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Lo DC, Hughes RE, editors. Neurobiology of Huntington's Disease: Applications to Drug Discovery. Boca Raton (FL): CRC Press/Taylor & Francis; 2011.

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Neurobiology of Huntington's Disease: Applications to Drug Discovery.

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Chapter 4Target Validation for Huntington’s Disease

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During the past several decades, pharmaceutical safety and efficacy have improved considerably with the elucidation of mechanisms of drug action in the disease context and a better understanding of the desired drug target profiles. The ability to fine-tune the target profile of many drugs has led to the premise that the optimal basis for the development of a safe and effective drug is the targeting of a single gene product. In the “omics” era of biology, the ability to rapidly identify and characterize the “druggable genome” in its entirety has created the opportunity to broadly test the validity of this premise. However, the torrent of potential drug targets resulting from this approach presents a new set of challenges, particularly the development of a robust and efficient target validation process. Such a process must be capable of screening a sizeable number of putative targets and identifying the best candidates for the lengthy, uncertain, and expensive process of drug development.

An effective target validation process depends on biological models or systems that exhibit the known properties of the disease and respond to intervention in a manner predictive of clinical outcome. Pre-existing benchmarks or “gold standard” treatments known to be effective in the clinic can be used to confirm the effectiveness of a target validation process by showing that the process is capable of “discovering” the benchmarks. The challenge is much greater for diseases without existing therapeutics. The absence of clinically successful benchmarks introduces significant uncertainty into all upstream drug discovery processes, as their validity cannot be confirmed. This is the difficult situation faced by therapeutic development for Huntington’s disease (HD) and arguably for all neurodegenerative diseases. This chapter will describe the approaches adopted by the CHDI Foundation—an organization that is dedicated to the singular mission of developing HD therapeutics—to manage these limitations while moving forward with HD drug discovery and development (see also Chapter 8, this volume).

The chapter is divided into four sections. In the first section, we briefly review the challenges inherent in target validation for HD. In the second, we present our current target validation processes and their deployment for the existing list of potential targets. In the third, we discuss the operational bottlenecks and scientific challenges in the target validation process, as well as several methods for meeting these challenges. In the last section, we discuss our longer-term goal of developing new approaches to improve our current processes. The ultimate validity of our target validation processes can only be demonstrated by the development of an effective drug for HD.


Because of the absence of clinically effective treatments for HD, research strategies and biological models for target validation are fraught with uncertainty and require constant re-evaluation and adjustment in light of new data. Moreover, the broad spectrum of biological processes that interact with Htt presents a multitude of potential biological approaches, none of which are supported by clinical evidence (reviewed in Borrell-Pages et al., 2006; Perez-De La Cruz and Santamaria, 2006; Ramaswamy et al., 2007). Furthermore, the slow, progressive neurodegeneration characteristic of HD, in which clinical onset (currently defined by motoric symptoms) lags behind molecular, biochemical, and cellular dysfunctions, also creates a higher standard, as optimal targets should be relevant both early and late in the disease process. Although we have tailored our current target validation process toward the identification of a single molecular target, it is not clear that a disease as complex as HD can be effectively treated with drugs that act on just one target. There is increasing evidence that the most effective treatments for complex diseases, such as cancer and HIV, target multiple steps in the mechanism of disease progression. It is possible, if not probable, that this will hold true for HD. Nevertheless, we believe that a robust target validation process to identify the best possible single molecular targets is a necessary first step. Despite its challenges, HD offers a critical advantage for drug discovery: it is a monogenic, dominant, and almost fully penetrant genetic disease with a high correlation between the size of the polyglutamine expansion and the age of disease onset. Ultimately, the optimal target validation process for HD will be defined by clinical successes and failures.


Step-Wise Target Validation—The CHDI Target Validation Scoring System

To effectively assess the long list of potential HD targets, disease models, and intervention modalities that range from small molecules to proteins, we have established an organizational framework of standardized metrics and coherent yardsticks to evaluate the targets. This framework includes a scoring system (the Target Validation [TV] score) with a uniform set of scores or metrics that define a scale of increasing validity in HD targets. The scale ranges from a low of TV 0 to a high of TV 5 and progresses from the identification of targets to the demonstration of direct roles of targets in HD and finally to the development of target-based therapies with proven efficacy in the clinic (Figure 4.1). These definitions are summarized below:

FIGURE 4.1. Target validation scoring criteria.


Target validation scoring criteria. Data collection for each target is done to progressively increase relevance in HD. Starting from the exploratory biology stages for acquisition of target and its association with HD, the target gene becomes a disease (more...)

  • TV 0 (target acquisition): Genes identified by any genome-wide screen with relevance to HD or any other neurodegenerative, polyglutamine repeat or brain disease.
  • TV 1.0 (target localization): Gene products expressed in HD-relevant brain regions (e.g., cortex or striatum) or otherwise linked to biological mechanisms of relevance to HD.
  • TV 2.0 (HD association): Gene products that interact with Htt or have altered expression or distribution in HD; targets at this level will also be used in our systems biology approach of building HD-relevant pathways and genetic networks.
  • TV 2.5 (functional association): Gene products and/or their pathways are altered or abnormal in HD or HD models.
  • TV 3.0 (causal relationship): The direct demonstration of a causal relationship between gene products and HD biology—a critical threshold in our target validation strategy; evidence consists of altered pathophysiology as a result of modulation of target function by genetic methods such as RNA interference (RNAi) and cDNA or by pharmacological probes and can be derived from derived from mammalian in vitro/ex vivo studies or nonmammalian whole organism disease models.
  • TV 3.5 (in vivo genetic proof-of-concept [POC]): To reach this level, alteration of the disease phenotype by modulation of the target gene product must be shown in vivo via genetic approaches in rodent HD models (e.g., viral gene delivery, RNAi, knockout [KO], knockin [KI], or transgenic rodents).
  • TV 4.0 (in vivo drug POC): The therapeutic potential of compounds or proteins is shown in vivo in rodent HD models; this stage marks the transition from disease target to therapeutic target.
  • TV 4.5 (preor Phase 2 clinical efficacy): At this level, the compound’s efficacy at modulating the target must be shown in nonhuman primates, large animals, or Phase 2 human clinical trials.
  • TV 5.0 (Phase 3 clinical efficacy): At this level, the compound’s efficacy at modulating the target must be shown in Phase 3 human clinical trials.

Although this scoring system appears to be a continuous spectrum, it is important to note discontinuities in the form of major gaps between some of the stages, for example, between TV 2.5 and 3.0 when the first causal functional relationship is made between a target gene and HD and between the preclinical target validation at TV 4.0 and clinical efficacy data at TV 4.5–5.0. These discontinuities also reflect the lack of a clinically validated drug discovery path for HD. Although this system defines a serial progression through the TV scores from low to high, some targets are initially assigned higher TV scores based on the strength of evidence appropriate for that particular stage, even in the absence of data supporting lower TV stages. For example, at TV 4.0 histone deacetylases (HDACs) lack some of the data demanded by TV 1.0 (localization in cortex and striatum) for many family members. However, HDACs as a class are supported by sufficient pharmacological efficacy data to be assigned to TV 4.0. Finally, the lack of clinically validated benchmarks (and thus no targets at stages TV 4.5 or 5.0) to guide the selection of appropriate assays and models to serve as the gatekeepers of each TV level leaves a degree of uncertainty in the target validation process that can only be resolved by success in clinical translation. In the meantime, as will be discussed later, the risks can be mitigated by applying multiple different assay systems and models, thereby increasing the chance of capturing the appropriate disease-relevant biological context. Therefore, the flow scheme of target advancement is not a rigid process but rather a work in progress. Opportunistic POC studies of advanced drug candidates, such as from drug companies, will more rapidly advance the pace of therapeutic development and attain a clinically successful drug to serve as the benchmark for guidance of future target validation efforts.

HD-Relevant Pathways and Mechanisms—Where Do Targets Come From?

Effective target validation depends on the proper acquisition of targets in appropriate disease-relevant systems. One might imagine that this is easier for a monogenic, dominant, and fully penetrant genetic disease such as HD, for which the mutation is known. However, as pointed out earlier, not only is a clinically proven benchmark drug lacking, but even basic questions on the pathophysiological mechanism of mutant Htt also remain unanswered, such as whether it is purely a gain of function associated with CAG expansion (Petersen et al., 1999; Tarlac and Storey, 2003) or also the result of loss of normal Htt function (Anne et al., 2007; Cattaneo, 2003). Moreover, Htt has no known intrinsic activity but is involved in myriad biological processes and serves as a scaffold for many proteins (Groves et al., 1999; Takano and Gusella, 2002; Truant et al., 2006) in various cellular compartments (Dorsman et al., 1999; Hackam et al., 1998, 1999; Petrasch-Parwez et al., 2007; Truant et al., 2006; Xia et al., 2003), making it difficult to sort out the specific mechanism of HD pathophysiology. Thus, even though Htt itself belongs at TV 4.0, the highest level achieved by any target thus far, it is difficult to envision how to modify mutant Htt to reduce its toxicity. The only avenue currently being pursued is the reduction of expression of Htt by antisense oligos or RNAi strategies. Although the many Httassociated pathways or mechanisms (Hannan, 2005; Leegwater-Kim and Cha, 2004; Li and Li, 2004; Subba Rao, 2007) provide a bounty of potential targets (Table 4.1 shows a partial list of HD-associated pathways and mechanisms; see also Chapters 2 and 3, this volume, for further details), it is not clear which of these pathways will have an impact on the progression of HD. Thus, a robust system for prioritizing targets for further prosecution is clearly needed.

TABLE 4.1. HD-Relevant Biological Pathways.


HD-Relevant Biological Pathways.

Observational data from HD clinical trials, particularly those that pertain to potential genetic modifiers or epigenetic phenomena that delay onset, clinical presentation, or course of the disease, are another valuable source of targets (Chattopadhyay et al., 2005; Di Maria et al., 2006; Djousse et al., 2004; Kishikawa et al., 2006; Li et al., 2006; Metzger et al., 2006b). These data take on particular significance in light of the current lack of clinically validated benchmarks for target validation. Targets relevant to other neurodegenerative disorders should also be considered if they are not disease-specific and play more general roles in neurodegeneration, such as apoptosis (Ekshyyan and Aw, 2004; Pattison et al., 2006), aggregation (Ross and Poirier, 2004; Trzesniewska et al., 2004; Wanker, 2000), or glial dysfunction (Bonifati and Kishore, 2007; Guidetti and Schwarcz, 2003; Maragakis and Rothstein, 2006; Nemeth et al., 2006). Finally, mammalian cell-based HD models and lower organism models such as Caenorhabditis elegans and Drosophila are amenable for whole genome screens of modifiers because of their fast turnaround, well-defined phenotypes, and widespread availability of genetic tools. These unbiased and mechanism-agnostic screens are a powerful method in discovering novel targets and pathways that play functionally significant roles in HD. However, without any other known association with the disease, it will be important to build a more robust dossier of supportive evidence before assigning targets derived from such approaches to TV 3.0.

Our HD target validation process does not suffer from a lack of targets but rather faces the challenge of how to prioritize and triage many potential targets to generate a short list of worthy candidates for drug development. In the following sections, we will summarize our current TV list, the particular bottlenecks and challenges facing the validation process, and several methods under consideration for overcoming them.

