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Neurome Technologies Discovery Screening Vaccine Gallery

Drug Target Discovery for Diseases of the Nervous System
Existing therapeutic strategies for the treatment of neurological disorders and mental illnesses focus almost exclusively on symptoms rather than the underlying pathogenic mechanisms causing disease. The available medications are thus poorly targeted as cures and essentially incapable of preventing or delaying the onset of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease and Amyotrophic Lateral Sclerosis (Lou Gehrig’s disease). Acknowledging this failed strategy, most pharmaceutical companies have now re-oriented their CNS research programs toward a disease mechanism-based approach to drug discovery. The principal research activity in these efforts centers primarily on characterization of disease-associated cellular pathways and their protein components. The next step is often the most difficult: determining which of the implicated proteins should serve as the next-generation drug targets. For many of these candidate molecules, it is not easily known ahead of time whether modulating their activity provides therapeutic benefit by affecting disease initiation or progression or by diminishing other important aspects of pathology. In addition, the availability and choice of animal model systems impacts the validity of data obtained for target assessment. More broadly, the evaluation of therapeutic strategies for human disease in imperfect animal models is often the road to drug development failure. Thus, successful drug discovery lies in focusing on the right targets and in applying the most precise animal models for any particular disease. This is especially true for diseases of the brain.

At Neurome, research focused on genes whose protein products demonstrably alter disease onset or pathology will enable discovery of disease-relevant drug targets for a variety of neurodegenerative disorders. Our strategy takes advantage of the strengths of mouse genetics, where Neurome’s quantitative methods have been remarkably successful at identifying genomic regions contributing to an array of disease-associated traits and susceptibilities that differ between inbred lines of mice. These genomic regions are known collectively as quantitative trait loci (or QTL), and the identity of the genes responsible for quantitative traits offers a powerful point of departure for the development of new pharmaceuticals. The encoded proteins of quantitative trait (QT) genes represent genetically validated targets, since studies that led to QTL mapping provide prima facie evidence that differences in the activities of the proteins directly contribute to differences in phenotype. Thus, in a mouse model for a particular disease for which it has already been shown by genetic studies that there are related QTLs, each QTL-identified protein represents a validated target for potential pharmaceutical targeting. A major goal of our research efforts will be to systematically discover QT genes for known QTLs related to human disease and to uncover disease-modifying QTLs and therapeutic targets for neurodegenerative diseases.

Identifying the Best Drug Targets for CNS Disorders
The search for susceptibility genes and drug targets for neurological and behavioral disorders has proven to be particularly difficult, even though heritability of genetic risk factors has been well documented for many mental illnesses. For many disorders, genetic linkage studies have proposed one or more chromosomal regions harboring genetic determinants, and plausible candidate genes from these regions have been tested to determine whether the gene co-segregates with disease within affected individuals of a pedigree. However, due to the complexity of these disorders and the limited statistical power of the linkage analyses, most of these studies fail to replicate and primarily serve to exclude hypothesized candidates within the genomic interval for which linkage was first established. Candidate genes or markers have also been evaluated in what are known as association studies, a form of linkage analysis in which polymorphic alleles or markers can be tested for association with susceptibility after determining the genotype of variants in individuals with disease and in unaffected controls in a large population. Major neurological disorders, including Alzheimer’s disease, multiple sclerosis, myasthenia gravis, Parkinson’s disease, epilepsy, migraine, and ischemic stroke, have all been investigated using genetic association approaches. With the exception of Alzheimer’s disease, replication has failed or is lacking for nearly all of these studies.

The difficulty in tracking complex trait genes for behavioral disorders in humans is due in part to the variable roles that environmental factors and experience play in establishing these illnesses and to the contribution of many genes of small effect, several of which may interact in a non-additive fashion to influence disease traits. The variability often observed in linkage studies is due to these inherent factors and therefore limits the power of human molecular genetics approaches in identifying genes for complex behavioral traits.

