**12. References**

52 Amyotrophic Lateral Sclerosis

increasing evidence to support functional roles for at least a subset of these transcripts (Kaikkonen et al 2011). There are broadly two types of ncRNA, infrastructural (including transfer RNA and small nuclear RNA) and regulatory RNA (including microRNA, Piwiinteracting RNA and small interfering RNA). The function of ncRNAs remains largely unknown. However, research into microRNA (miRNA) has led the field in recent years. miRNAs are a class of small, ncRNA molecules predicted to post-transcriptionally regulate at least one third of human genes (Lewis et al 2005). Each miRNA can potentially target hundreds of genes and play key regulatory roles in a diverse range of pathways including development, differentiation and pathological processes such as neurodegeneration (Enciu et al 2011). The study of miRNA in ALS is at a very early stage. However, given the proposed role for TDP-43 in miRNA biogenesis and the recent discovery of a beneficial effect of miRNA-206 in the mutant SOD1 mouse model, this will be an interesting area of

There is no diagnostic test for ALS, so diagnosis currently relies upon clinical assessment involving the exclusion of "ALS-mimic" syndromes (such as multifocal motor neuropathy and cervical radiculomyelopathy), causing an average delay of one year from symptom onset to a confirmed diagnosis (Silani et al 2011; Zoccolella et al 2006). In such a rapidly progressive disease this delay is a significant obstacle to potential neuroprotective therapies. ALS is clinically heterogeneous, with multiple subtypes associated with different survival times and symptoms making prognosis challenging. This heterogeneity is also a confounding factor for clinical trials as patient phenotype will impact upon survival data and may influence responses to therapeutic intervention, with some subtypes more responsive to therapy than others (Turner et al 2009). Robust biomarkers would therefore be valuable for the initial diagnosis of disease, the classification of various subtypes, monitoring responses to therapeutic agents and tracking disease progression (Turner et al 2009). Gene expression profiling offers a useful tool for biomarker discovery allowing patient and control biofluids, such as blood and CSF, to be compared on a genome wide scale. These tools have already been employed to improve classification and diagnosis of multiple diseases including neurodegenerative conditions such as Huntington's disease and

In conclusion, microarray analysis has been pivotal in understanding the transcriptional alterations occurring in response to genetic mutations associated with ALS and the sporadic disease (SALS). The cellular model has generated a therapeutic target and transcriptional activation of *Nrf2* is currently being assessed *in-vivo*. Use of spinal cord and peripheral tissues from transgenic mouse models has provided a mechanism to look at the progression of the disease and specifically to identify early changes in the motor neurones and astrocytes. These dysregulated pathways provide future therapeutic targets. In addition, gene expression profiling has allowed crucial insights into the mechanisms affecting different areas of the motor system, with the combination of LCM and microarray technology able to discriminate changes in specific cell types and understand how these affect each other and contribute to disease progression. Finally, the use of human material

investigation for the future (Buratti et al 2010; Williams et al 2009)

Parkinson's disease (Borovecki et al 2005; Scherzer et al 2007).

**9.3 Biomarkers in ALS** 

**10. Conclusion** 


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**3** 

**Dynamic Meta-Analysis as a Therapeutic** 

*Georgia Institute of Technology and Emory University, Atlanta, Georgia,* 

In this chapter, we present a new method, dynamic meta-analysis, which allows the examination of the underlying system dynamics of ALS utilizing the wealth of existing published experimental and/or clinical literature. We perform a small-scale feasibility study of the G93A SOD1 mouse model to show that dynamic meta-analysis can also be utilized to

In 2010 alone, 980 articles were specifically published on the fatal neurodegenerative disease Amyotrophic Lateral Sclerosis (ALS), and the cumulative total literature base for this single intractable pathology exceeds 10,300 articles, according to a PubMed search. One might think that with this wealth of information we would have ALS well in hand. Yet, there is no available, life-extending treatment despite the extensive and detailed information obtained by thousands of researchers at the cost of billions of dollars. ALS remains one of the most intractable neurological diseases; there is no apparent quick fix, no smoking gun, and no obvious answers—just mountains of intertwined experimental observations recorded across a host of individual publications. Furthermore, ALS has been remarkably resistant to reductionistic attempts to pinpoint the underlying problem. Potential contributing defects, mutations, and regulatory failures have been cited across a broad range of categories, including axonal transport (Bilsland, Sahai et al.), cellular chemistry (Hayward, Rodriguez et al. 2002), energetics (Shi, Gal et al.), excitotoxicity (Roy, Minotti et al. 1998), free radicals (Bogdanov, Ramos et al. 1998), genetic damage (Nagano, Murakami et al. 2002), inflammation (King, Dickson et al. 2009), necro-apoptosis (Vukosavic, Dubois-Dauphin et al. 1999), proteomics (Wood, Beaujeux et al. 2003), as well as systemic origin (Dobrowolny, Aucello et al. 2008). Yet experimental correction or "treatment" of any individually identified potential contributor has failed to translate into

predict treatment outcomes in a high-throughput manner.

clinically significant and reproducible results (Peviani, Caron et al.).

**1.2 Identifying and utilizing the system dynamics of ALS for combination therapy**  Based on current evidence, ALS may exhibit system-level abnormalities that emerge from the complexities and interactions of their underlying mechanisms (Mitchell 2009; Rothstein

**1.1 Leveraging the wealth of data** 

**1. Introduction** 

**Prediction Tool for Amyotrophic** 

**Lateral Sclerosis** 

*United States* 

Cassie S. Mitchell and Robert H. Lee