Summary of Current TV List

The current status of our target list according to TV score is summarized in Figure 4.2. The target list was generated from literature searches and ongoing HD drug discovery research at partner laboratories. There are currently more than 225 targets at stages TV 1.0–2.5, 81 targets at TV 3.0, and a sharp decrease to only 12 at TV 3.5 and 8 at TV 4.0 (Figure 4.2a). The targets at or above TV 3.5 are identified in Table 4.2. Many of the targets at TV 1.0–2.0 were identified in the HD interactome derived from expression of full-length or partial fragments of Htt in yeast (Goehler et al., 2004; Kaltenbach et al., 2007; Tam et al., 2006; Wanker et al., 1997) or from coimmunoprecipitation studies (Kaltenbach et al., 2007; Kegel et al., 2005; Li et al., 2002; Sittler et al., 1998). Although in most cases it is unclear whether they play a critical role in the course of the disease, some of the proteins that interact with Htt have also been shown to be modifiers in a Drosophila model of HD (Kaltenbach et al., 2007). Figure 4.2a, a snapshot of the current state of the target list, reveals a bottleneck at TV 3.5—the first direct demonstration of in vivo efficacy for a target. This bottleneck is expected to worsen as systematic target acquisition from whole genome screens in mammalian cell-based systems and model organisms yields large datasets. In the following section, we will address some of our efforts to relieve this bottleneck.

FIGURE 4.2. Number of targets binned by scoring criteria.


Number of targets binned by scoring criteria. (a) Current number of targets scored by the TV scale and binned by associative (TV 1–2), mechanistic (TV 2.5), causal in vitro (TV 3.0), causal in vivo (TV 3.5), and drug-like molecules (TV 4.0). A (more...)

TABLE 4.2. HD Targets Scored at TV 3.5-4.0.


HD Targets Scored at TV 3.5-4.0.

In Figure 4.2b, the biological relevance and chemical tractability of some of the targets at stages TV 3.5 and TV 4.0 are plotted. As shown, we have yet to identify an ideal target—one that is both highly relevant and chemically tractable, as indicated by the star in the upper right corner of Figure 4.2b, a point at which the full power of modern pharma drug development can be brought to bear. The current list of TV 4.0 and TV 3.5 targets is shown in Table 4.2. Because a very limited number of these targets is expected to advance in terms of small molecule development, it is critically important to steadily replenish this TV level. Also, the HDAC family of 11 isozymes at TV 4.0 is a prime candidate to be deconvoluted to identify the specific HDAC target of relevance to HD. Finally, some targets interrogated in POC studies using drug candidates already in clinical trials (see Chapter 12, this volume) showed no convincing efficacy for treating HD. Antidepressants in the serotonin reuptake inhibitor class are prescribed as symptomatic treatment of psychiatric symptoms in HD (Higgins, 2006; Slaughter et al., 2001), but the serotonin reuptake transporter SERT is scored only at TV 4.0, as its modulation has not yet been shown to delay onset or slow progression of the disease (Duan et al., 2004; Mattson et al., 2004; Wang et al., 2007). As an illustration of our target validation process, we selected several targets at TV 4.0 (fibroblast growth factor 2 and its receptor), TV 3.5 (brain-derived neurotrophic factor [BDNF] and its receptor), TV 3.0 (huntingtin-interacting protein 1), and TV 2.5 (phosphodiesterase 10a) and summarized in Table 4.3 the dataset and rationale that qualified them for these TV scores. Although the current TV scale does not provide finer granularity within each TV category, data quality, richness, robustness, and reproducibility are of utmost importance in prioritizing targets for further evaluation. BDNF, for example, is supported by a number of independent genetic approaches showing effects on HD-relevant outcomes and would be given a higher priority than a target with a single positive genetic validation approach.

TABLE 4.3. Representative Targets at TV 4.0-2.5 and Their Associated Evidence.


Representative Targets at TV 4.0-2.5 and Their Associated Evidence.


Prioritization of TV 3.0 Targets by In Vitro and Invertebrate Organismal Models in Early Target Validation

As is apparent in Figure 4.2a, there is a large and ever-growing list of targets at stages TV 1.0–3.0 that must be efficiently prioritized to face the TV 3.5–4.0 hurdles, which already constitute a major bottleneck. In this section, we discuss our effort to triage these targets with intermediate target validation assays that are efficient and have putative face validity for HD. Unfortunately, there is only a limited repertoire of in vitro and ex vivo assays and in vivo nonmammalian model organisms, and the value of these assays and models is uncertain, as none have yet been proven to be predictive of TV 3.5–4.0 in vivo rodent model testing. Thus, we currently use the entire existing panel of assays, and targets that show activity in a higher number of assays receive priority. Over time, however, we intend to improve and expand the current panel of assays, and eventually we will use only the most appropriate assays for the particular target under query, as well as mechanism-agnostic assays that are predictive of in vivo testing and therefore can serve a gating function for triaging targets.

Cell-Based Validation Assays

We currently use a series of cell-based screening assays as a validation panel to ascertain the biological consequences of manipulating a particular target, either by RNAi knockdown or by overexpression of its native or mutant forms. Because the relevance of these assays for any particular target is unknown, we use them as a panel and assign a higher priority to a target that shows positive effects in multiple assays. These assays include mutant Htt-induced cytotoxicity in the transformed cell line PC12 (Aiken et al., 2004), the striatal progenitor cell line ST14A (Bosch et al., 2004; Bottcher et al., 2003), primary striatal neurons, and conformational changes of mutant Htt in a cell-based assay (Arrasate et al., 2004; Brooks et al., 2004). There is clearly a heavy bias toward acute cytotoxicity of transformed cell lines induced by overexpression of mutant Htt, and we are striving to expand the repertoire of these assays by biasing toward neuronal cell types and functional readouts, including transcription (Cha, 2007; Cui et al., 2006; Sadri-Vakili and Cha, 2006; Sadri-Vakili et al., 2007; Zhai et al., 2005), clearance of mutant Htt (Bursch and Ellinger, 2005; Ravikumar et al., 2004; Rubinsztein, 2006; Shibata et al., 2006; Yamamoto et al., 2006), and neurite outgrowth (Lynch et al., 2007). We are investing in new assay development, including cocultures of cortical and striatal neurons as well as of neurons and glia (for interrogating targets such as kynurenine monooxygenase [KMO]). We are also generating neuronal lines from stem cell or neural progenitor cells to develop a system with greater physiological relevance for central nervous system (CNS) neurons. However, even primary cocultures of neurons and glia will not model to any significant degree the in vivo circuitry and physiological context of HD. On the other hand, cell culture is one area in which the single clear-cut advantage of HD research—the monogenic, Q repeat length-dependent causation of disease—may be best leveraged to search for endpoints that clearly depend on this most salient of clinical features.

Ex Vivo Validation Assays

The major disadvantage of cell-based cytotoxicity assays is that they are acute and wholly out of the context of the in vivo CNS complexity and the slow, progressive nature of neurodegenerative diseases. To partly address these issues, we use a more complex tissue-based platform in which survival of biolistically transfected neurons in early postnatal rodent brain slices are monitored over the course of 4–7 days (Khoshnan et al., 2004; Lo et al., 1994; Riddle et al., 1997; Varma et al., 2007; Wang and Qin, 2006) (see also Chapter 5, this volume). The expression of mutant Htt in these CNS neurons in situ results in their demise. This platform can be multiplexed to coexpress the target gene product (overexpression or knockdown), Htt in its wildtype or mutant form, and a fluorescent protein marker, allowing the effects of target gene expression on mutant Htt-dependent toxicity to be assessed. However, because the level of overexpression or knockdown in this system cannot be easily examined on a routine basis, negative data will not be conclusive. Thus, the failure to observe an effect of target gene expression is not a kill criterion for a target. The accumulation of positive data in multiple types of assays will move a target forward in the queue. Pharmacological validation in this ex vivo brain slice platform is straightforward; compounds can be directly applied to the slices to assess their effects (Varma et al., 2007; Wang et al., 2006). As with the target gene expression assays, only a positive result is meaningful, as a negative result could be the result of insufficient compound penetration into the tissue; actual concentrations of compounds at their site of action inside the tissue are difficult to ascertain.

The limitations of the ex vivo platform are similar to those of the cell-based assays; it relies on overexpression of mutant Htt as the toxic insult and cytotoxicity as the endpoint. Brain slices also are harvested from early postnatal animals (days P7–10). Although an improvement over the reconstitution of embryonic neurons in culture, the cells in this system may be too immature to serve as an appropriate substrate for HD-relevant cell death. Possible adaptations of the platform to address some of these issues include the use of transgenic HD animals to bypass the need for acute overexpression of mutant Htt, as well as advances in automation, image capture, and quantization to measure alternative endpoints such as neurite outgrowth. However, the throughput of this platform will always be lower than any cell-based platform.

A version of the brain slice ex vivo assay that takes advantage of known electrophysiological changes in conventional brain slices is currently under development. In this assay, brain slices from adult transgenic HD animals are maintained in culture for a single day. This approach bypasses the issues of developmental immaturity, inappropriate levels of overexpression of mutant Htt, and exclusive focus on cytotoxicity. Unfortunately, this method only allows for pharmacological validation studies at present, as it has not yet been adapted to allow genetic manipulation of target gene expression. One possibility for future development is the overexpression or knockdown of target genes in vivo in transgenic HD animals by infection with recombinant viral expression vectors, followed by the preparation of brain slices for electrophysiological measurements. However, such protocols are labor-intensive and challenging to institute on a large scale and are therefore only appropriate for a limited list of targets.

Whole Organism-Based Validation Using Invertebrate Models

Invertebrate, whole organism models of human disease provide in vivo context in which to assay progressive, multimodal disease-linked phenotypes. The best-characterized models are C. elegans (Brignull et al., 2006; Faber et al., 1999; Link, 2006; Parker et al., 2001) and Drosophila (Link, 2001; Sipione and Cattaneo, 2001), both long-standing models that recapitulate the adult-onset, progressive nature of HD on transgenic expression of mutant Htt (see also Chapter 8, this volume). In these models, multiple endpoints can be monitored, including cell loss, behavioral deficits, and shortened lifespan (Faber et al., 1999; Marsh and Thompson, 2004; Marsh et al., 2003; Morley et al., 2002; Parker et al., 2001). Interestingly, the behavioral phenotypes in Drosophila have not yet been associated with specific neuropathology in situ and may be caused by subtle cellular and ensemble defects, as in transgenic mouse models. These model systems provide a valuable tool to assist in target validation by providing both high-throughput and complex phenotypes reflective of cell circuitry and cell–cell interactions that are impossible to replicate in any in vitro or ex vivo context. However, the nonmammalian biological context will produce flyspecific positives and miss genes not present in Drosophila. Whole genome screens with mutant Htt in these models have not yet been done, but there are two examples of pharmacological validation in which positive hits in Drosophila have been successfully translated to transgenic mouse models: the HDAC inhibitor suberoylanilide hydroxamic acid (SAHA) (Hockly et al., 2003) and the mutant Htt aggregation modulator C-8 (Chopra et al., 2007). These data suggest that the invertebrate models will be useful for genetic target validation. A major challenge will be the transitioning of positives from testing in these models into mammalian systems, as the rationale of the former is their ability to provide in vivo validation, whereas the latter are mostly in vitro cell-based assays. Targets that already have some link to HD at the TV 1.0–2.5 levels (e.g., localization in brain, association with Htt, alteration in HD models) can be qualified for TV 3.0. Targets that are novel and have no known association with HD will require a focused effort to provide additional supporting data. Obviously target hits from both model organism tests and cell-based tests will have higher priority than those from only a single type of test.

