then selected. Because of how BLAST works, its parameters must be chosen carefully when searching using relatively short sequences like siRNA.

Conclusion

RNA interference and its application in medicine is still in its infancy. There remains a lot of research, studies, and trials to be done before RNAi therapies will become available to the public. There have been hundreds of successful experiments in cell cultures, and dozens in lab animals, but experts in the field still see obstacles ahead for treating most diseases. The main obstacle is how to deliver RNAi drugs to the intended target.

Several methods for siRNA design have been developed and all show merit. A full elucidation of siRNA design will likely emerge both through competing design methods as well as through emerging understanding of the proteins and complexes involved in the RNA interference machinery. RNAi databases for siRNA and microRNA for various genomes will likely improve prediction techniques, as well as potentially speed off-target filtering. A full understanding of RNAi machinery and effective structures may lead to a method of compacting the searchable genome in such a way that optimizes exhaustive genome searches specifically for RNAi purposes.

Researchers are optimistic that many RNAi therapies will enter clinical trials in the next five years and possibly get FDA approval in the next decade. RNAi is a powerful method for targeting specific proteins created by certain genes. With a strong biotech interest in RNAi, we can be certain that huge advances in medicine will come from RNAi and perhaps many diseases may be cured along the way. Resources:

Animation of the RNAi Process http://www.nature.com/focus/rnai/animations/animation/animation.htm

Interference RNA http://fig.cox.miami.edu/~cmallery/150/gene/siRNA.htm

RNAi http://www.sirna.com/sirnascience/rnai.html

The RNAi Cure? http://www.pbs.org/wgbh/nova/sciencenow/3210/02-cure.html

RNA Interference http://en.wikipedia.org/wiki/RNA_interference

RNA Interference and Gene Silencing: History and Overview http://www.ambion.com/techlib/hottopics/rnai/

Web tools for siRNA prediction siDirect: http://design.rnai.jp/

Whitehead Institute siRNA: http://jura.wi.mit.edu/bioc/siRNAext/

Wistar Bioinformatics Gene-specific siRNA selector: http://bioinfo.wistar.upenn.edu/siRNA/siRNA.htm

Ambion siRNA design and databases: http://www.ambion.com/techlib/misc/siRNA_tools.html

Web RNAi databases http://www.rnainterference.org/ http://nematoda.bio.nyu.edu/cgi-bin/rnai/index.cgi

Bibliography

Review: Gene Silencing in mammals by small interfering RNAs, (McManus, Sharp) Genetics Vol. 3 Oct. 2002, 737-747

Rational siRNA design for RNA interference (Reynolds, Leake, Boese, Scaringe, Marshall, Khvorova) Nature Biotechnology Vol. 22:3 Mar. 2004, 326-330. siDirect: highly effective target-specific siRNA design software for mammalian RNA interference, (Naito, Yamada, Ui-Tei, Morishita, Saigo) Nucleic Acids Research Vol. 32 2004, 124-129.

Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference, (Ui-Tei, Naito, Takahashi, Haraguchi, Okhi-Hamazaki, Juni, Ueda, Saigo, 2004) Nucleic Acids Research Vol. 32:3 2004 Potent and Persistent in-vivo anti-HBV activity of chemically modified siRNAs, (Morrisey, Lockridge, et. al.) Nature Biotechnology July 2004