A Direct Route to the Targets
Fortunately, the task of identifying quantitative trait (QT) genes is made much simpler by the use of inbred mouse strains. As noted earlier, the observation that traits and susceptibilities differ among different inbred lines has given rise to approaches that identify quantitative contributions of multiple polymorphic genes to disease-related phenotypes. Although initial QTL mapping has proven to be rapid in mice, the molecular identification of a QT gene and its encoded protein is often a formidable bottleneck in QTL analysis, requiring years of intensive laboratory effort. At Neurome, we have developed an innovative molecular genetic strategy to overcome this obstacle. Neurome’s strategy will employ measurements of mRNA abundance, utilizing TOGA® technology, in a way that correlates gene expression with genotype in order to vastly reduce the effort involved in resolving individual loci.

TOGA® Technology
Over the past decade, advances in robotics, informatics and molecular biology have led to the development of genomics technologies that enable gene expression measurements to be taken for thousands of genes in a highly parallel fashion. The technology platform best suited to discover QT genes and their corresponding protein therapeutic targets is Neurome’s TOGA® (an acronym for TOtal Gene expression Analysis). There are two principle reasons for this. First, TOGA® is an ‘open system’ approach to gene expression analysis. The method was designed to detect and measure the abundance of any mRNA species present in a biological sample, regardless of whether the gene was previously known or sequenced. This design feature empowers TOGA® to interrogate a sample and comprehensively survey all genes. The PCR-based detection system provides a level of sensitivity that is sufficient to detect genes expressed even as low as 1 part in 1,000,000. The molecular biology steps of the procedure are outlined in the schematic below. Second, in TOGA®, known genes are identified based on sequence properties that provide a unique digital address for every mRNA (see figure). For any previously uncharacterized mRNA, a corresponding TOGA® DNA fragment can be easily isolated and sequenced. Thus, the TOGA® open system approach is ideal for discovery applications, since no prior knowledge or assumptions need to be made regarding a gene’s expressed sequence.

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Figure 1a: The TOGA® method can be viewed as a 3-part process involving molecular biology protocols and robotics. The first series of steps (top box) accomplishes the efficient capture of a 3’ fragment for every mRNA. RNA from a biological sample serves as template for cDNA synthesis initiated with a biotinylated anchor primer, and the cDNA is subjected to enzymatic digest by a restriction endonuclease with 4-base recognition specificity (i.e. MspI CCGG) followed by fragment capture on streptavidin-coated beads. The second part (middle box) utilizes in vitro transcription (IVT) and reverse transcription (RT) reactions to enable highly reproducible linear amplification and production of templates for PCR. In the third part (bottom box), PCR is performed to systematically parse the entire set of expressed RNAs and generate sequence-specific subpools for analysis. PCR reactions are set up on high-throughput robotics stations to contain a fluorescent universal 3’ primer and a unique 5’ primer taken from a 256 member set whose 3’ termini cover all sequence possibilities across the four bases (labeled N1N2N3N4) adjacent to the restriction digest site. Following amplification, each of the resulting 256 PCRs is analyzed by capillary electrophoresis to measure DNA fragment size and fluorescent intensity. Each resulting fragment represents a single mRNA species detected by TOGA®. The resulting gene expression data from a sample, which typically contains expression measurements for 15,000 genes, is then analyzed together with a logical group of samples, such as in a comparison of normal versus diseased tissue.

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Figure 1b: Messenger RNAs are assigned a unique digital address by TOGA®. The addressing feature allows a TOGA® database to be established for all detected RNAs and enables prediction of TOGA® addresses for nearly all transcripts contained in genomic databases. An 8-nucleotide sequence, comprising the sequence specificity of the restriction endonuclease together with the parsing sequence used in PCR, is combined with the fragment length to create the TOGA® address. In the example, the restriction sequence and parsing sequence combine to give CCGGATCG and the fragment length is calculated from the 5’ restriction cleavage site to the poly A addition site to be 148 nucleotides, which together create CCGGATCG148. A TOGA® graph for CCGGATCG148 is shown in the bottom half of the figure.

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