Deconvolution of Target Family

In certain cases, pharmacological probes that have shown efficacy in HD models are not sufficiently selective to clearly reveal their mechanisms of action and cognate targets. Nevertheless, the identification of such efficacious probes can lead to the deconvolution of the responsible target(s). An interesting example is that of the HDAC Class I, II, and IV families, which consist of 11 distinct enzymes. In concert with histone acetyltransferases (HATs), these family members regulate transcription by modifying histone acetylation state and chromatin structure (Sadri-Vakili and Cha, 2006). These enzymes also regulate the activity of other proteins such as transcription factors and tubulin, mediate pleiotropic biological actions, and are prime targets for several disease indications, including cancer. It is believed that isozyme-selective HDAC inhibitors may be more effective and less toxic than the currently available broad-spectrum HDAC inhibitor (Langley et al., 2005). Early on, three lines of evidence suggested the relevance of HDACs in HD. First, weak pan-inhibitors of HDACs, such as SAHA, sodium butyrate, phenyl sodium butyrate, and valproate, showed moderate efficacy in some trials of transgenic HD mouse models (Fava, 1997; Grove et al., 2000; Hockly et al., 2003; Minamiyama et al., 2004). Second, genetic modification of Drosophila HDACs (four orthologues in Class I and II; Marsh et al., unpublished data), and potent pan-inhibitor trichostatin A showed efficacy in HD flies (Steffan et al., 2001). Third, mutant Htt appears to interact with HATs such as CREB-binding protein to modify transcription, and with axonal transport regulated by HDAC6, possibly as part of pathogenesis in HD (Dompierre et al., 2007; Iwata et al., 2005; Nucifora et al., 2001; Steffan et al., 2000). On this basis, HDACs were scored at TV 4.0, but it is not yet known whether a single isozyme or some combination of the 11 family members constitutes a therapeutic target for HD.

Parallel efforts currently underway include pharmaceutical development of isozyme-selective HDAC inhibitors, of which HDAC6 is the most notable success (Dokmanovic et al., 2007; Khan et al., 2008), and genetic validation studies in which HDAC KO mice are crossed into HD models to assess the effect of each HDAC isozyme on the HD phenotype. Because some of the HDAC isozyme KOs are embryonically lethal (Chang et al., 2006; Menegola et al., 2006; Vega et al., 2004), conditional KOs must be created. Meanwhile, HDAC inhibitors have shown no effects in most of the intermediate-stage in vitro and ex vivo assays, including mutant Httdependent cytotoxicity models, and limited information has been generated in the brain slice neuroprotection platform by using biolistics transfection to knock down expression of specific HDACs. An alternative brain slice assay may involve testing of HDAC inhibitors by electrophysiological methods, as the broad-spectrum inhibitors are known to increase long-term potentiation (Alarcon et al., 2004), a well-characterized cellular readout of synaptic plasticity that is affected in HD slices (Cummings et al., 2006, 2007; Gibson et al., 2005; Lynch et al., 2007; Picconi et al., 2006). It is hoped that these multiple parallel efforts will pinpoint the HD-relevant HDAC(s) on which to focus pharmaceutical development.

Pathway-Functional Network Development as a Tool for Additional TV Scoring

The large and continually increasing number of targets residing at TV 3.0 and lower reinforces the current and ongoing need to prioritize the list to focus our efforts on the best candidates. One method for prioritizing the list is to group these targets into functional networks and pathways, an exercise that may also lead to an improved understanding of HD pathophysiology. At the simplest level, this process will reveal whether there are multiple members of particular networks or pathways residing on the TV scoring list. Presumably, the members of such pathways should receive a higher priority. On a deeper level, such pathway analysis may provide a systems biology-oriented perspective on HD pathophysiology by providing valuable insights that would not be observed with a gene-by-gene approach. Although this approach is still in its beginning stages, the increased availability of genome-wide datasets and knowledge is expected to make this endeavor feasible in the short term. Our approach to the development of a gene network view of HD is described further at the end of this chapter.

Optimizing the Process of Late-Stage Target Validation

Choice of Genetic HD Models for Target Validation

As summarized in previous chapters, several different mouse and rat models of HD have been described. Although each model displays features of HD, none is clearly superior to the others, and all have yet to be validated by the development of a clinically successful “gold standard” treatment. The older, acute neurotoxin-based models, such as quinolinic acid and 3-nitroprionic acid, which are based on acute cytotoxic insults that target striatal neurons in vivo have largely been superseded by the development of a variety of transgenic models after the identification and cloning of the Htt gene. A recent side-by-side comparison of transcriptional deficits of the R6/2, yeast artificial chromosome (YAC) 128, Q111 KI, and Hdh150 KI mouse models showed a remarkable similarity in gene sets that are upor down-regulated (Kuhn et al., 2007). The main difference observed between the models is the age of onset of transcriptional changes that occurs earliest in the most aggressive R6/2 model (6 weeks) but is more delayed in the KI models (18 and 22 months, respectively, for Q111 and Hdh150) and most delayed in the YAC128 full-length model (onset of changes at 24 months). These data further support the hypothesis that fragment-based, KI, and full-length transgenic models may have common disease features and that the appropriateness of each model depends on the specific target and the particular set of phenotypic outcomes to be assayed. The short fragment R6/2 model develops HD phenotypes within a 3-month timeframe and is thus the most feasible choice for large-scale validation studies. Thus, purely for practical considerations, the R6/2 model is used for the primary testing of compounds. The full-length YAC128 and bacterial artificial chromosome 103 (BAC103) models develop their phenotypes within a 6- to 12-month timeframe, which poses logistical barriers for both genetic and pharmacological validation studies. In addition, these YAC and BAC models still overexpress full-length mutant Htt and show weight gain (Van Raamsdonk et al., 2006), features that are not manifest in HD patients. However, in cases in which the target under query requires a full-length HD model, one of these must suffice. In contrast, the KI mouse models, which include Q140, Q111, and Hdh150, are arguably the best genetic mimics of HD but do not develop robust behavioral deficits until advanced ages (~2 years). In some instances, however, the KI models may be suitable for monitoring specific outcome measures, such as inclusion bodies or transcriptional signatures, as the protracted timeframe allows for a thorough regional and temporal analysis. Finally, the HD transgenic rat (von Horsten et al., 2003), which expresses a rat mutant truncated Htt with 51 CAGs driven from the rat Htt promoter, has been shown to develop motoric deficits and neuropathology in the brain in a 1-year timeframe, as well as decreased survival (median of ~20 months). However, the transgenic rat model is only appropriate for TV 4.0 pharmacological testing because of the lack of transgenic rat models of most targets of interest. The rat model’s main utility may be for testing geneor cell-based therapies, as the model affords more accurate stereotaxic injections and availability of a variety of robust behavioral paradigms, particularly in terms of cognitive functions, that may be of interest in the context of HD.

Genetic Validation of Targets In Vivo (TV 3.5)

For TV 3.5, the first mammalian in vivo hurdle in the target validation pipeline, target gene expression must be altered genetically in the context of a rodent HD model to assess its effect on the progression of HD. In principle, altering expression of the target gene would mimic the actions of a chemical agonist or antagonist and thus predict the outcome of the pharmacologic tests required for TV 4.0. The genetically modified mice include transgenic overexpressers, dominant negatives, mutant KI mice, conventional or conditional KO mice, and transgenic RNAi mice, each of which modifies the target gene in a different way. The murine HD models used are typically the R6/2, YAC128, BAC103, or Q140 KI as described previously. Because no phenotypic outcome in these HD models has yet been shown to be predictive of clinical success in human HD trials, all possible phenotypic outcomes must be considered potentially relevant. Currently, the outcome measures associated with these models include motor, cognitive, histopathological, and biochemical readouts (e.g., aggregation, transcriptional changes) and survival.

Although the genetic validation studies of TV 3.5 precede the pharmacological studies at TV 4.0, the slow and expensive genetic approach may be bypassed in favor of a pharmacological demonstration of efficacy if a good pharmacological agent is available (Li et al., 2005). In such a case, pharmacological demonstration of efficacy can directly elevate the target to a bona fide “therapeutic target.” (This is also true for gene therapy strategies such as viral expression of a protein or delivery of antisense RNA or RNAi.) Before initiating efficacy testing of an agent, certain critical information, including brain penetration, selectivity toward the presumed target, and tolerability in chronic dosing, should be obtained. Typically, even if pharmacological agents penetrate the brain in sufficient quantities to act on their putative targets, they are often not as selective as originally believed, making definitive TV score assessments problematic. It then becomes necessary to undertake efficacy testing of related compounds to generate a pharmacological profile, and these studies must be preceded by the same sequence of brain penetration and tolerability studies. It is also possible that a compound’s efficacy is the result of action on multiple targets, which may lead to discordance between genetic and pharmacological validation approaches. Thus, pharmacological validation studies must be carried out with a fair amount of preparatory work, and the time saved compared with genetic validation may be less than expected. Nevertheless, we will undertake both genetic and pharmacological validation studies in parallel whenever possible, as we believe that the development of a complementary dataset using both approaches will make the most powerful argument in moving the target forward in drug development.

A major issue we must confront is the number of targets (currently 81) at stage TV 3.0. Genetic validation experiments, which can take at least 1–2 years to complete, form a significant bottleneck. To develop a more rapid, higher-throughput technology for advancing targets through the TV 3.5 genetic validation studies, we have adopted RNAi transgenic mouse technology from Taconic-Artemis (Cologne, Germany). In this system, small hairpin RNA (shRNA) sequences directed against the gene of interest are inserted into a tetracycline-inducible expression cassette at the mouse rosa 26 genetic locus (Figure 4.3). This resident cassette allows rapid insertion of different shRNA sequences at the identical genomic site (the modified rosa 26 locus), driving the tetracycline-regulated expression of each shRNA, and thus the knockdown of gene targets, in a temporally and spatially identical manner. We are currently evaluating shRNA transgenic mice engineered to knock down expression of the adenosine receptor 2a (A2a), KMO, and caspase-6 (C6) gene products, which will then be crossed into the appropriate murine HD models. The RNAi mice for A2a will be crossed with R6/2 mice because A2a mRNA and binding activities are altered in the R6/2 mouse (Tarditi et al., 2006). The RNAi mice for KMO will also be crossed with R6/2 mice because 3-HK, a toxic metabolite generated by the kynurenine pathway, is up-regulated in the brains of these mice (Guidetti et al., 2006), suggesting that KMO inhibition could be therapeutic. C6, which putatively cleaves the huntingtin protein at amino acid 586 to generate a toxic proteolytic fragment (Graham et al., 2006), must be crossed with one of the full-length Htt mouse models; the R6/2 exon 1 transgene does not contain the relevant C6 cleavage site. If successful, this technology platform would greatly accelerate the genetic target validation process, helping to relieve the bottleneck by allowing faster development of the genetic models required for target validation.

FIGURE 4.3. Scheme for generating targeted tetracycline-inducible shRNA transgenic mice.


Scheme for generating targeted tetracycline-inducible shRNA transgenic mice. A caspase-6 (C6) shRNA is inserted in a landing pad cassette resident at the rosa 26 locus of the mouse genome. The recombination is afforded by the action of FLP enzyme (expressed (more...)

