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Nov 1, 2006 (Vol. 26, No. 19)

Multidisciplinary Approach to RNAi

Determining Gene Function for New Diagnostics and Therapeutics

  • Encompassing molecular genetic, biological, and mathematical approaches, functional genomics is designed to study all genes or proteins simultaneously and systematically. Using the information provided by structural genomics, functional genomics attempts to discover the biological function of particular genes and uncover how sets of genes and their products work together in health and disease.

    RNAi is a gene-silencing technique used in studying the absence of normal gene action by disrupting its activity in vivo. RNAi can help to determine gene function by blocking the expression of a specific messenger RNA (mRNA). By simply, effectively, and specifically down-regulating the expression of genes in mammalian cells, researchers can develop new diagnostic and therapeutic strategies.

    As illustrated at Cambridge Healthtech’s “Beyond the Genome” conference in San Francisco, researchers are investigating altered microRNA (miRNA) expression levels and the functional consequences of these changes in a wide range of tissues, solid tumors, and other disease manifestations and cellular processes. General miRNA expression has been investigated in normal human tissue, blood, saliva, and fixed tissue, as well as in disease processes from cancer to diseases of the brain, heart, and other organs.

  • microRNA

    Mike Wilson, senior scientist and array R&D manager at Asuragen (www.asuragen.com), is investigating the global miRNA expression patterns in pancreatic cancer using Ambion’s (www.ambion.com) mirVana microRNA array system that represents 384 different miRNAs. Asuragen, an Ambion spinoff, currently offers the next-generation microRNA array containing 640 miRNAs applicable to human, mouse, and rat experiments.

    “Conventional 2-D hierarchical clustering results are useful for organizing and visualizing the relationships across a collection of samples that represent normal pancreas tissue, samples from patients with chronic pancreatitis, pancreatic carcinoma, and pancreas-derived cell lines,” Wilson says. “For better visualization of comparisons of microRNA expression patterns between two groups, such as the cancerous and normal pancreas, a novel type of visualization method can be particularly informative.”

    Wilson’s group identified 20 miRNAs with significantly altered changes in expression when comparing normal pancreas to pancreatic carcinoma. After validation of those results by QRT-PCR, the researchers showed how the expression of a small subset of miRNAs can be used as biomarkers for pancreatic detection. The researchers also investigated the downstream consequences of adding miRNAs to an experimental in vitro cellular system and monitored the effective changes that this had on the mRNA gene-expression profiles.

    Asuragen’s validation experiment demonstrated how the system could give relevant results if the changes in gene expression were interpreted in the context of pathway analysis. Finally, pathway analysis revealed that among the top pathways with altered mRNA patterns were those related to cell death, cancer, cellular growth and proliferation, and cell cycle, Wilson adds.

    According to Devin Leake, Ph.D., associate director of R&D at Dharmacon(www.dharmacon.com), “Both miRNA mimetics and inhibitors will be essential for defining the role of miRNAs in specific disease states as they simulate gain- and loss-of-function scenarios. The types of disease most likely to be affected are long-term, chronic conditions associated with internally caused disorders, such as cancer, rather than acute diseases with some external cause.”

    The miRNAs, which are small, noncoding RNAs, target multiple genes simultaneously. An active or mature miRNA is a short ~22 nucleotide single-stranded RNA that numbers anywhere from tens to thousands of molecules per cell, Dr. Leake explains.

    In their active form, these RNAs are incorporated as part of the ribonucleoprotein complex, also known as the RNA-induced silencing complex or RISC complex. The sequence of an miRNA determines, via complementary base pairing, the locations at which its RISC-like complex will bind to the 3´ untranslated regions (3´ UTRs) of target messages.

    “Complementarity of as few as seven bases is, in most cases, sufficient to direct this binding,” he continues. “Therefore, one miRNA can find binding sites in the 3´ UTRs of many genes, and as long as there are sufficient copies of active miRNA in a cell, all of those genes could, presumably, be targeted simultaneously. Researchers are asking whether or not all potential binding sites are actually used and whether there is preference of some sites over others in cases where the miRNA is limiting.”

    Multiple parameters contribute to improved inhibition including length, composition, and modification.