An additional benefit of this approach is that RNAi knockdown mice may be better genetic mimics of drug treatments than conventional KOs, as their reduction of target gene expression is typically in the 70%–90% range in vivo. Similarly, pharmacological inhibition will reduce but not completely eliminate target protein activity. However, if target gene expression is not sufficiently reduced in the RNAi mice, a conventional KO approach will be required. In the case of C6, conventional KO mice are made in parallel to be crossed with YAC128 and BAC103 full-length Htt mice. Given the results of Graham et al. (2006) showing that mutation of the C6 cleavage site on mutant huntingtin at amino acid 586 (C586R) results in complete rescue of the HD phenotype in YAC128 mice, direct genetic manipulation of the C6 target is of particularly high priority. Although these experiments are seemingly sufficient to advance the TV score of C6 to 3.5, it is absolutely necessary to demonstrate that direct inhibition of the target in question, the C6 enzyme, is capable of altering phenotypic outcome in an HD mouse model. Such a demonstration is necessary to achieve the TV 3.5 level and thus justify a comprehensive search for C6 inhibitors suitable to allow TV 4.0 experiments and drug development efforts to go forward.

The experimental evidence required for a target to achieve TV scores of 3.5 and 4.0 is reasonably well defined, which allows for a systematic standardized approach and direct comparisons of data. At this stage of the TV process, the experiments have moved from the realm of exploratory biology to that of drug discovery. Therefore, standardized experimental practices are necessary to allow clear decision-making for drug development campaigns.

Pharmacologic Validation of Targets In Vivo (TV 4.0)

A number of pharmacologic agents (i.e., agonists or antagonists) have been tested in HD mouse models, primarily the R6/2 model and to a lesser extent the N171-82Q (Schilling et al., 1999) and YAC128 models (Slow et al., 2003). We have implemented standardized protocols with our partner, PsychoGenics Inc. (Tarrytown, NY), and have systematically evaluated and compared each of the murine HD models to determine each model’s suitability for compound screening. The most significant differences between the models are whether they carry a mutant Htt fragment or the full-length mutant Htt gene and the expression level of the transgene. The genetic background of the mouse strain may be another important factor. The models showed a broad spectrum of severity of phenotypes, including motoric and cognitive deficits and neuropathological changes such as inclusion body formation. The R6/2 mouse, the most widely used model, is a transgenic C57BL6/CBA hybrid that expresses a mutant human Htt gene fragment encoding the first exon with a CAG repeat size that originated as 142 in the founder (Mangiarini et al., 1996, 1997). The advantages of the R6/2 mouse in drug testing are its robust and reproducible HD-like phenotype and the short duration of the studies (i.e., 3–4 months) compared with the other transgenic models. However, the mutant Htt expressed in this model contains only the first exon of a very large gene. As a result, this model is not suitable for testing compounds whose mechanisms of action depend on regions of the protein downstream of exon 1. Additionally, the rapid disease progression in the R6/2 mouse, thought to mimic juvenile HD and represent midto late-stage disease, creates a high standard for drugs to show benefit. Furthermore, the R6/2 mouse suffers CAG repeat instabilities over generational time. If not closely monitored, the CAG number can increase or decrease, which would affect the onset and severity of disease phenotypes (Stack et al., 2005) and confound drug studies. For these reasons, compounds will also be tested in the full-length models described previously. The BAC103 and YAC128 models offer the best compromise between rapidity and robustness of phenotypes and the practicality of large-scale drug testing.

Compound evaluation to attain a score of TV 4.0 follows a three-stage testing design to increase throughput (Figure 4.4). In Stage 1 testing, compounds are scored for tested motoric, general health, survival, and, if appropriate, cognitive endpoints. If results are statistically significant or if trends (0.05 < P < 0.3) are observed in multiple outcomes measures, the compound will enter Stage 2 testing, which uses multiple drug doses and higher numbers of mice to extend and confirm the Stage 1 findings. Cognitive and neuropathology endpoints are also evaluated more fully in Stage 2. Stage 1 allows for the testing of large numbers of compounds to determine which ones will most likely succeed in later stages of testing. Because this approach will lead to false-negative results in Stage 1, compounds that are negative in Stage 1 will be set aside for re-evaluation if additional supporting data become available. Compounds that succeed in the multiple outcome measures of Stage 2 will then be evaluated in Stage 3, which currently uses the BAC103 or YAC128 mice to evaluate motoric, general health, neuropathology, and cognitive endpoints. In certain instances, compounds of high priority and with a strong preclinical data package will bypass the first stage and go directly into evaluation using the paradigm shown for Stage 2 and/ or Stage 3.

FIGURE 4.4. Staged testing design for evaluation of compounds in HD mouse models.


Staged testing design for evaluation of compounds in HD mouse models.

Standardization of Testing at TV 4.0

We believe that compound evaluation for TV 4.0 assessment must be highly standardized to allow consistency and cross-comparisons of data over time and between different compounds. The testing of compounds in murine HD models typically follows the path shown in Figure 4.5. First, the best-in-class compound that hits the target of interest is identified. Then the optimal formulation for in vivo delivery is determined; the optimal route of administration to maximize plasma and brain concentrations, the maximum tolerated or optimal dose, and the optimal doses for efficacy studies are determined by additional testing.

FIGURE 4.5. Preparation for testing of compounds in HD mice requires pre-efficacy work.


Preparation for testing of compounds in HD mice requires pre-efficacy work. PK, pharmacokinetic; IV, intravenous; PO, oral; SC, subcutaneous; IP, intraperitoneal. Each refers to the mode of dosing in the mouse.

The efficacy testing in the HD mice follows a protocol of “Best Practices” that have been implemented to provide consistency of testing at contract research organizations, including PsychoGenics Inc., as well as at academic laboratories. The protocol includes a well-controlled, balanced, and blinded study design, consistency in the genetic background and CAG repeat size of the HD mice being used, and optimal husbandry to ensure care and overall general health of the mice being studied.

A critical component of the Best Practices is the husbandry of mice. All mice are housed in same sex groups in which HD and wild-type mice from different litters are mixed. Mixing R6/2 transgenic and wild-type mice has been shown to improve overall body weight and prevent early deaths of the mutant mice (Carter et al., 2000). The R6/2 mice show reduced social behaviors, such as nest building and huddling with cagemates, and inclusion of wild-type mice is postulated to enhance normal social interactions and to aid the R6/2 mice in maintaining better thermoregulation as a result of increased huddling (Carter et al., 2000). Fifty to seventy percent of the variation in the onset of HD is the result of CAG repeat size (Li et al., 2006), and the remaining 30%–50% of the variation is thought to be the result of genetic modifiers and environmental factors. A role for environmental factors is supported by studies of monozygotic twins that have shown differences in phenotypic presentation of disease (Anca et al., 2004). Therefore, we believe that testing compounds in mice housed under enrichment conditions, meant to simulate a more natural environment and to promote a basic level of social interaction and decreased stress, will more accurately predict therapeutic benefits. The enrichment consists of the addition of play tunnels, shredded paper, and plastic bones for all mice and has been shown to improve motor function and cortical brain atrophy in R6/2 mice (Hockly et al., 2002; van Dellen et al., 2000). R6/2 mice also have difficulty eating pelleted food from cage hoppers and reaching water bottle spouts (Carter et al., 2000). Therefore, to prevent dramatic weight loss caused by malnutrition, wet powdered food at bedding level and modified water spouts that reach further down into the cage are provided. These conditions ensure that R6/2 mice tested are more likely to succumb to effects of the HD mutation rather than to effects of malnutrition or dehydration, which would complicate the interpretation of results. In addition to the standardized husbandry conditions outlined above, the Best Practices also include the following considerations:

  • Mice from different litters have been shown to have subtle phenotypic differences (Bates and Hockly, 2003). Therefore, transgenic and wild-type mice are randomized into testing groups to avoid “litter effects.” Mice should also be weighed early after weaning, and each testing group should be counterbalanced according to mouse body weight.
  • Mice should be housed in groups of four or five and separated by gender. In each cage, two wild-type mice of the same gender but different litters should be included in an attempt to provide normal social stimulation.
  • Mice should be allowed to acclimate to the experimental room for at least 1 hour before the beginning of any experiment.
  • Experimentation should be conducted in a blinded manner regarding genotype of the mouse and whether it has received drug or vehicle.
  • Data analysis should not be performed until the end of the study to prevent bias in ongoing measurements.
  • Tail samples should be taken at the end of the study for verification of genotypes and CAG sizes of individual mice.
  • Validated protocols for phenotypic measures, which have been shown to detect significant differences between wild-type and the HD mouse, should be used. Such protocols should not be altered mid-experiment. Validated protocols generated by PsychoGenics Inc. are available to the research community.

In Vivo Target Validation at 4.5 and 5.0

Once a target has achieved validation at TV 4.0, the preclinical data package will be reviewed to determine its worthiness for the clinical studies required to achieve TV 4.5 and TV 5.0 scores. TV 4.5 requires positive outcomes in Phase 2 clinical trials in HD patients and/or positive outcomes in higher species such as nonhuman primates or even pig or sheep models of disease. Many of these nonhuman genetic models of HD are still in the process of being established, but if and when they become available, they may offer the opportunity to test therapeutic candidates in animal models that have brain neuroanatomy more comparable to that of humans. Testing of many therapeutic agents may not be necessary in nonhuman TV 4.5 models, as Phase 2 human clinical trials may be a more logical choice. However, in cases such as gene therapy, where site of brain delivery, degree of brain penetration, and extent of brain coverage are major issues, testing in higher mammalian models may be warranted. For example, delivery of adeno-associated virus (AAV)-small interfering RNA against Htt into the brains of transgenic HD mice shows beneficial outcomes (Harper et al., 2005). However, translation of these results to humans may require a demonstration of positive outcomes on the delivery of the AAV agent to a larger brain structure to justify the initiation of a human clinical trial. Progress has been made toward establishing genetic models of HD in sheep, pigs, and nonhuman primates. For the latter it is hoped that impairment of cognitive functions will better mirror the deficits observed in HD patients than those that can be observed in rodent HD models.

TV 5.0, the highest level of validation, is achieved when a pharmaceutical agent shows clinical efficacy in Phase 3 trials in HD patients. Our standard of clinical effectiveness is that a pharmaceutical agent must “delay onset or slow progression of HD” and cannot merely relieve symptoms. For that reason we do not currently consider Phase 3 clinical success of tetrabenazine (Huntington Study Group, 2006; Kenney and Jankovic, 2006), an inhibitor of the vesicle monoamine transporter 2 target, to be sufficient in ameliorating chorea symptoms to qualify this target for TV 5. Further investigation of the potential of tetrabenazine to affect HD onset or progression is warranted in preclinical HD models and in additional clinical trials. Furthermore, targets that reach TV 5.0 will be of great importance in the re-evaluation of preclinical cell-based and in vivo models of disease to determine which models and assays are the most predictive of success in human trials.


As shown in the target scoring chart, it is difficult to translate the large number of potential targets at the TV 1.0–2.0 levels into pathway or mechanism-relevant targets at TV 2.5. We attribute this observation to several factors: technological advances that enable frequent interrogation of genome-wide resources and produce a torrent of candidate targets; the complexity of HD biology and Htt function, which gives rise to a large dataset supportive of many functional roles for Htt; residence of nonstandardized datasets in various public and private locations, which leads to difficulty in correlation of these datasets; and publication pressure to further interrogate the most novel targets, which leaves a much larger list of targets with potentially greater value to drug discovery research unexamined. Thus, there is a critical need to address the handling of these early targets over the longer term.

One solution is to reduce the numerical complexity and clutter of these early data by assigning targets into gene networks (Zabel et al., 2006). Indeed, most gene products do not function alone but in pathways or networks in concerted action with other gene products. The abundant published literature regarding the cellular functions of many genes can then provide a basis for deducing the pathway-relatedness of the target genes. As a result of this approach, we may make indirect connections to key regulators and ultimately acquire better targets (see also Chapters 3 and 6, this volume). For a complex disease such as HD, this approach may also lead to the identification of the few key genes that regulate interlocking pathways involved in disease onset or progression. Ultimately, this approach may accelerate decision-making in target validation, although it may also demonstrate that a single gene approach may not be sufficient.

One way to assign targets to pathways and networks is to merge “wet-bench data” with in silico information curated at PubMed (MeSH categories), HD and neurodegenerative network overlaps at Kyoto Encyclopedia of Genes and Genomes (Limviphuvadh et al., 2007), and Gene Ontology. Data regarding tissue expression and distribution can also be overlaid with these datasets. HD-relevant information for the creation of HD gene networks should use the HD interactome (Giorgini and Muchowski, 2005; Goehler et al., 2004; Horn et al., 2006; Kaltenbach et al. 2007; Zabel et al., 2006), transcriptional changes observed in HD brain tissues (Hodges et al., 2006; Kuhn et al., 2007; Runne et al., 2007), and the list of potential genetic modifiers for HD onset identified from human and/or model organism studies (Chattopadhyay et al., 2003, 2005; Gusella and Macdonald, 2006; Li et al., 2006; MacDonald et al., 1999; Metzger et al., 2006a, 2006b; Wexler et al., 2004). In addition, modifiers of Htt expression in murine models can be included; this information is partly available at WebQTL ( Several potential methods to assign targets into pathways or networks can then be developed for HD. Significant effort in annotating these groupings, with iterative hypothesis testing of newly realized pathways or networks, should improve the list of HD-relevant mechanisms summarized in Table 4.1. Because some pathways or mechanisms may be shared among other neurodegenerative disorders, we also seek to include knowledge from the study of these disorders. Open resources at Alzforum and the SWAN project (Clark and Kinoshita, 2007) are additional sources of information to further hone hypothetical strategies for building networks. The goal is to develop a systematic method to (1) prioritize targets within each TV level to be promoted for testing; (2) to diversify target prosecution and broaden coverage of pathways and mechanisms of HD progression; and (3) to select targets that function either early or late in affected pathways.


We view a stepwise process toward target validation as a key to building and filling the “target pipeline” with disease-relevant targets for drug discovery in HD. Some process hurdles remain, and significant scientific gaps must be filled. We propose short-term solutions to some of these issues, such as standardization of processes and scientific risk management, and also offer our long-term thoughts on the development of an integrated systems biology approach to gain new insight into HD and generate new hypotheses for target prosecution. Through these efforts, we will continue to identify and promote the most promising targets for drug discovery toward an effective therapy for HD.


We thank Allan Tobin, Ethan Signer, Robert Pacifici, Michael Palazzolo, and Robi Blumenstein for their input and critical evaluation of the manuscript.