    Dharmacon miRIDIAN™ Inhibitors alter the rate kinetics of the miRNA and inhibitor association. The inhibitors act as an artificial nonhydrolyzable mRNA target that irreversibly binds the miRNA-RISC complex. Once a miRISC complex has bound to an inhibitor, it does not easily dissociate. The inhibitors are assumed to act as competitors for miRISC targeting. Thus, they affect RISC function by preventing the microRNA-programmed RISC complexes from binding to their ordinary targets, he concludes.

  • Off-target Effects

    “Off-target effects of siRNA—false positives that occur when one or a few genes not specifically targeted show loss of gene function following the introduction of an siRNA (short interfering RNA) pool—are a major cause of misleading results when performing genome-wide screening used in target identification studies,” according to Irene Nooren, Ph.D., associate director of bioinformatics at BioFocus(www.biofocus.com). “The true/false positive rate depends on the biological environment, i.e. cells, assay, or pathway.”

    According to Dr. Nooren, BioFocus is working with target discovery technology involving adenoviruses that efficiently introduce human gene sequences into a wide variety of human cells to knock down specific proteins, functionally selecting for proteins having a causative effect on certain models of human disease and validating these protein targets as novel drug candidates.

    The company is using an integrative, in silico biology approach to predict off-target effects of siRNA sequences in genome-wide screening programs and then validating the approach through the use of experimental results from a genome-wide screening program.

    “Using proprietary shRNA (short hairpin RNA) design algorithms, we design adenoviral knock-down constructs by assembling 3 KD shRNAs per gene,” Dr. Nooren explains. “Based on validation statistics, 98 percent of the genes have one or more KD constructs that show at least 70 percent knock-down in at least two different cell lines.”

    Sources of off-target effects include the miRNA pathway, immune responses, and nonspecific mRNA sequence matching, says Dr. Nooren, who adds that some of these effects can be assessed computationally by such methods as sequence-specific (pattern) effects of structural elements. These have been included in BioFocus’ design algorithm, minimizing off-target effects.

    “In silico prediction methods thus help to assess off-target effects in an earlier stage for rapid target identification,” Dr. Nooren concludes. “Using multiple constructs eliminates functional hits due to off-target effects.”

  • Maximizing siRNA Specificity

    Also concerned about off-target effects is Jie Kang, Ph.D., vp, R&D at Qiagen (www.qiagen.com). Qiagen’s new siRNA design architecture is designed to maximize siRNA specificity, incorporating all new discoveries. The company’s siRNA validation project “allows researchers to focus on the results of RNAi, not on the quality control of siRNA,” Dr. Kang says, adding, “A functioning siRNA will not silence the gene when it is not transfected into the cells effectively. The experiment result will be a false negative.”

    According to Dr. Kang, RNAi screening experiments require a variety of controls. A negative control will indicate whether changes in phenotype or gene expression are nonspecific. During start-up experiments, a positive control siRNA can be used to determine optimal conditions. A positive control for siRNA transfected in every experiment will also indicate if conditions become suboptimal. The AllStars Negative Control, a newly developed siRNA, introduces no change of gene-expression level in all pathways, making it a good negative control siRNA. Knockdown-level controls that target the CDC2 gene are used to monitor the experiment.

    The HiPerformance siRNA Design Algorithm, based on an artificial neural network algorithm licensed from Novartis(www.novartis.com)and used at Qiagen, combines design and homology analysis for the design of siRNA that is both potent and specific, enabling high knockdown with minimal risk of nonspecific effects, Dr. Kang adds.

    “Using gene-centric design, we predesign the siRNAs with the software our bioinformatics group built,” Dr. Kang explains. “We can select candidate siRNAs and use the proprietary homology analysis tool and an up-to-date, internally curated, nonredundant sequence database to search for regions of homology in the genome. We can detect even very short regions of homology that are often missed by conventional tools such as BLAST. Users basically only need to inform us of their gene of interest.”

    “RNAi is a powerful tool in functional genomics and drug target identification,” Dr. Kang concludes. “Now there’s no need to work with nonfunctional or poorly functional siRNA.”

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