  • Aiken CT, Tobin AJ, Schweitzer ES. A cell-based screen for drugs to treat Huntington’s disease. Neurobiol Dis. 2004;16(3):546–555. [PubMed: 15262266]
  • Alarcon JM, Malleret G, Touzani K, Vronskaya S, Ishii S, et al. Chromatin acetylation, memory, and LTP are impaired in CBP+/− mice: a model for the cognitive deficit in Rubinstein-Taybi syndrome and its amelioration. Neuron. 2004;42(6):947–959. [PubMed: 15207239]
  • Anca MH, Gazit E, Loewenthal R, Ostrovsky O, Frydman M, et al. Different phenotypic expression in monozygotic twins with Huntington disease. Am J Med Genet A. 2004;124(1):89–91. [PubMed: 14679593]
  • Anne SL, Saudou F, Humbert S. Phosphorylation of huntingtin by cyclin-dependent kinase 5 is induced by DNA damage and regulates wild-type and mutant huntingtin toxicity in neurons. J Neurosci. 2007;27(27):7318–7328. [PubMed: 17611284]
  • Arrasate M, Mitra S, Schweitzer ES, Segal MR, Finkbeiner S. Inclusion body formation reduces levels of mutant huntingtin and the risk of neuronal death. Nature. 2004;431(7010):805–810. [PubMed: 15483602]
  • Bailey CD, Johnson GV. Tissue transglutaminase contributes to disease progression in the R6/2 Huntington’s disease mouse model via aggregate-independent mechanisms. J Neurochem. 2005;92(1):83–92. [PubMed: 15606898]
  • Baquet ZC, Gorski JA, Jones KR. Early striatal dendrite deficits followed by neuron loss with advanced age in the absence of anterograde cortical brain-derived neurotrophic factor. J Neurosci. 2004;24(17):4250–4258. [PubMed: 15115821]
  • Bates GP, Hockly E. Experimental therapeutics in Huntington’s disease: are models useful for therapeutic trials? Curr Opin Neurol. 2003;16(4):465–470. [PubMed: 12869804]
  • Bjugstad KB, Zawada WM, Goodman SI, Freed CR. IGF-1 and bFGF reduce glutaric acid and 3-hydroxyglutaric acid toxicity in striatal cultures. J Inherit Metab Dis. 2001;24(6):631–647. [PubMed: 11768583]
  • Bonifati DM, Kishore U. Role of complement in neurodegeneration and neuroinflammation. Mol Immunol. 2007;44(5):999–1010. [PubMed: 16698083]
  • Borrell-Pages M, Zala D, Humbert S, Saudou F. Huntington’s disease: from huntingtin function and dysfunction to therapeutic strategies. Cell Mol Life Sci. 2006;63(22):2642–2660. [PubMed: 17041811]
  • Bosch M, Pineda JR, Sunol C, Petriz J, Cattaneo E, et al. Induction of GABAergic phenotype in a neural stem cell line for transplantation in an excitotoxic model of Huntington’s disease. Exp Neurol. 2004;190(1):42–58. [PubMed: 15473979]
  • Bottcher T, Mix E, Koczan D, Bauer P, Pahnke J, et al. Gene expression profiling of ciliary neurotrophic factor-overexpressing rat striatal progenitor cells (ST14A) indicates improved stress response during the early stage of differentiation. J Neurosci Res. 2003;73(1):42–53. [PubMed: 12815707]
  • Brignull HR, Morley JF, Garcia SM, Morimoto RI. Modeling polyglutamine pathogenesis in C. elegans. Methods Enzymol. 2006;412:256–282. [PubMed: 17046663]
  • Brooks E, Arrasate M, Cheung K, Finkbeiner SM. Using antibodies to analyze polyglutamine stretches. Methods Mol Biol. 2004;277:103–128. [PubMed: 15201452]
  • Bursch W, Ellinger A. Autophagy—a basic mechanism and a potential role for neurodegeneration. Folia Neuropathol. 2005;43(4):297–310. [PubMed: 16416394]
  • Carter RJ, Hunt MJ, Morton AJ. Environmental stimulation increases survival in mice transgenic for exon 1 of the Huntington’s disease gene. Mov Disord. 2000;15(5):925–937. [PubMed: 11009201]
  • Cattaneo E. Dysfunction of wild-type huntingtin in Huntington disease. News Physiol Sci. 2003;18:34–37. [PubMed: 12531930]
  • Cha JH. Transcriptional signatures in Huntington’s disease. Prog Neurobiol. 2007;83(4):228–248. [PMC free article: PMC2449822] [PubMed: 17467140]
  • Chang S, Young BD, Li S, Qi X, Richardson JA, et al. Histone deacetylase 7 maintains vascular integrity by repressing matrix metalloproteinase 10. Cell. 2006;126(2):321–334. [PubMed: 16873063]
  • Chattopadhyay B, Baksi K, Mukhopadhyay S, Bhattacharyya NP. Modulation of age at onset of Huntington disease patients by variations in TP53 and human caspase activated DNase (hCAD) genes. Neurosci Lett. 2005;374(2):81–86. [PubMed: 15644269]
  • Chattopadhyay B, Ghosh S, Gangopadhyay PK, Das SK, Roy T, et al. Modulation of age at onset in Huntington’s disease and spinocerebellar ataxia type 2 patients originated from eastern India. Neurosci Lett. 2003;345(2):93–96. [PubMed: 12821179]
  • Chien S, Reiter LT, Bier E, Gribskov M. Homophila: human disease gene cognates in Drosophila. Nucleic Acids Res. 2002;30(1):149–151. [PMC free article: PMC99119] [PubMed: 11752278]
  • Cho SR, Benraiss A, Chmielnicki E, Samdani A, Economides A, et al. Induction of neostriatal neurogenesis slows disease progression in a transgenic murine model of Huntington disease. J Clin Invest. 2007;117(10):2889–2902. [PMC free article: PMC1978427] [PubMed: 17885687]
  • Chopra V, Fox JH, Lieberman G, Dorsey K, Matson W, et al. A small-molecule therapeutic lead for Huntington’s disease: preclinical pharmacology and efficacy of C2-8 in the R6/2 transgenic mouse. Proc Natl Acad Sci U S A. 2007;104:16685–16689. [PMC free article: PMC2034257] [PubMed: 17925440]
  • Clark T, Kinoshita J. Alzforum and SWAN: the present and future of scientific web communities. Brief Bioinform. 2007;8(3):163–171. [PubMed: 17510163]
  • Cui L, Jeong H, Borovecki F, Parkhurst CN, Tanese N, et al. Transcriptional repression of PGC-1alpha by mutant huntingtin leads to mitochondrial dysfunction and neurodegeneration. Cell. 2006;127(1):59–69. [PubMed: 17018277]
  • Cummings DM, Milnerwood AJ, Dallerac GM, Vatsavayai SC, Hirst MC, et al. Abnormal cortical synaptic plasticity in a mouse model of Huntington’s disease. Brain Res Bull. 2007;72(2–3):103–107. [PubMed: 17352933]
  • Cummings DM, Milnerwood AJ, Dallerac GM, Waights V, Brown JY, et al. Aberrant cortical synaptic plasticity and dopaminergic dysfunction in a mouse model of Huntington’s disease. Hum Mol Genet. 2006;15(19):2856–2868. [PubMed: 16905556]
  • Curtis MA, Penney EB, Pearson AG, van Roon-Mom WM, Butterworth NJ, et al. Increased cell proliferation and neurogenesis in the adult human Huntington’s disease brain. Proc Natl Acad Sci U S A. 2003;100(15):9023–9027. [PMC free article: PMC166431] [PubMed: 12853570]
  • Di Maria E, Marasco A, Tartari M, Ciotti P, Abbruzzese G, et al. No evidence of association between BDNF gene variants and age-at-onset of Huntington’s disease. Neurobiol Dis. 2006;24(2):274–279. [PubMed: 16905325]
  • Djousse L, Knowlton B, Hayden MR, Almqvist EW, Brinkman RR, et al. Evidence for a modifier of onset age in Huntington disease linked to the HD gene in 4p16. Neurogenetics. 2004;5(2):109–114. [PMC free article: PMC1866166] [PubMed: 15029481]
  • Dokmanovic M, Perez G, Xu W, Ngo L, Clarke C, et al. Histone deacetylase inhibitors selectively suppress expression of HDAC7. Mol Cancer Ther. 2007;6(9):2525–2534. [PubMed: 17876049]
  • Dompierre JP, Godin JD, Charrin BC, Cordelieres FP, King SJ, et al. Histone deacetylase 6 inhibition compensates for the transport deficit in Huntington’s disease by increasing tubulin acetylation. J Neurosci. 2007;27(13):3571–3583. [PubMed: 17392473]
  • Dorsman JC, Smoor MA, Maat-Schieman ML, Bout M, Siesling S, et al. Analysis of the subcellular localization of huntingtin with a set of rabbit polyclonal antibodies in cultured mammalian cells of neuronal origin: comparison with the distribution of huntingtin in Huntington’s disease autopsy brain. Philos Trans R Soc Lond B Biol Sci. 1999;354(1386):1061–1067. [PMC free article: PMC1692596] [PubMed: 10434306]
  • Duan W, Guo Z, Jiang H, Ladenheim B, Xu X, et al. Paroxetine retards disease onset and progression in Huntingtin mutant mice. Ann Neurol. 2004;55(4):590–594. [PubMed: 15048901]
  • Ducker CE, Stettler EM, French KJ, Upson JJ, Smith CD. Huntingtin interacting protein 14 is an oncogenic human protein: palmitoyl acyltransferase. Oncogene. 2004;23(57):9230–9237. [PMC free article: PMC2908390] [PubMed: 15489887]
  • Ekshyyan O, Aw TY. Apoptosis: a key in neurodegenerative disorders. Curr Neurovasc Res. 2004;1(4):355–371. [PubMed: 16181084]
  • Ernfors P, Lee KF, Jaenisch R. Mice lacking brain-derived neurotrophic factor develop with sensory deficits. Nature. 1994;368(6467):147–150. [PubMed: 8139657]
  • Faber PW, Alter JR, MacDonald ME, Hart AC. Polyglutamine-mediated dysfunction and apoptotic death of a Caenorhabditis elegans sensory neuron. Proc Natl Acad Sci U S A. 1999;96(1):179–184. [PMC free article: PMC15113] [PubMed: 9874792]
  • Fava M. Psychopharmacologic treatment of pathologic aggression. Psychiatr Clin North Am. 1997;20(2):427–451. [PubMed: 9196923]
  • Gauthier LR, Charrin BC, Borrell-Pages M, Dompierre JP, Rangone H, et al. Huntingtin controls neurotrophic support and survival of neurons by enhancing BDNF vesicular transport along microtubules. Cell. 2004;118(1):127–138. [PubMed: 15242649]
  • Gibson HE, Reim K, Brose N, Morton AJ, Jones S. A similar impairment in CA3 mossy fibre LTP in the R6/2 mouse model of Huntington’s disease and in the complexin II knockout mouse. Eur J Neurosci. 2005;22(7):1701–1712. [PubMed: 16197510]
  • Gil JM, Mohapel P, Araujo IM, Popovic N, Li JY, et al. Reduced hippocampal neurogenesis in R6/2 transgenic Huntington’s disease mice. Neurobiol Dis. 2005;20(3):744–751. [PubMed: 15951191]
  • Giorgini F, Muchowski PJ. Connecting the dots in Huntington’s disease with protein interaction networks. Genome Biol. 2005;6(3) [PMC free article: PMC1088934] [PubMed: 15774033]
  • Goehler H, Lalowski M, Stelzl U, Waelter S, Stroedicke M, et al. A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington’s disease. Mol Cell. 2004;15(6):853–865. [PubMed: 15383276]
  • Gorski JA, Balogh SA, Wehner JM, Jones KR. Learning deficits in forebrain-restricted brain-derived neurotrophic factor mutant mice. Neuroscience. 2003;121(2):341–354. [PubMed: 14521993]
  • Graham RK, Deng Y, Slow EJ, Haigh B, Bissada N, et al. Cleavage at the caspase-6 site is required for neuronal dysfunction and degeneration due to mutant huntingtin. Cell. 2006;125(6):1179–1191. [PubMed: 16777606]
  • Grove VE Jr, Quintanilla J, DeVaney GT. Improvement of Huntington’s disease with olanzapine and valproate. N Engl J Med. 2000;343(13):973–974. [PubMed: 11012330]
  • Groves MR, Hanlon N, Turowski P, Hemmings BA, Barford D. The structure of the protein phosphatase 2A PR65/A subunit reveals the conformation of its 15 tandemly repeated HEAT motifs. Cell. 1999;96(1):99–110. [PubMed: 9989501]
  • Guidetti P, Bates GP, Graham RK, Hayden MR, Leavitt BR, et al. Elevated brain 3-hydroxykynurenine and quinolinate levels in Huntington disease mice. Neurobiol Dis. 2006;23(1):190–197. [PubMed: 16697652]
  • Guidetti P, Schwarcz R. 3-Hydroxykynurenine and quinolinate: pathogenic synergism in early grade Huntington’s disease? Adv Exp Med Biol. 2003;527:137–145. [PubMed: 15206726]
  • Gusella JF, Macdonald ME. Huntington’s disease: seeing the pathogenic process through a genetic lens. Trends Biochem Sci. 2006;31(9):533–540. [PubMed: 16829072]
  • Hackam AS, Hodgson JG, Singaraja R, Zhang T, Gan L, et al. Evidence for both the nucleus and cytoplasm as subcellular sites of pathogenesis in Huntington’s disease in cell culture and in transgenic mice expressing mutant huntingtin. Philos Trans R Soc Lond B Biol Sci. 1999;354(1386):1047–1055. [PMC free article: PMC1692613] [PubMed: 10434304]
  • Hackam AS, Singaraja R, Wellington CL, Metzler M, McCutcheon K, et al. The influence of huntingtin protein size on nuclear localization and cellular toxicity. J Cell Biol. 1998;141(5):1097–1105. [PMC free article: PMC2137174] [PubMed: 9606203]
  • Hannan AJ. Novel therapeutic targets for Huntington’s disease. Expert Opin Ther Targets. 2005;9(4):639–650. [PubMed: 16083335]
  • Hansson O, Nylandsted J, Castilho RF, Leist M, Jaattela M, et al. Overexpression of heat shock protein 70 in R6/2 Huntington’s disease mice has only modest effects on disease progression. Brain Res. 2003;970(1–2):47–57. [PubMed: 12706247]
  • Harper SQ, Staber PD, He X, Eliason SL, Martins IH, et al. RNA interference improves motor and neuropathological abnormalities in a Huntington’s disease mouse model. Proc Natl Acad Sci U S A. 2005;102(16):5820–5825. [PMC free article: PMC556303] [PubMed: 15811941]
  • Hebb AL, Robertson HA, Denovan-Wright EM. Striatal phosphodiesterase mRNA and protein levels are reduced in Huntington’s disease transgenic mice prior to the onset of motor symptoms. Neuroscience. 2004;123(4):967–981. [PubMed: 14751289]
  • Higgins DS Jr. Huntington’s disease. Curr Treat Options Neurol. 2006;8(3):236–244. [PubMed: 16569382]
  • Hockly E, Cordery PM, Woodman B, Mahal A, van Dellen A, et al. Environmental enrichment slows disease progression in R6/2 Huntington’s disease mice. Ann Neurol. 2002;51(2):235–242. [PubMed: 11835380]
  • Hockly E, Richon VM, Woodman B, Smith DL, Zhou X, et al. Suberoylanilide hydroxamic acid, a histone deacetylase inhibitor, ameliorates motor deficits in a mouse model of Huntington’s disease. Proc Natl Acad Sci U S A. 2003;100(4):2041–2046. [PMC free article: PMC149955] [PubMed: 12576549]
  • Hodges A, Strand AD, Aragaki AK, Kuhn A, Sengstag T, et al. Regional and cellular gene expression changes in human Huntington’s disease brain. Hum Mol Genet. 2006;15(6):965–977. [PubMed: 16467349]
  • Horn SC, Lalowski M, Goehler H, Droge A, Wanker EE, et al. Huntingtin interacts with the receptor sorting family protein GASP2. J Neural Transm. 2006;113(8):1081–1090. [PubMed: 16835690]
  • Hu H, McCaw EA, Hebb AL, Gomez GT, Denovan-Wright EM. Mutant huntingtin affects the rate of transcription of striatum-specific isoforms of phosphodiesterase 10A. Eur J Neurosci. 2004;20(12):3351–3363. [PubMed: 15610167]
  • Huang K, Yanai A, Kang R, Arstikaitis P, Singaraja RR, et al. Huntingtin-interacting protein HIP14 is a palmitoyl transferase involved in palmitoylation and trafficking of multiple neuronal proteins. Neuron. 2004;44(6):977–986. [PubMed: 15603740]
  • Huntington Study Group. Tetrabenazine as antichorea therapy in Huntington disease: a randomized controlled trial. Neurology. 2006;66(3):366–372. [PubMed: 16476934]
  • Iwata A, Riley BE, Johnston JA, Kopito RR. HDAC6 and microtubules are required for autophagic degradation of aggregated huntingtin. J Biol Chem. 2005;280(48):40282–40292. [PubMed: 16192271]
  • Jin K, LaFevre-Bernt M, Sun Y, Chen S, Gafni J, et al. FGF-2 promotes neurogenesis and neuroprotection and prolongs survival in a transgenic mouse model of Huntington’s disease. Proc Natl Acad Sci U S A. 2005;102(50):18189–18194. [PMC free article: PMC1312383] [PubMed: 16326808]
  • Kaltenbach LS, Romero E, Becklin RR, Chettier R, Bell R, et al. Huntingtin interacting proteins are genetic modifiers of neurodegeneration. PLoS Genet. 2007;3(5) [PMC free article: PMC1866352] [PubMed: 17500595]
  • Kegel KB, Sapp E, Yoder J, Cuiffo B, Sobin L, et al. Huntingtin associates with acidic phospholipids at the plasma membrane. J Biol Chem. 2005;280(43):36464–36473. [PubMed: 16085648]
  • Kenney C, Jankovic J. Tetrabenazine in the treatment of hyperkinetic movement disorders. Expert Rev Neurother. 2006;6(1):7–17. [PubMed: 16466307]
  • Khan N, Jeffers M, Kumar S, Hackett C, Boldog F, et al. Determination of the class and isoform selectivity of small molecule HDAC inhibitors. Biochem J. 2008;409(2):581–589. [PubMed: 17868033]
  • Khoshnan A, Ko J, Watkin EE, Paige LA, Reinhart PH, et al. Activation of the IkappaB kinase complex and nuclear factor-kappaB contributes to mutant huntingtin neurotoxicity. J Neurosci. 2004;24(37):7999–8008. [PubMed: 15371500]
  • Kishikawa S, Li JL, Gillis T, Hakky MM, Warby S, et al. Brain-derived neurotrophic factor does not influence age at neurologic onset of Huntington’s disease. Neurobiol Dis. 2006;24(2):280–285. [PubMed: 16962786]
  • Kuhn A, Goldstein DR, Hodges A, Strand AD, Sengstag T, et al. Mutant huntingtin’s effects on striatal gene expression in mice recapitulate changes observed in human Huntington’s disease brain and do not differ with mutant huntingtin length or wild-type huntingtin dosage. Hum Mol Genet. 2007;16(15):1845–1861. [PubMed: 17519223]
  • La Spada AR. Huntington’s disease and neurogenesis: FGF-2 to the rescue? Proc Natl Acad Sci U S A. 2005;102(50):17889–17890. [PMC free article: PMC1312425] [PubMed: 16330780]
  • Langley B, Gensert JM, Beal MF, Ratan RR. Remodeling chromatin and stress resistance in the central nervous system: histone deacetylase inhibitors as novel and broadly effective neuroprotective agents. Curr Drug Targets. 2005;4(1):41–50. [PubMed: 15723612]
  • Lazic SE, Grote H, Armstrong RJ, Blakemore C, Hannan AJ, et al. Decreased hippocampal cell proliferation in R6/1 Huntington’s mice. Neuroreport. 2004;15(5):811–813. [PubMed: 15073520]
  • Leegwater-Kim J, Cha JH. The paradigm of Huntington’s disease: therapeutic opportunities in neurodegeneration. NeuroRx. 2004;1(1):128–138. [PMC free article: PMC534918] [PubMed: 15717013]
  • Li JL, Hayden MR, Warby SC, Durr A, Morrison PJ, et al. Genome-wide significance for a modifier of age at neurological onset in Huntington’s disease at 6q23-24: the HD MAPS study. BMC Med Genet. 2006;7 [PMC free article: PMC1586197] [PubMed: 16914060]
  • Li JY, Popovic N, Brundin P. The use of the R6 transgenic mouse models of Huntington’s disease in attempts to develop novel therapeutic strategies. NeuroRx. 2005;2(3):447–464. [PMC free article: PMC1144488] [PubMed: 16389308]
  • Li SH, Cheng AL, Zhou H, Lam S, Rao M, et al. Interaction of Huntington disease protein with transcriptional activator Sp1. Mol Cell Biol. 2002;22(5):1277–1287. [PMC free article: PMC134707] [PubMed: 11839795]
  • Li SH, Li XJ. Huntingtin and its role in neuronal degeneration. Neuroscientist. 2004;10(5):467–475. [PubMed: 15359012]
  • Limviphuvadh V, Tanaka S, Goto S, Ueda K, Kanehisa M. The commonality of protein interaction networks determined in neurodegenerative disorders (NDDs) Bioinformatics. 2007;23(16):2129–2138. [PubMed: 17553855]
  • Link CD. Transgenic invertebrate models of age-associated neurodegenerative diseases. Mech Ageing Dev. 2001;122(14):1639–1649. [PubMed: 11511401]
  • Link CD. C. elegans models of age-associated neurodegenerative diseases: lessons from transgenic worm models of Alzheimer’s disease. Exp Gerontol. 2006;41(10):1007–1013. [PubMed: 16930903]
  • Lo DC, McAllister AK, Katz LC. Neuronal transfection in brain slices using particlemediated gene transfer. Neuron. 1994;13(6):1263–1268. [PubMed: 7993619]
  • Lynch G, Kramar EA, Rex CS, Jia Y, Chappas D, et al. Brain-derived neurotrophic factor restores synaptic plasticity in a knock-in mouse model of Huntington’s disease. J Neurosci. 2007;27(16):4424–4434. [PubMed: 17442827]
  • MacDonald ME, Vonsattel JP, Shrinidhi J, Couropmitree NN, Cupples LA, et al. Evidence for the GluR6 gene associated with younger onset age of Huntington’s disease. Neurology. 1999;53(6):1330–1332. [PubMed: 10522893]
  • Mangiarini L, Sathasivam K, Mahal A, Mott R, Seller M, et al. Instability of highly expanded CAG repeats in mice transgenic for the Huntington’s disease mutation. Nat Genet. 1997;15((2)):197–200. [PubMed: 9020849]
  • Mangiarini L, Sathasivam K, Seller M, Cozens B, Harper A, et al. Exon 1 of the HD gene with an expanded CAG repeat is sufficient to cause a progressive neurological phenotype in transgenic mice. Cell. 1996;87(3):493–506. [PubMed: 8898202]
  • Maragakis NJ, Rothstein JD. Mechanisms of disease: astrocytes in neurodegenerative disease. Nat Clin Pract Neurol. 2006;2(12):679–689. [PubMed: 17117171]
  • Marsh JL, Pallos J, Thompson LM. 2003. Fly models of Huntington’s disease Hum Mol Genet 12 Spec No 2: R187 193 . [PubMed: 12925571]
  • Marsh JL, Thompson LM. Can flies help humans treat neurodegenerative diseases? Bioessays. 2004;26(5):485–496. [PubMed: 15112229]
  • Mattson MP, Maudsley S, Martin B. BDNF and 5-HT: a dynamic duo in age-related neuronal plasticity and neurodegenerative disorders. Trends Neurosci. 2004;27(10):589–594. [PubMed: 15374669]
  • McBride JL, Ramaswamy S, Gasmi M, Bartus RT, Herzog CD, et al. Viral delivery of glial cell line-derived neurotrophic factor improves behavior and protects striatal neu-rons in a mouse model of Huntington’s disease. Proc Natl Acad Sci U S A. 2006;103(24):9345–9350. [PMC free article: PMC1482612] [PubMed: 16751280]
  • Menalled LB, Sison JD, Wu Y, Olivieri M, Li XJ, et al. Early motor dysfunction and striosomal distribution of huntingtin microaggregates in Huntington’s disease knock-in mice. J Neurosci. 2002;22(18):8266–8276. [PubMed: 12223581]
  • Menegola E, Di Renzo F, Broccia ML, Giavini E. Inhibition of histone deacetylase as a new mechanism of teratogenesis. Birth Defects Res C Embryo Today. 2006;78(4):345–353. [PubMed: 17315247]
  • Metzger S, Bauer P, Tomiuk J, Laccone F, Didonato S, et al. The S18Y polymorphism in the UCHL1 gene is a genetic modifier in Huntington’s disease. Neurogenetics. 2006a;7(1):27–30. [PubMed: 16369839]
  • Metzger S, Bauer P, Tomiuk J, Laccone F, Didonato S, et al. Genetic analysis of candidate genes modifying the age-at-onset in Huntington’s disease. Hum Genet. 2006b;120(2):285–292. [PubMed: 16847693]
  • Minamiyama M, Katsuno M, Adachi H, Waza M, Sang C, et al. Sodium butyrate ameliorates phenotypic expression in a transgenic mouse model of spinal and bulbar muscular atrophy. Hum Mol Genet. 2004;13(11):1183–1192. [PubMed: 15102712]
  • Morley JF, Brignull HR, Weyers JJ, Morimoto RI. The threshold for polyglutamine-expansion protein aggregation and cellular toxicity is dynamic and influenced by aging in Caenorhabditis elegans. Proc Natl Acad Sci U S A. 2002;99(16):10417–10422. [PMC free article: PMC124929] [PubMed: 12122205]
  • Nemeth H, Toldi J, Vecsei L. Kynurenines, Parkinson’s disease and other neurodegenerative disorders: preclinical and clinical studies. J Neural Transm. 2006;(70):285–304. [PubMed: 17017544]
  • Nucifora FC Jr, Sasaki M, Peters MF, Huang H, Cooper JK, et al. Interference by huntingtin and atrophin-1 with cbp-mediated transcription leading to cellular toxicity. Science. 2001;291(5512):2423–2428. [PubMed: 11264541]
  • Oliveira JM, Chen S, Almeida S, Riley R, Goncalves J, et al. Mitochondrial-dependent Ca2+ handling in Huntington’s disease striatal cells: effect of histone deacetylase inhibitors. J Neurosci. 2006;26(43):11174–11186. [PubMed: 17065457]
  • Ona VO, Li M, Vonsattel JP, Andrews LJ, Khan SQ, et al. Inhibition of caspase-1 slows disease progression in a mouse model of Huntington’s disease. Nature. 1999;399(6733):263–267. [PubMed: 10353249]
  • Palmer TD, Ray J, Gage FH. FGF-2-responsive neuronal progenitors reside in proliferative and quiescent regions of the adult rodent brain. Mol Cell Neurosci. 1995;6(5):474–486. [PubMed: 8581317]
  • Parker JA, Connolly JB, Wellington C, Hayden M, Dausset J, et al. Expanded polyglutamines in Caenorhabditis elegans cause axonal abnormalities and severe dysfunction of PLM mechanosensory neurons without cell death. Proc Natl Acad Sci U S A. 2001;98(23):13318–13323. [PMC free article: PMC60868] [PubMed: 11687635]
  • Pattison LR, Kotter MR, Fraga D, Bonelli RM. Apoptotic cascades as possible targets for inhibiting cell death in Huntington’s disease. J Neurol. 2006;253(9):1137–1142. [PubMed: 16998646]
  • Perez-De La, Cruz V, Santamaria A. Integrative hypothesis for Huntington’s disease: a brief review on experimental evidence. Physiol Res. 2006;56(5):513–526. [PubMed: 17184144]
  • Petersen A, Mani K, Brundin P. Recent advances on the pathogenesis of Huntington’s disease. Exp Neurol. 1999;157(1):1–18. [PubMed: 10222105]
  • Petrasch-Parwez E, Nguyen HP, Lobbecke-Schumacher M, Habbes HW, Wieczorek S, et al. Cellular and subcellular localization of Huntingtin [corrected] aggregates in the brain of a rat transgenic for Huntington disease. J Comp Neurol. 2007;501(5):716–730. [PubMed: 17299753]
  • Phillips W, Morton AJ, Barker RA. Abnormalities of neurogenesis in the R6/2 mouse model of Huntington’s disease are attributable to the in vivo microenvironment. J Neurosci. 2005;25(50):11564–11576. [PubMed: 16354914]
  • Picconi B, Passino E, Sgobio C, Bonsi P, Barone I, et al. Plastic and behavioral abnormalities in experimental Huntington’s disease: a crucial role for cholinergic interneurons. Neurobiol Dis. 2006;22(1):143–152. [PubMed: 16326108]
  • Pineda JR, Canals JM, Bosch M, Adell A, Mengod G, et al. Brain-derived neurotrophic factor modulates dopaminergic deficits in a transgenic mouse model of Huntington’s disease. J Neurochem. 2005;93(5):1057–1068. [PubMed: 15934928]
  • Ramaswamy S, Shannon KM, Kordower JH. Huntington’s disease: pathological mechanisms and therapeutic strategies. Cell Transplant. 2007;16(3):301–312. [PubMed: 17503740]
  • Ravikumar B, Vacher C, Berger Z, Davies JE, Luo S, et al. Inhibition of mTOR induces autophagy and reduces toxicity of polyglutamine expansions in fly and mouse models of Huntington disease. Nat Genet. 2004;36(6):585–595. [PubMed: 15146184]
  • Reiter LT, Potocki L, Chien S, Gribskov M, Bier E. A systematic analysis of human disease-associated gene sequences in Drosophila melanogaster. Genome Res. 2001;11(6):1114–1125. [PMC free article: PMC311089] [PubMed: 11381037]
  • Riddle DR, Katz LC, Lo DC. 1997. Focal delivery of neurotrophins into the central nervous system using fluorescent latex microspheres Biotechniques 23 5 928–934, 936 927 . [PubMed: 9383561]
  • Ross CA, Poirier MA. Protein aggregation and neurodegenerative disease. Nat Med. 2004;10(Suppl):S10–17. [PubMed: 15272267]
  • Rubinsztein DC. The roles of intracellular protein-degradation pathways in neurodegeneration. Nature. 2006;443(7113):780–786. [PubMed: 17051204]
  • Runne H, Kuhn A, Wild EJ, Pratyaksha W, Kristiansen M, et al. Analysis of potential transcriptomic biomarkers for Huntington’s disease in peripheral blood. Proc Natl Acad Sci U S A. 2007;104(36):14424–14429. [PMC free article: PMC1964868] [PubMed: 17724341]
  • Sadri-Vakili G, Bouzou B, Benn CL, Kim MO, Chawla P, et al. Histones associated with downregulated genes are hypo-acetylated in Huntington’s disease models. Hum Mol Genet. 2007;16(11):1293–1306. [PubMed: 17409194]
  • Sadri-Vakili G, Cha JH. Histone deacetylase inhibitors: a novel therapeutic approach to Huntington’s disease (complex mechanism of neuronal death) Curr Alzheimer Res. 2006;3(4):403–408. [PubMed: 17017871]
  • Schilling G, Becher MW, Sharp AH, Jinnah HA, Duan K, et al. Intranuclear inclusions and neuritic aggregates in transgenic mice expressing a mutant N-terminal fragment of huntingtin. Hum Mol Genet. 1999;8(3):397–407. [PubMed: 9949199]
  • Shibata M, Lu T, Furuya T, Degterev A, Mizushima N, et al. Regulation of intracellular accumulation of mutant Huntingtin by Beclin 1. J Biol Chem. 2006;281(20):14474–14485. [PubMed: 16522639]
  • Singaraja RR, Hadano S, Metzler M, Givan S, Wellington CL, et al. HIP14, a novel ankyrin domain-containing protein, links huntingtin to intracellular trafficking and endocytosis. Hum Mol Genet. 2002;11(23):2815–2828. [PubMed: 12393793]
  • Sipione S, Cattaneo E. Modeling Huntington’s disease in cells, flies, and mice. Mol Neurobiol. 2001;23(1):21–51. [PubMed: 11642542]
  • Sittler A, Walter S, Wedemeyer N, Hasenbank R, Scherzinger E, et al. SH3GL3 associates with the Huntingtin exon 1 protein and promotes the formation of polygln-containing protein aggregates. Mol Cell. 1998;2(4):427–436. [PubMed: 9809064]
  • Slaughter JR, Martens MP, Slaughter KA. Depression and Huntington’s disease: prevalence, clinical manifestations, etiology, and treatment. CNS Spectr. 2001;6(4):306–326. [PubMed: 16113629]
  • Slow EJ, van Raamsdonk J, Rogers D, Coleman SH, Graham RK, et al. Selective striatal neuronal loss in a YAC128 mouse model of Huntington disease. Hum Mol Genet. 2003;12(13):1555–1567. [PubMed: 12812983]
  • Stack EC, Kubilus JK, Smith K, Cormier K, Del Signore SJ, et al. Chronology of behavioral symptoms and neuropathological sequela in R6/2 Huntington’s disease transgenic mice. J Comp Neurol. 2005;490(4):354–370. [PubMed: 16127709]
  • Steffan JS, Bodai L, Pallos J, Poelman M, McCampbell A, et al. Histone deacetylase inhibitors arrest polyglutamine-dependent neurodegeneration in Drosophila. Nature. 2001;413(6857):739–743. [PubMed: 11607033]
  • Steffan JS, Kazantsev A, Spasic-Boskovic O, Greenwald M, Zhu YZ, et al. The Huntington’s disease protein interacts with p53 and CREB-binding protein and represses transcription. Proc Natl Acad Sci U S A. 2000;97(12):6763–6768. [PMC free article: PMC18731] [PubMed: 10823891]
  • Subba Rao K. Mechanisms of disease: DNA repair defects and neurological disease. Nat Clin Pract. 2007;3(3):162–172. [PubMed: 17342192]
  • Takano H, Gusella JF. The predominantly HEAT-like motif structure of huntingtin and its association and coincident nuclear entry with dorsal an NF-kB/Rel/dorsal family transcription factor. BMC Neurosci. 2002;3:15. [PMC free article: PMC137586] [PubMed: 12379151]
  • Tam S, Geller R, Spiess C, Frydman J. The chaperonin TRiC controls polyglutamine aggregation and toxicity through subunit-specific interactions. Nat Cell Biol. 2006;8(10):1155–1162. [PMC free article: PMC2829982] [PubMed: 16980959]
  • Tarditi A, Camurri A, Varani K, Borea PA, Woodman B, et al. Early and transient alteration of adenosine A2A receptor signaling in a mouse model of Huntington disease. Neurobiol Dis. 2006;23(1):44–53. [PubMed: 16651003]
  • Tarlac V, Storey E. Role of proteolysis in polyglutamine disorders. J Neurosci Res. 2003;74(3):406–416. [PubMed: 14598317]
  • Tooyama I, Kremer HP, Hayden MR, Kimura H, McGeer EG, et al. Acidic and basic fibroblast growth factor-like immunoreactivity in the striatum and midbrain in Huntington’s disease. Brain Res. 1993;610(1):1–7. [PubMed: 7686078]
  • Truant R, Atwal R, Burtnik A. Hypothesis: Huntingtin may function in membrane association and vesicular trafficking. Biochem Cell Biol. 2006;84(6):912–917. [PubMed: 17215878]
  • Trzesniewska K, Brzyska M, Elbaum D. Neurodegenerative aspects of protein aggregation. Acta Neurobiol Exp (Wars) 2004;64(1):41–52. [PubMed: 15190679]
  • van Dellen A, Blakemore C, Deacon R, York D, Hannan AJ. Delaying the onset of Huntington’s in mice. Nature. 2000;404(6779):721–722. [PubMed: 10783874]
  • Van Raamsdonk JM, Gibson WT, Pearson J, Murphy Z, Lu G, et al. Body weight is modulated by levels of full-length huntingtin. Hum Mol Genet. 2006;15(9):1513–1523. [PubMed: 16571604]
  • Van Raamsdonk JM, Pearson J, Slow EJ, Hossain SM, Leavitt BR, et al. Cognitive dysfunction precedes neuropathology and motor abnormalities in the YAC128 mouse model of Huntington’s disease. J Neurosci. 2005;25(16):4169–4180. [PubMed: 15843620]
  • Varma H, Voisine C, DeMarco CT, Cattaneo E, Lo DC, et al. Selective inhibitors of death in mutant huntingtin cells. Nat Chem Biol. 2007;3(2):99–100. [PubMed: 17195849]
  • Vega RB, Matsuda K, Oh J, Barbosa AC, Yang X, et al. Histone deacetylase 4 controls chondrocyte hypertrophy during skeletogenesis. Cell. 2004;119(4):555–566. [PubMed: 15537544]
  • von Horsten S, Schmitt I, Nguyen HP, Holzmann C, Schmidt T, et al. Transgenic rat model of Huntington’s disease. Hum Mol Genet. 2003;12(6):617–624. [PubMed: 12620967]
  • Wang H, Guan Y, Wang X, Smith K, Cormier K, et al. Nortriptyline delays disease onset in models of chronic neurodegeneration. Eur J Neurosci. 2007;26(3):633–641. [PubMed: 17686041]
  • Wang JK, Portbury S, Thomas MB, Barney S, Ricca DJ, et al. 2006. Cardiac glycosides provide neuroprotection against ischemic stroke: discovery by a brain slice-based compound screening platform Proc Natl Acad Sci U S A 103(27):10461 10466 . [PMC free article: PMC1481664] [PubMed: 16793926]
  • Wang LH, Qin ZH. Animal models of Huntington’s disease: implications in uncovering pathogenic mechanisms and developing therapies. Acta Pharmacol Sin. 2006;27(10):1287–1302. [PubMed: 17007735]
  • Wanker EE. Protein aggregation in Huntington’s and Parkinson’s disease: implications for therapy. Mol Med Today. 2000;6(10):387–391. [PubMed: 11006527]
  • Wanker EE, Rovira C, Scherzinger E, Hasenbank R, Walter S, et al. HIP-I: a huntingtin interacting protein isolated by the yeast two-hybrid system. Hum Mol Genet. 1997;6(3):487–495. [PubMed: 9147654]
  • Wexler NS, Lorimer J, Porter J, Gomez F, Moskowitz C, et al. Venezuelan kindreds reveal that genetic and environmental factors modulate Huntington’s disease age of onset. Proc Natl Acad Sci U S A. 2004;101(10):3498–3503. [PMC free article: PMC373491] [PubMed: 14993615]
  • Xia J, Lee DH, Taylor J, Vandelft M, Truant R. Huntingtin contains a highly conserved nuclear export signal. Hum Mol Genet. 2003;12(12):1393–1403. [PubMed: 12783847]
  • Yamamoto A, Cremona ML, Rothman JE. Autophagy-mediated clearance of huntingtin aggregates triggered by the insulin-signaling pathway. J Cell Biol. 2006;172(5):719–731. [PMC free article: PMC2063704] [PubMed: 16505167]
  • Yanai A, Huang K, Kang R, Singaraja RR, Arstikaitis P, et al. Palmitoylation of huntingtin by HIP14 is essential for its trafficking and function. Nat Neurosci. 2006;9(6):824–831. [PMC free article: PMC2279235] [PubMed: 16699508]
  • Zabel C, Sagi D, Kaindl AM, Steireif N, Klare Y, et al. Comparative proteomics in neurodegenerative and non-neurodegenerative diseases suggest nodal point proteins in regulatory networking. J Proteome Res. 2006;5(8):1948–1958. [PubMed: 16889417]
  • Zala D, Benchoua A, Brouillet E, Perrin V, Gaillard MC, et al. Progressive and selective striatal degeneration in primary neuronal cultures using lentiviral vector coding for a mutant huntingtin fragment. Neurobiol Dis. 2005;20(3):785–798. [PubMed: 16006135]
  • Zhai W, Jeong H, Cui L, Krainc D, Tjian R. In vitro analysis of huntingtin-mediated transcriptional repression reveals multiple transcription factor targets. Cell. 2005;123(7):1241–1253. [PubMed: 16377565]
  • Zhang Y, Ona VO, Li M, Drozda M, Dubois-Dauphin M, et al. Sequential activation of individual caspases, and of alterations in Bcl-2 proapoptotic signals in a mouse model of Huntington’s disease. J Neurochem. 2003;87(5):1184–1192. [PubMed: 14622098]
  • Zourlidou A, Gidalevitz T, Kristiansen M, Landles C, Woodman B, et al. Hsp27 overexpression in the R6/2 mouse model of Huntington’s disease: chronic neurodegeneration does not induce Hsp27 activation. Hum Mol Genet. 2007;16(9):1078–1090. [PubMed: 17360721]
  • Zuccato C, Belyaev N, Conforti P, Ooi L, Tartari M, et al. Widespread disruption of repressor element-1 silencing transcription factor/neuron-restrictive silencer factor occupancy at its target genes in Huntington’s disease. J Neurosci. 2007;27(26):6972–6983. [PubMed: 17596446]
  • Zuccato C, Cattaneo E. Role of brain-derived neurotrophic factor in Huntington’s disease. Prog Neurobiol. 2007;81(5–6):294–330. [PubMed: 17379385]
  • Zuccato C, Ciammola A, Rigamonti D, Leavitt BR, Goffredo D, et al. Loss of huntingtin-mediated BDNF gene transcription in Huntington’s disease. Science. 2001;293(5529):493–498. [PubMed: 11408619]
  • Zuccato C, Tartari M, Crotti A, Goffredo D, Valenza M, et al. Huntingtin interacts with REST/NRSF to modulate the transcription of NRSE-controlled neuronal genes. Nat Genet. 2003;35(1):76–83. [PubMed: 12881722]
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Bookshelf ID: NBK55997PMID: 21882416


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